Salesforce launched Financial Services Cloud as its first vertical-specific customer relationship management (CRM) product in August last year, giving it a full year to form its plans for the platform and collect some customer case studies during Dreamforce this week.
Rohit Mahna, general manager of financial services at Salesforce, said Financial Services Cloud was built to solve "big, complex, industry problems" across banking, wealth management and insurance.
He expanded a little, saying: "In banking everyone is thinking about getting this 360-degree view of customers to become trusted advisors. In insurance it is about the policy holder and creating a seamless claims experience. In wealth management we help teams create a 'one team' mentality to get time back to spend with clients."
The key here is personalisation. As Mahna said: "How do we bring that high touch personalised experience across the entire enterprise? Because customers are getting that level of interactivity outside of industry."
The answer is, unsurprisingly, data. In comments that are even more relevant following Salesforce's recently failed bid to buy the professional social network LinkedIn, Mahna said: "Your customers are broadcasting signals on a daily basis in industry, these are in transactions and things they are posting publicly.
"Just imagine the signals they are sending on LinkedIn, maybe they have changed their job. These are things we need to be capturing in real time and bringing them into our enterprise and analysing this."
Salesforce Einstein for Financial Services Cloud
This is where Salesforce Einstein comes in, by providing an underlying artificial intelligence layer to push predictive recommendations to agents, bankers and wealth managers.
"We need to be using intelligence to spot patterns and make the right recommendations, when we should be interacting and in which channel. It is all about connecting at the right time," Mahna said.
Mahna gave four use cases that Financial Services Cloud users can perform with the new AI powered Einstein capabilities:
"It means our relationship managers are able to discover relationships among the clients in their books they didn't know about before.
"Marketing teams can make predictions based on predictive scores so they will know when a customer is likely to buy something.
"A recommendation engine, this is the idea of a coach so you're relationship managers are coached on the next thing to do to keep customers on their personal and financial journeys.
"Automation, so everything those relationship managers do on a daily basis, the pen and paper jobs need to be automated so they get time back to spend with clients."
Customer case study: Bank of America
Mahna then introduced two large launch customers to talk about Financial Services Cloud. The Bank of America case study is particularly relevant to the personalisation drive mentioned earlier.
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