Acquiring, upselling and keeping profitable customers: it’s the Holy Grail of the financial services industry. But, in recent years, that quest has become easier said than done. New competition from emerging FinTech players, mobile channel expansion and changing customer expectations have rendered traditional approaches to customer acquisition and retention ineffectual.

Although banks have long used various types of segmentation models to design their sales and marketing programs, these are typically very broad in nature. Efforts to attract and upsell new customers are more generic; without the laser-sharp personalization people now expect.

And, because the data collected from customer interactions across multiple channels are not linked, most banks have no way of pulling together a comprehensive, longitudinal view of their customers.

Without this insight, upsell, cross-sell and retention efforts are hit or miss, at best.

By setting up a Center of Excellence framework that leads with data analytics, financial institutions have the opportunity to transform their sales operations.

This paper explores how a modernized, analytics-driven sales function speeds new customer acquisition and upsell engagement, reduces attrition, and enables banks to increase revenue by making smarter use of their marketing dollars.

Analytics and Acquisition: Finding the Right Customers

In other industries, acquisition is often a sheer volume play: get as many people to shop at this store, on this site, or to use this service. For the financial services industry, the target is far more qualitative. Banks want to attract those prospects with the highest long-term value.

Historically, if an institution wanted to attract customers in a certain area, the marketing or sales executive purchased a standard list of prospects who fit a basic demographic profile and live in certain zip codes. While these segmented lists are far more effective than the blanket, mass mailings of old, they are still fairly generic, without an informed data set.  That means, although there may be some viable customers on the list, it could take three or four cycles of communication to acquire them, which significantly increases marketing costs. At the same time, the institution is wasting a lot of money communicating with people who are never going to respond.

In a modernized outreach program, financial institutions sharpen these prospect profiles, so they can fine-tune their sales messaging, acquire customers faster, and maximize their marketing dollars.

A few factors make this possible.

There are also new analytics and digital tools that continually use response and prospect behavior to refine the prospect profile dynamically.

By starting with a better understanding of target need, and crafting messaging accordingly, institutions can shorten the sales cycle—bringing in more, higher-value new customers, in a shorter timeframe, at a lower cost. Marketing dollars are spent where they generate the greatest return.

It’s important to note that this targeted approach is not simply about replacing one list with another. Every prospect contact and response is collected to begin stage one of the ongoing customer journey—an essential process not only for fine-tuning messaging in subsequent contacts, but to begin creating longitudinal memory of that individual. This becomes the foundation of ongoing cross-selling and retention efforts after that prospect has been converted to a bank customer.

In a transformed organization, every piece of online and offline channel outreach and response is documented and analyzed, and as well as prospect activity on other, relevant digital sites. If a prospect exhibits a behavior that indicates he or she needs a specific product, like a home improvement loan, that action immediately triggers a marketing message about that product for that person. If a new business incorporates, or orders internet service online, that triggers communication with an offer for opening a business account.

Outreach moves from monthly email blasts or mailings into a more channel agnostic, hyper-customized marketing strategy based on the hyper-contextualization of analytic data. Not only is the message customized to need, but that message is timed contextually, appearing exactly when that prospect needs the product or service.

In short, the right prospects get the right offers at the right time, so they are more likely to respond.

Data-driven Cross-sell and Upsell Strategies

It’s proven fact: the more products and services a customer uses, the less likely he is to leave the institution. So, as new customers onboard, initiating an effective, personalized cross-sell and upsell program is key to retention, as well as profitability.

Banks are historically organized like big box retailers, with every department—deposits, lending, wealth management, mortgage— operating as separate units, often with their own database of customers. So, the focus is the specific process or product, not the customer.

Bank consolidation has added to the challenge, with mixed assets and often, disparate systems housing account data.

Even if institutions maintain their internal structures, their account profiles have to be more holistic to provide a single customer view. That’s the only way the entire organization can effectively service customers from a needs perspective.

In a transformed environment, new customers aren’t automatically sent into a generic onboarding queue, where they receive standardized communications. Like the prospecting model, upsell and cross- sell offers are based what banks know about those customers and what they learn through continually analyzing interactions and behavior.

The longer the customer is with the bank, the more data the institution has to personalize the offers to what that individual or business owner needs—even though those needs will change throughout the course of the banking relationship.

