Tag: Big Data

How Data Analytics Can Transform Your Business | Entrepreneur

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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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June 5, 2023
7 Ways Data Helps Your Restaurant Succeed | Entrepreneur

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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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May 29, 2023
How Contextual Data Is Revolutionizing Advertising | Entrepreneur
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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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May 17, 2023
How Data-Driven Marketing Strategies Help You Achieve Growth | Entrepreneur

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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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May 12, 2023
MEPs cling to TikTok for Gen Z votes

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Voiced by artificial intelligence.

It may come with security risks but, for European Parliamentarians, TikTok is just too good a political tool to abandon.

Staff at the European Parliament were ordered to delete the video-sharing application from any work devices by March 20, after an edict last month from the Parliament’s President Roberta Metsola cited cybersecurity risks about the Chinese-owned platform. The chamber also “strongly recommended” that members of the European Parliament and their political advisers give up the app.

But with European Parliament elections scheduled for late spring 2024, the chamber’s political groups and many of its members are opting to stay on TikTok to win over the hearts and minds of the platform’s user base of young voters. TikTok says around 125 million Europeans actively use the app every month on average.

“It’s always important in my parliamentary work to communicate beyond those who are already convinced,” said Leïla Chaibi, a French far-left lawmaker who has 3,500 TikTok followers and has previously used the tool to broadcast videos from Strasbourg explaining how the EU Parliament works.

Malte Gallée, a 29-year-old German Greens lawmaker with over 36,000 followers on TikTok, said, “There are so many young people there but also more and more older people joining there. For me as a politician of course it’s important to be where the people that I represent are, and to know what they’re talking about.”

Finding Gen Z

Parliament took its decision to ban the app from staffers’ phones in late February, in the wake of similar moves by the European Commission, Council of the EU and the bloc’s diplomatic service.

A letter from the Parliament’s top IT official, obtained by POLITICO, said the institution took the decision after seeing similar bans by the likes of the U.S. federal government and the European Commission and to prevent “possible threats” against the Parliament and its lawmakers.

For the chamber, it was a remarkable U-turn. Just a few months earlier its top lawmakers in the institution’s Bureau, including President Metsola and 14 vice presidents, approved the launch of an official Parliament account on TikTok, according to a “TikTok strategy” document from the Parliament’s communications directorate-general dated November 18 and seen by POLITICO.

“Members and political groups are increasingly opening TikTok accounts,” stated the document, pointing out that teenagers then aged 16 will be eligible to vote in 2024. “The main purpose of opening a TikTok channel for the European Parliament is to connect directly with the young generation and first time voters in the European elections in 2024, especially among Generation Z,” it said.

Another supposed benefit of launching an official TikTok account would be countering disinformation about the war in Ukraine, the document stated.

Most awkwardly, the only sizeable TikTok account claiming to represent the European Parliament is actually a fake one that Parliament has asked TikTok to remove.

Dummy phones and workarounds

Among those who stand to lose out from the new TikTok policy are the European Parliament’s political groupings. Some of these groups have sizeable reach on the Chinese-owned app.

All political groups with a TikTok account said they will use dedicated computers in order to skirt the TikTok ban on work devices | Khaled Desouki/AFP via Getty Images

The largest group, the center-right European People’s Party, has 51,000 followers on TikTok. Spokesperson Pedro López previously dismissed the Parliament’s move to stop using TikTok as “absurd,” vowing the EPP’s account will stay up and active. López wrote to POLITICO that “we will use dedicated computers … only for TikTok and not connected to any EP or EPP network.”

That’s the same strategy that all other political groups with a TikTok account — The Left, Socialists and Democrats (S&D) and Liberal Renew groups — said they will use in order to skirt the TikTok ban on work devices like phones, computers or tablets, according to spokespeople. Around 30 Renew Europe lawmakers are active on the platform, according to the group’s spokesperson.

Beyond the groups, it’s the individual members of parliament — especially those popular on the app — that are pushing back on efforts to restrict its use.

Clare Daly, an Irish independent member who sits with the Left group, is one of the most popular MEPs on the platform with over 370,000 subscribed to watch clips of her plenary speeches. Daly has gained some 80,000 extra followers in just the few weeks since Parliament’s ban was announced.

Daly in an email railed against Parliament’s new policy: “This decision is not guided by a serious threat assessment. It is security theatre, more about appeasing a climate of geopolitical sinophobia in EU politics than it is about protecting sensitive information or mitigating cybersecurity threats,” she said.

According to Moritz Körner, an MEP from the centrist Renew Europe group, cybersecurity should be a priority. “Politicians should think about cybersecurity and espionage first and before thinking about their elections to the European Parliament,” he told POLITICO, adding that he doesn’t have a TikTok account.

