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Tag: Data Analysis

  • How Data Analytics Can Transform Your Business | Entrepreneur

    How Data Analytics Can Transform Your Business | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    The digital age has ushered in a new era where data reigns supreme, providing businesses with valuable insights into customer behavior, market trends and overall business performance. In order to thrive in today’s highly competitive landscape, entrepreneurs must not only recognize the significance of data analytics but also leverage its power to drive their organizations forward.

    At its core, data analytics involves the systematic examination of raw data with the purpose of drawing meaningful conclusions. By embracing this approach, businesses gain the ability to understand their operations at a granular level, make data-driven decisions, accurately predict future trends and ultimately foster growth and profitability. Let us delve deeper into the ways in which data analytics can revolutionize your business.

    Related: Eight Ways Data Analytics Can Revolutionize Your Business

    How data analytics can transform your business

    Enhancing customer experience:

    One of the greatest benefits of data analytics lies in its capacity to help businesses better comprehend their customers. By analyzing various data points, such as purchasing habits, social media interactions and website visits, organizations can create comprehensive profiles that encompass customers’ preferences and behaviors. Armed with this knowledge, businesses can tailor their product offerings, personalize marketing messages and ultimately enhance the overall customer experience. Consequently, this leads to increased customer satisfaction, loyalty and a competitive edge in the market.

    Streamlining operations:

    Data analytics serves as a powerful tool for uncovering inefficiencies within a business’s operations. By examining production data, for example, businesses can identify bottlenecks within their manufacturing processes. Similarly, studying sales data may shed light on underperforming products or regions. Armed with these insights, businesses can take the necessary steps to streamline their operations, reducing waste and enhancing overall efficiency. Ultimately, this results in cost savings and improved productivity, thereby giving businesses a competitive advantage.

    Mitigating risks:

    Inherent to any business endeavor is an element of risk. However, data analytics empowers businesses to anticipate and mitigate potential risks effectively. By closely analyzing data, businesses can identify patterns and trends that may indicate forthcoming issues. This allows organizations to take proactive measures, ranging from real-time detection of fraudulent transactions to predicting future market volatility. By staying one step ahead, businesses can better protect their interests, reduce financial losses and ensure long-term stability.

    Guiding strategic decision-making:

    Data analytics eliminates much of the guesswork associated with decision-making processes. By providing factual insights, it serves as a reliable guide when it comes to making strategic choices. Whether it involves entering new markets, launching innovative products or investing in cutting-edge technology, businesses can rely on data-driven decision-making to reduce uncertainty and increase the likelihood of success. Armed with accurate information, entrepreneurs can make informed choices that align with their long-term objectives.

    Related: Leverage the Power of Data to Boost Your Sales — and Your Customer Connections

    How can you effectively harness the power of data analytics within your business?

    Embrace a data-driven culture:

    To embark on a successful data analytics journey, it is crucial to foster a data-driven culture within your organization. This entails training employees to understand and utilize data in their day-to-day work, encouraging them to base their decisions on concrete data rather than relying solely on intuition.

    Invest in the right tools:

    The market offers a wide array of data analytics tools, catering to various business sizes, industries and specific needs. From robust business intelligence platforms, such as Tableau and Power BI, to advanced machine learning tools, it is essential to carefully select the tools that align with your organization’s unique requirements.

    Hire or outsource expertise:

    Interpreting data and extracting meaningful insights necessitates specific skills. If your organization lacks in-house expertise, consider hiring data analysts or data scientists to fulfill these roles. Alternatively, you may choose to outsource your data analytics needs to specialized firms that possess the necessary knowledge and experience.

    Prioritize data privacy:

    In an era marked by frequent data breaches and privacy scandals, handling data responsibly is of paramount importance. It is crucial for businesses to ensure that their data practices comply with relevant regulations and industry standards. This includes implementing robust data privacy measures to protect sensitive information and maintaining transparency in how customer data is collected, stored and used. By prioritizing data privacy, businesses can build trust with their customers and safeguard their reputations.

    In conclusion, data analytics has the potential to be a game-changer for businesses in today’s information-driven landscape. By harnessing the power of data, organizations can gain valuable insights into customer behavior, optimize their operations, mitigate risks and make informed strategic decisions. However, reaping the benefits of data analytics requires a deliberate and strategic approach.

    It begins with embracing a data-driven culture within the organization, where employees are empowered to utilize data in their decision-making processes. Investing in the right data analytics tools is crucial, as it enables businesses to effectively collect, analyze and interpret data. Depending on the organization’s resources and expertise, hiring data analysts or outsourcing data analytics services may be necessary to extract meaningful insights from the data.

    Furthermore, businesses must prioritize data privacy and ensure compliance with relevant regulations. Protecting customer data and maintaining their trust is essential in the age of increasing privacy concerns. By adopting these practices, businesses can unlock the full potential of data analytics and drive growth, efficiency and innovation.

    Related: Using Data Analytics Will Transform Your Business. Here’s How.

    In today’s digital landscape, data is no longer just a byproduct of business operations. It has evolved into a valuable asset that holds the key to unlocking opportunities and staying ahead of the competition. Embracing data analytics is no longer an option but a necessity for businesses that aim to thrive in this dynamic and data-centric environment.

    So, seize the power of data analytics, and embark on a journey to transform your business. Embrace the insights that data can offer, streamline operations, enhance customer experiences, mitigate risks, and make informed decisions that propel your organization toward success. Remember, in the age of data, the possibilities are endless, and the businesses that effectively leverage data analytics will gain a significant competitive advantage in the marketplace.

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    Aidan Sowa

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  • 7 Ways Data Helps Your Restaurant Succeed | Entrepreneur

    7 Ways Data Helps Your Restaurant Succeed | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Data makes the world go around. While not every restaurant takes advantage of the wealth of data, it’s essential in making smarter decisions. Cloud-based POS systems are equipped with numerous restaurant analytics and insights that generate data every time your staff takes an order, processes a credit card payment or closes a check.

    While each piece of information may provide some insight into your restaurant’s sales performance, when collected and analyzed together, they tell a complete and compelling story about your business.

    But once you have all this data, what can you actually do with it?

    1. Optimize your menu

    It’s easy to assume that your most popular menu item is also your most profitable. This might not be the case, however. By analyzing your data, you can get a clearer picture of your menu performance and understand which items bring you repeat customers and make you the most money. For example, if burgers are one of your best-selling items, but those customers don’t return, it’s time to investigate.

    The same is true for your lower-selling items. Some of your lower-performing items could have a lot of untapped potential. Your restaurant analytics software can tell you which items have a higher-than-average return rate for guests. With this new data, you can make decisions to improve your menu, like highlighting a particular item or updating the description and picture to tap into that potential.

    Related: Here’s How Data Analytics Is Improving Dining Experiences While Helping Increase Revenues for Restaurants

    2. Measure staff performance

    How well do you know your staff? Staff performance can be directly linked to your profitability. Staff reports let restaurants track productivity, efficiency and customer service levels. While some staff might be doing great, others might need more training. With this data, you can quickly identify rockstar employees and reward them, but also determine which employees aren’t measuring up to the mark and give them additional training to reach their potential.

    3. Uncover strengths – and weaknesses

    Is one server a pro at upselling? If a server is the best at selling high-priced menu items like wine bottles, this is an opportunity to pair them with other staff for training purposes. Pair high-performing staff with servers with low-performance numbers for shadowing and other exercises to help improve their sales.

    Is your best customer coming in next weekend? Make sure you schedule at least one of your best-performing staff members to make their experience memorable.

    4. Decrease turnover

    Turnover is a huge issue in the restaurant industry. Restaurant owners have been scrambling to find new ways to hire and keep staff. Keeping a closer eye on their performance could be the difference between staff that stays for the long haul or finding a new employee. By regularly looking at staff performance, you can better understand the employees that are struggling and might need more training or a change of role.

    Related: Using Data-Driven Concepts To Unlock Incremental Growth

    5. Increase staff happiness

    Staff performance can also give you insights into employee happiness levels. Sometimes the environment needs to change to keep staff happy and performing at their best. If you notice a pattern of decreased productivity across staff, it might be time to sit down for a chat with them or to start looking at how the current environment might be affecting the team.

    6. Create repeat customers

    How often are customers coming back? What are they ordering? Knowing these key pieces of data will help you determine how to shape your menu and how you upsell or interact with customers. With 360 analytics tools that connect operations, customer data and payments into your reports, you can get eye-opening data you can act on.

    Each time a credit card is swiped, the restaurant analytics software generates a unique profile for every guest. This provides insights into their preferred menu items, purchase history, frequently used payment methods, preferred location and other details. With this information, you can pinpoint VIP customers and elevate their experience with tailored promotions or complimentary items.

    Related: 25 Ways You Can Turn a One-Time Buyer Into a Repeat Buyer

    If a guest has dined at your restaurant six times in the last four weeks, you can access their guest profile to identify their favorite drink or appetizer and offer it to them as a complimentary item. This gesture is an excellent way to show your appreciation and build customer loyalty.

    7. Improve stock management and reduce waste

    If you’re constantly running out of ingredients or always have specific ingredients leftover from under-ordered items, it’s time to take a look at your inventory.

    Proper inventory management is an essential part of running a successful restaurant. By analyzing inventory data, restaurants can identify trends in food waste and improve profitability. Restaurants can also use inventory data to optimize ingredient usage and reduce the risk of running out of popular menu items.

    With inventory management software like Lightspeed Inventory, restaurants can make the most of their ingredients, eliminate manual stock counting, reduce human error and simplify their inventory management with real-time deductions as items are sold and automatic replenishment when you get fresh inventory.

    Every day is an opportunity to get new insights into your business. Data can help you do everything from optimizing your operations to improving the overall guest experience. Not sure where to start? All it takes is partnering with the right restaurant management software.

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    Peter Dougherty

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  • How Contextual Data Is Revolutionizing Advertising | Entrepreneur

    How Contextual Data Is Revolutionizing Advertising | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Advertising has come a long way in the last few decades. With the rise of digital marketing, advertisers have access to more data about consumers and businesses than ever. This data feeds into vast new compute power resulting in increasingly effective ways for advertisers to convey messaging.

    Enter the next generation of AdTech. This new wave of technology combines AI and contextual data to curate ads tailored to consumers at the individual level. By analyzing data about a person’s interests, preferences and behaviors, advertisers can deliver content to the target audience that resonates in very specific moments of time.

    The key to this new approach is contextual data. Rather than simply looking at a person’s demographic information or search history, advertisers are now looking at a person’s context — where they are, what they’re doing and what they’re interested in, measured in real-time along thousands of data points. By understanding a person’s context and automating custom content creation in seconds, advertisers can deliver ads to millions of consumers simultaneously that are highly relevant.

    By using machine learning algorithms, AI can analyze vast amounts of data to identify patterns and insights that are impossible to monitor and act on manually.

    Related: How New Age Technologies Are Changing the Ad-Tech Industry

    Here’s how each of these technologies plays a role in generating highly personalized content for each individual:

    • Machine learning: Machine learning algorithms enable AdTech companies to analyze vast amounts of data about each user, including their browsing history, search queries, social media activity, and other interactions. These algorithms use this data to identify patterns and make predictions about what content is most likely relevant and engaging to each user.
    • Predictive analytics: Predictive analytics is the use of statistical algorithms and machine learning techniques to analyze data and make predictions about future events or behaviors. In AdTech, predictive analytics is used to anticipate user needs and preferences before they even express them. By analyzing patterns in user behavior and other data points, AI algorithms can make highly accurate predictions about what content will be most engaging and relevant to each user.
    • Natural Language Processing (NLP): NLP is a branch of AI that enables computers to understand, interpret and generate content in the human voice. By using NLP, AdTech companies can analyze and generate highly curated content tailored to individual users’ interests and needs. This technology enables computers to understand the nuances of human language, including context, intent, and sentiment, which is essential for generating highly personalized and relevant content.

