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Understanding your customers, IA, ethics, the role of external contexts in a Behavioral CDP (customer data platform).

October 18th, 2021

Along the pre-digital times customer service was a human activity taking the best of human brain to understand the customer to adapt the offering and the messaging while creating empathy, a powerful trust builder. Bank tellers, insurance or travel agents were part of the lives of the people and perceived as problem-solvers from friendly companies.

In the digital world, digital channels have been replacing human channels (branch, contact center, etc). A myriad of digital channels (web, app, chatbots, voice assistants, IVR, etc.) are becoming the only point of contacts for most of consumers with their providers (banks, insurers, travel ticketing, telco, etc.).

Companies need to increase the LTCV (lifetime customer value) and need to create new ties (upselling) with the customer and also keep them loyal to be able to offer new products and services along time. A churned customer leads to a new investment (CAC, customer acquisition cost) either to reacquire the churned one, or to acquire a new customer.

The reality is that there is an extensive use of brute force marketing: a bank offering every single day for years a credit to a customer that never took a credit, a simple search in Google leading to a lot of re-targeting ads for weeks, hundreds of non-opened marketing emails with a never changing cadence...

IA has come to stay, and from the point of view of customer services it is being applied to conversation channels (understanding language) and in some way understanding customer actions, but mostly without context.

Analyzing customer actions to predict a NBA (next best action) in terms of communication, offering or both, need “contexts”. The customer contexts being considered are different:
  • Understanding his/her customer journey; it is important to know by the contact-center agent that 
  • Using historic purchase/action records.
  • Using social profiling (that needs customer consent due legal and ethical reasons).
The use of IA to extract value from contexts has produced some improvement in customer services and many companies talk proudly about increased NPS (Net Promoter Score) but most of the end-customers of banks, insurance companies, telcos, e-commerce feel overwhelmed by the force brute marketing that does not understand the individual customer and his/her needs.

At Whenwhyhow we figure out each customer mindset (and the aggregate segment based persona-mindsets) combining customer actions and external world related phenomena. An aggressive investor can invest when the market falls and a risk-averse is in a rush to sell when an unexpected downturn appears. The message or warning to those customers cannot be the same; one “communication” size does not fit all. We want to digitalize the “last mile” of human customer service: understanding the customer and staying relevant and supportive. A personal banker would never insist to very risk-averse customers that they should buy shares or bonds to the less risk-averse customers. A travel agent will call in advance to the customers that use to call to confirm the flights (and they will feel understood) and not to the ones that said that they use the airline app or do not need confirmation (sothey do not feel disturbed).

Whenwhyhow makes it possible to define external phenomena to which the end-customers could be more or less reactive as a time series. Customer actions are studied through Machine Learning not just as “pure maths” but in a more human way by creating hypothesis and confirming the effects on customers, and creating new segmentation based on external events reactivity. Some examples: advertising pressure of competitors, own or competitor reputational impacting news, etc. 

Also we encourage confirming with end-customers if the detected “mindset pattern” is correct or not. If a bank detects that some individual is quite conservative and risk-averse, the customer could use chatbot channels, or in-app prompted question to rate herself or himself in a scale. 

The digitalization trend is here to stay, and perhaps the hundreds of thousands of customer-facing jobs will not be back, but humanizing the customer service will need more skilled marketing jobs. Each organization/vertical may be thinking and testing which external events could impact their customers, detect those customers, create confirmation strategies and use that customer mindset understanding to improve CX to create digital empathy to improve the loyalty and to improve the offering to be more relevant and leading to a better monetization.

Whenwhyhow allows combining internal information of customers (CRM previous segmentation, usage of digital channels, historical actions) with external phenomena time series to profile, using ethically the AI/ML, the reactivity maps that could lead to a better personalization. Also, the “no code” data model design helps non-techy marketing people to participate more actively in the process of customer understanding.

And of course, this approach, the need of replacing brute force by digital human touch alike, is here to stay too :-) so if you are interested in knowing more from us, reach us at
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