How Retailers Can Use Data Analytics

to Revitalize Customer Loyalty Programs


When the first retail loyalty cards appeared in the 1980s, there were few computers and no internet. A loyalty program consisted mainly of a card, points and discounts at the point of sale. Fast forward to 2018, and we find an entire industry of software solutions, applications, consultants and “loyalty analytics” in use to help retailers breathe new life into their loyalty programs. The loyalty programs of yesteryear and the techniques for managing them are ineffective against today’s backdrop of digitization, personalization, falling margins, eCommerce disruption and intensified retail competition. In this age of the buyer’s market, building customer loyalty to a retail brand is not only an advantage, it is a requisite for survival. In one recent poll of retailers, roughly 60% of companies listed upgrading loyalty programs as a top priority. Data science and retail analytics are at the core of the movement to bring innovation to retail loyalty programs. StrategyWise has identified seven ways that retailers can use data analytics to modernize their loyalty programs.

Identify and Recognize the Best Customers

Loyal customers typically seek preferential treatment from retailers. Investment in building loyalty makes sense when retailers can identify their most loyal customers, recognize them as such, and do so in personalized ways. Companies must use data to identify and track who their best customers are first, understand their shopping habits, values and lifestyle, and then find a way to appropriately and personally recognize them. Data can enable a retailer to recognize its best customers when they enter the store or at the point of purchase. Coffee retailers, for example, are often exceptionally good at this. Retail analytics can provide useful information that helps to cultivate the most loyal customers through tailored marketing messages and anticipation of their needs and desires.

Increase Retention, Reduce Attrition

Arguably, the goal of loyalty programs is customer retention. Retention ensures steady revenue streams and reduces customer acquisition costs. Data mining of customer information can inform and support retention strategies, putting valuable information into the hands of marketing managers and employees, thereby positively influencing the way they approach different types of buyers. They will be more informed about customer characteristics and purchasing behaviors. Data mining can also serve to identify at-risk customers. For example, if a customer breaks a long-time purchasing pattern or stops redeeming earned program points, that may be an indication they are moving on to another store. Knowing this, an appropriate retention strategy can be devised.

Retail analytics and big data for retailers provides personalized attentionTargeted Product Recommendations

Sending targeted promotions is one way to keep customers engaged with a brand. Retail is now all about personalization. Unique customer data points, such as purchase information, demographics, preferences and habits, when analyzed, can present a picture of a shopper that makes it possible for retailers to offer well-targeted, personalized product recommendations. By combining real-time data with historical purchase data, loyalty program managers and leaders in retail analytics teams can create specific recommendations that are appropriate to each customer and in real time.

One national food chain tasked StrategyWise with empowering their loyalty program to offer free food based on individual customer taste profiles. By extracting data from customer purchase history, our team was able to build insights and divide customers into “healthy eaters”, “indulgent eaters”, and so forth. These custom profiles then enabled the restaurant to offer unique loyalty awards designed to surprise and delight their customers on a more personal level. Redemption rates were monitored, and constant iterations using machine learning allowed the program to continuously improve the reward process.

Well-Timed Promotions

Consumers are overwhelmed with promotional offers on a daily basis, often from a variety of competing loyalty programs. To be differentiated and effectively gain attention, promotions need to fit into a consumer’s buying habits and lifestyle. By monitoring sales data, retailers can learn when and how customers typically redeem codes in order to deliver the right offer at the right time, thereby increasing the likelihood that a promotion will be successful. Seasonality can be key in understanding how and why customers engage with a brand. This can be particularly important for retailers with multiple locations serving the same customer, such as a bank or restaurant chain that has different branches serving someone on their commute vs. lunch break or weekend outing with the family.

Customer Segmentation

One of the big mistakes traditional loyalty programs make is generalizing a loyalty program across customer segments by assuming that each customer values the same promotional offers and rewards. Studies show that such generalization disengages customers. Segmentation, supported by data analysis, enables a retailer to offer relevant discounts and promotions to different consumer profiles and, at a granular level, be as specific as one-to-one marketing. Retailers can use a variety of data and methods for segmentation, such as transaction data, credit reports, member profiles and customer demographics. Analytics can also track the movement of customers between segments as they are influenced by a loyalty program or provide insights into the behaviors of members versus non-members. A retailer can then look at all factors driving retention of various customer groups to determine how to drive loyalty within each segment.

Personalize Loyalty Rewards

Personalization drives loyalty with today’s consumers. A loyalty program where everyone receives the same benefits and rewards has long become outdated. Today, a firm needs to determine which rewards specific customers value. To do so, they need to collect and analyze relevant data, such as customer-specific monetary transactions, shopping frequency, lifestyle choices, favorite products and survey responses. They can use this analysis to create appropriate rewards around specific customer profiles, so that every customer feels appreciated. The most profitable customers should, again, be identified and rewarded accordingly.

Determine the Effectiveness of a Loyalty Program

Fragmented data and costly legacy systems can hamper a retailer’s ability to determine loyalty program effectiveness, ROI and profitability drivers as well as partnerships and joint promotions’ contributions to overall program success. To determine the value of various loyalty program components, retailers must be able to integrate and analyze the status of a program by tracking budgets, identifying membership trends, keeping track of point balances and associated data, and identifying reward and redemption trends over time. They also need to track behavioral, transactional, social media and other data to be able to take real-time corrective action during a promotion when needed. Using data to identify the methods, products and services that have a positive impact on promotional successes enables a retailer to also understand the factors that motivate consumer loyalty.

Having a loyalty program is not enough to create a competitive advantage in 2018. To be differentiated, firms must innovate around loyalty program features, delivery channels, communication and measurement. Data is the core component of customer loyalty program differentiation.

Retailers who embrace data-driven loyalty programs will succeed in creating a competitive advantage. At StrategyWise, we help national brands leverage data to understand how to better engage with their customers in a meaningful and relevant way. If your firm is looking to harness customer data to create such a competitive loyalty program, we can help. Call one of our specialists today to take the next step in leveraging all your data has to offer!