During the past several months, many of my clients and prospective clients have turned their attention to mining their customer databases.
Data mining is the umbrella term for processes designed to identify and interpret data for the purpose of discerning actionable trends and formulating strategies based on those trends.
As firms scrutinize their spending on marketing activities, they begin to focus on their data mining capabilities.
How can they learn more about customers, use that information to make appropriate offers to customers and understand which offers succeed?
My last article focused on distributing customer information across an enterprise for use in analysis and marketing. Information about customers is gathered from a variety of sources across the enterprise, assembled in a consistent, reliable and usable format and provided at an appropriate level throughout the firm. Once a firm begins to use customer information to make decisions, it may begin to develop more sophisticated means of using customer data.
Data mining, data exploration and knowledge discovery are all terms that create an image of the demanding and sometimes tedious search to uncover insights that are neither obvious to competitors nor easy for competitors to duplicate. Customer relationship management depends on data analysis activities that are designed to uncover directions and opportunities and highlight warning indicators for CRM initiatives.
CRM uses data mining to understand how to reach out to and communicate with customers. Data analyses can range from simple, intuitive determination of who to contact, when and where, to applying complex algorithms in real time to deliver customized responses to customer-initiated interaction. The following is a review of two broad categories of data analysis in order to see how they might be used to prioritize CRM initiatives.
Descriptive analysis. Not all data mining relies upon complex statistical analytics. Segmentation and clustering techniques are commonly used to group customers by shared characteristics to highlight patterns that can be used in developing marketing plans.
Basic segmentation is often used to group customers by easily identified, mutually exclusive characteristics such as demographics, product ownership or usage. Segments can be as simple as females versus males, or females 55 or older versus those younger than 55. As long as the grouping leads to insights, which can be used to drive marketing initiatives, it can be a segment.
Clusters are often used to describe mutually exclusive subsegments according to a list of preselected characteristics, usually those thought to be key indicators of consumer behavior. Large firms often use geodemographic clusters to target brand marketing. Some firms use value clusters to drive marketing activities based on the current or potential value of a customer group.
Nonexclusionary segments require more sophisticated analytic techniques and allow customer behavior to drive the creation of segments. In nonexclusionary segmentation, a customer may spend as if affluent on one product type, such as travel, and not spend at all on associated products such as room service. These spending patterns might place the customer in two segments.
Other types of descriptive analyses include market basket analysis, which links products based on customer purchase behavior, and clickstream analysis, which uses behaviors such as Web browsing, site path, shopping and shopping cart abandonment to describe customer activities on a given Web site.
Predictive modeling. This is a powerful data mining tool using statistical methods to compare and contrast customers on a wide variety of factors. Predictive modeling determines which factors are highly correlated and measures the degree of correlation and statistical reliability. The result of a predictive model is a mathematical formula or score that may be applied to customers to predict likely behavior.
There are several common types of predictive models. Univariate models test a single factor against a series of other factors to see which has the highest correlation. Product purchase may be tested against age, income, computer usage, pet ownership or any other factor to discover which attribute has the highest association.
CHAID or CART analyses create decision trees of the most predictive attribute combinations by testing multiple factors against each other. These tree analyses are popular because of their easy-to-describe, visual output relating predictive attributes. Each attribute adds branches to the tree. Branches predicting product purchase may include age groups of younger than 25, 25 to 55, 55 and older. Each age branch will have a percentage associated with it, such as “the younger than 25 node (or cluster) has a 60 percent likelihood of purchasing the product.”
Multivariate regression analysis tests multiple factors against one another to generate a score that indicates the probability of displaying the targeted behavior. In a multivariate regression, several attributes will be combined to predict the outcome. Product purchase may be highly correlated with age, somewhat correlated with income and negatively correlated with computer use. Each of these attributes will be required for every customer you wish to score.
A neural network or neural net is a type of sophisticated analysis that imitates the workings of the human brain by learning from each observation. Like a multivariate regression, a neural net generates a score that indicates the probability of displaying the targeted behavior. Neural nets are often run in conjunction with other predictive modeling techniques, as the analysis performed is fairly “black box” and the results difficult to explain.
Using predictive models. Models can be used to predict response to a targeted offer. Individual customers or businesses may be scored on their likelihood to respond to an offer. The model scores may be used to run economic and what-if scenarios.
Risk models may be used to determine the likelihood of default or nonpayment, and they typically rely on credit bureau data. These models require a fairly long time frame to validate. Attrition models also require a longer time horizon to validate. These models identify customers at risk of defecting.
A simple segmentation, requiring significant effort to understand customer value, may be one of the most effective ways to use descriptive analyses and predictive modeling. Firms can create a two-by-two matrix and assign customers to a quadrant based on their current and potential value. CRM initiatives may be organized around the customers in each quadrant. Quaero LLC calls this customer value segmentation strategy the MUST segmentation.
• Quadrant I: High current value/high potential value — maintain. Depending upon the industry, the most profitable 10 percent of customers may represent 50 percent to 80 percent of a firm’s profits. Ongoing retention or loyalty efforts should be aimed at the customers in Quadrant I.
• Quadrant II: Low current value/high potential value — upgrade. These customers may increase in value through cross-selling and account management efforts. Perhaps these customers have not received appropriate offers in the past or they may be delaying purchases. Efforts should be aimed at increasing the depth and breadth of the relationship of each Quadrant II customer with the firm.
• Quadrant III: High current value/low potential value — study. In some segmentation matrices, the recommended strategy for Quadrant III customers is to milk them for current revenue. We recommend studying these customers to determine which ones can be converted to profitable clusters in the future and how they can be converted.
• Quadrant IV: Low current value/low potential value — table. We assume that you cannot focus on every segment at once, so we suggest tabling Quadrant IV customers while your firm works to improve relationships with customers in other quadrants. Some experts recommend proactively ending relationships with Quadrant IV customers while others focus on information gathering to determine ways to convert unprofitable customers to profitable customers.
The combination of good customer information, data mining and technology enables companies to better understand their customer base and communicate with them more effectively. Once a firm is actively using customer information to make decisions about how, when and what to market to customers, it often increases the volume of targeted customer contacts. This increase leads many firms to look for new ways to automate mining and marketing processes to make the most of their newfound lessons regarding customers.
