Data Science for Customer Knowledge

For a long time, data science was only the concern of the IT department and the technical  functions, and was  only used  to optimise processing. The reappropriation of Data Science by the business lines has opened up and continues to  openup prospects for new business levers.

We were the first to see the potential of data science to improve the customer experience, to engage in better customer relationship performance.

Our expertise in data and digital issues relating to  customer knowledge, allows us to base ourselves on feedback and to take real action, to anticipate customer behaviour and to be able to determine the weight of a product  according to each of its criterion.

Micropole Digital Business Data Client

Whether it's predicting a customer's behaviour on a promotion, predicting when a member will terminate their contract... data science is a valuable aid.

How to ensure that the source of the information analysed will predict what you want to find.

Whether it's predicting a customer's behaviour on a promotion, predicting when a member will terminate their contract, or ....., data science is a valuable aid.

Coupled with data which we work on to guarantee its quality, richness and diversity, we develop more adapted and more efficient algorithm models, with which you will be able to better understand your customers and adjust your marketing and sales operations accordingly.

Turning regulatory constraints into an opportunity to rethink customer relations.

In view of the constantly evolving and restrictive GDPR regulations, the question arises as to how to ensure that the digital print of a prospect or customer is known and recognised.

For this we have been working for some years to create models: to find algorithms to support, predict what is already known and to define correlation matrices.

Other answers must be found to the question of identifying the target customer. And to understand the representative data, the volume of useful/non-useful history, while being compliant with the CNIL...

Overcoming the loss of bearings generated by the C19 crisis

However, all the statistical assets have been turned upside down with the health crisis that began at the beginning of 2020, all behaviours have been modified, making existing models obsolete.

So since then, what can we do with unstable models? How can we use such a changeable past, with such unrepresentative data, to predict the future?

This is where our hybrid teams, experts in digital, customer journey and data, make the difference : by imagining and establishing confusion matrices to stress-test models and quickly readjust them.

Setting up score factories

Then it is a question of envisaging the whole thing as a score factory, with streamlined, reliable processes, to succeed in collecting the information of tomorrow, making better use of the data, the useful data, and knowing how to manage the unexpected, even if it means initiating it to provoke discovery.

Whether it is for one model or several, the watchword is "piloting", not undergoing and being able to "see" to react very quickly, even if it entails  correcting just as quickly if the model does not turn out to be the right one.

Companies that dare to democratise the use of data science and no longer confine it to a minority are opening up real opportunities: offering new methods, generating new thinking, inspiring new collaboration, these are all levers for thinking about global strategy, for creating new business models, while enriching the work of teams -all of which adds to the customer and employee experience.

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