Marketing (Un)Intelligence

I had a great first job out of college. I was introduced to Business Objects, Teradata, and using them as technologies against what was at the time, the world’s largest non-government data warehouse. I stayed in the same group for my seven year run with that company working in the Strategic Marketing organization. Our goal was to design reporting systems and associated applications which made our marketing strategy more effective. Other aspects of marketing on platforms like Facebook and Youtube; those views came from and we didn’t have to concern ourselves with that. From a high level, while it was a beast (mainly because of the huge base of data and customers we were dealing with) it was an established revenue generator with lots of smart things built into it. As I flip through a day’s worth of mail and see countless direct mail pieces, I’m left wondering, who came up with this marketing (un)intelligence?

Today’s post comes much more from the perspective of an architect that wants to optimize the use of data in both generating revenue and creating cost avoidance opportunities when it comes to marketing efforts. I think some other day I’ll take some time to talk more about a technical approach to a Marketing Intelligence system.

Let’s start with Generating Revenue

An organization that uses any kind of data to more effectively grow a business should be paying attention here. Using your existing data for ACCURATELY targeting customers for direct mail and telemarketing, a historical accounting of the performance of campaigns, and in a predictive fashion to determine who will most effectively purchase a good or service is critical.

Use Your Data

Your blossoming data warehouse is likely a gold mine of intelligence. The important stuff here may be data on customers and prospects. What their demographics, firmographics, purchasing history, existing services, etc. are in order to determine if a campaign is going to be effective or not. So ask your data the burning questions, such as “Does my campaign to sell more of service X specifically exclude customers that have already subscribed to it?” or “Is my customer I’m targeting even eligible to utilize this service?”.

Baseline the Current Population

Before embarking on a new campaign, be sure to capture a metric that measures the current penetration. Why? At the end a given campaign lifecycle, measuring the effectiveness of that campaign by re-evaluating the new penetration of the product or service and correlating directly to sales attributed to the campaign will serve as the KPI on whether or not we are effective in our strategy.

Get Predictive

Trying to get insightful on what people have the propensity to buy is a constantly evolving process. I’ve been a part of several organizations that have attempted to use predictive analytics to create strategies around marketing. I think my favorite implementation of predictive analytics was one in which we had a dedicated team of statisticians on the business side defining and creating the models and a process for scoring our customers against those models for inclusion in the data warehouse. This in turn created a mechanism in which when doing lead segmentation, our business partners could immediately reference statistical models when trying to decide who to market to.

This is all pretty abstract in this post, I realize. To put a bow on using Marketing Intelligence to generate revenue from a Business Objects perspective, I think it’s important to wrap a technical brain around what this all means:

  • A collection of data about customers, prospects, their behaviors, and ideally statistical probabilities that they may buy your stuff
  • A lead segmentation tool (i.e. universe) that allows you to profile and continually analyze what customers fall into your campaigns before you market to them
  • A campaign execution tool that allows you to target leads, market, and collect real data on the response rates
  • Reports and useful dashboards to measure and evaluate campaign effectiveness

Next let’s talk about Cost Avoidance

My particular beef that drove this post. . . I get more mail spam than I care to count. I have to shred at least two credit card offers per day. This might include:

  • Offers from new banks
  • Offers from the same bank that sends me something every other week (not kidding)
  • Offers from BANKS I ALREADY DO BUSINESS WITH. Seriously, they send me mail like I’m not a customer today.

I do realize why I get these offers, but if the banks were using BI effectively, there is a lot to be gained on knowledge of my behaviors like I discussed above. It seems that they are not looking at:

  • Response rate – have I ever called back or responded to a mail piece?
  • Has this name/address already been marketed to?
  • When did we last contact Mr. Vallo?
  • Does Mr. Vallo’s father really live with him (no, not in the three times I’ve moved in the last 11 years)? Then why do we keep mailing him?

Believe me I get that prospecting is a tough game and a lot of times you may be dealing with 3rd party data that is not clean. But for the time it takes to scrub data for a campaign, I’m guessing a significant amount of dollars can be avoided annually in a little bit of caution in who you are marketing to and can lead to more effective penetration in campaigns. Construction of a Marketing Intelligence system that is closely knit with SAP Business Objects is a win.

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