    Imagine a world where you are walking down the street and receive a notification on your phone for a nearby coffee shop you haven’t tried before. The notification is personalized to your interests and preferences since it is historically the type of coffee you like, at the prices you usually pay, set in an ambiance you tend to enjoy for a coffee shop, at the time of day you typically drink coffee when out and about. The notification also includes a discount for a beverage you have purchased in the past. This is an example of AI and contextual data working together to deliver a highly targeted and personalized ad.

    But this approach is not without its challenges. There are obvious concerns about privacy and the ethical implications of using personal data to target consumers.

    Although policymakers have taken an active stance on regulating the industry by way of the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States, keeping bylaws current in this rapidly evolving ecosystem poses a challenge to say the least. In the near term, transparency will ultimately dictate efficacy for both advertisers and end consumers as we get closer to a convergence point in value-driven and derived.

    Related: Safeguarding Digital Identities: Why Data Privacy Should Matter To You (And Your Business)

    Despite these challenges, the benefits of this approach to engagement are significant. Solving for relevancy and timing creates a win-win for all stakeholders across all verticals in consumer and business.

    Every second passed represents millions of data recorded — especially in advertising. This correlates directly to the models and algorithms getting better in a positive feedback loop leading to the overall ideal of personalized advertising growing — with now just being the start of what can only be related to an exponential “J-curve” growth story for the industry and underlying technology.

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    Karl Eshwer

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  • How Data-Driven Marketing Strategies Help You Achieve Growth | Entrepreneur

    How Data-Driven Marketing Strategies Help You Achieve Growth | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    In the midst of economic turmoil, CEOs and entrepreneurs are focusing on a bright future. Nearly 75% of leaders surveyed during a joint Hello Alice-Mastercard initiative said they planned to grow in 2023. This means business owners nationwide aren’t allowing the heat of inflation to squelch their optimism. However, they can only generate good results with equally good data-driven digital marketing strategies.

    Fortunately, this isn’t a revelation to most leaders. Everyone has heard about the importance of data. Yet, many companies spend less time mapping out a successful, data-backed, growth-centered plan than the average family does when preparing for a vacation. It’s just not enough to choose some data points to measure.

    To see growth — and scalability when your team is ready for it — your business needs to know where it wants to go. When you have a destination in mind, you can reverse-engineer your process to determine which data you need to make your growth dreams a reality. You’re bound to wander off course when you don’t have a destination set in stone. That’s costly but fortunately avoidable.

    To start, you need to do a deep dive to understand what “growth” looks like for your company. Instead of picking metrics based on what you think you should measure or setting up data reports, answer four questions. First, where do you want your business to go in the coming 12 months? Pinpoint specific goals. Second, do you have assets in place that are helping you reach those goals? These could be anything from audiences and offers to channels.

    With these questions answered, evaluate how your existing assets are working. In other words, where are the gaps? Be very honest with what you see, or else you won’t be able to respond to the last question: Is your current plan helping you reach those goals?

    Once you’ve taken this deep dive into your overall sales and marketing objectives and strategies, you can employ data-focused, successful digital marketing measures. Each of these measures will nudge you closer to the growth you want and protect you from preventable roadblocks.

    Related: 3 Steps to Assemble the Right Infrastructure Building Blocks to Successfully Scale Your Business

    1. Set up metrics that are personalized to your stated goals

    You’ll never be confident that you’re moving in the right direction unless you measure the right metrics. One of the biggest errors many leaders make is not testing their metrics or KPIs against their overall growth strategy objectives. Your metrics must have an impact and not just be chosen at random.

    A 2021 Adverity announcement indicated that around one-third of all CMOs don’t trust their marketing data. That is, they’re reluctant to believe the metrics their dashboards show. You can’t afford to be in this position because it hinders your ability to make informed decisions. This is why you need to be choosy and particular when it comes to metrics.

    Run each possible metric that you might measure through an assessment. How will you use the metric? Why will it show whether you’re on or off track? Are there other corresponding metrics that could shed light on the metric?

    Spending time on this kind of upfront evaluation will pay off later. Just be sure that you examine your metrics every few months. You may want to decrease or add data points as you move closer to your goals.

    2. Take a “big picture” approach to your data

    With your metrics in hand, you can start getting data insights. The insights may or may not be valuable, though. Plus, they might not say what you think they’re saying. Believe it or not, sometimes you have to interpret the numbers. This is where stepping back and being able to look at everything from a 35,000-foot view makes sense.

    Our company works with many leaders who, in their eagerness to examine the data, haven’t skimmed it beyond the surface. As a result, they’ve sometimes been surprised when they discover that their data is showing red flags — and that they’ve ignored those red flags.

    For instance, one of our clients was showing high-profit margins via the metrics and assumed the company was on a serious growth trajectory. Just in case, we poked around a few additional data points. What was really happening was that two or three of the client’s customers were very profitable, but about 10 other customers were dropping in profitability.

    The company realized that it had to get to the bottom of why such a high percentage of customers were unprofitable. If their leaders hadn’t been open to the big picture, they could have found themselves without the growth they sought.

    Related: How to Collect Digital Marketing Data in 5 Easy Steps

    3. Include catastrophe management in your data-driven digital marketing strategy

    Catastrophic things can happen to any company. Just ask the countless companies that reported a collective total of 1,802 data breaches or compromises in 2022 per Identity Theft Resource Center. Every time you add a new data entry or endpoint to your workflows, such as a cloud-based software tool, you’re opening the door to being hacked. Nevertheless, you shouldn’t allow fear to shut down your data-driven digital marketing campaigns. Instead, leverage the experience of vendors and partners who’ve seen it all and want to help you avoid being a worst-case scenario.

    You can use certain metrics to help you shed light on the unknown and be proactive. Being able to get real-time data on internal and external security protocols, subscription sign-ons and more can help you avoid heartache and headache. Remember, not all catastrophes come from nefarious places.

    Another client of ours said their product turnover was 90 days. They built a thriving, data-driven digital marketing strategy around that belief. Orders started coming in, and their metrics, including SEO-created online authority, looked amazing. All except one: fulfillment. They were wrong about the 90-day prediction and couldn’t fulfill orders. Their business tanked because they couldn’t support the growth they sought and we achieved.

    Essentially, your job is to unveil buried information so you can grow without faltering. Let others pay the “school of hard knocks” tuition. You have better places to spend your money, like consistently tweaking and honing your digital marketing plan throughout the year.

    Getting bigger and better requires that you identify your baseline objectives and then construct data-driven strategies around them. It’s the healthiest way to keep your business ticking and humming straight toward your goals.

    Related: A Practical Guide to Increasing Startup Success Through Data Analytics

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    Ross Denny

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  • How to Power Up Negotiations with Credible Data | Entrepreneur

    How to Power Up Negotiations with Credible Data | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Negotiation is an essential skill for entrepreneurs in creating business relationships that provide value for each party and position you for growth. At its core, negotiating is about reaching terms that create a balance that meets the needs of both parties.

    Achieving that balance is a natural struggle as both sides push to secure the best deal. Market and performance data and insight give you the leverage to ensure a fair and favorable outcome.

    In this article, we’ll examine why data is the foundation that supports a strong negotiating position, how to employ the data, and how to leverage tech and advisors to collect, analyze and present insights.

    Related: The Art of Negotiation is Misunderstood. Here Are Some Lesser-Known Tactics I Use to Win.

    Data is vital to supporting your position

    Negotiating is a challenging endeavor. It requires a specialized skill set and experience. And in a tough economy where inflation is high, liquidity is low and supply and demand are in flux, it’s more difficult to find terms all parties to a deal (of nearly any type) will accept. You have to work smart to get the terms that will position your business to succeed while satisfying the other party’s expectations.

    Some deals fail to produce optimal outcomes when one or both parties don’t have a clear picture of the economic and operating environment. Developing that sight (e.g. situational awareness) by collecting and presenting relevant data boosts each party’s confidence in the terms they can feasibly accept.

    Moreover, pursuing a data-backed negotiation strategy ensures you’re making the best decision going into the transaction or agreement and that you know your financial and operational situation — and the criteria for a deal that won’t sink your ship.

    What types of data should you collect? Some essential categories include:

    • Macro, regional and niche market demand and supply indicators.
    • Personal and organizational financials.
    • Pro forma financial statements (revenue and expense projections).
    • Asset, service and corporate performance benchmarks.

    Related: 5 Steps to Master the Art of Negotiation

    How to leverage data in negotiations

    In what types of negotiation is data valuable?

    For nearly all forms, but most commonly for entrepreneurs in the process of:

    • Swaying investors and partners — raising capital.
    • Leasing or buying operating space and equipment.
    • Selling and securing products or services.
    • Contracting with suppliers/vendors.
    • Hiring staff — presenting employment offers.

    How can we use this data in negotiations?

    Most importantly, leveraging data in the negotiating process helps demonstrate the upside for both parties to the agreement. Even when the agreed terms aren’t ideal or what they were expecting, if they feel the outcome will improve their position and they got the best deal for the circumstances, a contract is more likely.

    A crucial role of data in negotiations is supporting bidding and asking value/prices. Market data and performance metrics can demonstrate a sector’s demand and supply factors and relationships. Even if the numbers don’t work in your favor, they ensure all parties are comfortable with the terms. If the price or terms are contentious, comparable analyses based on market pricing and sales data can validate or encourage a reevaluation of pricing.

    Related: A Negotiation Expert Shares Tactics from Elon Musk’s Twitter Deal Every Entrepreneur Should Know

    Objective data provide evidence of feasibility for your proposal and the stated objective. The perceived viability of your venture is fundamental when raising capital and pitching investors. Providing data that supports your market assumptions and projections, including demand fundamentals and market growth, makes your pitch more credible and facilitates investors’ due diligence processes.

    Performance metrics pertaining to your assets, products, companies or units highlight your core competencies and illustrate your track record. Important data points include revenue, relative profit among offerings, expense ratios and numerous other KPIs.

    Presenting these data points and insights in a polished and upfront manner lets your potential stakeholders know you’re serious, organized and equipped.

    Related: 4 Things to Do When You’re in a Negotiation

    Leveraging tech and advisors to source, organize, interpret and report the data

    The data and analyses presented are only as credible as the sources, methods, tools and analysts contributing to their aggregation and preparation. Incomplete, inaccurate or irrelevant data will undermine a deal as fast as a sinking foundation.

    Therefore, a business or entrepreneur must have the systems, time and expertise to assemble and interpret the data.

    To accomplish this, build an integrative strategy comprising data management technology and an internal or external team of analysts and advisors.

    Data management and analysis systems, of which there are industry-specific solutions for most sectors, enable entrepreneurs to collect performance and market data continuously and automatically. The results are valid and timely insights available when they’re needed to formulate terms and evaluate counteroffers.

    When time is of the essence, the best opportunities go to those who are prepared and ready to act with assurance. If your core competencies( e.g. your strengths or personal value proposition) aren’t in data research and analysis, there’s an opportunity to build an in-house and outside team of experts to bridge the knowledge and experience gap.

    Additionally, respected team members enhance your organization’s credibility and capabilities.

    Related: Make Your Next Negotiation a ‘Win-Win.’ 3 Tips for How to Do That.

    Fair and fruitful

    Introducing quality data and analyses into negotiations gives credibility to assertions and projections and validates any offers, proposals and ventures.

