In Marketing Analytics, Business Needs Drive Technology Adoption

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You might think that when a technology is trendy, you would have questions of business ownership. Organizations would buy the technology before knowing what to do with it. But in the case of marketing analytics, a fairly hot area within the broader realm of CRM, my experience with a range of clients, small and large, in financial services, automotive, pharma and high tech, is that business needs truly drive technology use, and the questions of ownership fall by the wayside.

There are some areas where marketing analytics has really blossomed, and the trend seems to be accelerating. Marketing analytics has made headway in campaign management, measuring customer value and in data mining.

The tracking of marketing campaigns has become much more rigorous and intricate over the past few years with the introduction of sophisticated campaign management software. It is possible now to execute campaigns of enormous complexity—with customized messages and offers for different segments—and track response over time across different channels. This, of course, is particularly true in direct and online channels, but it is increasingly the case for traditional media, as well.

In addition to campaign management tools, business intelligence software has made measurement and tracking of campaigns a great deal easier. At Quaero, where we work to help businesses improve their marketing performance, one of our large financial services clients has built a very sophisticated tracking and reporting system that provides daily updates on how different print, media, direct and online campaigns are doing, not only in terms of response generated but also for actual revenue generation. Users throughout the company, not just in marketing, leverage the system, which is very intuitive and easy to use. But it didn’t look as though it would be that popular in the beginning.

Initially, when the system was proposed and being built, most of the marketing people—particularly those in advertising—tried to ignore it and starve it of data. . For instance, our folks were having difficulty getting access to data about media buys details of campaigns, such as target markets and message content, to initially populate the system. Once the system was rolled out (with partial data) and the top executives started using it—and based allocation decisions on it—the non-cooperators started cooperating in a hurry. Now most of the data feeds are automated and results are usually available within a week.

Delicate balance
We have noticed, though, that the ability to track individual campaigns sometimes leads marketers to ignore the forest for the trees. We increasingly see many companies measuring individual campaigns with great precision but not doing as good a job of measuring longer-term effects that accumulate over multiple campaigns and also neglecting the impact on customer value over time. There is always a delicate balancing act between getting short term ROI and building long term brand equity, although the two don’t necessarily need to be in conflict.

Analytics has proven particularly useful in measuring customer profitability and value. This can be done on a historic, potential or lifetime basis and is an important part of marketing analytics, because, presumably, marketing investments increase customer value over time and one of the ways to analyze the effectiveness of marketing is by understanding what impact it has had on customer value.

Data mining and statistical modeling techniques, such as Markov chains and different types of regressions, play a critical role in helping pinpoint the drivers of short- and long-term customer value and also to prioritize how best to increase this customer value.

One bank we worked with was able to use Markov chains to better understand the progression of customers along a path of increasing or decreasing value over time, as well as the factors that led to these transitions. Understanding the drivers of customer value shattered many myths about what kind of marketing programs would be best to deploy against different types of customer segments. It also resulted in a major overhaul of product pricing, sales incentive structures and call center scripts which, in turn, led to significant savings and increases in profitability.

There have been an increasing number of data mining tools available over the past decade. One reason they have become popular with business users is the ease of use, which has made these tools more accessible to people without Ph.D.s in statistics. Data visualizations tools, such as Quadstone, make it easier to explore large quantities of data before running formal statistical models. Real-time analytical engines make it easier for companies to analyze customer behavior on the fly and react in a very personalized way, almost instantaneously.

Marketing analytics is a hot and growing area because it has proven its value in many companies. Companies that invested in CRM infrastructure, focused on sales and call center operations, in past years are realizing that this kind of analysis can help them leverage their existing investments and boost their return on investment. These technologies, thankfully, are not solutions looking for problems.

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