Q&A: Salesforce’s Ketan Karkhanis on Einstein Analytics for Financial Services

Ketan Karkhanis, SVP and GM of Salesforce Einstein Analytics, has been leading the analytics team since 2016. After almost a decade at Salesforce, he’s spent countless hours learning what customers need when it comes to taking action based on data to support decisions. After only a few years in market, Einstein Analytics has been rated as the top cloud business intelligence vendor by Dresner Advisory Services.

We talked with Ketan to learn more about today’s news on Einstein Analytics for Financial Services, and the evolution of the customer experience.

You have some exciting news about the newest analytics offering, Einstein Analytics for Financial Services. Can you tell us a little about it?

You probably heard at Dreamforce we introduced Einstein Analytics Plus, and in the short period of a few months it has already become a leading AI-augmented platform. Our innovation and momentum continues, and now we are taking the next step to deliver verticalized analytics products built on the Einstein Analytics platform. We now have a one-of-a-kind, industry-specific product for financial services with pre-built components that focus on retail banking and wealth management. It’s fully integrated with Salesforce Financial Services Cloud, and can be deployed quickly and easily to get bankers and advisors up and running with actionable insights in no time. Speed to value is key.

Customers often spend a very long time deploying analytics because they have to figure out what to measure, how to leverage their data, and more. Our solution automates much of the process. Out of the box, the product comes with more than 100 dashboards including current clients, clients at risk and clients likely to add funds, and more than 200 KPI-focused views such as assets under management, wallet share, and held-away assets based on our knowledge of the financial services industry. And that’s just the appetizer. The main course is AI. Einstein Analytics is augmented with AI capabilities to infuse intelligence into your workflow. One example of this is pre-built models that show predicted churn, predicted change in assets under management and much more that you can easily take action on.

For example, if I am wealth manager, I want to see what customers are moving money around, maybe removing large amounts of money from their account, so that I can reach out and understand what their changing needs are. I can see what other factors may be contributing to their propensity to churn and take action immediately.

Do you expect to deliver more tailored analytics solutions for specific industries?

Yes absolutely — for us, the challenge is not to just give customers across different industries a set of blocks and then leave them at the mercy of the technology. The most important advantage of a pre-built, industry-specific application is it handles the lead-up and runway work, so you can take off immediately with powerful analytics. Secondly, it has pre-built KPIs that adhere to industry best-practices. The latter is a huge benefit — customers are not just looking for technology, but they are hungry for best-practices to guide them. It is made for you, for your business, rather than requiring you to build out all of the intelligence from scratch.

And this is just the beginning. We’re not going to stop at financial services. In fact, we have a long and exciting roadmap leading up to Dreamforce. You’re going to see us launch dedicated solutions for other industries spanning health & life sciences, manufacturing, consumer goods, insurance and more.

Analytics has been a horizontal solution for companies, adding business intelligence for sales teams, service teams, and more. What factors led to analytics becoming verticalized with financial services?

The biggest factor that drives our product roadmap is our customers. When we launched Einstein Analytics Plus, they adopted it widely. But they also told us, "Look, we need you to have a vertical flavor of Einstein Analytics Plus that speaks the language of our industry."

So this innovation is not our idea, it is our customers’ idea. They helped drive what we are delivering. And just like that, we have many other customers now inspiring us to continue this tailoring for their industries.

What are some other trends you’re seeing in the analytics space?

The biggest trend we’re seeing is that analytics is moving away from being a back-office function — this required sending a request to a data analyst and waiting days or even weeks for a report that was then outdated. That was the case for decades. The reason that Einstein Analytics Plus is so widely adopted is that it embeds easy-to-generate, actionable insights right where people work.

Another big trend is that AI now comes standard with analytics. If I show you a chart, would you like to see it as a static chart that only shows what has happened in the past? Or would you like to see an interactive chart that not only tells you the history, but also gives you a glimpse of the future? Predictive intelligence weaves AI and analytics together, and frankly, it’s no longer optional. Its an imperative.

Finally, actionable insights were never truly available until now. When customers see data and charts, they want to see how what they’re looking at connects with their mission and how they can optimize their business processes. So analytics solutions are now connected with specific business processes for the most relevant kinds of actionable insights right where you’re working — for example, a recommendation to connect with a customer that is likely to deposit more money in an account and increase your assets under management, right in the account page. Connected is key. I mean, an insight you can’t act on is just a dumb chart.

Any advice for companies who are thinking of starting their own analytics journey?

The most important piece of advice is to not have a system-oriented view of things, but a customer-oriented view of things. Work your way backward from your front office to your back office. Start by thinking about what experience you want to deliver. Then, be a little bit provocative and ask, "What insights can we use to transform the customer experience?” And don’t forget that agility is important. Yes, it’s good to have a 5-year strategy — but it’s equally important to have a 90-day action plan and iterate rapidly.

One other thing — don’t be intimidated by all the jargon and the misconception of “it’s hard” or “we are going to need people with PhDs to work on this.” That may be true for other products in the market, but with Einstein Analytics you can build, test, and operationalize a predictive model without writing a single line of code or having to understand what “one hot encoding” means. Most importantly, we have a relentless focus on surrounding you with all the resources and education you will need to succeed. You can find all the training you need to get started today, and it’s free to learn.

What do you see in the future for analytics?

Connected Intelligence is where the market is going. And by Connected Intelligence, I mean the three points we talked about: front office (where people work), actionable insights, and AI built-in.

We are also going to see more of what we call trusted AI. This is not just about coming up with new algorithms. The world has lots of algorithms already. This is AI that is transparent, responsible and accountable. This is why Einstein Analytics has features built in to protect customers from making leaps and assumptions that they shouldn’t. In Einstein Analytics,a variable like race or gender that may bring bias into predictions can be removed from a model, and any biases that may surface get flagged (a warning, for example, that zip code is often another way to designate race).

I think customers have realized that analytics-driven insights need to connect to all parts of their businesses. And, they have told us that they want pre-built analytics solutions that speak the language of their industries.