A dilemma has been reached in database marketing and its root cause is a lack of visualization. We're not talking about a lack of vision or creative inspiration, but the inability to capitalize on visually displayed trends emerging from the overwhelming flow of customer information--the result of increased competition, mass customization, lifetime value analysis, contact history development and new, electronic ways of doing business. In marketing circles, the importance of relationship marketing is undeniable. Studies show that the average cost of acquiring a new customer is five to 10 times greater than the cost of retaining a current customer. And those customers who stay long-term with your organization contribute substantially to its profitability. In fact, according to Frederick Reichheld in The Loyalty Effect, a five percent improvement in a company's customer attrition rate can push that company's profits up 75 percent.
With the increased storage power of data warehouses, traditional methods of data analysis have not been able to keep up. In the past, spreadsheets, charts and graphs allowed us to see smaller amounts of data in two-dimensional, linear displays. However, data warehouses and marketing databases require multivariate displays and the ability to explore tens of thousands of data items at a time. Data visualization uses graphical elements such as color, shape, size, spatial layout and geometry to represent complex information in highly intuitive ways. With this metaphorical representation, users can easily see patterns and trends in large amounts of data. The more powerful visualization systems are interactive, allowing users to gain a big picture of their data before filtering out uninteresting information and drilling down to important details.
So how does visualization help? Let's look at a scenario from the telecommunications industry to show how data visualization could increase profitability by enhancing the loyalty effect of top customers and decreasing attrition. A couple of years ago, telephone companies paid $50 to customers who switched from a competitor's service. The result was that consumers began churning from company to company. Data visualization could have been used to analyze the customer database as follows.
First, by assigning color to represent the average amount of money customers spent on long distance each month and using filtering to find the customers who had churned, a marketer could see that the majority of customers who left spent less than $20 a month in telephone calls. The marketer could then filter and drill down to the record level to create a list of churn customers with an average monthly spending rate of $100 or more. This list could then be used in a win-back campaign. Next, after returning to the full data set, the marketer could find customers who had not yet churned. Here, the marketer might notice that their best customers, those who spend more than $500 a month, had been with the company for more than three years and make a lot international calls.
Again, by accessing customer records, the marketer could launch a campaign providing reduced international calling rates for this segment. He could also help improve customer care by marking the record of best customers, indicating to call center personnel that this customer should receive a premium offer if requested.
Visualization makes this type of analysis quicker and easier, making information more accessible for marketing professionals by decreasing the dependency on statistical analysis and using visual metaphors. The only way to truly implement a relationship marketing model is to reestablish the ability for campaign creators to see the customer. Visualization allows marketers to better understand and respond to customer needs and compete successfully in today's market.
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