Don’t waste money on analytics

Don't waste your money on Analytics


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Analysis is an instrument of learning, defined as “a process of acquiring modifications in existing knowledge, skills, habits, or tendencies.” There are substantial volumes of academic research produced over the years on a subject of relationship between learning and beliefs. You can search for them with the keywords “deep learning”, “deep belief networks”, etc. The gist of these academic inquiries points to an observation that deeply held beliefs impede learning process, and network (think groups or organizations) shared beliefs have tendency to suppress learning process aggressively.

As long as you and/or your executives are not ready to question your current beliefs, no amount of evidence will make you or them to act.

The power of analytics is in its ability to expose patterns of data that can help us to learn. When new knowledge is rejected/ignored by the organizational belief system, all the cost of learning is wasted. If you think the last sentence does not apply to you because your use “free” tools, think again. The time, effort and political capital you have to invest in use of “free” tools for learning can be substantial. The probability of acceptance by your “network” of new knowledge, discovered with use of “free” tools, is even lower.  That is because you bypassed an opportunity to socialize the idea that you may discover something your organization does not know yet, and to gain conceptual adoption of such result. Use of “free” tools rarely require any approval process within organization. Therefore nobody knows what you are doing, and as a result are not prepared to consider any findings, unless they support and re-enforce existing beliefs. Presenting new findings, that challenge status quo as a surprise, is very bad idea. The process of selection and acquisition of a tool prepares your audience to consider the findings, as participation exposes them to a potential value.

People and organizations are most likely to consider a challenge to their beliefs at the times of extreme “pain”. At such times the leaders open their minds and examine their beliefs to learn how they need to act to improve their lot. The rest are looking for excuses and complain about circumstances beyond their control. Here is an example describing such a moment at Best Buy in 2012:

The one critical thing we offer the world is choice,” said the Best Buy chief executive officer Brian Dunn in a BestBuy customers analysisMarch 2012 phone interview. He was trumpeting in particular his company’s role in guiding customers through the expanding smartphone universe.

“We provide the latest and greatest choice of all technology gear, from Apple products to Google products, and that brings more opportunity to help people put technology to use. That is a great place for us to be.” A week later, reality intruded. The consumer electronics retailer posted a $1.7 billion quarterly loss and announced it would close 50 stores nationwide. On Tuesday, Dunn resigned.

The belief of the Best Buy CEO (at the time) – “The only critical thing we offer the world is choice” was challenged by customer intelligence that exposed the evidence of “most critical” things from “the world” perspective are in-store service quality and products reliability. The evidence was ignored and a new CEO had to come in.

Here are a few suggestions on how to deal with this challenge:

If you focus on intelligence that can help to improve probability of enterprise to increase its market share, your challenge to status quo is more likely to be tolerated. Business executives are motivated by two desires:

  • increase in revenue or market share and
  • reduction of expense, i.e. increase of profit margin

and two fears:

  • Decrease in revenue or market share and
  • Decrease of profit margin.

Intelligence that improves probability of realizing their desires, and/or forewarn that they are on the path of realizing their fears, is aligned with their system of values and therefore deserves their attention.

New knowledge that does not conform our beliefs is a natural suspect.  We credit our beliefs with helping us to achieve our past successes, while new intelligence has no “resume”. Applying the new intelligence to historic data can overcome the trust challenge if that application successfully expose patterns that correlate with actual results in the past.

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