Data Mining vs. Mining Data In Multichannel Retailing
If there's one thing I've learned during my first four months as a small business owner, it is that multichannel retail executives are hesitant about "data mining", but are very enthusiastic about "mining data".
Assume you have a catalog/online CEO who wants to understand how website customers behave between a first visit and a twentieth visit.
A statistician might provide a series of reports and analyses that thoroughly explains the process from soup to nuts, publishing exciting findings along with complex statistical information to support his findings.
The multichannel CEO nods politely, even offers verbal kudos, then leaves the room feeling like she still doesn't understand how her customers behave.
The person who "mines data" identifies the handful of key findings that every CEO must know, then puts the findings into a context, a "story", that the CEO can use to create actionable strategies that drive sales and profit. There is something that the CEO can "do" with the findings, and it is easy for the CEO to "know" what the next steps are.
Mathematically, this type of work is much less satisfying. Professionally, this kind of work can be more gratifying.
Data mining software and data mining experts are generally plentiful and affordable in multichannel retailing.
Folks who "mine data" are generally in short supply, and are desperately needed by multichannel executives.
Labels: data mining, Multichannel Retail
posted by Kevin @ 8:36 AM
7 Comments:
Right on Kevin! It's a similar phenomenon in my world of customer research, where companies are locked in to meausring overall customer satisfaction. For example, let's say you measure overall satisfaction on a 5-point scale and this time your score was 4.1, and last quarter it was 4.8. Can you tell your CEO what causes your score to go up or down each time and what you will do now to increase it? If you don't, why are you measuring it?
You've hit the nail on the head here. It's frightening how many analytical types can produce a ton of reports, factoids and analysis and yet still fail to distill all that data into something meaningful.
The goal of all data/analytical geeks (and I am one, so I can call us geeks) is to make sure that there is meaning behind findings. That there is a story in the data (or lack of data). All too often, it is the story that is missing, and all that analytical work is for naught.
This post has been removed by the author.
I killed the last message, too many words spelled incorrectly.
Paul / Suzanne --- I, too, am a geek.
I'd be willing to bet that the majority of my failures in my career came from an inability to simplify complex information into the soundbytes that business leaders need to hear.
All too often, we try to give our executives an episode of Frontline, when they were looking for CNN Headline News.
How much of this shortage is self-inflicted? The overwhelming majority of hiring managers I've spoken with over the years are more interested in a specific college majors, degrees, and specific statistical procedures. It is very unusual to be asked about the ability to put things into context.
I'm not sure I agree with the concept of self-inflicted problems. At some point, I hold analytics folks accountable --- they need to get off 'tech island' and learn how to communicate with business leaders.
Undoubtedly, hiring managers can do better, and can ask better questions. At most of the companies I've worked at, I was able to give 'tests' to analytics folks, a good tool to understand communication skills. At one comapny, I wasn't allowed to give tests, and that made the job a bit harder.
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