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Consumers share an incredible amount of master data

Last week, I wrote about Customer Motivation Management (CMM), a term I coined to describe a use case of MDM data, functions and processes. CMM puts customers in the driver’s seat and lets them tell the merchant exactly what they want, enabling merchants to target them more effectively and precisely.

Most businesses use clever techniques like data mining on massive quantities of historical data to try to predict a consumer’s next move, but they can’t. Some organizations incorporate real-time insight from transactional systems, trying to extract meaning out of social media postings to gauge sentiment or propensity to buy.

Again, this only improves the chances of understanding a consumer’s next move, without providing near-certainty of intent, budget, timing, quality, etc. They can only guesstimate that Jim, to use my prior post’s example, belongs to a similar sample group who would buy item category x next month for price w since he bought items y and z last quarter (hopefully all from the same merchant).

They might only find out months later that he moved – or not at all. They know nothing about his wife’s gardening passion, why it was on hold for years, or what re-ignited it. Overall, they also do not know the other people in the household or how their lifestyles – and motivations – have changed.

What type of business would best leverage CMM?

Ultimately, CMM could help any sort of business: tax/investment advisors, local retailers, pest control or lawn care providers. Essentially, anything a person would post about on Facebook or Craigslist, beyond pure commodities.

Again, this is a master data issue. Some will disagree because it smells like transactional insight and is squarely in the realm of BI or predictive modeling. These technologies crunch, structure and relate numbers for easy navigation (in hindsight) to estimate or predict the future by looking at relationship changes in multiple variables.

However, I posit that such social data is master data. Despite potentially fluctuating data point-in-time motivations (and surrounding facts, like budgets), CMM is useful across multiple applications, processes and core to a bottom line.

This data can contribute to campaigns to sell and inform, while supporting actual sales and service. Granted, the data’s half-life is shorter than an address change, but master data has already been compartmentalized by each consumer attribute’s reach and latency.

As we move from purely analytical to transactional analysis to incorporating social profiles, more of the customer’s profile will be in flux. Purchase motivation (e.g. any social media post) is just another piece of this fluctuating master data, placing low latency on top of a typically static customer profile.

Reality is catching up with theory. I am not in the same financial, emotional or social position as a few weeks ago (especially given market fluctuations). Neither are you. Ergo, why does my merchant still see me as the same customer?

So why has nobody tackled this today? Well, it isn’t easy. CMM is a drastic shift from the current modus operandi and the large “content platforms” that would be in a position to capitalize on it face numerous roadblocks:

• Not profit-oriented (Craigslist)
• Still figuring out on how to use their gathered data (Facebook)
• In the “wrong” domain space (match.com, chemistry.com)
• Not truly social in nature (eBay)
• Use an ad-based model (YouTube, Google)
• Do not track transaction details (credit card companies)

CMM is also technically tricky, as it requires:

• A high-transaction volume way to sift through big data streamed in real time
• A sophisticated metadata model to extract the useful from the useless (text mining)
• A glossary pre-loaded, self-learning system (such as IBM Watson) to adjust existing matching rules to product or service offers, given user instructions, and then hand it off for processing and follow-up.

CMM requires companies to shift the burden of governing increasingly larger chunks of data to the customer. By default, this also means that the company now has to invest more time, money and skills to ensure customer input is well-formed, digestible, understood and properly used.

This is a whole new ballgame for organizations, even for organizations who thought they are already doing MDM by implementing software in one division with a committee overseeing related projects.

Today, customers do not voice or manage much, if any, of this data. Most companies would not think to go back to the “good old days” when people talked face-to-face across the register instead of through surveys and statistical analysis of a very finite set of transactions.

Don’t get me wrong. There is value in CMM, but as we become increasingly demanding as consumers and as commerce becomes increasingly real-time and competitive, the window of opportunity to (repeatedly) sell becomes smaller.

If you approach someone too late, too often, through the wrong channel, or without a clear understanding, he may not buy. Even worse, you may not even know that he went to a competitor – and why – because you didn’t listen to what he was saying all along.