customer lifetime value formula

Marketing researchers are increasingly turning their attention towards the behavioral sciences to understand what triggers purchase behavior among customers – especially sentiment analysis. The more these triggers are studied, the more it seems that people buy when they make emotional connections. An interesting hypothesis is that people have what is termed as “premature cognitive commitment“.

This wordy phrase simply means that people “buy into” ideas/concepts/products first based mostly on their emotional attachment to them and then rationalize later about why they did what they did. At a recent sales seminar which I attended, the speaker did an interesting demonstration to prove this point. He first flashed a slide which showed the face of person who seemed to be in a lot of pain or grief and asked the audience to identify what emotion they saw in the picture. The answers from the audience were of course immediate and unambiguous. Next he showed a slide which had an arithmetic operation: 24 X 76 and asked people what the answer was. Clearly this was a rhetorical question, because most people need a calculator to do this. 

The point was simple – our brains are hardwired to detect and understand emotions faster and without uncertainty – apparently it takes less than 7 microseconds to detect facial emotions! Considering the complexity of this task : try to build a machine learning algorithm which can predict the emotion from a photograph! The technology is not quite there yet, but will be soon, it is amazing that people – even little children can do this without trouble. Rational or logical questions require the cognitive portion of the brain which is much slower than the limbic portion of the brain that is responsible for emotional problem solving. To put this in context, the cognitive part of the brain is only a few million years in evolutionary terms where as the limbic or lizard brain has been evolving for more than 300 million years!

Against this backdrop it makes it much more easier to understand why there is such a great interest in something like sentiment analysis among marketing folks. Angry tweets or glowing praise for a new product launch in a blog can now break or make product launches. For businesses that are (correctly) focused on their customers and prospects, it becomes very important to keep a vigilant eye on the voice of the consumer. In a way you can call predictive analytics for doing sentiment analysis a marriage between the cognitive and limbic response systems, except that the cognitive portion of the solution is provided by a machine learning algorithm!

We have been helping one of our customers with precisely this objective. Our customer is a membership driven non-profit that hopes to provide technology based solutions for small and medium manufacturers (SMM) to help them improve their productivity and efficiency. Their main goal is to understand their member pain points, especially the SMMs. Some companies are ready to adopt new technologies while others are not yet mature enough to adopt new ideas. The measurable objectives of our engagement with them is to quickly classify member companies into important categories such as innovators, early adopters and early majority, so that our customer can effectively determine how and what to market to their members.  They have conducted several surveys to learn about their members and to identify problem issues among them, but the challenges are several. Here are two of the challenges we are helping them with:

• Challenge 1: how to quickly determine customer/member’s category based on responses
• Challenge 2: how to improve survey questions to enable quicker and better categorization

Combining numerical survey responses along with text mining their open ended responses can help to build more accurate categorization, for example. Another goal of this type of exercise would be to use sentiment analysis to understand the emotions conveyed in the survey responses to understand how member companies look at new technology adoption. Predictive analytics and data mining, enable companies not only understand clearly the needs of customers, but also provide effective ways to quantify the value customers bring to the bottom line of any business.

Originally posted on Thu, Mar 20, 2014 @ 07:34 PM

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