However, presenting the right offer at the right time is just one component of the solution. The customer experience is the heart and soul of cross-sell and upsell success.

This experience is reliant on six different interventions:

  • Hyper-customization of messaging
  • Integrity
  • Expectations—self-service options and response times that mimic the metrics of other vendors the customer deals with
  • Effective, non-frustrating issue resolution
  • Time and effort required (on customer’s part) to resolve the issue
  • Employee/contact center empathy

Using data and analytics to fine-tune these areas and equip employees to elevate the service levels they provide will enable institutions to increase wallet share, market share and customer stickiness.

Upsell and cross-sell efforts are no longer simply a marketing function. They are part of creating a customer journey that strengthens the relationship throughout the customer lifecycle.

Recognizing Attrition Risk— and Stopping It in Its Tracks

All of the efforts spent to attract and upsell these customers would be wasted if the institution can’t retain them. In the transformed sales organization, institutions also harness the power of analytics to reduce customer churn. The goal is to understand why people are leaving the bank, what actions a person on the verge of attrition takes before leaving, and enable branch and contact center personnel to manage churn with analytics-driven incentives.

On the micro level, the bank gains the insight and agility to prevent individual accountholders from switching to another institution. On the macro level, bank leaders have a churn analytics model to help identify the top three or four reasons for customer churn, so they can solve any systemic issues impacting retention within their organizations.

The Churn Management structure focuses on three distinct areas:

  • Pre-churn
  • At Churn
  • Post-churn

The key is creating a program that applies data and interventions to mitigate loss and improve customer relations at each level.


Studies indicate that the greatest attrition risks are newly onboarded customers.

Annual churn rates for this segment hover in the 20%-25% range during the first year, with 50 percent of these not making it past the first 90 days after opening their accounts. If interest rates increase over the next three- to-five years, as projected, retention will become an even greater challenge.

Banks could reduce churn by always paying the highest rates and offering the lowest fees across the board, but this strategy comes with a significant impact on profitability.

However, with aggressive client management powered by data, banks have the opportunity to keep more of their hard- earned customers—and their hard-earned profits—by recognizing customers at risk early on, and taking action to turn things around.

For example, customer X cancels a credit card, or transfers money from an account to another bank, or sends another warning signal of coming attrition. This immediately triggers back office communication, using analytics start the intervention, based on the customer’s history. By analyzing recent activity, the bank could also identify probable cause for the exodus—a better offer elsewhere or a recent bad experience—and counter with the appropriate incentive.

Historically, when the customer has called the bank or visited a branch to move deposits or close down an account, bank personnel either handled the transaction without a conversation, or followed a generic script that offered up a reason to stay.

The conversation ended, the customer was gone, and until the win-back process began, that was that.

With an analytics-powered Sales Center of Excellence in place, banks can enable branch and contact center personnel with interventions at the point-of-churn based on customer history, value and propensities. They now have the information they need to have a real conversation with the customer. Even if they can’t stop the attrition, they will, at the very least, be able to show the customer that the bank knows him, values him and wants him to stay on as a customer.

This increases the chances of reacquiring this customer at a later date.


In most existing organizations, when a bank loses a customer, that person goes back into the “prospect” cycle. In six months, he or she is receiving the same kinds of messaging and contacts as new prospects. Even if the institution has specific win-back communications, it’s typically very generic in nature, with a few tested incentive offers.

In a transformed environment, banks have longitudinal data on every past customer. So, they can customize those win-back incentives, based on the proven response triggers for each individual. Just as important, they have the historical data to determine whether or not that consumer or business owner was a profitable, viable bank customer—or someone who just shopped rates.

Instead of a blanket effort to recoup every lost customer, banks can narrow that list down to the most profitable accounts. Then, through focused messaging, can create a post-churn offer that’s appropriate to the specific customer’s needs and value, based on everything they know about that individual or business owner.

Today, financial institutions have the opportunity to increase revenues and accelerate their growth by modernizing their sales organizations. By moving to a Sales Center of Excellence framework, powered by data and analytic insight, banks can more effectively target high-value prospects, get greater return from their marketing expenditures, and more readily anticipate what prospects and customers need.

Ultimately, this framework enables institutions to map personalized customer journeys that deliver what all customers want: for the bank to know them, engage them, solve their problems and delight them.

And that’s a true competitive advantage.



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