Big Data is piling up – so how can businesses make best use of this resource? CustomerMatrix believes the solution is a Big Brain, as the startup has created a customised cognitive computing suite. “CustomerMatrix leverages artificial intelligence software to help salespeople identify opportunities automatically,” says Guy Mounier, CEO and co-founder of CustomerMatrix. The solution is ideal for relationship managers in the world of banking, adds Mounier, as an algorithm identifies opportunities and saves users a lot of time: “[Our product] detects a whole bunch of opportunities that they would have missed otherwise. This allows them to really focus on closing these opportunities.” The results are tangible: CustomerMatrix estimates its product results in a 10% to 25% increase in deal closure rates.
CustomerMatrix is a New York company, but Mounier is calling from Paris today – that’s where the company first started. Paris is also the location of 40 R&D people working on maintaining the company’s artificial intelligence engine – you could also call it machine learning or cognitive computing. In any case, what the CustomerMatrix platform does is attempt to perform tasks in a manner similar to the human brain: take in lots of information, identify patterns, draw out meaning, and make decisions. IBM’s Watson is the headline example for how this can work, but Mounier says the CustomerMatrix approach is more focused: “We’re already connected to all the customer data sources on the internet. For example, we go on LinkedIn, we go on FactSet, we have relationship with Factiva, and in the US we even have a relationship with eBay.” The result is a solution that comes pre-loaded with a good understanding of the industry: “We can plug these insides directly into [companies’] workflow and make them more productive right away.”
From regulation to reputation
After some initial skepticism, the financial sector is starting to come around to the idea of using cognitive computing. Asked why he thinks this is happening now, Mounier starts talking about a “significant acceleration of disruption” and a “significant acceleration of scale and complexity of data, so you seem to be pedalling harder and harder to stay in the same place”. This means the old tools are now insufficient and outdated: “Banks are under a lot of pressure to reduce their costs, sometimes by as much as 30%, while at the same time being asked to grow revenue. The only way they can do that is by using a transformative tool,” says Mounier. “[Cognitive computing] is raising the bar in the industry. [Banks] see that, and they recognise the opportunity.”
Regulation has long been a hurdle for banks looking to innovate around data handling, but Mounier says it’s now less an issue of rules, and more about reputational risk. Financial institutions often have strict standards ruling third-party access to customer data, meaning an outsider like CustomerMatrix needs to jump through more than a few hoops: “You have to demonstrate that you have a compliant data flow and, in short, that they can trust you – that their client list isn’t going to make it to competitors or be misused by someone who doesn’t mean you well.”
An empowerment solution
CustomerMatrix is already working with a number of major banks, including BNP, Credicorp, Santander, UBS, Standard Chartered, Capital One, ING and HSBC. The company has raised $16 million in funds to date, most recently in January 2016 when it secured a $10.5 million in a round led by HSBC. The startup has been up and running for about four years now, after spending its first three years building the service. Mounier explains that the company is the result three companies coming together: “One was in the data acquisition space, the other in the text mining space, and the third in the robotics space, which was to understand behavior analytics.”
Mounier says how the motivation behind CustomerMatrix was one of “empowerment”, as the co-founders found that salespeople were struggling to understand their customers in the new data-rich environment. “We started to realise that unless you apply some form of advanced intelligence, none of these rudimentary tools would address the problem. It would have meant the end of the relationship-based selling, because the old model simply isn’t scalable. … CustomerMatrix started as a frustration at not succeeding at fully solving a problem in the world of data integration. I have to say, the results are surpassing our expectations.”