    When offers and counteroffers are supported by objective data that illustrates why the proposed terms are fair and provide the most upside to both parties, reaching an agreement and forming a fruitful business relationship are simpler and more likely.

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    Robert Finlay

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  • How to Leverage Data and Analytics to Connect With Customers | Entrepreneur

    How to Leverage Data and Analytics to Connect With Customers | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    When it comes to marketing, a one-size-fits-all approach just doesn’t cut it anymore. Luckily, and as my CTO at the Strategic Advisor Board always says, “With big data and advanced analytics, businesses can create customized experiences tailored to each customer’s unique needs and preferences.” This personal touch increases engagement and conversions and fosters a more profound sense of brand loyalty. Let’s dive into the exciting world of personalized marketing and discover how your business can implement this strategy.

    Imagine receiving an email that feels like it was crafted just for you. Every product recommendation and piece of content speaks to your interests and needs. This is the power of personalization in marketing. By gathering and utilizing data on your behavior and preferences, brands can create a truly unique and tailored experience for each individual customer. From behavioral to contextual, demographic to predictive, there are many ways to personalize marketing tactics and make every interaction feel special.

    Behavioral personalization involves tailoring the customer’s experience based on their past behavior. Ecommerce websites, for example, recommend products based on past purchases, browsing history and search queries. In contrast, contextual personalization is based on a customer’s current context, such as their location or time of day. Demographic personalization involves tailoring a customer’s experience based on their location. Predictive personalization uses data analysis and machine learning algorithms to predict customers’ interests and provide personalized recommendations.

    Personalization in marketing involves using data and technology to create a more relevant and personalized customer experience, which can drive better engagement. It is a critical strategy for businesses looking to stay ahead of the competition and connect with customers more meaningfully. One of my golden rules in all my companies is to keep it customized and personalized to the audience we need to speak to.

    Related: 3 Tips for Using Consumer Data to Create More Personalized Experiences

    The benefits of personalization

    Personalization in marketing has become a critical strategy for businesses because it can increase customer engagement, drive higher conversion rates and improve customer loyalty. By delivering personalized experiences, companies can create stronger customer connections, improving brand perception and repeat purchases.

    According to a study by Experion, personalized emails have an open rate of 29% higher than non-personalized emails. Additionally, a Segment study found that customized product recommendations can increase conversion rates by up to 300%. These statistics demonstrate the powerful impact of personalization on driving business results.

    In addition to these benefits, personalization can also improve customer loyalty. An Infosys study found that 74% of customers feel frustrated when website content is not personalized to their interests. Moreover, 59% said personalization influences their shopping decisions. By delivering customized experiences, businesses can show their customers that they understand their needs and preferences, leading to increased loyalty over time.

    Best practices for personalization

    Personalization in marketing campaigns can boost customer engagement, loyalty and conversion rates. To succeed in personalization, data collection and analysis are crucial. Here are some tips for personalization in marketing campaigns:

    First, collect as much data as possible about customer behavior and preferences. This includes their purchase history, browsing behavior and social media activity. Analyze this data to identify patterns and trends and use it to tailor marketing messages and offers to individual customers.

    Second, segment customers into smaller groups based on shared characteristics such as location, behavior or demographics. This enables businesses to create targeted marketing messages and promotions relevant to specific customer identifiers.

    Finally, testing and optimization are essential for successful personalization. Use A/B testing to experiment with different personalization strategies and optimize campaigns based on the results.

    Several companies have successfully implemented personalization in their marketing campaigns. For example, Amazon uses data to recommend products and provide personalized shopping experiences. Netflix uses customer viewing data to suggest customized content, while Spotify utilizes customer data to create personalized playlists and recommendations. Through its Beauty Insider loyalty program, Sephora leverages customer data to provide tailored product recommendations and offers.

    Related: These Are the Biggest Takeaways from 2022. What Does 2023 Have in Store for the Customer Experience?

    Overcoming personalization challenges

    Personalization in marketing campaigns has benefits, but challenges such as data privacy and scaling personalization efforts must be addressed. Businesses must be transparent about data collection and use practices to build customer trust. Investing in technology like AI-powered tools can help companies automate personalization efforts and create personalized experiences at scale.

    Several companies have successfully implemented personalization in their marketing efforts, achieving impressive results. For example, Coca-Cola used personalization to create unique bottles for its “Share a Coke” campaign, featuring customers’ names on the label. This campaign resulted in a 2.5% increase in sales and more than 500,000 photos were shared on social media using the campaign hashtag.

    Another successful example is Spotify, which uses personalization to create personalized playlists and recommendations for each user. This has significantly increased user engagement and retention, with over 60% of users listening to recommended music regularly equaling a total of 30% of total listening.

    These companies demonstrate how personalization can create more engaging and effective marketing campaigns. By leveraging customer data and delivering personalized experiences, businesses can increase customer engagement, boost loyalty and drive sales.

    The future of personalization in marketing

    As technology evolves, personalization in marketing will also grow, with emerging technologies such as AI potentially revolutionizing businesses’ personalization. AI can analyze vast amounts of customer data in real time, allowing companies to deliver highly personalized customer experiences at scale. In the future, businesses may use technologies like facial recognition or VR to create even more individualized experiences.

    Businesses must invest in emerging technologies like AI and machine learning to implement personalization effectively. Yet, challenges such as data privacy concerns and scaling personalization efforts must be addressed. Transparency about data collection and investment in technology that can automate personalization efforts can help.

    Modern marketing relies heavily on personalization to increase customer engagement, loyalty and sales. Amazon, Spotify, Sephora and Coca-Cola have all implemented personalization successfully. To stay competitive, businesses must prioritize personalization, invest in emerging technologies and build customer trust through transparency and privacy. Creating more engaging and effective marketing campaigns in the modern era will require businesses to utilize personalization.

    Related: Owning Your Customer Data Is the Key to Profitability. Here’s Why.

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    Jason Miller

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  • 5 Ways to Use Data to Make Faster — and Better — Business Decisions | Entrepreneur

    5 Ways to Use Data to Make Faster — and Better — Business Decisions | Entrepreneur

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    For Subscribers

    Business leaders need to have instant data access and analysis to make rapid decisions, yet getting what they need is often hampered by a lack of access and analysis, siloed teams and overwhelmed IT departments. Here are five ways you can break down siloes and get your employees what they need to collaborate and make business function decisions faster.

    Opinions expressed by Entrepreneur contributors are their own.

    Your marketing team needs to move at the speed of customers. They need to know if their new marketing campaign is working today so they can adjust it tomorrow. Your inventory management team wants supply chain products and supply visibility to quickly shift inventory to locations running low. Your sales team knows time is money and wants to know which accounts need an in-person visit to close the deal or if a sales campaign is more appropriate at this stage in the buyer’s journey.

    These needs require instant access to data from a multitude of sources. Typically, these departments ask IT for a report. But the sophisticated data analytics of today requires a specialized IT talent that’s scarce, and the velocity of data makes those requests a frustration for all. The data is needed immediately and the IT team is unable to respond in real time.

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    Girish Pancha

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  • 3 Data Gathering Strategies That Benefit Businesses and Consumers | Entrepreneur

    3 Data Gathering Strategies That Benefit Businesses and Consumers | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Data is powerful. It can generate leads, inform customer interactions and ultimately grow revenue.

    Data is also a force that businesses can use for good (or evil). The poster child of the latter is third-party data, which has given the analytics industry a bit of a bad rap in recent years. As third-party data is being phased out, though, it’s opening the doors for companies to rewrite the script on data through better data-gathering methods.

    Related: 4 Steps to Become a Data-Driven Business

    The benefits of healthy data collection

    Healthy data collection is the process of collecting, organizing and utilizing data in a legal, honest and safe manner. It’s an approach to data that is in everyone’s interest.

    When a business has certain pieces of consumer and customer data, it can personalize experiences. From customized emails to unique sales offers, customers have better overall experiences when the right data flows between them and the brands they patronize.

    Healthy data flow also impacts both ends of the sales funnel by generating a higher number of quality leads from prospective customers. For those close to the point of sale, key pieces of data (such as an email address) can generate fewer abandoned carts. All of this translates to better revenue, which is in the best interest of every company.

    The question is: How can companies tap into this positive, respectful approach to using data as we move toward a third-party-data-less future?

    Here are a few strategies that companies can use to collect both potential and existing customer data in a manner that benefits businesses and consumers alike.

    Related: How Marketers Can Prepare for the Removal of Third-Party Cookies

    1. Use on-site software to capture first-party customer data

    As third-party data becomes irrelevant, it puts a fresh emphasis on first-party data — data consumers offer businesses with their explicit consent. There are multiple ways to collect this data independently, including directly on your website.

    Software solutions can be installed onto a company website, allowing a brand to collect first-party data directly from visitors. This can match anonymous digital identifiers to customer profiles — critically, using data collected with the awareness and acknowledgment of its owner.

    The need for healthy first-party data collection is great, especially in a world that is increasingly skeptical of third-party information. The collection of first-party data enables brands to confidently create personalized browsing, individual product offers and targeted cart abandonment emails.

    2. Surveys are a neat and clean data-gathering strategy

    If a business wants to collect data through its efforts, one of the best ways is through surveys. This is a great way to glean information from online customers as they’re on their way out the door.

    The most obvious way a survey can help with data is by collecting important personal information. By asking a customer for certain preferences and proclivities, you open up the doors for personalized marketing in the future.

    You can also use surveys to gain insights into your customers as a whole. Survey Monkey highlights the importance of closed-ended questions that create clear, quantitative data.

    For instance, consider a scenario where a company asks existing customers how easy it was to navigate its website. It offers specific answers in the form of a five-point scale ranging from “difficult” to “super easy.”

    This provides a growing set of data that comes directly from the customers with their explicit consent. The company can then use it to improve its ecommerce shopping experience, benefiting both the business and consumers in the process.

    This can turn one-time interactions into enduring customer relationships. Even better, both parties are fully on board with the exchange of data taking place.

    Related: 5 Ways to Build Killer Relationships With Customers

    3. Offer giveaways (with reasonable strings attached)

    Another way to proactively collect consumer data, even from those who aren’t your customers yet, is by using giveaways.

    Contests are a great way to encourage engagement and spread brand awareness. They also double as an easy way to get an individual’s data with their blessing.

    Entering something like a product giveaway often comes with certain stipulations. These might include sharing a post, leaving a comment or signing up for an email list. That last option is a great way to begin generating customer data. Once you have a person’s email, you can begin communicating with them and using things like surveys to expand on the data they’ve already given you.

    It’s a good idea to use a double opt-in solution to respect the customer, too. This is also referred to as confirmed opt-in, and it consists of a confirmation email that a person must accept before truly being added to a list.

    Related: The Demise of Third-Party Cookies: Retaining the Sweet Spot

    Campaign Monitor points out that the double opt-in approach has the important benefit of creating higher-quality leads. The additional step of opting into an email list twice indicates that the consumer in question has increased interest in your company to the point that they’re willing to put in the extra effort.

    From double opt-in giveaways and post-point-of-sale surveys to comprehensive solutions like Resolution, there are multiple ways companies can gather data. These are strategies that benefit businesses and consumers alike, allowing both to mutually benefit from a new, third-party-cookie-free future where data will remain as relevant as ever before.

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    Rashan Dixon

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  • How Do I Know I’m Getting the Best ROI? Use First-Party Data. | Entrepreneur

    How Do I Know I’m Getting the Best ROI? Use First-Party Data. | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Today’s digital world has opened up many opportunities for small businesses to leverage data and maximize their marketing ROI. First-party data allows businesses to develop customer insights, optimize campaigns, track performance metrics and create targeted strategies.

