Dynamic decision-making in an era of constant change.
Posted Oct 1, 2005
The rapid speed and degree to which all things in today's marketplace change have made traditional marketing models obsolete and expendable. The traditional five Ps of marketing--positioning, packaging, promotion, persuasion, and performance--no longer hold. If a marketer packages a product, sets a price point, and creates a promotion today, the consumer will likely move onto the next big thing tomorrow. Instant messaging, the Internet, and consumers' general fickleness all contribute to the shrinking life cycle of most marketing efforts. Today, savvy marketers know that the answer to this crisis is to make marketing more nimble, and they can do this using a just-in-time strategy.
Marketers not only need to shrink their marketing cycles to keep pace with the rapidly changing market, but they also need to create dynamic marketing strategies that can change course on a dime and shake up the five Ps. The result will be better response rates and improved results for marketers.
Too many marketers today use static elements, like demographic and geographic data, as the basis of their marketing practices. This approach merely segments customers; it does nothing to get at the root of consumer behavior and motivation. If marketers delve into customer motivations, values, and behaviors, they'll be better able to appeal to consumers' true needs and wants by creating customer-specific marketing efforts. The key to success is to avoid static criteria and instead create business models using dynamic behavioral categories that focus on--and require--constant monitoring, such as "Where did the customer shop?" and "What did the customer buy?" This results in real-time recategorization and reclassification of customer data, creating a nimble marketing strategy for any situation.
Given the current state of information management systems, almost all marketers have access to this type of data, but too few use it to create useful just-in-time strategies. The tools for this type of practice are widely available; it simply comes down to using the tools properly and effectively. Online retailers are probably the leaders in this arena. A number of brick and mortar companies also do this well, but most fail to leverage their resources to the best possible extent to customize offerings for their customers. Other industries, such as banking, insurance, and even direct mail, lag much farther behind.
Some online retailers use a dynamic marketing practice called collaborative filtering to make offers based on past customer behavior. For example, if a customer buys a book through Amazon.com, it will send the customer follow-up email promotions for related books. Travel sites, such as Orbitz, offer discounts customized on the basis of customers' prior travel histories. Some brick and mortar retailers analyze patterns of customers' previous purchases to propose next offers, such as Macy's, which sends customers discount coupons for items in the same product category as a recent purchase. These retailers are on the right track, even if they aren't all harnessing the full power of true collaborative filtering that compares purchase patterns across many customers to come up with predictions of interest in a wide range of possible next products.
So how can other companies create dynamic strategies? Here's how to start:
Stop using static categories and start using observed behavior to better understand consumers. For instance, most marketers look at a customer's age, gender, and hometown to lump customers into various segments. But this approach merely offers an indirect view of the purchase behavior the marketer wants to understand and influence. The smartest marketers know that understanding a person's complete shopping history--for example, what she bought, where, when, and at what prices--provides the most reliable segmentation for tailoring marketing messages.
Understanding the complete shopping profile provides a basis for customized marketing--what product, at what price point, and delivered through what channel or retail experience?
Don't look for one-time solutions. Segmentations need to be dynamic enough to fluctuate and keep pace with the speed in which information changes. Strictly speaking, marketers need to get beyond the idea of segments altogether--rather, they should think of consumer behavior in terms of positions in a big continuous data map. Some people are closer together than others--we predict a consumer's behavior on the basis of the behavior of others who are closest to him or her. But consumers move around all the time (for example, their patterns of purchases and shopping destinations change), bringing them closer to others with different profiles. Marketers need to think of their job as tracking this behavior and react appropriately.
The foundation of dynamic marketing is the efficient capture and organization of vast quantities of information. Increasingly that information is already being captured but its full power isn't being used. The good news is that modern IT infrastructures are fully capable of providing the necessary storage capacity and processing speed to achieve the dynamic marketing vision. The biggest challenge may be to enable the people in your organization to unleash this power.
Marketers also need advanced analytics to harness the potential of the data. The data-mining revolution of the past 10 years has made available sophisticated data analysis and visualization tools that bring cutting edge analytic models within the reach of most marketing programs.
By integrating all of these tools and techniques, marketers will be able to track consumers' wants and needs much better than they have before--and improve marketing practice as a result.
About the Author
Martin Ahrens is vice president of methodology and quality assurance at Inductis. He develops quantitative analytic solutions to business challenges and advises all Inductis project teams on the selection and application of scientifically sound methodologies. He also plays a lead role in communicating analytic solutions to the broader business and technical community. Ahrens earned his Ph.D. in ecology at McGill University. He can be reached at firstname.lastname@example.org.
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