Demystifying Salesforce Data Cloud for banking

The first question we usually ask our clients is, “Do you have a unified profile of your customer or member?” 

With multiple origination systems, indirect channels, service channels, and card systems, it can actually be quite difficult to get a full view of a customer that combines all of their information and activity from different sources.

On top of that, how do you translate the varying data formats from each system into one common format, while also fixing data quality or conflicts at the same time. This is referred to as a common data model, meaning you define the ideal profile and attributes ahead of time, and map all of your data to it so the end result is a clean, consistent, and easy to use set of data. 

With a unified profile, customer engagement on every channel–both inbound and outbound–dramatically improves because knowledge of each customer is accurate and up to the moment across all touchpoints on which they have engaged.

The next question we ask our clients is, “Are you generating unique insights using your customer data?”

Having your data available and unified is a great first step, but to really get the ROI you are looking for you need to be differentiated in your understanding of the data. Generating insights such as propensity scores, engagement scores, and sentiment analysis can enable you to be much more effective in how you interact with your customer, either in person or through digital channels. This data can also help you segment customers. One common theme we hear is “the lonely customer.” In banking, a lonely customer is typically a higher asset customer who has the basic products such as a checking account, but who almost never comes into a branch or calls in. These customers are typically ignored because they don’t “need” anything and aren't making noise, yet they are likely to be a great candidate for other products the bank offers. Using analytics and calculations can help you identify various customer segments, and use data to tell a story that leads to better serving your customers.

With the combination of near-real-time data, deep discovery, and generated recommendations on the fly, your customer's digital experience can evolve the moment they take action

The last question we ask is about data activation. With all the work we talk about above, it'd be a shame to let it just sit there and not be used! 

Data activation typically is split into two parts; direct customer engagement through digital channels, and organizational efficiency through internal systems. As we mention above, many banks still rely on nightly batch processing, and if it’s real-time, it's typically just refreshing the data you already have in case something changed. Both are necessary, but don't usually drive action. 

The best way to think about this is seeing everything a customer does as an event, and the goal is to capture that event as soon as it happens–and send it (activate it) to where it should go to turn into a valuable interaction. For example, let's say a customer's purchase was declined because of potential fraud. Ideally you capture that event from the card processor, send a text to the customer asking if it was them or not, capture the response, and validate the merchant so they can try the purchase again without issue. Or better yet, you can capture that event, check the customer’s segment to determine their preferred method of resolving potential fraud, and maybe for this customer you route an action to the call center to make an outbound call and verify the person over the phone if they aren’t favorable to text.

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