    With first-party data in hand, small business owners have access to valuable consumer preferences, which they can utilize when planning engagement activities or launching new products/services. Additionally, using this form of data helps build a clear view of customers’ journeys while creating personalized experiences at every touchpoint along the way — without spending huge amounts of money on expensive market research tools.

    Related: In the Fight for Privacy, Web Cookies Are Disappearing. Here’s What That Means for Your Company’s Advertising Strategy.

    Get your hands on Google Analytics 4

    Small businesses have to be smart in their marketing efforts. Leveraging first-party data can help you get the most out of your marketing budget, ensuring that every dollar spent drives value and a higher return on investment (ROI). Small business owners can adjust their strategies accordingly by processing website data such as customer behavior and engagement metrics.

    Leveraging Google Analytics 4 for your first-party data can help you rank higher in Google search results. There are numerous advanced features of GA4 that allow businesses to collect valuable insights about their customers, allowing them to create targeted campaigns with more accuracy and efficiency than ever before. This new platform also offers powerful tools that enable you to measure the success of your digital marketing efforts and make quick adjustments as necessary.

    Understanding user journeys across different pages provide a further understanding of what works well for an organization’s digital presence, allowing them to focus time and effort more effectively on areas that drive conversions or revenue opportunities. Research shows investing in targeted campaigns using first-party data generates up to 20% higher return on investment than non-targeted approaches like social media ads.

    Place your customers in the right buckets

    Customer segmentation is a powerful way to leverage first-party data and boost marketing ROI. By gaining deeper insights into customer behavior, businesses can target their most valuable customers with specific content that resonates more effectively than ever before.

    Email segmentation, for instance, can help businesses leverage first-party data to create personalized, targeted messages. By grouping customers into segments based on similar profiles and behaviors, companies can tailor their communications and promotions accordingly. With this strategy in place, organizations have greater control over the messaging they deliver to specific cohorts of customers, helping them develop stronger relationships with the people who matter most.

    Behavioral segmentation helps small businesses create a tailored experience that resonates with each audience, rather than simply creating general messaging campaigns without any context or personalization attached. This personalized approach has the potential to drive higher engagement from customers by providing them with experiences catered just for them — resulting in greater brand loyalty and increased revenue opportunities further down the line.

    Related: Forget Third-Party Data. You’re Already Missing Out on Most of Your First-Party Data

    A personal touch for your customers

    First-party data can also help in strengthening relationships with customers. This is done by improving customer communications, creating personalized conversations and experiences, enabling segmentation of audiences for better targeting and providing relevant content to the right customers at the right time. Also, by leveraging this data, businesses can gain insights into buyer behavior, allowing them to become more responsive while helping build trust among their customers.

    For instance, personalized text messages can help create a closer connection between the business and its customers. This strengthens the relationship over time, creating a better understanding of what each customer wants and needs — further increasing customer loyalty and creating greater opportunities for revenue growth.

    First-party data helps with this by allowing you to understand shoppers’ habits by collecting behavioral information such as email open rates or click-through links. By doing so, companies can give customers a tailored and targeted experience that increases engagement and loyalty.

    Facebook pixel and other tags

    The Facebook pixel is a powerful tool for businesses to gain access to rich first-party data. This allows them to better understand customer behavior and build tailored campaigns that match their customers’ interests. With a Facebook pixel, you can track website visitors across devices, create custom audiences, measure conversions from ads and optimize ad spend for improved results. Utilizing this valuable first-party data enables businesses to make targeted decisions quickly, increase ROI and maximize performance.

    Utilizing first-party data to track and optimize marketing ROI requires dedication but can be immensely rewarding. Tracking customer behavior across various platforms is essential for efficient optimization and successful campaigns.

    With appropriate investments into understanding first-party data and employing ways to analyze its effectiveness efficiently, small businesses can equip themselves with what they need to increase their profits through increased ROI.

    Related: Data in 4 Flavors, and the Demise of the Cookie

    Ensuring accurate, reliable data collection

    Having accurate data is an essential part of any successful marketing campaign. With the right first-party data, businesses can ensure they’re targeting their audience in the most relevant and effective way possible. To ensure reliable data collection, small business owners should strive to use multiple sources that are consistent with each other, such as social media platforms and customer databases.

    Additionally, tracking analytics on a regular basis will allow for ongoing insights about trends or changes in consumer behavior over time to adjust strategies accordingly. Finally, investing in sophisticated tools like GA4 can enable companies to go beyond basic demographics and build better profiles around customer needs or preferences. This will help them create meaningful interactions with potential customers, leading to higher ROI from campaigns.

    Small businesses often have limited resources and need to be smart about how they spend them. Fortunately, first-party data is an inexpensive way to reach potential customers and understand consumer behavior. By collecting the right user information, small businesses can use this data to better understand individual buyer behaviors and create data-driven customer journeys and targeted campaigns tailored specifically toward them.

    This allows the business to maximize its return on investment by sending out highly personalized communication that instantly connects with potential buyers in a powerful way.

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    Sonu Yadav

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  • The Importance of Data in Outdoor Advertising | Entrepreneur

    The Importance of Data in Outdoor Advertising | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    It’s no secret that big data has changed the world. Capturing and processing information at scale have impacted every sphere of our lives, including health, wealth, business and leisure activities. Advertising is no exception, and marketers continue to discover how data empowers them to rethink promotional strategies. The enormous outdoor advertising market, which is projected to be worth $34.4 billion by 2027, is a prime arena for companies to apply data to boost their marketing activities.

    Discover how to optimize outdoor advertising and spur your company’s growth by harnessing and applying data in your marketing plans.

    Related: It’s Time to Shift Your Advertising Budget to Outdoor Media. Here’s Why and How to Do It.

    Using data in outdoor advertising

    All advertising strategies depend on specific criteria for success. To implement an out-of-home (OOH) campaign, marketers need in-depth knowledge of the target audience, recognition of the prospect’s media channel preferences and an understanding of consumer behavior concerning the product.

    Big data provides valuable intelligence on these issues, including what advertising methods work and which don’t. Analyzing data and applying the insights enables your enterprise to maximize specific OOH media, such as billboard advertising, digital out-of-home (DOOH) and even wild posting tactics.

    With the detailed analytics available and the right tools to use them, you can now optimize your outdoor campaigns beyond any previous capability.

    Related: Outdoor Advertising Is Conquering. Why Aren’t You Using It?

    Applying data analytics in outdoor advertising

    Data analytics is the processing and examination of datasets and the drawing of conclusions about the information they contain. Business intelligence derived from analytics enables you to create OOH advertising strategies focused on the needs and preferences of the people passing through each touchpoint.

    The types of outdoor advertising analytics you can generate include descriptive analytics and predictive analytics. The first interprets historical data to identify trends and patterns and determine what happened in the past, spot potential problems, or uncover opportunities for improvement. The second — predictive analytics — uses statistics and modeling techniques to project future outcomes and performance.

    Generating useful metrics and insights

    The intelligence delivered by data analytics allows OOH marketers to segment their audiences effectively, determine media placements and audience movement patterns, and use performance metrics to optimize their outdoor advertising campaigns.

    For example, location analytics add a layer of geographical information to datasets to generate more valuable insights. These might include audience saturation, the population’s purchasing power and brand affinities, and the product categories that usually do well in each location.

    Mobility analytics provide information about foot traffic, peak hours in specific locations, and the hours of highest demand. When you know in detail which consumers will be exposed to your advertising, your company is in a much better position to maximize its return on investment.

    Determining data validity

    It’s one thing to have access to a ton of data and quite another to know that it’s valid and you can rely on it to improve your campaigns. Bad data is a perennial problem, with Gartner estimating that poor-quality information costs organizations an average of $12.9 million annually.

    If inaccurate or unreliable data make their way into a company’s outdoor advertising analytics, the resulting poor strategy choices could be devastating for both the marketing department and the company as a whole. To ensure decision-making accuracy, you need to ensure you’re using data that has been validated. Companies can use first-party data that belongs to them, combined with second- and third-party data purchased from reputable suppliers. Examples of this type of quality data include Geopath’s impression numbers and their Insights Suite for audience measurements.

    Related: Marketers, Turn Your Data Literacy into a Data Superpower

    Benefits of data analytics in outdoor advertising

    Data analytics delivers multiple benefits for outdoor advertisers. The intelligence provides a 360-degree, unified view of the customer’s journey, including all their interactions with the company.

    Purchase insights: Evaluating OOH impressions in tandem with your website activity, social media engagement and digital chat records offers insight into customer needs and wants and helps you clarify the consumer’s typical path to purchase.

    Audience targeting: With analytics, you can see which customers contribute the highest percentage of your revenue. You can also identify customers with the highest lifetime value and those who consistently share positive information about the brand. This information enables you to define your “ideal customer” characteristics, which creates valuable insights for audience targeting based on these metrics.

    Customer personalization: Consumers these days typically expect highly relevant offers and messaging across all their media channels, not just OOH. Analytics insights support more personalized outdoor advertising designed for each target segment. This tactic benefits the customer journey and increases your chances of making a sale.

    Effective iteration: Analytics enables precise performance measurements across all your campaigns and marketing channels, including OOH. These insights allow you to identify real-time improvement opportunities and iterate your actions mid-campaign to achieve them.

    Accurate forecasting: Performance projections are complex because they include multiple campaign variables, but analytics enable you to examine past performance and make more accurate future predictions. Scenario modeling helps identify likely outcomes of an OOH campaign depending on external factors. This allows you to accurately forecast lead volumes and conversion rates and take more effective actions.

    Improved ROI: By analyzing the performance of each channel and platform, including OOH, social media, email, websites, smart TV and direct marketing, you can identify those performing best for any given market segment and customer journey point. Based on this data, you can reallocate your ad spend and improve your ROI.

    Organizations that use data-driven insights to inform their outdoor advertising strategies typically experience a 10% to 30% improvement in overall marketing performance. Data takes the guesswork out of outdoor and billboard advertising. It helps you optimize your marketing budget, improve your customer experience, and understand which channels, touchpoints, and strategies work. And insights like that are invaluable when it comes to increasing ROI.

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    Gino Sesto

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  • The Key to Elevating Your Market Research Strategy | Entrepreneur

    The Key to Elevating Your Market Research Strategy | Entrepreneur

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    Opinions expressed by Entrepreneur contributors are their own.

    Like all science, research began as an entirely manual process. Responses to surveys were gathered in person or by mail in the 1940s, and the results underwent rigorous coding, tabulation and analysis for insights. Since then, developments in computer science and the internet have made it possible for researchers to collect data swiftly and affordably at scale.

    Today, thanks to further advancements in data collection, researchers can gather insights from anywhere in the world (through various channels like email, social media and websites). What’s more, they can leverage the enormous computing capacity of the cloud to simultaneously examine billions of data points — all made possible through market research software.

    As SaaS market research platforms become widely available, we no longer require a team of researchers to carry out a study. Anyone in an organization, including marketers and product analysts, can initiate a research study at the press of a button and obtain real-time insights.

    Let’s start from the basics and discover more about market research platforms and the role they play in the market research industry today.

    Related: 4 Phases of Market Research to Ensure Success

    What is an integrated market research platform?

    An integrated market research platform takes care of end-to-end market research, including conception, recruitment, sampling, data gathering and analysis. It streamlines market research methodology and processes into an online platform for ease of use and ongoing insight creation.

    Companies, individuals and research agencies utilize research platforms to gather and analyze data, aiding in decision-making. Users can conduct a study, acquire results and turn data into insights — all on one platform — due to the combination of survey tools, analytics and reporting tools.

