How to Teach Your CRM to Think and Learn from Big Data

The problem with big data is, well, it’s big.

Even with the best CRM to record and organize every interaction with your customer, if you don’t know how to leverage that data, you could risk damaging the customer experience, and leaving money on the table.

But what if your CRM could take all of that data, extract exactly the information you need, and make recommendations to not only give the best possible solution to a service question, but empower sales staff to upsell and cross-sell with confidence? In short: What if your CRM could think?

Sounds like a tall order, but Guy Mounier, CEO of CustomerMatrix, says his company’s new enterprise cognitive system, Cognitive Intelligence Engine for CRM, does just that.

The Brains Behind the CRM


“Customer intent and best practices are things we can compute with extreme precision from big data,” he told CMSWire.

“The reason is that, under the hood we have an unfair advantage to data. We can extract patterns of unstructured data to create a 10,000 pixel view of your customer instead of a 10 pixel shadow view.”

The CustomerMatrix system reportedly helps companies maximize customer interactions by allowing current CRM systems and processes to better use all available data and knowledge about a company's customers.

How? Through machine learning.

According to Mounier, the platform learns from big data in real-time by using internal and external sources, including social media posts, to link all customer information and interactions, and read through all text to extract and enrich knowledge.

“Big data is the DNA of that relationship with the customer,” said Mounier. “You can really unearth a lot of the best practices and needs and issues in customer interactions.”

Recommendations to Satisfy and Sell

Once the platform has identified and analyzed the necessary data, it recommends best sales actions or issue resolutions, called Action Alerts, to agents while identifying upsell and cross-sell opportunities, as well as risks – all with a level of confidence attached.

Mounier calls this the “empathy meter” – a precise measure of intent in which the system provides an explanation and a percentage associated with the likelihood that a customer will take a particular action.

For example, the platform can state that it is 80 percent certain that a customer is calling about a particular issue, or 50 percent certain that they want to buy another product.

“Computing precisely about how certain or uncertain you are with a customer is very important to build trust with the end user,” said Mounier. “It really matters a lot because it’s very easy with the wrong recommendation to destroy the customer experience.”

The system also uses a proprietary algorithm that evaluates and prioritizes alerts based on revenue impact.

All of this happens within the context of your existing CRM workflows and user interfaces.

“We don’t change the customer’s CRM,” said Mounier. “It’s all embedded in the existing workflow, so there are no end user adoption problems.”

A Little Help from Technology

Mounier wrapped up by discussing the complexity of providing relevant, personalized recommendations as a company’s customer base grows.

“If you have a large volume of accounts, and up to a million products in your catalog, then showing empathy and understanding at every customer context, as well as following best practice is an impossible thing to do without technology helping along the way.”

Creative Commons Creative Commons Attribution 2.0 Generic License Title image by d26b73.