DSC Tech Library
This section of our technical library presents information and documentation relating to CRM Solutions and Customer relationship management software and products. Providing customer service is vital to maintaining successful business relationships. Accurate and timely information provided in a professional manner is the key to any business and service operation.
Telemation, our CRM software application, was built on this foundation. But the flexibility to change is just as important in this dynamic business environment.
Telemation call center software was designed with this concept from the very beginning.
That is why so many call center managers, with unique and changing requirements, have chosen and continue to use Telemation CRM software as their solution.
Our Telemation CRM solution is ideally suited for call center service bureaus.
Data Mining and CRM
By Ro King Executive Vice President
Over 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 capability. 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, they 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 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 analyzes 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. Let's review two broad categories of data analysis and see how they might be used to prioritize CRM initiatives.
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 or product ownership or usage. Segments can be as simple as females versus males or females over 55 years old versus those under 55 years old. As long as the grouping leads to insights, which can be used to drive marketing initiatives, it can be a segment.
"Clusters" is often used to describe mutually exclusive sub-segments according to a list of pre-selected characteristics, usually those thought to be key indicators of consumer behavior. Large firms often use geo-demographic 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.
Non-exclusionary segments require more sophisticated analytic techniques and allow customer behavior to drive the creation of segments. In a non-exclusionary segmentation schema, a customer may spend as if affluent on one product type, say, 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 analyzes include market basket analysis, which links products together 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 is a powerful data mining tool using statistical methods to compare and contrasct 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. For instance, 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 analyzes create decision trees of the most predictive attribute combinations by testing multiple factors against each other. These tree analyzes are popular for their easy-to-describe, visual output relating predictive attributes. Each attribute adds branches to the tree. For instance, branches predicting product purchase may include age groups of under 25, 25 to 55, 55 and older. Each age branch will have a percentage associated with it, such as "the under 25 node (or cluster) has a 60% 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. For instance, 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 pretty "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 non-payment and they typically relay 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 understanding customer value, may be one of the most effective ways to use descriptive analyzes and predictive modeling. Firms can create a two-by-two matrix and assign customers to a quadrant based upon 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% of customers may represent between 50% and 80% of a firm's profits, so losing a customer from this group may be very costly. On-going 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 VI customers while your firm works to improve relationships with customers in other quadrants. Some experts recommend proactively ending relationships with Quadrant VI customers, 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, they often increase 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 learnings about customers.