    Why do we need market research platforms?

    Researchers are less traditional now than they were, and this can be owed to the demands of the market research industry today. Researchers are required to strike the balance between accuracy and speed. They are expected to embrace technology while staying true to research methodologies. They are asked to surface richer insights but deliver them succinctly.

    These demands, however, are easier stated than done.

    Not only are these tasks extremely challenging and time-consuming to pull off, but they also create roadblocks to what could be an otherwise straightforward market research process by using a tool.

    Related: The Impact of Technology on Market Research

    The benefits of integrated market research platforms

    Integrated research platforms provide several benefits that address the current industry’s anticipated demands:

    • For the convenience of every user on the platform, all the tools needed for market research can be integrated into one single space. For instance, qualitative and quantitative research are used for different use cases and hence require different tools. With an integrated market research tool, both functionalities can be consolidated.

    • Since market research platforms are hosted online, they can be accessed anytime, anywhere, ensuring seamless collaboration between all stakeholders (thereby improving the transformation of insights into action). Studies indicate the future is SaaS-powered; according to Statista, the SaaS market was worth approximately $145.5 billion in 2021 and hit $172 billion in 2022 and is only expected to grow further.

    • Unlike disparate market research solutions, singular platforms do not require programming knowledge and are easy to use, thereby removing the skills gap or the training required to implement them.

    • Through direct access to pre-profiled participant panels, integrated platforms are equipped to accommodate both short-term and continuous (long-term) research projects.

    • By using a tool, you can run multiple studies at once and generate insights more quickly, boosting the likelihood that your ROI will increase. For instance, a marketer can test different advertisements and launch the one that’s the most engaging for the best reach.

    Qualtrics’ Market Research Trends report suggests that 67% of organizations planned to acquire new market research technology in 2022 (depicting a 7% increase from 2021).

    Steps to adopt market research platforms

    There are numerous market research tools available today. Here are some objectives to keep in mind before implementing one:

    • Prioritize speed and accuracy: In an era of wavering customer loyalty, generating insights isn’t enough. The key is to generate higher-quality insights faster — before your competitors beat you to it.

    • Move your consumer research online: To ensure maximum speed and efficiency in research without compromising quality, moving research-based activities online is the solution in the digital era we live in currently.

    • Increase adoption with research and marketing teams: While it is true that researchers aren’t as traditional today, the change is unfortunately not fast enough to keep up with the changes in the market. It is critical to convey the benefits of integrated market research software within research agencies and organizations to increase its awareness and in the long run, its adoption.

    • Choose an integrated research platform: There is a difference between a market research platform and an integrated research platform. It is advisable to implement a tool that can integrate seamlessly with your present (and future) systems to ensure less development and implementation effort and an overall smoother research process.

    Related: 3 Ways to Win Consumer Confidence with Market Research

    Because integrated platforms are naturally adaptable, they allow for continuous development and innovative evolution (as they can incorporate any future tools and methodologies). This means that businesses can shape research platforms to meet their specific requirements at any time. New demands can be incorporated to the greatest extent possible, allowing for longevity that other non-integrated platforms cannot achieve.

    Streamlining processes such as researcher-participant communication, data reporting and visualization techniques, while fully automating research, is no longer just a concept, but an attainable reality, thanks to integrated market research platforms.

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    Reshu Rathi

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  • How to Use Predictive Analytics in Your Business

    How to Use Predictive Analytics in Your Business

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    Opinions expressed by Entrepreneur contributors are their own.

    Predictive analytics is a field of data analysis that uses past data to make future predictions. By understanding customer behavior, you can better anticipate what they want and need — and therefore create products and services that appeal to them. In this article, we outline seven simple steps for using predictive analytics in your business. We hope these will help you get started and that the insights generated will help you achieve your business goals. In this article, we’ll discuss:

    1. What is predictive analytics?

    2. Why is it important in business?

    3. How does predictive analytics work?

    4. The different types of data that can be used in predictive analytics

    5. Steps for using predictive analytics in your business

    Related: How Predictive Analytics Can Help Your Business See the Future (Infographic)

    1. What is predictive analytics?

    Predictive analytics is a method of using data to make predictions about future events or behavior. It can be used in a number of different fields, including marketing, sales and customer service.

    Predictive analytics can be used to predict how people will behave in the future based on their past behavior. This can help businesses plan their marketing campaigns or sales initiatives better by knowing which type of customer is likely to respond well to a particular product or service.

    It can also be used to predict how customers will respond to changes that are made to the company’s website or product offerings. By understanding where and how customers are clicking on the website, for example, you can make sure that all information is presented in an effective way.

    Finally, predictive analytics can be used in order to improve customer service by predicting which customers are likely to require more attention than others. This allows staff members to allocate their time accordingly so that everyone receives the care they need.

    2. Why is it important in business?

    Predictive analytics is a powerful tool that can help you make better decisions in your business. It’s used to predict future events and trends, which can then be used to influence decision-making throughout the organization.

    There are a number of reasons why predictive analytics is important in business. Some of them include:

    • It helps you optimize your operations.

    • It helps you identify and prevent risks before they become problems.

    • It allows you to make more informed decisions about pricing, marketing and product development.

    • It can help you improve customer retention and loyalty by understanding how customers behave and what motivates them.

    Related: Why Industry Leaders Are Turning Towards Predictive Analytics

    3. How does predictive analytics work?

    Predictive analytics is a method of predicting future outcomes based on past data. By understanding how people behave and what affects their behavior, you can make better decisions about the future. There are three different ways that predictive analytics can work:

    1. Predictive modeling: This is the most common type of predictive analytics, and it uses mathematical models to predict future outcomes. These models are usually powered by data sources like historical sales data or customer preferences.

    2. Predictive segmentation: This is used to identify specific groups of people who are more likely to behave in a certain way. For example, you might use predictive segmentation to know which segments of your customers are more likely to switch brands or spend more money.

    3. Predictive analysis: This is used to understand how various factors (like pricing, product design, etc.) affect overall customer behavior. It can also be used to improve performance by identifying problems early on and fixing them before they become major issues.

    4. The different types of data that can be used in predictive analytics

    There are many different types of data that can be used in predictive analytics, and each offers its own benefits. Here are the four types of data that can be used in predictive analytics:

    1. Demographic data: This includes information about people’s age, gender, location and other personal details. It is often used to predict who will buy a product or service, or to understand customer trends over time.

    2. Behavioral data: This includes information about how people behave, including their shopping habits and preferences. It is often used to target ads and content with the right audience.

    3. Social media data: This includes information about who is talking about what on social media and how this conversation is evolving over time. It is often used to understand which topics are being talked about most frequently and to identify potential marketing opportunities.

    4. Economic data: This includes information about economic trends such as inflation rates and GDP growth rates. It is often used to make business decisions based on predictions about future customer behavior.

    Related: 3 Steps to Building a Predictive Analytics System

    5. Steps for using predictive analytics in your business

    There are a lot of different ways to use predictive analytics in your business, so it can be hard to know where to start. Here are seven simple steps that will help you get started:

    1. Set your goals for using predictive analytics in your business. What do you want to achieve? What outcomes do you want to see?

    2. Define what you need to measure to accurately assess the results of your predictive analytics efforts. Are there any key indicators that will tell you whether your predictions were accurate?

    3. Develop a strategy for how you will use predictive analytics data in order to make informed decisions. How will you use it to improve your business operations?

    4. Train your staff on how to use the data and how it can be helpful in their work. Make sure they understand the data’s limitations and why predictive analytics is important for their work.

    5. Implement a process for monitoring and adjusting your strategy based on feedback from the data-collection process, analysis and decision-making processes. Are there any changes that need to be made? Do they warrant a new set of predictions?

    6. Use predictive analytics technology as part of an overall effort toward improving decision-making across all parts of your business operation, not just with respect to marketing or sales activities.

    In today’s digital world, where customer behavior is changing at a rapid pace, you can use predictive analytics to put out relevant products and services that keep customers happy and satisfied. You can also add other techniques to your arsenal as necessary. For instance, you may focus on customer satisfaction by tracking their emotional state while using your product or service. With such powerful tools at your fingertips, you can now be more confident and informed before making any major decisions!

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    Piyanka Jain

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  • How to Advertise to Customer Emotions Without Invading Privacy

    How to Advertise to Customer Emotions Without Invading Privacy

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    Opinions expressed by Entrepreneur contributors are their own.

    It’s probably not difficult to grasp that our customers’ purchase behaviors are deeply entangled with moods.

    There’s a reason that we call shopping therapeutic. Purchasing things we want sends a serotonin surge to the brain that can temporarily make us feel better if we’re stressed, depressed or anxious. Moreover, according to widely-cited research by Gerald Zaltman, 95% of purchase decisions are made subconsciously and driven by emotions — so it’s no surprise that advertisers have been interested in understanding and evoking particular mood states for generations.

    Now that data about internal states of mind is becoming more available, the stakes are higher when we consider how to act on this sensitive consumer information. For example, how far should brands go to utilize emotional data to encourage purchases?

    Let’s take a look at where we’re at and how brands can take a human-centered approach to the use of this sensitive information.

    Related: 5 Insights Into Human Behavior That Will Boost Your Sales and Marketing

    How we gauge emotions

    Let’s start with how we gauge emotions. Until recently, our data about feelings relied on self-reporting by consumers since it’s impossible to embody another person’s emotional experience. Self-reporting means that consumers answer direct questions about how they are feeling at a given time or in a given context. Usually, this happens via market research surveys.

    Neuroscience is advancing to the point that we may be able to accurately predict emotional states without relying on overt consumer admissions. This type of emotional assessment may prove to be even more accurate than direct consumer reporting since many people struggle to predict how they’ll feel in particular contexts.

    Technology that assesses activity in our brains is getting more advanced and better capable of predicting mood states. While most of this innovation is happening in research labs, we’re getting closer to realizing this technology as a marketing tool.

    Neuroscience and wearables

    The Art of Shopping, a subconscious shopping experience between art retailer Saatchi and eBay, is one of the most direct campaigns that aimed to utilize this technology in shopping.

    During the experiential retail event, attendees browsed an art gallery while wearing headsets that were designed to track a consumer’s mental engagement. When the software suggested that viewers were inspired, eBay added similar items to the patron’s shopping cart.

    While the activation was interesting, getting consumers to voluntarily and consistently wear mind-tracking headsets is far-fetched in our current environment. Although, it may become more common as more consumers adopt augmented and virtual realities.

    Today, wearables like fitness trackers and smartwatches are becoming more ubiquitous and can aggregate mood data inferentially or from the self-assessment of consumers. The devices can assess everything from our heart rate and breathing patterns to our mindfulness activity. This can imply or correlate to stress levels or provide more direct mood data on apps like Calm and Halo that encourage emotional reporting.

    Related: 4 Neuromarketing Hacks to Reach More People and Maximize Results

    Inferring emotional data

    There are other ways to gauge the mood of consumers, and some of them have a troubling history.

    Meta, formerly Facebook, was famously under the microscope for conducting a large-scale emotion experiment aimed at understanding if emotions spread through networks.

    It actively manipulated the algorithm of nearly 700,000 users without their informed consent, in order to serve them positive or negative content and to gauge the apparent mood in their resulting posts. Among other goals, the company was interested in how emotions might make the site more or less engaging.

    The more engaged users are on the platform, the more valuable they are to Meta’s advertisers. Critics worried that the company wanted to understand how to manipulate emotions to bolster its bottom line and increase purchases for its advertisers, without apparent regard for the impact on the consumer.

    Meta isn’t the only tech company making actionable inferences about emotions. Search engines like Google track emotional effects by utilizing software to assess language for positive and negative sentiments in search, among other tactics.

