Dynamics CRM meets Machine Learning – Part 1 : The Business Problem & Solution Architecture

Continuing the journey of our Dynamics CRM and Machine Learning series, in this post I will define the business problem we are trying to solve using Machine Learning (referred as ML hereafter) and how the solution will work. You can read the premier that introduces this blog series if you haven’t already.

 

The Business Problem

When an organisation implements a CRM solution, the most basic goal they try to accomplish is to manage the interactions and dealings they have with their customers through this CRM solution.

Any organisation, Any CRM – customer interactions are always tracked inside CRM.

Interactions build relationships. And they damage them too !!

Take email for example – the most trustworthy and traceable medium of a customer interaction.

Every deal starts with an email and is sealed with an email (sign-off), appreciations are made using emails so are the complaints. Emails are the mirror through which CRM users see their customers .

Think about it – when a new sales exec or a customer rep joins your organisation and is about to deal with one of your existing clients, where do they go to find out what is this client up to ??

 

Guess where ??

guess

Courtesy: funnyjunk

 

They go through all the recently exchanged emails pertaining to the work they are about to start. So that they can take it forward from where it was left by the last rep.

 

Emails –  A dimension of Customer Satisfaction

So I hope by now we have established that it is  an undeniable fact, that emails (and their content) is the most important asset of any CRM system. An email from your customer with an appreciative tone instils confidence and hope inside you.

Whereas an email marred with apprehension and displeasure heralds  the ominous moves coming your way. 

But how does an organisation pick up on these early cues of a crackling relationship so that it can be repaired before it’s too late ??

A solution could be to utilise existing emails from the customers and determining satisfaction based on their content.  We can assign a score to every email based on the gist of the message and its indicative tone. Then a statistical measure can be applied up the chain, to rollup the score over to the customer record.

 

Customer Happiness Index

So that score which will rollup to the customer record is the magic number that I will refer in this post as the Customer Happiness Index

More pleasing emails from the customer, higher the index – happy customer

More displeasing emails from the customer, lower the index – unhappy customer

 

Simple … Lets roll.

 

There, often are thousands, if not millions emails in any CRM system. So let us not even contemplate doing this work manually.

After all we have better things to do in life than judging the content of emails (especially if you, like myself, happen to be living in sunny country like Australia) .

Let us use Machine Learning Intelligence to our advantage.

 

The Score

Customer Happiness Index will range from 0 to 4

0 – Extremely Dissatisfied

4 – Extremely Happy

 

An indicator will appear on every contact record in Dynamics CRM  that will show how happy they are

Dynamics CRM – Happiness Indicator 1 2 3 4
Index Less than 1 1 to 2 2 to 3 3 to 4
Interpretation Dissatisfied Neutral Satisfied Extremely Happy

 

Solution Architecture

A picture is worth a thousand words

To the wisdom of this famous saying, I will leave you with a picture of proceedings on the way. The design is simple and self explanatory from the captions, we’ll dig into the innards in the next post

 

archtec1

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