Collecting Data Should Not Become Your Strategy — It Should Enable It
Why are companies struggling so much with data use?
The conversation is the same regardless of the widgets or services your company may sell: What about the data? Small talk during a round of golf inevitably leads to: So, how are you guys using your data? Chance acquaintances on airplanes suddenly turn semi-serious: Well, I imagine you have a good data strategy, right?
While they suggest a sense of professional nonchalance with data, the questions are actually hidden pleas of desperation. What is being asked is: “Please, give me some pointers on better exploiting my data to grow sales. I am so confused and so overwhelmed.”
Overwhelmed — there is just too much data!
That is how many entrepreneurs and business professionals feel when they start talking about data. There is often a sense that something is being missed. The question that hangs in the air in meetings of top managers is: Are we doing enough? Can we do more with the tremendous amounts of data being amassed? Are we “data-driven” or being driven mad by the data?
Data should be an asset for your business and not a burden. Yet, too many companies pour resources into collecting and even analyzing the information gleaned from customers, operations, and logistics and then fail to extract the gems hidden in the numbers that can resolve the bottlenecks that often mean the difference between market leader and market straggler.
One prominent hurdle managers must overcome is getting their companies to think the same way about data — instilling a data ethos or a data culture is not as easy as just saying, “We should all be thinking about data, people.” Turning your company into an infocentric or infosavvy one is a lot easier said than done.
It is necessary to create an analytics strategy. What does this mean? Quite simply — but not really — a roadmap of the capabilities that will make your company a data-driven one must be brainstormed. By looking at best practices for data usage and identifying the right people, either internally or as an external data advisor, it is necessary to build a community in the company around data.
Another problem companies face in using data is the establishment of data silos. Information gets spread across departments, and managers don’t share what they have. This results in duplication of efforts. The overall operation of the company is hindered because everyone is reading music from different sheets. The problems with data use are many, and each company probably has its own unique issues with which it deals.
Examples of Companies Crushing It on Data
Above, we discussed some of the most significant problems facing companies in their quest to use the data. Every company has its data usage issue that only pertains to its business, so endlessly discussing them will be less beneficial. Let’s look at one of the better-known success stories of data-driven companies: Starbucks.
Starbucks
Starbucks is the perfect place to enjoy high-quality coffee and a cozy atmosphere. With over 30,000 locations worldwide, it is a trusted brand that values customer satisfaction above all else. By utilizing advanced data analytics, Starbucks can tailor their product offerings and store layouts to meet the demands of its customers. This data-driven approach ensures that you’ll always find a drink you love at Starbucks worldwide, but it might be the company’s real estate decisions that are more valuable to its leading position as a coffee -provider.
Starbucks contracts with a location analytics company called Esri to use its technology platform that helps analyze maps and retail locations. It uses data like population density, average incomes, and traffic patterns to identify target areas for a new store. Starbucks uses both local and corporate-level approaches to new stores. They have 20 analytics experts around the world analyzing maps and geographic information systems data but also empower regional teams to give input on location, store design, and other issues (Starbucks’ Secret Ingredient).
Experts at Starbucks implement a combination of predictive and prescriptive analytics. Esri provides a detailed spatial analysis of a given region, and the experts determine the average estimated customer count, ticket size, and customer spend using the predictive model. The economic viability of opening a new store is determined by incorporating the cost structure in this model.
Menu decisions
Starbucks creates its menu by conducting extensive research and testing to identify popular trends and customer preferences. They also consider factors like seasonality, regional tastes, and dietary restrictions. Once this information has been gathered, new menu items are developed and tested in select markets to see how customers respond. Based on the results, further adjustments are made before the products get rolled out nationwide. Starbucks regularly updates its menu to keep it fresh and exciting for customers.
Starbucks also uses data to help align its menu and product lines with consumer preferences. For example, when building out its grocery lines of K-cups and bottled beverages, Starbucks used data from its stores as well as customer market research to decide which products to create. One finding was that many tea drinkers don’t put sugar in their tea, so Starbucks created two unsweetened tea K-cups (Secret Ingredients).
Starbucks uses data to optimize its menu boards, which are digital displays that show customers the menu and prices by analyzing data on customer behavior, such as which items are most popular and at what times of day. Starbucks, in particular, hopes the digital boards will help increase sales during the after-lunch time, traditionally considered sluggish.
The company also uses the boards to promote seasonal items and limited-time offers, creating a sense of urgency and encouraging customers to try something new. Optimizing menu boards with data is just one of the many ways the company uses data to enhance the customer experience and drive business growth.
Loyalty
Starbucks has set a standard for other companies to follow with its highly successful loyalty programs. Among its many programs, the Starbucks Rewards program stands out for its effectiveness in driving customer loyalty and increasing sales. Customers earn points for every purchase they make at Starbucks, which can be redeemed for free drinks, food, and other rewards. The program has proven so popular that it has become a key driver of customer engagement and sales growth.
