Data, Big Data, Artificial Intelligence
Clients don’t understand their customers, they just think they do!
It’s not for the lack of trying or spending millions on developing and building huge data systems, the problems are many but can be traced back to one simple thing;
“Data only describes part of the what is happening and almost nothing of the why, let alone what should be done to change the situation”
Clients have been sold that data gives them the answers and that big data will close the loop for them to understand the upstream and downstream thinking of their customers, WRONG.
Douglas Adams noticed the real problem
Douglas Adam’s said “But even Amazon has only got part of the picture. Like real world shops, they can only record the sales they actually make. What about the sales they don’t make and don’t know that they haven’t made because they haven’t made them?” Douglas Adams “The Salmon of Doubt” by Permission of Pan Macmillan. That pretty much covers the problem if you extrapolate the thinking for Data Analytics, Big Data or even Artificial Intelligence based Data and Decision systems.
“Data is binary a yes or no (even complex views), it does not capture motivation, intention, desire, cognition, distraction or any other human reasoning or pattern”
A child pretending to be a robot just as data pretends to be the truth, he is a kind of robot and data is a kind of truth
A pure Data approach to understanding customers will provide the wrong data because data is an absolute and people are not. Even with Artificial Intelligence it only works from the starting point you give it, if any of the perimeters are wrong the whole data sample is wrong.
Guide to understanding Customers
- Data, Big Data, Artificial Intelligence – Tells you what
- People in target demographic – Tell you why
People in target demographic
User research answers the question Why have we not made the Sale? through the only people equipped to answer the question, consumers. This is not market research, its scientific without a predetermined agenda or outcome. User Research is a problem solving method that offers solutions by finding the right questions, finding the right people and asking the questions in a way that does not lead or direct the answers.
There are right questions and people to ask?
This may sound a little Adamsesque (if you ask the answer to Life, the Universe and Everything you get 42, because it the wrong question). Getting the questions or setup wrong is the real problem with an Analytics approach to a Diagnostic process. While it may be reasonably expected by a seller to directly ask, why didn’t a visitor become a buyer or register. Visitors may be asking themselves where am I? what does this do? this does not make sense, should that be happening? technology, why do I bother? Why has my screen gone pink? None of these “in mind” experiences are expressed in the data or even a consideration for the data schema design.
A visitors experience is not only defined by the online environment but they bring past experiences, desires and doubts about their current experience. Without these insights from research, it is difficult for clients to grasp potential problems, gain a good return upon their investment (ROI), innovate to fit the market and consumer needs or break into a new market sector.
Reasons that Data is Trusted and People are Not
It appears to come down to scale and a short sighted approach to costs. Buying an Analytics Solution appears to tick all the boxes, even if in reality it does not. While using Research Companies or in-house Research Teams seems expensive in comparison.
“The real trick is to understand you need both, you always did”
When I first started using Web Position Gold (the analytics tool), bought by Webtrends long before Google Analytics existed or the current proliferation of products promising the impossible, we used it to spot trouble only. We would then do some user testing in the area, working out possible failure scenarios, from there we would suggest two or three solutions and build them for A/B testing to see what worked and what did not. Everything was monitored and all the data from both analytics and user testing was collated into one final solution. Sometimes there was a single resolution, a re-architecting of a section, in one project I kept 16 pathways active because they all delivered transactions for different types of customers.
The thing is just as there is no absolute way to find out the problem, resolution or adaptive innovation except byDiagnostics a digital and human activity.
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