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CUSTOMER & FINANCIAL ANALYSIS + DATA MANAGEMENT
Executives seek our help in delivering:
IT terms for the skills used to get these results are:
In short, we organize your past and shape your future.
LOOKING BACK
Consider two customer-centric questions from a product manager seeking to aquire new consumers: "What distinguishes profitable customers from unprofitable customers? In what markets should I be investing my time and money?"
Using a rare blend of data warehousing and analytical skills, we quickly assemble relevant marketing, product and financial data. Transactional (i.e., behavior) data is a primary need and manipulating it will require a data mart. Transactions stored in our mart are allocated revenues and expenses (a difficult task), and summarized by customer.
Understanding that true profitability may not occur immediately with new customers, we also factor in a lifetime value calculation that modifies the first question to: "What is the profile of my most profitable customers who I've retained at least a three year relationship with?"
Having identified your most profitable customers, a representative subset are appended with demographic or corporate data on which to base a final profile. Statistical routines (e.g., factor and cluster analysis) are run which segment these individuals or businesses into groups with common characteristics proven to be important and which can be catered to.
Finally, summary information is exported to a multi-dimensional database (perhaps the one freely bundled with your other transactional database) providing lightening 'slice & dice' reporting capabilities. It is in this environment that you will track financial and marketing results on an ongoing basis.
MOVING FORWARD
Reporting systems that compare actuals-to-plan and support what-if scenarios are table stakes (though many firms feel the need for improvement, post-Enron!) Predicting the future is the next step towards maximizing investments.
Take telecos and financial service firms, for example. In addition to understanding factors behind customer churn, maximizing share-of-wallet is a central concern in these industries. Which cross-sells will find favor with a given customer, and how often should these offers be made? At what point do diminishing returns begin? Answering these questions conserves money by only spending where and when it makes sense!
DEMAND FORECASTING - THE RIPPLE EFFECT
Success in such 'analytic CRM' projects often lead to applying similar tools and techniques towards the tuning of supply chain systems.
Demand forecasting and product allocation programs are of particular interest to retailers, who need to know where to make product available and in what quantity.
In his book Data Warehousing - Using the Wal-Mart Model, Paul Westerman points out Wal-Mart's use of store and product traits to guide replenishment. Simplified, 'beach' products are assigned to 'beach' stores. But how many snorkels should go to a particular store? Paul advises that "a data warehouse can provide a good estimate based on another, similar product that has the same distribution."
Market basket analysis reveals these complementary products. Combined with demographic data, you understand the market forces at work (e.g., a correlation with young affluent families) and send masks and flippers to stores in neighborhoods with swimming pools, too.
Through careful planning, your past and future converge to tackle today's problems.
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