Starbucks also leverages personalized promotions through its mobile app, based on each customer’s unique purchase history and preferences, to further enhance customer engagement. Starbucks has demonstrated that a well-designed loyalty program can be a powerful tool in driving customer loyalty and sales and has set a high bar for others to follow.
Starbucks has also partnered with other companies to expand its rewards program. For example, they have teamed up with Spotify to allow customers to earn points for music streaming through the Starbucks app. This increases customer engagement with the app and provides valuable data on music preferences.
The expansive and dynamic loyalty program drives repeat business by using customer data to create personalized offers and incentives. This benefits the bottom line and enhances the overall customer experience.
The Starbucks loyalty program has been one of the most popular programs of its kind in the industry — if not the most popular. Starbucks estimates that around 40 percent of its sales come from promotions pushed through its loyalty program. With over 30 million active members, the company’s continued investment in its mobile interface increases the frequency and spend of both regular and occasional customers. Starbucks uses this tool to individually market specific products and features based on data collected from the app about its users.
Because the company is such a stellar example of a “best in class” example in the way it uses its data, when Starbucks beats you, many competitors can still reap the rewards.
The hot drinks market, of which coffee is the largest player, is expected to grow at an annual rate of 10.3% from 2020 to 2025. Starbucks not only will be part of that growth, its rewards program can take much credit for making craft coffee an everyday purchase (12 Ways Starbucks Loyalty Program).
Starbucks continues to invest heavily in its mobile app while highlighting the benefits of its reward program. Thanks to this commitment to both technology and its envelope-pushing use of data, reward program membership has almost doubled since 2020.
The COVID effect
The COVID drop, which hit all coffee shop chains, saw many of the company’s efforts at getting American consumers to “go out for” a cup of expensive coffee getting unraveled. Americans started to drink more coffee at home, and even after COVID restrictions were lifted in many states, sales were not rebounding as quickly as many had hoped. Trends for some of the leading chains show the slow return to pre-COVID consumption.
Thanks to its aggressive use of data, Starbucks was able to track changing coffee-drinking habits in different regions of the country and then target the ones where sales were lagging to accelerate the return a bit.
In 2021, an average 21% of Starbucks visitors return again within 3 days. A significant number of Starbucks customers return even more quickly: 10% of Starbucks visitors return again within 1 day and 16% within 2 days. Only 1% of Starbucks visitors are seen at another coffee shop during the same time. This indicates that Starbucks visitors are not only regular coffee drinkers, but extremely loyal to the Starbucks brand.
Viewing the same data on a map, Starbucks found that consumers in western states had retained their loyalty to the brand in the post-COVID period, while other parts of the country were slow to break their newly acquired coffee-drinking habits, at home or from gas station convenience stores, or were willing to switch to other chains if it was more convenient. On the East Coast, there was an assumption that consumer fatigue had slowed return visits.
While no data has yet been made available by Starbucks, one can make the assumption that the company is maximizing its rewards program in ways to overcome the diverging demand for its in-store products. Regionally crafted promotions are possible because Starbucks, in many ways, thanks to its aggressive data strategy, knows us as well as we know ourselves.
Conclusion
Using data helps companies better understand their customers, operations, and logistics. It enables them to make informed decisions and take actions that drive growth and improve efficiency.
By analyzing the information from various sources, companies can identify patterns and trends that can help them optimize operations, improve customer engagement, and increase sales. In short, using data can be a valuable asset for companies that want to stay competitive and succeed in today’s data-driven world.
There are several best practices that companies can follow to make the most of their data:
1. Establish clear goals and objectives: Companies should define their goals and objectives before collecting and analyzing data. This will help them focus on the most relevant information to their business and avoid getting bogged down by irrelevant data.
2. Collect and store data in a structured manner: It is essential to collect and store data in a structured manner to ensure that it is easily accessible and can be analyzed efficiently. This may involve setting up a centralized data repository or using a cloud-based storage solution.
3. Use appropriate tools and technologies: Companies should use appropriate tools and technologies to collect, store, and analyze data. This may involve using data analytics software, machine learning algorithms, or other advanced technologies.
4. Build a data-driven culture: Companies should foster a data-driven culture by encouraging employees to use data in decision-making and providing training and support for data analysis.
5. Ensure data quality and accuracy: It is essential to ensure that data is accurate and of high quality to avoid making decisions based on incorrect information.
6. Monitor and measure results: Companies should monitor and measure the results of their data analysis efforts to see if they are achieving their goals and objectives. This can help them refine their data strategy and make more informed decisions in the future.
If your company or brand is struggling, ask yourself one question: Am I using the data being collected?
