The techniques that assist data warehousing facilitate data fusion.
Posted Oct 18, 2004
Integrating data into warehouses or marts for marketing analysis means that the elements of the marketing mix can be as beneficial to the database as the database is to those elements. Knowing who has bought which product, and when, responding to which campaign, and through what channel is all very useful: Together it comprises the "who," "what," "when," and "how" driving the customer action.
What if we could add the "why" factor--the psychographic element that establishes true differential between customers? Why is a customer loyal? Why does the customer maintain a dialogue and value the relationship with a preferred supplier?
The techniques that assist data warehousing to facilitate data fusion, a process by which research data is introduced into the marketing database and matched by personal identifier or by segment to permit extrapolation right across the database. This reveals more specific profiles and establishes differentiators between clusters previously considered similar.
Knowing a customer's expectations of product and service, preferred channels, and extent of communication enables marketers to tune services and propositions to meet them. Knowing how the company matches up to these expectations in the minds of its customers provides important benchmarks.
While scoring techniques like RFM (recency, frequency, and monetary value) provide important bases for segmentation, they do not necessarily demonstrate loyalty. Customers will find their own level of relationship, and cannot be forced to assume a closer one. Any two customers may be equally satisfied and loyal but view the relationship differently--hence the need to understand the attitudes and aspirations of each and establish specialized indicators that show if the company is getting it right.
The data fusion process requires a deep understanding of the data that contributes to the analysis universe. An appraisal of the company's in-house database is also necessary to ensure that it is fit-for-purpose, highlighting and solving issues concerning data quality or integrity that may affect the creation of survey panels and the outcome.
Preparing for the research requires construction of personal and purchase profiles often introducing qualification data to establish far more meaningful segments, each referenced against the constituent customer records.
Raw response data should be matched back and extrapolated throughout the database, which is then analyzed ideally using train-of-thought exploration techniques. This means that the psychographics of a consumer or the KPIs of a business customer can be integrated in the selection criteria for communications, proposition, or customer relationship development, in real time, delivering direction to the contact center agent, the field salesperson, or even in the presentation of Web pages.
This methodology is being used to address some of the key business imperatives uppermost in the minds of marketers:
Knowing more about customers
Identifying and preempting churn
Improvement in customer service
Opportunities for cross-sell and upsell
The company can increase sales to current customers, improve retention and loyalty, and increase the effectiveness of prospect conversion. The analysis of fused data will also direct development of new products and provides a competitive edge in maintaining and increasing market share.
About the Author
Michael Collins is managing consultant of Database Marketing Counsel, a leading database marketing/CRM consultancy in London. He is an internationally acknowledged speaker and author, and lectures in database and CRM at Brunel University, London. He can be contacted at firstname.lastname@example.org or by visiting the DMC Web site at www.dmcounsel.co.uk
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