The recent explosion of interest in using analytics to make smarter marketing decisions is no fluke. Amid rising prices and raw nerves, organizations want to target their campaigns with pinpoint accuracy, limit waste, nurture their best customers, and zero in on profitability. To do so requires serious data-crunching -- data which is already available in monumental volume and continues to accumulate. Analytics, whether fully understood or not, is practically dinner-table conversation for executives these days.
Equally relevant is that improvements in both capability and interface have made powerful analytics much easier to access and use. Even to nontechnical marketing executives, analytics is no longer an alien concept. From customer profitability and purchase propensity to attrition and credit risk, analytics are increasingly helping companies execute smarter and more-effective marketing campaigns.
So individual campaigns are getting better. But of the many campaigns available to marketers, how do you know which one to use at any given time? How do you know from month to month that you are employing the proper mix to maximize your overall profit from these campaigns?
Better Marketing through Better Math
When running hundreds of campaigns a month, it's not enough to rely on intuition. Questions such as those above can only be answered using mathematical optimization.
Marketing optimization can be characterized as finding the best combination of campaigns (or offers, or contact policies) from the pool of all possible combinations. The optimization is based on the principle of limited resources: No one has unlimited budget or channel capacity, nor does anyone have unlimited customers. Given that we can't contact every customer with every offer, a mathematical approach is the most reliable way to determine who should get what offer.
The Case for Optimization
More than with most business opportunities, the case for optimization can readily be made from the numbers themselves. Consider the case of one insurance company, which relied both on direct mail and telemarketing campaigns as primary marketing channels. Initially this company attempted to create its own optimization routine to maximize returns. As you might expect, this homespun routine was slow and unreliable. It took days to perform the optimization and the process was subject to crashes, which meant starting over each time.
After upgrading to commercially available optimization software, the same optimization now takes place in a matter of minutes. The optimization process considers constraints such as a minimum number of customers for certain campaigns, as well as inputs such as loss ratio and pay rate to optimize on the Net Present Value of future profits. In this case, marketing optimization covered the software investment in just two months.
Other examples include a communications company that increased its monthly profit stream by $6 million in the first month and a credit-card company that achieved a 25 percent uplift in receivables from a balance-transfer campaign to 75,000 people through a single communication channel.
The benefits from optimizing marketing activities are real, demonstrable, and almost immediate.
Not all optimization routines are created equal, so beware of any software that claims it includes optimizing capabilities. By definition, optimization routines need to consider "all possible solutions." Some packages may use rules-based or segmentation-based approaches that only consider some of the possible solutions. As such, they're sure to leave money on the table. Although these approaches offer some incremental improvement compared with no optimization at all, they only get you part of the way. Math-based optimization takes into account all of the possible solutions and simply tells you which one is best.
Payback and Profits
Figuring out the best way to market multiple products through hundreds of campaigns that make thousands of offers to millions of customers is too much to do in an ad-hoc manner. But now more than ever, nearly instant payback and very large profit increases make the case for marketing optimization, and truly beg the question, "Why would you not optimize?"
About the authors
Brent Lever is a senior consultant for SAS Institute specializing in marketing optimization. He has worked with Fortune 500 companies to help develop a comprehensive customer intelligence strategy including customer analytics, campaign management, and optimization. Larry Mosiman has over 20 years experience leading marketing teams for high-tech companies. He is currently the worldwide product marketing manager for SAS Institute's Customer Intelligence Solutions.
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