    In conjunction with the rest of their consumer data, such as browsing and purchase history, these tech behemoths have real power to understand, contextualize and leverage consumer emotion without the use of neurological equipment.

    Related: If You Want to Win Over Customers, Appeal to Their Emotions

    How are we using this data?

    Marketers are curious about how mood impacts purchases, and thereby interested in creating purchase paths that are aligned with particular feelings. Payment providers are paying attention as well. In fact, in their latest Future of Payments research paper, Worldpay from FIS identified personalization, including emotional engagement, as a trend that payment providers are attending to.

    Creating payment journeys that utilize emotional information from consumers may sound troubling. But it’s worth noting that consumers increasingly expect these kinds of personalized experiences from brands — as long as they are additive to the consumer journey.

    When an experience provides convenience to a consumer and helps the brand connect meaningfully with them, it can make the consumer feel supported and improve emotional engagement and loyalty.

    Striking a balance between utilizing emotional data to offer mutual brand-consumer gains while respecting consumer rights and privacy is tricky. This is why we need to think deeply about creating consumer safeguards as we venture into the future.

    Related: Personalization: A Perspective On The Future Of Targeting

    Where do we go from here?

    There’s no shortage of data, and we’re only going to get better at detecting and reacting to emotional states in various contexts. As advertisers and marketers, we need to be thoughtful about how all this emotional data is applied.

    We’ve already seen social media companies exploiting negative emotional states like anxiety and depression to move users toward a purchase path of aspirational products in categories like beauty and fitness (What’s even more troubling is that the algorithms are likely contributing to the negative emotional state, but that’s a conversation for another day). We’ve seen the same algorithms promote negative headlines that are likely to elicit engagement, which results in exacerbated political polarization and negative societal impacts more broadly.

    As an advertising community, we need to implement safeguards to protect consumers. These safeguards should come from regulators, as well as individual brands. Creating an ethics playbook prior to locking in uses of emotional data in the purchase path, conducting thought experiments for secondary and tertiary impacts of the use of mood-based information, and defining and acting in accordance with a brand’s values can help to ensure marketers are responsible brokers of mood data.

    It’s worth remembering that understanding emotion can have powerful positive consequences as well. As humans, we’re emotional beings and brands that can meet consumers where they are in their internal experiences are likely to create better and more meaningful connections. It’s imperative for brands to think through how they’re using emotional information, not only to create lasting relationships with consumers but also to take a human-centered approach to innovation.

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    Tina Mulqueen

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  • The Beginner’s Guide to Understanding Data Science and Machine Learning

    The Beginner’s Guide to Understanding Data Science and Machine Learning

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    Opinions expressed by Entrepreneur contributors are their own.

    We are on the brink of a massive technological revolution as we slowly move from the water and steam-powered first industrial revolution to the artificial intelligence-powered fourth industrial revolution. The theories backing data science and machine learning have existed for hundreds of years. There used to be times when proto-computers would take almost forever to compute a billion calculations. No one dared think of artificial intelligence or related technology. All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second.

    Data science and machine learning are amongst the most popular disciplines that evaluate and analyze big data for beneficial purposes. Whenever big data or data, in general, is mentioned, our minds go straight to data science and machine learning. While both disciplines are noticeably different, they have a unique and symbiotic relationship. This article will explain in detail the concepts of data science and machine learning, their special relationship and practical examples.

    Related: How Data Science Can Help You Grow Your Business Faster

    The science of data

    As mentioned above, our world is about to be overrun by data. Data is fast becoming overwhelming and tedious to manage. Tons and tons of data are being generated every second. The advent of the internet further pushed this development to the edge. Everywhere you go, your data is being collected knowingly and unknowingly — from gestures as simple as opening a door through fingerprint sensor automation to shopping for groceries from a grocery store.

    Data science is the study of data and the processes involved in extracting and analyzing data for problem-solving and predicting future trends. Data science is a broad discipline that is interconnected with other fields, such as machine learning, data analytics, data mining, visualizations, pattern recognition and neurocomputing, to mention a few.

    Data scientists investigate, analyze, infer and present data that solve technology-related business problems. The science of data draws inferences, interpretations and conclusions from data that can be used for informed decision-making. This science is built on fundamental disciplines like statistics, mathematics and probability. In all its entirety, data science works to understand data and interpret it.

    Machine learning

    Machine learning studies data over time to create predictive models that can discern trends and solve problems without human intervention. Machine learning is a subset of data science. Through algorithms and development tools, machine learning engineers build expert systems that can be taught to work independently without human intervention. This is achieved through a series of algorithmic approaches divided into four categories: supervised, unsupervised, semi-supervised and reinforcement learning.

    Machine learning engineers study big data to simulate machines to behave and think like humans. Machine learning utilizes fundamental disciplines like strong programming knowledge skills in languages, like python and R, as well as mathematics and data processing. Machine learning is extensive on data; machines rely on this input to gain knowledge and understanding and also to act independently of human information after complete simulation. Through machine learning, artificially intelligent systems continue to grow in numbers as more intelligent agents are being developed.

    Related: 3 Ways Machine Learning Can Help Entrepreneurs

    The relationship between data science and machine learning

    The relationship between data science and machine learning is symbiotic. They work hand in hand. Data is the big link bridge between the two fields, as both disciplines use data for advanced problem-solving and prediction.

    Machine learning is a development tool for data science. Data scientists research, evaluate and interpret big data, while machine learning engineers, on the other hand, build predictive and simulative models that use decrypted data to further solve problems — for example, the betting companies.

    These companies use data science to study and interpret tons of data from decades of football games. They observe each club’s strengths, the footballers’ skills and consistency. This data was then used to build algorithmic solutions and models that predict the outcome of these games even before they are played. The odds and probability of occurrence are calculated even down to which player scores in these games and the number of shots that could be fired. You can also predict which player will be featured full-time and who will be played as substitutes. Another excellent example of the symbiotic relationship between data science and machine learning is natural language processing. Data from different backgrounds and cultures were collected and studied by data scientists. The data machine learning engineers utilized this data in the development of intelligent agents such as Alexa and Siri.

    You can not think of data without data science and machine learning coming to mind. They carry out specific activities but are strongly interwoven with each other. One is only complete with the other. Yes, you can perform some data analytics activities in data science, but you can only fully utilize that data with machine learning.

    On the other hand, machine learning is supposedly based on building models with this data rather than interpreting it, which can only happen with big data. Both disciplines work with data and work to solve problems with data. Data scientists create and clean these data, analyze them and use them for problem-solving, according to the subject matter. In contrast, machine learning experts study these data over time and build an algorithmic predictive model that uses these data to mimic human thinking, solve advanced problems and predict future trends.

    If I may add a subtext, a data scientist would be the senior colleague of a machine learning engineer. This is because data science is more encompassing and interwoven with different aspects of technology. A machine learning engineer would report to a data scientist because they have the interpreted model of what the machine learning engineer wants to build. The data scientist has a futuristic view of what the predictive model should do, so naturally, the machine learning engineer should report for a clearer picture and alignment of the model with the entire business objective of building the model.

    Having seen the unique and symbiotic relationship between data science and machine learning, let’s look at some use-case scenarios of these power disciplines.

    Related: Big Data Combined With Machine Learning Helps Businesses Make Much Smarter Decisions

    Use cases for data science

    Data science can be used in business for different beneficial purposes. Some of them are highlighted below:

    1. Simple data analytics with Excel (e.g., creating clusters, data collection and organization into structured and unstructured data).

    2. Root cause analysis. Several organizations adopt data science for root cause analysis and resolution. This is done by investigating all collected data on the subject matter and tracing down the root of the problem through different data analysis models/algorithms like classification, binary trees and clustering.

    3. Prediction of future trends by researching and interpreting big data

    4. Design and delivery of user/customer-focused business solutions

    5. User-centered product development and management

    6. Expert decision-making and inferences

    7. Building and development of strategic business models

    Use cases for machine learning

    Machine learning is the propelling force behind artificial intelligence. Highlighted below are some of the use case scenarios for machine learning:

    1. Design and construction of robotics

    2. Design and implementation of natural language processing

    3. Design and building of expert knowledge databases and inference engines

    4. Structure of predictive models for problem-solving

    5. Simulation and construction of artificially intelligent agents (e.g., facial recognition machines and lie detectors).

    Data scientists and machine learning experts are using the plethora of data produced daily to move our world rapidly into the machine age. Here is an era where machines might be as intelligent as human beings or even more intelligent than human beings — a time when devices have evolved beyond every scientific principle. While some believe that time is much closer than farther, it is almost here. In all, data science and machine learning are the two front wheels that are moving us toward singularity in technology.

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    Taiwo Sotikare

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  • 3 Kinds of Ecommerce Data Insights Brick-and-Mortar Retailers Must Use to See Significant Growth

    3 Kinds of Ecommerce Data Insights Brick-and-Mortar Retailers Must Use to See Significant Growth

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    Opinions expressed by Entrepreneur contributors are their own.

    Nowadays, it can sometimes feel like brick-and-mortar retailers are at a distinct disadvantage compared to their ecommerce peers. Reports of ecommerce’s global growth can make it seem like brick-and-mortar retailers are gradually going extinct.

    This isn’t the case. In fact, the National Retail Federation reports that retailers announced over 8,100 store openings in 2021 — more than double the 3,950 announced store closings. While shopper preferences clearly play a factor in the survival and success of brick-and-mortar retailers, so does a store’s ability to use the same kinds of data insights used by ecommerce businesses.

    By focusing on the right kinds of data, brick-and-mortar retailers can gain valuable insights into their customers, effectively helping them achieve even greater growth.

    Related: 4 Ways Brick-and-Mortar Stores Can Outsell Online Retailers

    1. Traffic patterns

    When it comes to tracking store traffic, ecommerce websites have it easy. Website analytics allow them to see how many people visit their website at any given time — and, of course, visitors can access their store 24/7.

    For brick-and-mortar retailers that can’t stay open around the clock, tracking foot traffic can be a bit more challenging yet even more important. Understanding how many customers enter your store at a particular time can help you understand when your store needs the most staff available and even determine key metrics like in-store conversion rates. This can help retailers understand when to run promotions or how to optimize shift scheduling — activities that directly influence sales numbers and customer satisfaction.

    One example of new tech that enables brick-and-mortar retailers to better track their foot traffic is Dor, a people-counting device that uses a thermal sensor to anonymously track how many people enter or exit a store — simply by being mounted over the entryway.

    By collecting traffic data, businesses have the baseline information they need to begin tracking conversion rates and improving their store operations, something that ecommerce retailers have long been able to take for granted.

    2. Tracking shopper behavior

    Ecommerce websites aren’t just able to track how many people visit the website. They can also track what pages they visit, what product promotions yield the most attention and more. Fortunately, brick-and-mortar retailers are increasingly gaining access to tools and devices that also allow them to see how shoppers behave in-store.

    One example of this comes from Shopic, which offers a clip-on smart cart device. In an interview with Cheddar News, Raz Golan, CEO of Shopic explained, “We have created a device that connects to standard shopping carts, turning them into smart carts only when shoppers are using it. So we’re basically allowing grocers to bring all the benefits of online shopping to their physical supermarkets. [In ecommerce], they can measure things online and know exactly what is happening — who clicked on what, how much time they spent, what page. We’re basically unveiling this data that was not available for them in the physical space.”

    The system is able to anonymously report on which items customers purchase, as well as create a heat map that shows which parts of the store they spent the most time in. Such information is helping grocers understand which products sell best at which times, as well as identify ways to optimize their store layout to maximize consumer purchases.