Others are finding workarounds to have it both ways.

“We will use a dummy phone and not our work phones anymore. That [dummy] phone will only be used for producing videos,” said an assistant to German Social-democrat member Delara Burkhardt, who has close to 2,000 followers. The assistant credited the platform with driving a friendlier, less abrasive political debate than other platforms like Twitter: “On TikTok the culture is nicer, we get more questions.”

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Eddy Wax and Clothilde Goujard

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March 23, 2023
The Importance of Data in Outdoor Advertising | Entrepreneur

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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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March 6, 2023
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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January 4, 2023
The Beginner’s Guide to Understanding Data Science and Machine Learning
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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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December 21, 2022
How to Prepare Your Digital Marketing Team For 2023

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As the shift to digital continues, traditional marketing is increasingly being disrupted by digital marketing. Digital marketing teams are under enormous pressure to manage this transition, seize the opportunities offered by digital marketing and deliver outsized gains. Here are seven tactics for doing that.

Automate to improve workflow efficiency

Improving workflow efficiency is a great way to enhance the client experience, reduce costs of service and improve agency profitability. A decade ago, venture capitalist Marc Andreessen said that “software is eating the world.” Today, it is truer to say that artificial intelligence (AI) is eating the world. AI is disrupting industry after industry, and digital marketing is no exception. One easily deployable tool is the use of conversational intelligence to unearth insights from conversations with clients and improve marketing strategies. For instance, conversational intelligence can determine which keywords clients use and shift marketing so that products and scenarios reflect customer language.

SEO remains important

Any marketing expert will tell you that search engine optimization (SEO) has grown beyond the days when all that mattered was sending a site up Google’s page ranking. Although rising up Google’s page ranking remains important, SEO has changed in fundamental ways. Digital marketers have to consider alternative SEO strategies. Although Google’s dominance among search engines continues to rise, search strategies on platforms such as Amazon, Instagram and Twitter will become increasingly important.

Stand out with thought leadership

Thought leadership is a novel and increasingly important way to grow a brand. It’s also a high-margin way of doing it, requiring very little economic investment. What it does require is an investment in developing unique insights. This may even require having someone whose job is to do nothing but read, think, write or post videos.

People are hungry for insight, and what they want is someone who can deliver profound insights that make it worth their time. If you can win people’s trust as a reliable authority, you will build the value of your brand.

Accumulate data

In the era of Big Data, data is the new oil. Possessing data is the foundation of a great business. Your business needs to invest in tracking your client’s activity not just on client websites, but also on third-party websites. On Apple products, they will of course have to explicitly consent to this, and you might even proactively seek their consent when they land on the customer’s website. The fraction of traffic that you can track will give you insights into what people want. Lead conversion tracking is vital.

If you can’t measure something, it’s hard to know what you are doing right. Data collection is key for understanding what’s working in your digital marketing strategy and therefore reducing the cost of acquiring each customer. It’s also important for doing right by your customers by giving them just what they want and need. Without data, you cannot maximize the return on investment (ROI) on your campaigns. In this way, you can increase your chances of retaining your customers.

Prepare for Google Analytics 4

From July 1, 2023, Universal Analytics will no longer process data. Although you will still be able to see Universal Analytics reports for some time, new data will be processed by Google Analytics 4 properties. Google Analytics 4 has been touted as the “next generation of Analytics”.

It’s advisable to start running both Universal Analytics and Google Analytics 4 in preparation for the transition so that by the time July arrives, you are comfortable with Google Analytics 4. Furthermore, you should start using Google Analytics 4’s tracking features to give yourself a little treasure trove of data once the July deadline arrives.

Build customer loyalty

Each customer’s lifetime value is determined by the degree of customer loyalty that you can foster. This is not only because clients will stay longer the more loyal they are, but it is also because loyalty will lead them to use more and more of your services, allowing you to “land and expand”.

In order to enhance customer loyalty beyond what your own operational excellence can achieve, you should offer customers freebies, loyalty and referral discounts, and you should over-communicate your fulfillment expectations to build trust. These things are even more pertinent in times like these when customers exist in a state of uncertainty and might even be experiencing declining revenues. These investments pay off in the long run. You want to go the extra mile for repeat customers so they understand just how valuable they are to you.

Forget third-party cookies

Third-party tracking is dying. Apple is one of the big reasons for this, as it leads a push to answer customer fears about privacy. For digital marketers, what this means is that you will have to reimagine how you advertise in a world without third-party cookies. More specifically, digital marketers will have to do more to collect first-party data. Data’s importance remains, but now, success will depend on if you own enough data to gain meaningful insights. Digital marketers who can succeed in building out their first-party data will be ahead of those who cannot and will be able to analyze and forecast the performance of their ads and conduct better market research.

