For marketing and sales executives, data mining is akin to taking vitamins – it’s something you suspect you should be doing, but don’t really know why.
A common practice among business-to-consumer giants like Proctor & Gamble, data mining is now gaining acceptance in the business-to-business world. What’s more, mid- and even small-sized companies are tuning in to the opportunity. While this is partly due to an increased volume of data at companies of all sizes, a better explanation is found in the breakthrough tools and services that have made data mining easier and less expensive.
Yet, for the overwhelming majority of BtoB companies, data mining is still an unknown, which creates a huge advantage for those who can figure out how to put it to work for them.
So, what is data mining and what can it do for you?
A Smarter Approach
Put simply, data mining turns customer and industry data into actionable business knowledge. For BtoB companies looking to improve their sales and marketing, some of the most important applications of data mining include:
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Modeling the traits of current customers to identify the best prospects for future customers
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Understanding the differences between high and low profit customers
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Identifying untapped up-sell and cross-sell opportunities
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Improving the ability to customize services, offers and messages to individual clients
The process of using data mining to improve BtoB sales and marketing performance begins with the most predictive information possible -- a company’s own data. Using information from virtually any source – ERP, CRM, order entry, even simple spreadsheets – a data miner can begin segmenting customers based on their similarities to each other AND their relationship to your specific criteria.
Take a couple examples from our work:
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In an effort to improve their sales efficiency, a client that sells services to banks hoped to identify variables that could predict which prospect banks would be a good fit for them. Our first step was to append a simple ‘sales-by-customer’ list with information that further defined each customer’s value (e.g., length of time as a customer, demand on resources, strategic value, etc.). Then, using tools to analyze a variety of bank characteristics such as fee and interest income, number of branches, location, ownership structure, and type of charter, we created profiles of our client’s best customers. Finally, we developed lists of non-customer banks that mirrored those profiles.
Armed with this insight, our client can now direct its sales team to those prospects with the greatest likelihood of turning into valuable customers.
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In an industry dominated by well-established competitors, a uniform rental client could not afford to chase the marketing budgets of its rivals. Instead, they hoped to limit their lead generation spending to getting the right message to the most receptive audience.
Using five years of historical sales data, we built a segmentation scheme that weighted products by strategic importance. This revealed seven “sweet spot” market segments based on product usage, cost to service and future revenue potential. Prospect lists that matched the sweet spot criteria were purchased, with targets then divided into one of the seven segments. Our client then launched separate direct marketing campaigns – with messages specific to the traits of a given segment.
New customer conversions within the sweet spot segments generated a 60% higher average revenue and a 50% lower churn rate than non sweet spot prospects. Over time, the net profit for these customers doubled.
Yes...you really can
For many, data mining’s value proposition is obvious. “Who wouldn’t be able to put this knowledge to good use?” they say. Yet we often hear from companies who assume that their customer list is “too small” to data mine...or who believe they don’t have enough customer data....or who think they can’t afford to do something “that sophisticated”.
But for most companies today, those fears are misplaced. No longer must you have thousands of customers and terabytes of data to justify the costs of a data mining effort. In fact, the ROI for data mining is often a “no-brainer” for even small companies. Just think of our client selling services to banks: the money they’ll save by marketing to only the “right” prospects will by itself pay for the data mining in the first few months.
If you think data mining might be a good move for your organization, here’s how to go about it:
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Start with your corporate objectives and consider how data mining can support them. This will also ensure that you have the support and buy-in from all parts of the organization that need to be involved.
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Understand the data that is available to you and how you can get it. If your goal is to increase sales and you decide to profile or model your best customers, is there data available? Historical sales, products purchased, industry SIC codes, number or employees, etc.? You may need to get IT on board to review the analysis goals and what data is available.
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Determine the help you need. There are a variety of analysis tools available with varying levels of sophistication and price. Perhaps start with a simple package that you can run on your desktop and build a success story or case study. Or, at least initially, engage data mining experts, such as the authors of this article.
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Have the right perspective. Data mining is not a one-time event. For maximum value, it should be viewed as ongoing and as a continual improvement process. Incorporate testing, tracking and refining the analysis into your plans.
Can data mining work for you? More than ever, the answer is probably, “Yes.” A recent study by Gartner Group found that the average company uses only 7% of its data-warehoused information. Clearly, the remaining 93% offers a tremendous opportunity to enhance the precision and power of your sales and marketing efforts.
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The Science of Sales
The Science of Cross-Selling and Up-Selling
Barry Rosen is President and a founding partner of The Pursuit Group. Over the last 20 years he has launched successful sales and marketing integration programs for companies in healthcare, technology, insurance and other high-ticket, complex sales industries. Contact him at brosen@thepursuitgroup.com.
Robert Migliara is founder and Director of Intelligence for Marketing Intelligence Group, a Cincinnati-based firm that helps clients leverage database intelligence and tools to facilitate positive customer interactions, improved sales performance, enhanced customer loyalty and world-class customer satisfaction His career experience includes analytical positions with companies such as GE Capital and Cincinnati Bell. He holds a B.S. and Masters' Degree in Applied Mathematics and Statistics from Stonybrook University in New York. Contact him at robert@migsite.com.
