MDM + BI = Customer Analytics

by Stephen J. Andriole, Senior Consultant, Cutter Consortium

Wikipedia described customer relationship management and, indirectly, customer analytics, along these lines:

1. Active: A centralized database for storing data, which can be used to automate business processes and common tasks.

2. Operational: The automation or support of customer processes involving sales or service representatives

3. Collaborative: Direct communication with customers not involving sales or service representatives ("self service")

4. Analytical: The analysis of customer data for a broad range of purposes

Operational {analytics} provides support to "front office" business processes, including sales, marketing and service. Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database as necessary.

Focusing on customer value is key to a successful CRM strategy. Different customers have to be treated differently. Variables like customer ranking, actual value and potential value are strategy drivers.

Collaborative {analytics} covers the direct interaction with customers. This can include a variety of channels, such as internet, email, automated phone/interactive voice response (IVR). It can generally be equated with "self service." The objectives can be broad, including cost reduction and service improvements. Many organizations are searching for new ways to use customer intimacy to gain and retain a competitive advantage. Collaborative {analytics} provides a comprehensive view of the customer, with various departments pooling customer data from different sales and communication channels.

Collaborative {analytics} also includes Partner Relationship Management (PRM) which enables organizations to manage their relationships with partners (consultants, resellers and distributors), and potentially the customers of those partners.

Analytical {customer analytics inspects} customer data for a variety of purposes including: design and execution of targeted marketing campaigns to optimize marketing effectiveness; design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention; analysis of customer behavior to aid product and service decision making (e.g., pricing, new product development, etc); management decisions, e.g. financial forecasting and customer profitability analysis; risk assessment and fraud {detection} for credit card transactions. Analytical CRM generally makes heavy use of Predictive analytics.

Several commercial software packages are available which vary in their approach to {what is generally known as} CRM. However, CRM is not just a technology, but rather a holistic approach to an organization's philosophy in dealing with its customers. This includes policies and processes, front-of-house customer service, employee training, marketing, systems and information management. CRM therefore also needs to consider broader organizational requirements. [1]

All of these investments in master data management, business intelligence, and customer analytics/customer relationship management should be purposeful. In fact, investments in MDM and BI should be made after the CA/CRM strategy is developed. Once this strategy is formed, then an MDM/BI investment map should be created.

This kind of reverse applications engineering should drive investments in data and analysis. The list of CA/CRM strategic and operational objectives should include the ability to:

• Identify and integrate customer data across applications and lines of business

• Securely store master and redundant copies of all customer data

• Profile customer behavior over time with patterns and trends

• Cross-sell across business units and products and services

• Up-sell existing customers with additional and more profitable products and services

• Develop customer acquisition and retention campaigns

• "Touch" customers physically and digitally based on analysis and receipt of preferences

• Build whole customer management and customer lifecycles that can be monetized over time

• Customize and personalize products and services

• Develop financial models of individual and classes of customers that yield specific cash-flow forecasts, cost management strategies, and overall revenue/profitability pictures for each customer and class of customer

These objectives suggest the kind of data necessary to optimize customer monetization.

What else?

• Empirical metrics for each of these objectives should be developed in order to provide evidence for effectiveness, trends, profiling, and the like. It's essential to understand what's accurate and inaccurate and what's working and what's failing.

• Alternative analytical methodologies should be leveraged on to the data. For example, there are several ways to forecast customer behavior based on techniques like multiple regression analysis all the way to the use of Bayes' Theorem of conditional probabilities. It's important to note the different forecasts that different methods generate and understand the differences among them. Much more importantly, alternative methods should be used to answer key questions and to provide some normalizing of the results.

• Reporting should be easy, flexible, and in the right form. While "content" is great, "form" is just as important to decision makers who must decide what to do -- and not do -- with their customers. Dashboards and similar easy-to-use, customized views of customer data and activity are absolutely essential to effective MDM/BI/CA/CRM. The data also needs to be "queryable," supporting real-time, what-if analyses. This is another capability that should be reverse-engineered back to the MDM and BI investments.

I welcome your comments on this issue of the Cutter Edge and encourage you to send your insights on the market in general to me at sandriole@cutter.com.

-- Stephen J. Andriole, Senior Consultant, Cutter Consortium

Notes

1. Wikipedia. "Customer Relationship Management." Quoted in Answers.com. "Customer Relationship Management."