Big Data vs. Actionable Data: 2 Steps for Capturing the Information You NeedFebruary 20, 2013 By Wayne Townsend
Let's face it, big data scares most marketers. The thought of hiring the talent, managing a huge database and purchasing specialized tools sends CMOs into their offices poring over spreadsheets trying to figure out how to cover the cost. Is it all really necessary?
Predictive analytics is one of the challenges associated with big data. The obstacle with predictive analytics is not the availability of well-crafted, easy to use modeling tools; it's the limited availability of skilled analytics professionals. While large enterprises routinely have in-house analytics teams, mid-tier companies can rarely afford to fund these positions. Moreover, in certain geographic areas, it's almost impossible to find people with the requisite skills.
Most mid-tier marketers need to capture the right information and make it work for their needs. Sophisticated Web analytics tools are available to collect every keystroke, but most of that information is anonymous and/or not particularly useful in helping marketers be more effective. What marketers really need to know are their acquisition and retention rates, what offers consumers are responding to, what subject matter (or content) they are interested in, and if and when they purchase products.
My advice to marketers is this: "Don't try to boil the ocean." Think about the data you need to drive your marketing programs and make yourself relevant and timely. Focus your efforts there and avoid the latest catchy phrase or term used to define something you might not even need.
So where should you begin?
- Start with using embedded predictive models. Cost-effective and easy-to-use, embedded predictive models help marketers turn data into actionable information. Pre-built, industry-specific models provide compelling, cost-effective alternatives to big data. Predictive models—such as likelihood to convert, time to next purchase, likelihood to attrite and next best product—can provide the most accurate and up-to-date customer understanding.
- Descriptive models, such as RFM (recency, frequency and monetary value) and customer categorizations such as new, lapsed, lost, reactivated, core-stable, core-grow, core-decline, core-best are also available—and help the marketer to better target and time offers and messages.
Making data actionable is easy if you stay focused and don't let the latest buzzword force your hand. Not all marketers are the same, just as every consumer is not the same. Understand your marketing needs and plan accordingly. Turning your data into actionable data is the best bet for your long-term marketing success. Bigger isn't always better.