    Related: 67 Fascinating Facts About Ecommerce vs. Brick and Mortar (Infographic)

    3. Inventory management dashboards

    Brick-and-mortar businesses depend on having an adequate inventory of in-demand products. Optimizing in-store inventory allows retailers to restock items on a predictive basis, using analytics trends to identify when and how much to stock each item. This way, they won’t have low-selling items taking up space on store shelves or find that they didn’t order enough of an in-demand item.

    Business intelligence dashboards that provide predictive analytics based on current and past customer behavior can help brick-and-mortar retailers avoid the type of issues Target has experienced recently.

    As The New York Times reports, “[Target] had $15.3 billion in inventory, a 36% increase from a year earlier. As shoppers have curtailed their spending on items deemed discretionary, squeezed by higher-than-usual prices in essential categories like grocery and gas, Target was left with electronics and apparel that people were not buying. Target said it was solving the problem by using discounts and canceling orders for the fall with vendors, which would result in lower profit.”

    A business intelligence dashboard that links with suppliers and helps businesses adapt inventory restocks as needed can help reduce the risk of such occurrences. Reliable inventory tracking, when paired with predictive analytics, will improve profitability.

    Related: How to Survive as a Brick-and-Mortar Retail Store

    While implementing sound data collection practices for a brick-and-mortar retailer may be somewhat more challenging than they would be for an ecommerce store, there is no denying that data can still become a powerful resource for your physical store.

    By taking advantage of the tech integrations that provide ecommerce style data, brick-and-mortar retailers will be better positioned to understand shopper behaviors and market to them appropriately — while also boosting supply chain efficiency to lower operating costs.

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    Andres Tovar

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  • You Have to Tap Into Your Customers’ Subconscious to Keep Them Coming Back

    You Have to Tap Into Your Customers’ Subconscious to Keep Them Coming Back

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    Opinions expressed by Entrepreneur contributors are their own.

    When your app or website was just a small seedling of an idea, you probably had big plans for how people would use it. As you built and tested it, you imagined your product becoming as integral to users’ days as brushing their teeth or checking their emails. That was the hope, at least. But making your product a recurring part of users’ lives is easier said than done.

    To understand why we must first look at the mechanics of human behavior. Per the Society for Personality and Social Psychology, about 40% of people’s daily actions aren’t tied to conscious decision-making. Instead, they’re automatically initiated by situational cues and other triggers. This isn’t necessarily a bad thing. Rather, it’s a way to compartmentalize the myriad decisions we have to make every minute, hour and day. By eating the same thing for breakfast every morning, for example, we free up our mental capacity for more important decisions.

    The question is: How can you make your product so inviting that users have no choice but to incorporate it into their subconscious routines? This is especially important today, as McKinsey & Company found that more consumers have switched brands in 2022 compared to 2021 and 2020. What’s more, 90% of them plan to continue doing so. Here are three tips for creating product usage habits in your users, so they are more inclined to stick with your brand:

    Related: 5 Ways to Set Good Habits That Actually Stick

    1. Dig into your product usage data

    No amount of self-study or controlled testing will teach you more about your user journey — the good, the bad and the ugly — than product usage data (i.e., the information users generate as they interact with your product). From geolocation to session length to tasks completed, these rich insights span numerous types of data and actions.

    For instance, when you open the Grubhub app, it’s not just logging your food order. It’s also looking at where you were when you opened the app, which features you explored versus which ones you bypassed, how long it took you to decide between chicken nuggets and a burger and how long it took for your order to be fulfilled and delivered.

    If that sounds like a lot of data, it’s because it is. But when segmented and analyzed, this treasure trove of information can help you tap into your product’s habit-forming potential. To that end, you should plot two key product usage data points: frequency (i.e., how often users repeat a specific behavior) and perceived utility (i.e., how useful and rewarding users perceive that behavior to be).

    Plotting these points is only step one, however. Next, you need to understand the bigger story behind the actions and what they tell you about the user journey. For example, imagine users are clicking a specific button at a higher frequency. Can you link those button clicks to higher retention among that group? That might tell you the button is a “sticky feature,” or a dependable engagement driver that encourages repeat uses. With that information, you can more easily identify and clear the friction points in your product to deliver greater value and encourage recurrent use.

    Related: Using Data Analytics Will Transform Your Business. Here’s How.

    2. Deploy user-centric reminders

    Unfortunately, developing products isn’t a “build it, and they will come” situation. If you want your product to become second nature to users, you need to develop a messaging strategy that taps into intrinsic motivators and helps users bust through inertia.

    Take 15Five, for example. The team management software platform allows employers to keep a pulse on their employees’ goals through weekly check-ins. Employees must log in to their accounts on a specific day to answer a series of questions and set goals for the upcoming week. But how does 15Five build and maintain engagement in its platform beyond the check-in? Well, mid-way through the week, it sends every employee an email reminding them of their goals.

    Because employees were the ones who set the goals, the reminder acts as an intrinsic motivator to provoke action toward goal completion or adjustment. The messaging that 15Five uses is effective because it’s inherently user-centric: Review your goals. Plus, even if employees don’t go into the app itself, the email nudges them to at least think about their goal progress.

    We know this kind of messaging works. Language-learning platform Duolingo, for example, prompts users via notifications to practice every day and continue their learning streaks. The company’s research shows that these reminders and streaks are highly motivating for users.

    Related: People Love Playing Games. Use These 4 Psychological Hacks to Keep Customers Coming Back for More.

    3. Use hooks to turn behaviors into habits

    Turning conscious behaviors into subconscious habits ultimately comes down to repeatedly linking your users’ problems to your solution. This methodology is what tech entrepreneur Nir Eyal calls the “hook model” in his book “Hooked.” The hook model is made up of a four-phase process with consecutive cycles:

    The first phase is the internal (e.g., users’ intentions or goals) or external (e.g., a “buy now” button) triggers that cue a particular behavior. The second is the completed in-app behavior or action in anticipation of a reward. The third phase is the variable reward, or the result of taking action that leaves users wanting more (e.g., connectedness or physical products). Fourth is the investment that sweetens the deal for future cycles through the hook model.

    When building hooks, you need to get to the heart of each phase in the cycle. For instance, when looking at internal triggers, ask yourself what users want and what pain points your product alleviates. In contrast, if you’re brainstorming external triggers, focus on what brings people to your specific product.

    When looking at actions, try not to overcomplicate things. Instead, look for the simplest action users might take if a reward is involved. Remember, if users don’t have sufficient motivation or ability to complete the action, they won’t. When it comes to the variable reward phase, ask yourself how you can fulfill the reward without veering into finite variability territory. The last thing you want is your experience to become so predictable or boring that users have no reason to return.

    Although variable rewards are about immediate gratification, investments are more focused on long-term rewards. Therefore, think about how much work users are willing to put into your product to enjoy those lasting rewards. Consider a product such as Pinterest, for example. A user might find satisfaction in an individual image on the platform, but that image alone isn’t what builds lasting engagement. Instead, the collection of images across all their Pinterest boards makes the platform more valuable and harder to leave. That’s the investment.

    Every business owner’s dream is to lead a company that’s indispensable to customers’ lives — but doing so requires more than just a good product. Habits are made, not born. So, follow these three tips and see how customers start to incorporate your offering into their routines.

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    Nick Chasinov

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  • How to Give Customers the Digital Experience They Crave

    How to Give Customers the Digital Experience They Crave

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    Opinions expressed by Entrepreneur contributors are their own.

    The discrepancy between the quality of digital experiences customers report and what businesses believe they are delivering online is proving to be more significant than previously thought.

    Only 10% of global customers agree that brands provide a good digital experience, while 82% of marketers believe they are meeting customer experience (CX) expectations. This abysmal statistic serves as a call to action for businesses everywhere — they must prioritize and optimize their online customer experience to meet customer expectations or risk revenue losses and a damaged reputation.

    Anticipating what customers want out of their digital experience through rigorous analysis can have a significant impact on a brand’s success. By adopting best practices, strategies and tooling, businesses across industries can close the gap between what they think they are delivering and what customers report experiencing.

    Related: What Customers Expect Out of Their Digital Experience

    Digital experience makes or breaks a brand

    The digital experience is essential to a company’s profitability and longevity, yet customers feel as though their expectations are not being met on digital platforms. A total of 54% of U.S. customers say the user experience (UX) of brand websites needs improvement. Brands must listen to customers and understand all issues within digital experiences, taking swift steps to address points of friction.

    Responding to problems as they arise is crucial, but it is just as important to be proactive when developing digital experiences. Brands must work to anticipate customer needs and design platforms with evolving customer preferences in mind.

    Eliminating company blindspots through CX enhancements

    Every company has blind spots — business leaders do not understand customers’ wants and needs, so they invest in the wrong areas. Knowing exactly where customers are experiencing pain points instead of guessing is key to delivering a better CX. Executives must take steps to investigate and close this “digital experience gap.”

    Using tools to surface hidden problem areas provides an opportunity to rectify them — giving customers a reason to come back and stay loyal to one’s brand and website. A recent Emplfi study broke down several key areas where customers experience the biggest pain points:

    • Nearly 20% of customers will abandon a website after just one bad experience.
    • Having a previous positive experience with a brand influences where they make a new purchase.
    • Half of customers will abandon a brand they have been loyal to for over a year due to poor CX.
    • Poor CX and low-quality products are equally harmful to a brand.
    • The main contributors to a negative CX are slow response times and a lack of 24/7 customer service. Customers expect a response within an hour.
    • Customers across the board want access to self-service options to resolve issues independently.

    All it takes is one wrong move for a customer to abandon goodwill toward a brand. Companies are increasingly relying on modern digital tools to help identify sources of customer frustrations and mitigate site abandonment.

    Related: 5 Ways to Show Your Customers You Understand Them in a Digital-First World

    Proven strategies to tackle problem CX areas

    A total of 86% of customers say that they are ready to pay more for a better customer experience, making digital experience improvements a revenue-driving opportunity. Implementing technology that can help businesses anticipate customer needs and respond to user issues in real time can lead to increased conversions and enhanced efficiency. Proven strategies include:

    • Leverage AI: Implementing an AI-driven digital experience analytics platform enables businesses to proactively identify and resolve problems surfaced through customer feedback and interactions data.
    • Prioritize a self-service model: Customers expect immediate answers to any issues they may encounter without having to deal with customer service representatives. Incorporating a chatbot, dynamic FAQs and semantic search engines help customers find their answers with ease.
    • Individualization: An individualized digital experience for each customer is essential, as nearly three-quarters of customers expect personalized interactions. Furthermore, 76% are frustrated when personal interactions aren’t delivered.

    The power of data and analytics

    Businesses cannot close the digital experience gap and meet their customers’ expectations if they do not have a thorough understanding of how customers are navigating their digital platforms. To achieve that understanding, they can integrate analytics solutions such as a Digital Experience Intelligence (DXI) platform to capture and analyze 100% of customer interactions across channels.

    As a DXI platform serves as a single source of truth, the analyses can be used by various teams, helping businesses prioritize and quickly make data-driven decisions about customer experience improvements. Teams are immediately alerted to technical issues on a brand’s website or mobile app so they can be solved before significantly impacting revenue or the customer experience, ensuring a frictionless journey.

    Related: 3 Tips for Using Consumer Data to Create More Personalized Experiences

    Improve digital experiences now for the future

    It has never been more important to close the digital experience gap. The customer journey is invaluable; maintaining an exceptional digital experience requires teams to work diligently behind the scenes to tackle any possible issues before they escalate.

    Implementing strategies that prioritize anticipating and meeting customer needs ensures long-term brand success. Through best practices, businesses across industries will soon deliver the quality experience customers say has been missing from their digital journeys.

