Banking on it: Customers, Big Data, and the Elusive Cross-Sell

The cross-sell is becoming less elusive with the right tools in the hands of the teams that know business best.

This article was written by Jason Demby, the Director of Business Development, Financial Services at Datameer.

As financial services institutions grow and diversify, the term cross-sell is often cited as a driver for that growth and diversification. The retail banking relationship with a customer will surely lead to the firm’s service of their personal investment accounts and mortgages, which will in turn lead to support of that person’s company IPO or M&A activity.

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Does this actually happen? Sometimes – but not as often as it should. With the dawn of big data, and true understanding of customer behavior and the customer experience, the ever elusive cross-sell may no longer be as difficult to realize.

What we have seen over the last few years is divestiture and increased focus on select business lines. This includes the exit of some banks from their investment banking and institutional securities business to focus on the consumer. Fintech upstarts that provide focused best-of-breed single business or product line solutions are creating increased pressure and competition.

The question is – how can the cross-sell potential be captured and how can your data help?

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Big Data and Analytics: The Opportunity and the Challenges 

With the increased push towards a digital customer experience, there are a number of benefits that financial services institutions are taking advantage of. There is a massive amount of data available to help you understand your customers’ every mouse-click and phone call, as well as their behavior regarding risk tolerance and spending.

The cross-sell lends itself to a number of questions – many of which can be answered with data:

  • Who should we target for cross-sell opportunities?
  • When should we target them?
  • What products would be best to offer?
  • Which channels are best to use to extend an offer?

The data is not simple and capturing and realizing the value in a large diversified institution brings analytics challenges including:

  1. Digital and fintech creates oceans of data – the push towards digital and fintech solutions is creating a HUGE amount of customer data – and of varied forms!
  2. Banks are complex – the structure of the modern financial institution mandates the need to have seamless and efficient collaboration across teams that impacts upon the customer experience.
  3. The customer journey is complex – the complexity of the customer journey over both short-term and long-term periods requires advanced analytics and the use of machine learning algorithms.
  4. Turning insights into results – the insights gained from the analytics need to drive action and results, increase revenue and efficiency, or reduce operational costs.

How does one overcome these challenges? The answer lies in the right data architectures and analytics tools.

The use of big data architectures such as Hadoop, and supporting analytics tools, are making the customer journey easier to understand and influence. Hadoop’s ability to handle storage and processing across massive amounts of structured and unstructured data has been a game changer for financial services institutions. The ability to identify the right customer and deliver the right cross-sell offer at the right time is being made easier.

The right self-service analytics tools on top of Hadoop can offer:

  1. Analytics that handle the full volume, variety, and velocity to ensure that you have a complete view of the customer journey.
  2. Collaboration and self-service analytics across functional, business line, and regionally aligned teams.
  3. Machine learning algorithms in the hands of business analysts and operational teams that can quickly illuminate the relevant data dependencies, clusters, decision trees, and product recommendations that drive behavior.
  4. Operation, automation, and governance to enable the resulting analytics to be driven directly into the business processes that will enable revenue growth and cost savings.

By taking advantage of the right analytics tools, and putting them in the hands of the business and operations teams that know the business the best, the cross-sell is becoming less elusive.

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