Beyond Purchase Histories

Many companies today rely primarily on customer purchase histories to power their customer segmentation strategies. Although a critical component in determining value, this strategy falls short in driving high wallet share and lifetime customer value due to the limited knowledge of the customer, his or her overall buying preferences, and his or her behavioral propensities.

Businesses need a complete picture of their most enthusiastic customers to be able to communicate consistently and compellingly across every channel. This includes tapping into customer purchase history data along with meaningful insights about who they are, why they're currently buying, what they are likely to buy next, and why they may churn. This data can also help reveal how, when, and where to best communicate with them—proactively and reactively—and, perhaps most importantly, how to identify more customers like them.

To gather this knowledge, more and more companies are enhancing their customer purchase histories with third-party marketing analytics. Such analytics allow them to glean rich consumer insights at the U.S. household level, including demographics, psychographics, lifestyles, and predictive segmentation tools built on rich data sets gathered from like-minded and like-situated consumers. And, with new privacy-friendly breakthroughs in segmentation analytics, these insights can be accessed using minimal personally identifiable consumer information, such as email addresses or mobile numbers.

Once an organization has tapped into these powerful predictive tools, it has the information it needs to personalize its interactions with consumers in real time, through the right channels, using messages that are likely to resound best with consumers in a particular marketing segment. This approach not only helps alleviate customer churn and identify pre-churn warning signs, but also drives high-value lifetime customer relationships.

Purchase History Alone Isn't a Predictor

Many companies in the B2C space apply recency, frequency, monetary (RFM) purchase models to analyze and segment their customers, model future purchases, and apply loyalty-building strategies. Take, for example, a consumer who purchases a $10 candle on her first visit to an online retailer, a $30 picture frame on the second visit, and then does not make another purchase for the next three months. The RFM model does not provide much insight here, and the retailer may be likely to make generic offers and offers geared toward home decor. As time goes by and the consumer doesn't return, the retail site will place decreased value on that customer.

Why? Judging the customer on purchase history alone, the company is making decisions based on incomplete information, as the retailer has no insight into the customer's lifestyle and shopping preferences. Furthermore, the online retailer has little to no insight into what particular offers would be most appealing to encourage future transactions.

Using predictive segmentation built on household-level marketing analytics, the retailer may learn that consumers in the same segments respond positively to certain kinds of marketing and become loyal customers. In fact, these customers may currently be a competitor's highly valued shoppers. Based on this information, the retailer can then make an immediate effort to engage these types of consumers with offers and messages tailored to their interests, ultimately resulting in increased conversions to high-value lifetime customers.

Reducing Churn

Most companies recognize that they will lose customers over time. But to anticipate and minimize churn, they must understand the attributes of customers who are more likely to jump ship. With this approach, companies can proactively reach out to other "high churn risk" customers to disrupt potential defection with customized messages and offers to re-engage them (e.g., a discounted renewal promotion or free gift with their next purchase).

An example of this is a large telecom company that used marketing analytics and predictive segmentation to determine what kind of customers were most likely to churn based on an analysis of previous customers' demographic, geographic, and rate plan data. The company combined this data with primary research to determine the reason these customers were switching providers. Using these insights, the company launched targeted retention campaigns to specific segments of their customers. For example, one customer group of recent college graduates entering the workforce was offered upgraded phones while maintaining the same minimum rate plan. The result: a 40 percent to 50 percent reduction in attrition.

Examining Path to Purchase

Another key component of maximizing lifetime customer value is performing channel analysis to determine what types of media customers consume—print, TV, direct mail, email, Internet, mobile—and how they interact with or respond to offers across inbound channels (e.g., on the phone, online, or in-store). For instance, a company sending weekly email offers to a large group of customers who made their last four purchases via phone in response to a mailed offer is missing the right channel and wasting time and money. The company could improve purchase potential by recognizing up front that these customers belong in a segment that prefers printed communications. By researching customers' channel behaviors prior to campaign execution, companies can better understand their preferences and insert themselves into the right channels along the path to purchase.

Companies that rely solely on customer purchase histories are missing out on critical insights about their customers' buying propensities. By applying marketing analytics with predictive segmentation that links to a customer's media, channel, lifestyle, and purchase behaviors, organizations can establish a more complete picture of their target customers to identify what they truly need and want, how to message to them, and where to reach them. In highly competitive marketplaces, this knowledge is necessary for attracting, converting, and optimizing the loyalty of high-value customers.

Paul McConville is the senior vice president of sales and marketing at Neustar Information Services, a division of Neustar. He has held numerous sales and management roles since joining the company in 2005. Previously, he spent seven years in global-management consulting at Strategic Management Group.