In the era of Big Data and growing competition, it’s no longer enough that Starbucks baristas know you like a tall skinny vanilla latte. The iconic Seattle coffee brand is relying on Demand Planners and Data Scientists behind the scenes to increase market share. They are using software and predictive analytics to learn more about you and boost sales in the process.

Micro-targeting and personalized sales are fast becoming the Holy Grail for retailers, as this attention to detail is key to keeping consumers coming through their doors or visiting their websites. This involves recording and analyzing every physical and digital touchpoint, allowing companies to understand what customers want and then allocate resources to better meet this demand. Most companies now routinely log every visit to a product page, every call made to an inquiry response center, and every email received.

How Starbucks Collects Your Data

Of course, in order to gain insight from predictive analytics, you need data. For Starbucks, the key to this digitalization of consumer insights is the Starbucks loyalty card, the likes of which were first made popular by grocery and mass merchant stores. With this kind of card, consumers can now go to a grocery store’s website, enter their loyalty card number, and retrieve a record of everything they have purchased from that store in the last 12 months. Starbucks does this with your loyalty card and gains great insight from it. They also analyze data captured by their mobile app, which customers use to pay for drinks and accrue loyalty points.

Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day.

Subsequently, Data Scientists at Starbucks know what coffee you drink, where you buy it and at what time of day. Paring this with data from millions of other users, along with other correlating data, they have very real and actionable insights.

Gerri Martin-Flickinger, the Chief Technology Officer and EVP at Starbucks, said in 2016 “With about 90 million transactions a week, we know a lot about what people are buying, where they’re buying, how they’re buying. And if we combine this information with other data, like weather, promotions, inventory, insights into local events, we can actually deliver better personalized service to other customers.”

Starbucks Uses Predictive Analytics To Personalize Your Experience

Starbucks along with many other retailers is going from just forecasting what may happen, to using Predictive Analytics and Artificial Intelligence (AI) to deliver a more personal experience. And this goes beyond sending customers emails on their birthdays – it sets the groundwork for merging digital marketing and physical stores. Starbucks now generates recommendations for customers approaching their stores, using location data to know when customers are approaching. Starbucks segments its customers with data and Machine Learning, then sets up rules based on decision trees mapping their purchase behavior [Ed: For practical insight into leveraging Machine Learning in your company, read this article here]. Then, the customers get offers, usually on their smartphones. With this they can tell you how much you spend on average and, for example, they know on Thursday you are most likely not alone and spend a little extra, buying a blueberry muffin and two venti lattes. From your purchase habits, along with other insights, they work on targeting you so that you increase that average spend and buy a cupcake crème frappuccino and a chocolate chip cookie on Monday to treat yourself.

Target Can Know When Your Daughter Is Pregnant Before You Do

Starbucks is not alone either. Many retailers are using predictive analytics to micro-target consumers to not only better forecast sales but also drive consumer behavior. With the right data and analytics, a large big-box retailer can even predict when someone is pregnant and when they may be due. Such was the case with a Minneapolis father who found out – from Target of all places – that his fifteen-year-old daughter was pregnant. After shopping at Target, the girl began receiving mail at her father’s house advertising baby items like diapers, clothing, cribs, and other baby-specific products.

Through looking at past purchases and seeing patterns and descriptive models, Target could make assumptions of what coupons to send what customers.

Her father was incensed at the company’s attempts to “encourage” pregnancy in teens and complained to the store’s management. Turns out that what Target was doing was collecting point-of-sale data and clustering that data and comparing it to demographics. Through looking at past purchases and seeing patterns and descriptive models, Target could make assumptions of what coupons to send what customers. A few days after the irate father called Target, an embarrassed dad phoned the manager back to apologize. It transpired that his daughter was, in fact, pregnant.

Predictive Analytics Increases Companies Revenues By 21%

Predictive marketing is clearly a very big deal right now, and the benefits are clear. Research firm Aberdeen found that companies homing in on customer needs and wants through predictive analytics increased their organic revenue by 21% year-on-year, compared to an industry average of 12%. We’re looking at a perfect storm of technological and cultural factors, driving Data Scientists to claim with ever greater confidence that they know what we’re going to do next – and why.

At the same time, for those of us who aren’t Google, Amazon, Facebook, or Starbucks, how do you take advantage of this digital revolution? For many, Forecasting and Demand Planning is still only a Supply Chain problem to generate a discrete demand signals to assist supply planning. For some companies, Big Data is as much a problem as it is an asset.

How You Can Use Predictive Analytics

If you haven’t done this in your company already, develop Forecasting and Demand Planning as a function with specific competencies. Look at different ways that Demand Planning can begin to add value to other functions in the organization. Consider augmenting your team with specialized roles and develop skill sets outside of your key function and focus on the core competencies of your overall team. There is a trend towards a more centralized function, taking forecasting analytics out of just Supply Chain, because an independent Predictive Analytics and Planning department can drive insights across all functions, insight of just driving Supply Chain efficiency.

Commit to using data as a competitive advantage and work on visualization of information.

Commit to using data as a competitive advantage and work on visualization of information. It’s now pretty simple to gather mounds of performance and predictive data and the opportunity to drive customer insights has never been greater. The cost of data storage is almost negligible and its importance is becoming non-negotiable. Even if you may not use everything now, look at what kinds of structured and unstructured data you can capture and store.

Understand advanced analytics and the implications it’ll have on your process. Consider launching predictive analytics and cognitive capabilities in a limited capacity to get started. There are countless tools available, many of which are free. Then, target a specific marketing initiative with the goal of leveraging these analytics in a practical way. Continue optimizing until you’re driving improved results.

Processing this data manually is cumbersome, daunting and leads to missed opportunities – automate this aspect of the process and save yourself the time and effort. While it is generally people first, then process, then technology – the cost and efficiencies are making all three critical to keep pace with the business of tomorrow. Technology has grown its capabilities exponentially in the past decade while becoming less and less expensive. Using technology with AI is affordable and removes the burden of transitioning insights into actions from your team members, resulting in better strategic engagement and a pace unachievable by humans.

Starbucks is presenting at IBF’s flagship Business Planning, Forecasting and S&OP Conference in Orlando from 16-19 October 2018. Don’t miss out – see details and register.