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The Need for Speed

The world of financial services has never stood still, but the pace of change today is positively blistering. The disruption caused by the global pandemic continues to drive rapid digital adoption. At the same time, consumers have more options today than ever before — upping the ante for legacy providers and fintechs alike. In this hyper-competitive landscape, the winners will be those who can move quickly to either lead the market or adapt on a dime to deliver the personalized mix of relevant experiences that consumers have come to expect. And with rates rising faster than anticipated just a few months ago, the pressure is on.

Many marketers are aware of this need for speed and are frustrated by the roadblocks that prevent it. They know that faster learning and execution lead to better results but are hamstrung by old processes and budget constraints.

The essential ingredient to achieving this pace of adaptation is experimentation. The faster your teams can learn, the faster they’ll be able to move towards impact. There are two particularly crucial windows where financial institutions need to get the pace of experimentation right: digital relationship building and lead velocity. How many tests are your product and marketing teams running in these two areas in a calendar year? If the answer isn’t in the thousands, you’re already falling behind.

Examples of a Rapid Testing Framework

Source: Curinos analysis


So what’s getting in the way of productive experimentation today? Curinos has identified a few major speed bumps:

  • Marketing departments are still largely campaign-driven. This approach, which is geared towards large blasts, puts a premium on planning and approval cycles in order to maximize the impact of one message. That makes it difficult to adapt to rapid changes in the market and consumer behavior. For example, in a rapidly rising rate environment, the market is likely to move on — maybe even twice — in the time it typically takes to get a campaign out the door.
  • Current personalization processes force the manual end-to-end creation of experiences for each segment. This quickly hits a point of diminishing returns. Instead of dynamically adapting to individual attributes and behaviors, today’s “personalization” efforts mostly yield one-size-fits-all experiences for very large segments (e.g. mass affluent). For example, marketers might set up different trigger-based deposit retention programs for mass affluent and mass market customers whose deposits the institution wants to retain as rates rise rapidly. But we know that neither segment is monolithic when it comes to deposit behaviors and attitudes about rate. How much is lost because it’s too time- and labor-intensive to target much more granular segments?
  • The long-standing tension between relationship-building and units sold gets in the way. On one end of this struggle are the product teams whose performance is evaluated on product profitability and unit sales. This can sometimes result in strategies that create experiences many customers perceive as “pushing product” rather than supporting financial success. On the other end are marketers who are taking steps to become more customer-centric in their engagement strategies, but KPIs that are focused too heavily on response and engagement will fall short of achieving sustainable business results.
  • Internal data siloes make it tricky to orchestrate rich experiences at the customer level. It’s essential to be able to identify and continuously provide the most value to customers as they research, select and use your financial products and get advice. But too often data take months and significant engineering resources to extract from warehouses and fuel execution engines. Adding insult to injury, analytics teams are often stretched too thin to support more than a dozen tests in any
    given month. 


Banks need to break free of these slow, linear processes and begin to embrace always-on testing and iterative optimization. Curinos has identified three steps that can help the process.

The good news is banks are at an advantage when it comes to the raw material needed for smarter and faster adaptation: access to extensive first-party transactional and interaction data.

Step 1: Invest in expanded customer scoring. Score customers based on their likely response to behaviors that you are trying to influence. The goal is to improve the response rate of the most attractive current customers and prospects. Financial institutions need to leverage insights generated from the full breadth of deposit and lending data to better predict customers’ potential value and evolving financial needs and preferences. Overlaying third-party non-financial data can offer greater insight into customers’ lifestyle and personal motivations. These insights are critical for presenting products, value propositions, guidance and support in ways that are relevant to each customer. Banks should fast track their testing by scoring customers for propensities to engage in specific value-based behaviors, such as acquiring a new product, activating new services or increasing utilization.

Step 2: Fine-tune the breadth of data that you use. Ensure your executional engines are equipped with relevant enrichment algorithms out of the box. Algorithms that clean and enrich customer data — in effect generating new and useful attributes that drive differential marketing decisions — are one of the most important determinants of speed because building them is both technical and time-consuming. Some examples of enrichment algorithms include days since last app login, average daily balance and loan-to-deposit ratio. The extent to which your marketing engines include a robust library of battle-tested metrics for financial services data will dramatically speed up your launch dates — and your speed to return on investment.

Step 3: Improve cycle time by adopting continuous testing. Start with a high-impact use case. Identify a specific outcome, such as improving quality of customer engagement at certain points of the customer journey. Then develop a set of hypotheses to test within high-value potential segments. Hypotheses can test different treatments based on audience attributes, incentives offered, channels employed or creative messaging. Dynamically assemble experiences from an existing library of treatments rather than creating each experience whole cloth. Leverage scores and AI to optimize treatments against a meaningful KPI to reduce the optimization timeframe from months to days. Break down barriers within the bank to speed approval processes.

While it may seem daunting, these steps can be taken in one leap with AI-driven marketing. Institutions that move away from manual test-and-learn and hard-coded trigger rules or campaigns to automated continuous learning cycles will be in pole position in these challenging times.

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