The Confluence of Data Mining and Market Research for Smarter CRM
Kenneth Elliott, Ph.D. Kenning Research Inc.
Richard Scionti SPSS Inc.
Mike Page The Kantar Group
Source: www.spss.com
Copyright SPSS, Inc. 2004

Part 3


What Customer Intelligence Questions do Data Mining and Market Research address?

Within the context of Customer Intelligence, Data Mining and Market Research are often used to support decision making in the areas of Customer Acquisition, Customer Segmentation, Customer Retention, and Cross-Selling. These applications are part of the field called Analytical Customer Relationship Management (A-CRM). As described below, the insights gained from these initiatives help organizations better manage their customer interactions, improve the level of customer service, and create richer longer-lasting customer relationships.


Customer Segmentation
Understanding customer segments is critical to any customer-focused organization. Market Research derives customer segments through surveys and demographic research. Data Mining uses clustering techniques to find naturally occurring groups within the customer database. While each approach individually provides insight into basic customer groups, combining these approaches yields deeper insight still. A simple illustration of this can be seen in the table below. The table shows variances between purchased Demographic Segments and Clusters that are derived by behavioral, transactional, and individual characteristics. Segment 1 seems to include two distinct behavioral clusters. An understanding of Clusters 1 and 2 may suggest varied marketing strategies within Segment 1. Segment 2 and Cluster 2 seem to validate each other. Clusters 1 and 3 contain two different demographics. While these two groups seem to behave the same, demographics may provide insight into differing intentions. Combining Data Mining and Market Research techniques for customer segmentation can lead refinement of segmentation strategies and to more accurate customer understanding.

                                                 FIGURE 2


Customer Acquisition
Data Mining is used to help improve customer-acquisition efforts by identifying the profile of potential buyers for a particular product or responders to a campaign. While these derived profiles can lead to improvements in marketing efforts, one can only infer the reasons these groups respond where others do not. With Market Research one can survey customers to understand why they buy a particular product or respond to a specific campaign. Used together, Data Mining and Market Research can provide more actionable results in a more efficient manner. Specifically, Data Mining can identify customer segments to survey and provide hypotheses as to purchase intent and Market Research can narrow field work to a tighter segment and more focused research objective.


Customer Retention
Market Research is well equipped to identify drivers of satisfaction and loyalty. By matching primary Market Research data to a customer data-warehouse, Data Mining can be used to identify behavioral links between reported satisfaction and loyalty. Additionally, Data Mining can be used to validate a relationship between reported loyalty and actual churn behavior. Used together, Data Mining and Market Research can more accurately identify key drivers of customer loyalty and enable an active management of customer churn.


Cross Selling
Data Mining is often used to identify naturally occurring associations between products. Marketing managers use these associations to develop joint-marketing and cross-selling campaigns. However, many times product associations are not obvious or only occur within specific customer segments. Data Mining is often ill-equipped to provide further insight into these patterns. In such circumstances, Market Research can be utilized to focus on what factors lead to these associations. This research can result in more effective cross-selling campaigns and product promotions.


Where Should Data Mining and Market Research Converge?
The convergence of Data Mining and Market Research can best be illustrated by examining the underlying research stages common to both disciplines. To this end, we define the underlying research processes as consisting of six distinct stages. These stages include:
• Define where the customer is articulated
• Capture where information is collected
• Store where information is managed and maintained
• Analyze where information is examined
• Understand where insights and conclusions are drawn
• Deploy where insights are operationalized throughout the organization

              FIGURE 3 COMPARISON OF RESEARCH PROCESSES


Data Mining Process
Data Mining most commonly defines the customer as a set of trackable behaviors. This is due in large part to the fact that Data Mining requires large data sets. These are more often produced by operational systems than surveys. This means that the customer is defined as an acting entity with less input from intentions, attitudes or outside behaviors. Therefore, Data Mining focuses on capturing what is accessible via operational systems that interact with the customer.
These systems produce massive amounts of transactional data including purchases, customer service inquiries, web visits, phone logs and more. The data is stored in large data warehouses. The analysis of this data requires highly scalable algorithms that churn through the data looking for common aggregate patterns. Customer understanding is derived from interpreting behavioral patterns. Intentions are then inferred from actions. Finally, the insights gained through Data Mining are represented in the form of 'models' that can be used to score databases and real-time applications.


Market Research Process
Market Research defines the customer as a thinking affective entity where intentions and attitudes are more important than actions. Market Research often defines the ‘customer’ as a group within the general population. Being freed from the internal corporate database, Market Research is able to explore questions such as competitive-product assessments, intentions to defect and general satisfaction. The data outputs are subjective comments and ratings. The data is often captured in the form of spreadsheets or text files and delivered in the form of written reports. The analysis of this data is a subjective summary of the results and interpretation of meaning across the responses. Customer understanding is gained by linking the attitudes of general population segments to the assumed makeup of a client’s existing customer base. Deployment of market research results occurs through presentations to decision makers.


Combining Processes
Combining Data Mining and Market Research will require synergy at each stage of the research process. While the customer deserves to be seen as a thinking and acting entity, combining these disciplines provides the unique ability to analyze the gaps that are known to exist between espoused plans and practice. Thus data capture must expand to include all information, subjective and objective, intentions and actions. The storage of data must come together so that the analysis stage can leverage both. In addition, the analysis stage must leverage new processes that take advantage of the best of both disciplines, including empirical behavioral modeling and qualitative research methods. Finally, the deployment of insight, whether to human or machine, should take advantage of the knowledge gained from both Data Mining and Market Research. Only when a full perspective of the customer is available can holistic conclusions be drawn and the most accurate insight can be deployed.

For a more detailed examination of the convergence of Data Mining and Market Research practice, see Convergent Research Patterns (Kenning Research Inc., 2003).