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Mark Pierce

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December 21, 2022
3 Kinds of Ecommerce Data Insights Brick-and-Mortar Retailers Must Use to See Significant Growth

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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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December 16, 2022
Drive Product Growth With A Metric That Guides You to Success.
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As China continues to crack down on how companies handle user data, it’s time for the business world to think about what exactly they’re collecting and measuring. The country’s strict data privacy laws make it more challenging to store and manage Chinese consumers’ data, but it could also have more wide-reaching ramifications if other countries decide to adopt similar regulations (much like the EU’s General Data Protection Regulation). This will lead to a new digital marketing landscape when it comes to data and metrics.

Not long ago, marketing and growth teams relied on just a handful of metrics to analyze campaigns and measure business performance: revenue, expenses and profit. Then, the internet exploded, ushering everyone into the information age. The rapid proliferation of technology, product development and data-collection methods created a feeding frenzy of sorts.

Marketers and product teams began capturing and measuring anything and everything they could get their hands on. Their intentions were good: They thought if they collected every piece of data available, then voila, those metrics would reveal what was and wasn’t working in their products. In practice, however, they simply created a game of “find the needle in the haystack.” And unfortunately, there’s no winning that game.

When it comes to product growth metrics, more isn’t always better. Having too many metrics is as bad as having none at all. Simply look at the sheer amount of data people generate to understand why. Research estimates that humans collectively will create more than 180 zettabytes of data by 2025. To put that in perspective, that’s equivalent to the storage of 2,587 iPhone 13 Pros per second (1 terabyte model).

Imagine the resources and time it would take to track this much data. Plus, some of the information could be old or obsolete. Other metrics might be readily available but ultimately lack relevance and practicality. In the end, you’re data-rich but insight-poor — not a good position to be in.

Why do you need a North Star metric?

Rather than chasing down any metric that feels remotely related to your product, consider centering your product growth strategy around a singular guiding metric. Just as sailors used the North Star located directly above the Earth’s northern celestial pole to navigate oceans, you can use a North Star metric to align your team around the top-line goal of product growth.

Of course, the sales, engineering, product and marketing teams can still have their own subgoals and metrics. But having that North Star shining brightly overhead keeps everyone moving in the same general direction. Because a North Star metric is focused on overall product growth, there’s a built-in level of teamwide transparency and camaraderie not found in other team-specific initiatives.

However, what makes a North Star metric such an effective measure of success is its intrinsic relationship to users. By definition, a North Star metric is the number that best reflects the value your product delivers to users. Therefore, your teams will always be aligned and working together to grow your product.

Related: Customer Experience Will Determine the Success of Your Company

What constitutes a North Star metric?

So, what exactly is a North Star metric? It’s important to note that revenue isn’t a North Star metric. When you track your product’s revenue, you track how much money you made at the end of the month, quarter or year. Though this is a decent indicator of success, it’s not user-specific. For example, revenue alone can’t tell you how much the average user spends on your products and how long they remain loyal.

In general, there are five categories of North Star metrics:
1. Customer growth: Customer growth-focused North Star metrics include market share and number of paid users, among others.
2. Consumption growth: Consumption goes beyond mere site visits. Instead, think about this category through the lens of product usage, such as messages sent or classes attended.
3. Engagement growth: If your product is an app, you might use engagement metrics — such as monthly or daily active users — to track the number of unique users within a specific time period.
4. Growth efficiency: When comparing the value of a new user relative to the cost of acquiring one, you might leverage metrics around lifetime value and customer acquisition costs as your North Star.
5. User experience: User experience metrics, such as net promoter score, provide data that helps you measure user satisfaction and product experience.
Related: 4 Reasons Sharing Performance Metrics Will Accelerate Your Business

What’s your North Star?

Your North Star metric should be the one that’s most predictive of your product’s sustained success and how users get value from the product. Therefore, it will vary based on your industry, audience, offering, etc. For instance, a fintech product might coalesce around the total assets under its management or daily active users. In contrast, streaming company Netflix uses total hours streamed as their North Star metric.

Of course, the metric you choose must be regularly measurable. It also needs to fulfill two other criteria to be considered a North Star metric: help generate revenue and mirror customer value.

1. Help generate revenue

A metric that doesn’t measure advancement toward goals in a way that informs your next steps won’t be useful at all. So, make sure you can directly tie your North Star metric to product growth. Airbnb‘s North Star metric, for example, is number of nights booked. This reveals platform growth and correlates with the value customers and hosts receive from good experiences.

