Under the retail microscope: Seeing your customers for the first time

How much are your customers worth to you? That hasn’t been an easy question to answer. The massive amounts of information about customers available today, however—from point-of-sale transactions to loyalty programs to social media—provide a view of customers that is orders of magnitude more detailed, nuanced, and personal than it’s ever been. This unprecedented degree of clarity means companies, especially retailers, can understand the lifetime value of a customer and manage for it.

This approach, called Customer Lifecycle Management (CLM), allows retailers to target their marketing efforts to get much greater returns. In fact, we’ve seen companies add 20 percent or more to a company’s profits when they use CLM techniques.

Retail is living the data dream

Companies, especially retailers, can understand the lifetime value of a customer and manage for it
Retailers are uniquely positioned to benefit from CLM, thanks to the scope and richness of their customer data. One major US retailer, for instance, processes one million customer interactions every hour, feeding databases that hold more than 2,500 terabytes of data. Think about it. Embedded in each transaction are the specs, price, and category of a product, as well as the date, time, and place of the purchase. Add to this all the personalized data gathered from social media and loyalty-card programs over long periods of time, not to mention location data from coupon redemption and store check-ins.

“Of course we can communicate with customers in a number of ways,” says Daniela Mündler, senior executive in charge of marketing at Douglas, the leading European fragrance and beauty retailer. “But the best way to get closer to customers is to understand what their behavior tells us.”

CLM is particularly effective for doing the basics… but doing them much, much better:

What Customer Lifecycle Management is particularly effective for is doing the basics… but doing them much, much better
Acquire new customers. CLM helps develop marketing programs that cost less and return more. Advanced analytics, for example, can quantify the impact of specific promotions, enabling retailers to allocate their marketing spend where it brings the highest returns. CLM techniques also identify not only the most valuable prospective customers, but also the places where they spend their time, from stores to social networks. Gilt Groupe, for example, added a million customers in just one year by identifying and targeting people on blogs and social networks who had a specific interest in apparel and designer brands. Gilt extended a tailored invitation to these select individuals and rewarded them for inviting their friends.

Develop existing customers. CLM tools encourage customers to buy more, and more often. Analysis of the unique interests of each customer open “upsell” and “cross-sell” opportunities, while sophisticated predictive analytics can identify the “next product to buy” that retailers can use to tailor specific offers. Predictive algorithms allow Harrah’s, for example, to tailor their offers based on expected customer value.

Interactions every hour that one major US retailer processes, feeding databases that hold more than 2,500
Retain existing customers and win back former ones. CLM analysis exposes vulnerable areas of likely customer attrition, and helps with developing interventions, such as offering incentives if purchase behavior falls off. Marketers can also identify those customers who matter most and reward them with special loyalty tiers, exclusive offers, or VIP events. eBay, for example, tailors its offers and discounts based on predictions of how likely a valued customer is to defect—the greater the likelihood, the more compelling the offer.

What CLM leaders do

In our experience, the companies who are most successful turning CLM into growth do four things well:

Target the best customers. CLM leaders relentlessly comb through their data to quantify how much their customers are worth to them, not just today but in the future as well. Based on the results, these companies develop customer lifetime value models and prioritize segments. Understanding which customers have traded down or churned out or increased their average purchase is critical for safeguarding the future economic health of the company.

Build predictive models. Customer value is not a constant, but a moving target. Leading companies follow that target closely, and develop programs that maximize customer profitability. Well-developed analytics models can take in point-of-sale (POS) transaction data, product specs, payment details, aftersales service logs, customer data, social media data, etc. and develop accurate “next product to buy” (NPTB) and optimum timing recommendations for specific customer segments. Gathering as much historic data as possible increases the accuracy of NPTB models. Best Buy, for example, sends an email to a customer within three days of a product purchase, to recommend must-have accessories or “cross-aisle” purchases.

Continually test and learn. CLM pioneers constantly test new approaches in multiple channels and at various stages of the consumer decision journey. Such testing and learning creates a constant feedback loop so that companies adapt and evolve both their marketing vehicles and their customer segments. Predictive models outlined above are most successful when supported by real-time feedback loops.

Build for the long term. Successful players make customer value an integral part of their organization. They assign a dedicated manager with overall responsibility for CLM programs, with budget support and authority to implement campaigns. The team needs to include an analyst with strong quantitative skills and experience with relevant software (SQL, SAS). A steering committee from various functions often helps to keep the team from overinvesting in expensive analytics solutions. Most importantly, executives on the CLM team need to build relationships with line and business-unit managers so that their insights are delivered to the people who can actually use them. One example of how to do this is by displaying relevant purchase histories of registered customers on sales reps’ terminals. Information flow, decision patterns, and incentives are at least as important as having the right data.

CLM is not about data and tools. It’s a mindset that puts the customer at the center of business decisions, and guides marketing and sales decisions to maximize the value of each customer.