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    Asim Zaheer

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  • How to Become Master of Your Data with Microsoft SQL For Only $40

    How to Become Master of Your Data with Microsoft SQL For Only $40

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    Opinions expressed by Entrepreneur contributors are their own.

    Data is vital in business. Every entrepreneur, no matter how big their company is, can use data to draw valuable insights about their business and customers. But not all data sources are created equal and not all are as usable as you’d like them to be. That’s why it’s valuable to have an understanding of tools like Microsoft SQL.


    StackCommerce

    SQL is a special programming language designed for managing data held in relational database management systems. It helps you retrieve, organize, and manage data much more effectively, making it more usable for your aims. If you’re struggling with your data, it’s time to delve into The 2023 Professional Microsoft SQL Database Development Bundle.

    This eight-course bundle is curated and taught by Packt Publishing (4.0/5-star instructor rating). Packt has published more than 4,000 e-books and videos to date, helping IT professionals and newbies alike learn the actionable knowledge they need to thrive in a competitive space.

    This beginner-friendly bundle will start you out with the absolute basics. You’ll learn the difference between the query language and databases, understand SQL Server structure, and learn how to insert and delete values in a table using SQL Statements. From there, you’ll learn how to use SQL for practical skills like marketing by writing statements to select data and learning how to aggregate data to perform functions like counting.

    As you get more comfortable with SQL, you’ll expand your knowledge by learning how to build applications that utilize SQL, understanding modern web development with MySQL and Heroku, and much more. There are also courses on using SQL in the cloud, PostgreSQL, and more.

    Work with data like a pro. Right now, you can get The 2023 Professional Microsoft SQL Database Development Bundle on sale for a limited time for just $40.

    Prices subject to change.

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    Entrepreneur Store

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  • What SaaS Companies Need to Focus on to Survive Market Downturns

    What SaaS Companies Need to Focus on to Survive Market Downturns

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    Opinions expressed by Entrepreneur contributors are their own.

    If this is your first market downturn, you may be especially confused by the conflicting advice arising from such an event. To some, the sky is falling, and you should quickly change your model. To others, the pastures are green, and you should take advantage of the weakened landscape. Which one you are depends on what the data tells you about your business.

    Right now, the data from the venture capital world can feel bleak: Global VC funding fell 33% quarter-over-quarter in Q3 2022. SaaS, specifically, has seen valuations slide since the beginning of 2022. However, not all companies are created equal.

    The valuation decline has been the steepest for companies not focused on their data, specifically their unit economics. In those unit economics, you’ll discover whether you should bear down to weather the storm or attack the market to expand your dominance. Either way, the decisions you make now should be strongly rooted in your unit economics.

    Related: 2022’s Top Trends Impacting SaaS Company Funding and Growth

    The pendulum swing

    We all benefited from larger funds and higher valuations. A rising tide raises all ships; unfortunately, that includes the leaky ones. The glut of available capital meant companies performing at mediocre and poor levels from an efficiency perspective could still grow quickly. In some cases, investors were pushing companies to take more chances and bet on future growth, sacrificing efficiency and certainly profitability.

    Those days of “growth at all costs” seem behind us. As markets sank and capital tightened, funders scrutinized their deals harder. They now seek companies demonstrating the fundamentals of running a scalable SaaS company, with efficiency and a strong path to profitability as hallmarks.

    The metrics that matter

    To be clear, SaaS companies cannot survive without growth — dominating your space requires it. But growth can no longer come at all costs, and companies must display certain fundamental metrics to support faster growth. SaaS companies should track dozens of metrics, but to attract investment in the current market, companies must address their efficiency metrics, especially:

    • Gross retention, with a goal of 90%+;

    • Net retention, with a goal of 110%+;

    • Gross margins, with a goal of 75%+;

    • Cost of acquisition (CAC), with a payback goal of <2 years

    Achieving these efficiency metrics will help companies maintain or exceed their valuations. If you’re already achieving these metrics, then you’ve earned the right to discuss deploying more capital in exchange for growth. If you aren’t, consider slowing growth and redirecting your strategy, especially if capital is tight.

    Related: Four Ways To Ensure Your Company Will Survive A Market Downturn

    The cost of capital without efficiency

    The higher cost of capital may prove incredibly expensive for companies buying time to achieve efficient growth. Beyond tightened funding requirements and depressed valuations, investors are placing more funder-friendly structures into deals with less fundamentally sound companies, including liquidation preferences, voting rights and even board control to reduce their downside risk. In fact, overly flawed later-stage companies may struggle to find funding on acceptable terms and may have to explore an exit or consolidation. But those wanting to tough it out and buy time to see better metrics have options.

    What can leaders do now?

    Start by scrutinizing your business fundamentals and assessing the efficiency of your core operating teams, then adjust to reduce inefficient spending.

    • Sales: Review metrics like pipeline-to-bookings ratio (with a goal of 4-5x+) and average seller’s quota attainment (with a goal of 65%+). This information will focus your efforts and help you find needle-moving improvements before simply growing your sales teams without correcting underlying issues.

    • Marketing: Focus on efficiency metrics like your cost per opportunity across every channel and over-invest in high-performing channels.

    • Product teams: Consider tracking efficiency based on a product productivity benchmark and monitor user-to-issues ratios. You might invest more in customer features and platform stability over new builds to increase retention and enable higher converting upsells.

    • Customer success: Examine retention rates across various customer segmentations to understand your customer base’s strengths and weaknesses. Optimize your book of business-to-customer rep ratios, and heed customer Net Promoter Scores and other sentiment metrics.

    As you adjust, you may need to shrink your teams and rightsize your operation. It’s an unpleasant reality, but you should fill any cracks in your ship before renewing your push for growth. This can help control your burn rate and buy the time needed to convince an investor you’re on the path to efficiency.

    Related: 4 Tips To Keep Your Business Afloat in a Downturn

    Where is the funding?

    Valuations likely won’t reach 2021 numbers, but companies with strong fundamentals will find funding. Companies correcting their fundamentals and needing to buy time with capital will find tougher markets. So, where else can you go?

    Start with your current investor base. They have as much to lose as you do, and in the case of venture capitalists, they often have allocated “dry powder” for situations like these. They may also behave more moderately as bad valuations and more structure can often hurt their previous positions. Another way to avoid a down round in the short term is by raising via convertible notes.

    If equity is not an option, climbing interest rates have made debt providers more active, creating an opportunity to explore debt financing. If it’s available to you and makes sense financially, leveraging debt lets you raise non-dilutive capital that buys you time to achieve better efficiency metrics. Timing matters, however, as the debt market can ebb fast should monetary policies change further.

    The funding silver lining

    Companies that rightsize their operations and control their burn for the next year might find a funding pool at the end of the proverbial rainbow. Funds with charters to invest in private tech companies are riding out the troubled market on the sidelines. As the market improves, funds will further open their checkbooks to companies with healthy efficiency metrics.

    Valuations may not have completely rebounded by then, but companies will keep raising at good multiples if they demonstrate solid fundamentals and maintain healthy efficiency metrics alongside growth rates. These companies are best prepared to ride out the falling wave and catch the rising tide again.

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    Afif Khoury

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  • Your Documents Aren’t Safe. Here Are the Best Practices for Document Security

    Your Documents Aren’t Safe. Here Are the Best Practices for Document Security

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    Opinions expressed by Entrepreneur contributors are their own.

    With the advent of 5G technology and Industry 4.0 putting more pressure on businesses to fast-track their digital transformations, the demand for document-management solutions has exploded. The worldwide market for document-management software is projected to reach $10.17 billion by 2025. Along with this revolution comes inherent concerns about properly securing all this information. Documents often contain sensitive and private information that, if compromised, could be detrimental to individuals, businesses or governments. That is why companies need to incorporate the highest levels of document-management security.

    Related: Keep Your Information Moving At The Speed Of Your Business

    Don’t wait to secure digital documents

    With the continued release of new vulnerabilities regularly and the ease at which a digital document can be compromised — compared to a physical piece of paper — ensuring the security of those documents has become more important than ever to keep private information from being exposed.

    It is common to read the news and learn about a new security breach. Impacting small and large companies, nearly 2000 data breaches occurred in the first half of 2022 alone. To many companies, their data is among their most valuable assets, so it must be protected.

    Ransomeware, a form of malware designed to encrypt files and deny users access to them until a demand ransom is paid, is one clear threat. Phishing attacks, where hackers try to get account credentials (username and password), represent an ongoing and ever-evolving danger. Hackers typically lay low for a time, then eventually start logging in as that user so as not to draw suspicions. Then they download documents that the user can access or, if sophisticated enough, attack network administrator privileges.

    Just who is trying to hack into systems to get documents? Anyone who can find value in the type of data a company possesses. Hackers typically don’t know the type of data a company possesses until they get their hands on corporate documents or know enough about a company to recognize the types of information that might be available, such as financials or employee personally identifiable information (PII). It’s really any documents that they can use for profit.

    What to look for in a document-management partner

    Numerous outsourced document-management vendors exist in the marketplace today, and not all are created equal when it comes to offering the highest levels of security. Below are four necessary security features to look for from a document-management partner:

    1. End-to-end chain of custody and tracking: It’s important to know who has had access to both physical and digital documents. Chain of custody is crucial throughout a document’s life cycle. Any access should be logged so that you can see who opened a particular document, when and what their reason was. Partners should be able to show audit and chain-of-custody logs. This also helps ensure that only people with the proper privileges can access particular documents — and no one else.
    2. Disaster recovery, failover, redundancy, and guaranteed access: With a reduction in paper documents, systems and processes need to be in place to ensure that your digital documents are accessible in the event of a single point of failure. At the partner’s data center, if the internet goes down, you still should have a backup, redundant way to access those docs. Partners should be able to provide written reports that show testing on an ongoing basis along with results, so you feel confident that if disaster strikes, you know the failover will work properly.
    3. Compliance with industry standards: Compliance standards, such as PCI for credit card information, HIPAA for health information and SOC 2 Type II for policies and processes, ensure complete accountability for the security and related processes around any document. Compliance usually involves an independent third-party assessment to ensure that partners are following industry guidelines, performing the necessary tasks and have the appropriate controls in place to ensure the highest levels of security. Partners should be able to provide evidence of certifications, indicating they meet the necessary compliance standards for the types of documents that you’re storing.
    4. Utilization of a “continuous ongoing compliance” model: One of the drawbacks of compliance is that it’s an annual assessment, so sometimes companies get lax throughout the year — then get ready just at compliance time. Partners should be able to demonstrate compliance not only at assessment time but also throughout the year.

    Related: How To Develop Security Policy For Your Company

    Best practices companies can implement

    In addition to wanting the best technology solutions to help facilitate the digitization of documents, companies should also make security a top priority. Whether you have a Chief Security Officer, Chief Technology Officer, Head of IT or are working with a third-party service provider, there are several best practices that companies themselves should implement to ensure they’re doing their part to secure their digital documents:

    • Make security a primary, proactive focus and not an afterthought;
    • Perform a complete audit of all access to and actions taken on each digital document;
    • Ensure proper data classification, retention, and destruction protocols are established and followed;
    • Test and document disaster-recovery and business-continuity solutions;
    • Run regular vulnerability scans of the environment and remediation of all critical vulnerabilities found;
    • Hold recurring security-awareness training with 100% required staff participation; and
    • Conduct regular chain-of-custody and security audits to ensure best practices are being followed and documented.

    To obtain the highest levels of security for digital documents, collaboration on strategy should involve all stakeholders — including document-management providers, IT, security and operations.

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    David Winkler

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