Just remember that it’s important to balance this criterion with the other two. For instance, if you hang your hat on a money-centric metric to the detriment of customer satisfaction, you’ll ultimately drive users away. On the other hand, you can’t prioritize customer satisfaction at all costs, or you’ll run yourself out of business.

2. Mirror customer value

Your North Star metric needs to encompass what users find valuable about your product. If you fail to understand what they appreciate, then you’ll end up measuring the wrong thing. For instance, users disliked having to log in to Meta‘s virtual reality headset with a Facebook account. Meta was too focused on boosting its social media platform to realize that its audience wanted more flexibility and anonymity.

To define your North Star metric, gather key stakeholders to outline your company’s needs and the value your product adds to users’ lives. Determine whether a metric helps users achieve the intended results of your offering. Look at the external factors that might impact your North Star metric, as well as the internal ones within your control.

Related: How to Keep Leaders Focused on a Company’s Most Important Metrics

Long ago, sailors turned their eyes to the sky to determine where they were going and what adventures awaited. In the same way, you can use your North Star metric to inform your product growth strategy no matter what the future holds.
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Nick Chasinov

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October 21, 2022
Leader in Internet of RoadWork™ Announces Formation of the R.O.A.D™ Database

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Press Release
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Mar 2, 2022

CATO, N.Y., March 2, 2022 (Newswire.com)
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iCone Products LLC announces today the creation of a cloud-based data service for digitally marked work zone events called the R.O.A.D™ Database.

Smart and connected work zones are becoming commonplace in North America, supported by integrated Internet of Things (IoT) technology. Consolidating this set of traffic control and roadway infrastructure devices – otherwise known as an “Internet of Road Work (IoR^™)” – allows drivers, contractors, traffic managers, and others to receive real-time status information about key traffic control devices being used in roadway construction operations and any disruptive anomalies they may cause to surface transportation.

The Real Ontime Accurate Data (R. O. A. D.)™ for Work Zones is a cloud-based database of road event data supplied by preeminent equipment and infrastructure developers, IoT equipment retrofit companies, construction operators, maintenance and traffic control companies. These organizations all work together to provide data for safer roads. This database represents an industry-first in that it is populated by the actual producers and generators of the work-zone data, collectively known as the Work-Zone Data Industry (WZDi™).

The R.O.A.D Database will be the clearinghouse for work zone-generated attributes that may require corrective or attentive action or alertness from roadway users. The launch of the R.O.A.D database will involve a significant portion of the WZDi, made possible through relationships with several major traffic control equipment manufacturers, pavement marking companies and other significant producers of smart work zone technology and data that perform tasks on a daily basis such as temporary work zones, lane closures, protecting lives and equipment, painting lanes, closing roads, and performing maintenance, through automatic communications from devices integrated inside roadway assets.

One of the founding data suppliers to the R.O.A.D Database is the originator of the Internet of RoadWork (IoR^™), iCone Products’ ConnectedTech™ Data Community (CTDC), which combines universal smart retrofit kits for work zone equipment and includes data from devices built by most of the companies within the portable traffic control equipment market.

The R.O.A.D Database will adhere to the established protocol of data feeds for the collection and distribution of information related to the location and activity of road work developed by The Federal Geographic Data Committee Transportation Subcommittee Work Zone Data Working Group. R.O.A.D. intends to enhance this feed with additional certification of valid traffic control components with secure, documented data transfers, with a verified chain of custody from the field device to the client. This extra layer of compliance is the WZDi’s process of ensuring that the data produced is accurate, timely, and meaningful.

About iCone Products:

iCone Products, LLC is guiding the future of Work Zone Safety. As the creator of the IoR – Internet of Roadwork^TM, iCone has developed a suite of ConnectedTech™ products that collects and transmits real-time information about the status of the roadways into the cloud. Navigation applications, traffic control centers, and contractors receive the information to assist motorists in navigation, protect crews in work zones, and create an overall safer environment on public roadways around the world. As an integrated technology platform, iCone’s devices can mark virtually anything that may intrude upon a public right-of-way that would cause a vehicle to change either its speed or direction of travel.

iCone’s ConnectedTech Data Community is designed to provide a unique and broad digital collection of surface roadway operators that collectively contribute information along with iCone Internet of Roadwork (IoR™) – enabled equipment to a repository of data that provides increased safety through an additional layer of security for both workers and oncoming motorists. This data is also provided to the navigation community, improving route navigation through direct communication with in-car navigation systems such as Google Maps, Waze and eventually directly displayed in the dash of automobiles.

All Inquiries & Press, please contact:

Garvin Forrester, COO

g.forrester@iconeproducts.com, Ph: (315) 626-6800

Source: iCone Products LLC

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March 2, 2022