With IBM's recent press release announcing a new consulting organization dedicated to advanced business analytics, I thought to myself, "I sure hope this new organization helps to incorporate advanced analytics/data mining into mainstream marketing this time around." Because up to now, other than a few BIG exceptions (i.e. Amazon.com), the promise of integrating data analytics and marketing (in a scalable and repeatable manner) has yet to be realized. It's quite frustrating, as data mining technologies have been around for a number of years and incorporating data mining into marketing seems like such a natural fit. For example, customer exhibits pattern of behavior for product cross-sell opportunity so complementary product offering is dynamically presented to customer and add on sale is made. Done. End of story. Not so fast. Here's what's throwing a wrench into this seemingly straight forward scenario:
- The PhD's in machine learning and mathematics (in the Reporting and Analytics Division) and the MBA's (in Marketing) are wired completely different. Here, the PhD's are concerned about accuracy (to the n'th degree), methodology, sample size...etc. and the marketers are concerned about "just getting the stuff out the door".
- For direct marketing, most marketers are still obsessed with list quantity versus list quality. "You mean my list count is only 150,000, I thought it was going to be more like 2,000,000?" In this case, if a list (generated via data mining) has a smaller count than expected, the marketer may request that the list be pulled using more traditional data selection/segmentation methods to increase the list count (completely missing the point).
- The raw data (to be mined) is never right. So the data mining folks spend most of their time "getting the data ready to mine" versus actually mining the data.
- Very few companies have figured out how to integrate data mining and marketing execution efficiently (or at all). As such, a lot of interesting discoveries are made, via data mining/analytics, but no direct action is taken to help generate additional sales from these discoveries. For example, through mining its sales transaction data, an on-line retailer may have discovered that customers who purchase products in category A, are likely to purchase products from category B as well. Unless there's a process/technology in place to alert the customer about products in category B (i.e. automated e-mail, website message...etc.) this discovery, while perhaps interesting, has limited or no monetary value.
So, with the above realities in mind, my advice to IBM (if the company wants to be successful at selling data analytics products and services to marketers) would be the following:
- Figure out a way to bridge the communication barrier between the data modelers/analysts and the marketers.
- Set expectations for senior management on the process of data mining. Many senior executives believe the process involved with advanced data analysis and modeling is likened to the Ronco "Set It and Forget It" Rotisserie Oven. Here the assumption is that a magical software tool is turned loose against a sea of data and (miraculously) revenue generating discoveries pop out the back end with limited or no human intervention. Obviously there is a lot more to data analytics/modeling than this, so senior management's expectations need to be managed accordingly.
- Make sure the raw data that's being analyzed/mined is complete, accurate and structured correctly on an on-going basis. Otherwise you will have highly skilled data modelers spending most of their time doing manual, mundane data "grunt work" instead of the work they are hired (and paid handsomely) to do.
- Educate marketers on the value of data quality versus data quantity. Here various tests could be set up to compare the marketing ROI between segments derived via data analytics and segments derived using more traditional data selection methods.
NOTE: I find that numbers speak much louder than words when it comes to proposing new marketing techniques and technologies to marketers. - Don't focus solely on analytics and discovery. Make sure you also provide the customer with a scalable and repeatable method to "take action" when key, revenue generating discoveries are made. In other words, help the customer fill the gap between marketing discovery and marketing execution.
If IBM is mindful of the above recommendations, I believe this newly formed consulting organization will be highly successful at selling data analytics products and services to marketers.
P.S. Here's an interesting video I found that helps answer the question "what is data mining?":
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