89% of financial institutions polled are not fully confident in the value of the offers they give to customers.
The financial services industry runs on relationships, and banks are striving to optimize every aspect – from acquisitions to establish new relationships, to keeping those customers engaged and growing their business later down the line. However, with increasing competition, the fight for customers’ business has never been fiercer. Banks are caught in an arms race to give the most appealing offers to customers, and spending on credit card rewards has skyrocketed.
But how do banks know if these costly offers truly pay back over time? Which customers will remain after the steep drop off at the end of promotional periods such as the common “no annual fee for the first year” offer? Or, how much spend should be required to earn a rich sign up bonus and over what period of time? Accurately and regularly forecasting the long-term value of campaigns is challenging, and banks struggle to know which offers are best for which customers. A recent poll of more than 35 leading financial services organizations sheds light on some of these challenges.
We found that 89% of financial institutions polled are not fully confident in the value of the offers they give to customers. This statistic illustrates a problem with current decision-making processes; banks need a better way to understand the long-term value of their campaigns and offers, so they can make smarter decisions that will drive profitability in the long run.
So why the lack of confidence? This uncertainty can stem from the fact that financial institutions do not have the right processes and forecasting methods in place to build and update rigorous forecasts at a granular level in order to optimize campaigns.
We found that 81% of financial institutions polled do not use standardized modeling practices for most of their forecasts. This inconsistency renders full optimization across a bank’s portfolio impossible. Banks should empirically develop a standardized best-practice process for generating and reporting forecasting results.
Further, two-thirds (66%) of financial institutions polled indicate they refresh their forecasts quarterly or less, and they completely rebuild their forecasts even less frequently. With new data constantly flowing in, a quarterly cadence is not sufficient to capture real-time insights that could impact critical investment decisions. Making decisions based on outdated results can lead to large losses and missed opportunities. However, forecasting is extremely time-, data-, and resource-intensive, so banks are challenged to improve.
Additionally, the polled revealed that most (61%) of respondents track long-term performance for less than half of their campaigns. Banks have a lot of room to increase the number of campaigns they are actively tracking, so they can constantly iterate and improve future campaigns. However, tracking all campaigns is difficult because banks, in some cases, run thousands of campaigns each year, which span several products, channels, and offers. The sheer volume of campaigns makes it challenging to perform a complete analysis for each one.
These challenges are difficult to overcome, but the investment required to solve them is worthwhile, as getting forecasting right can generate significant value. For example, consider a credit card issuer that wanted to increase share of wallet among existing cardholders by running a cross-sell campaign with a large sign-up bonus offer. This offer might cause some customers to reduce spend on their existing cards to levels that make the whole program unprofitable for these customers, and others may even close their existing accounts entirely. However, if the bank did not update its forecasts until several months after the campaign began, it would not identify the cannibalization effect until it was too late to adjust the campaign. By having up-to-date forecasts, the issuer would realize this negative impact and be able to reach different types of customers going forward, avoiding significant losses.
Having the right analytic tools in place can alleviate the issues of standardization, speed, and scale and empower banks to improve their forecasts significantly. These forecasts can then be incorporated into decision-making for both individual campaigns and overall campaign strategy. Being able to quickly and accurately forecast the long-term performance for all campaigns at scale will not only bring confidence to banks’ decision-making, but also improve long-term profitability.
Senior Vice President, APT, A Mastercard Company
Mr. Clarke is Head of APT’s Financial Services practice, based in Washington D.C. He previously worked at Capital One, where he led the Venture Card business and was Head of Credit Card Strategy. He has also worked at Bain & Company, as a consultant on financial services and private equity engagements as well as at LexisNexis, leading their Credit Risk & Analytics business. He holds a Master of Business Administration from Harvard Business School and Bachelor of Science in Mechanical Engineering from Virginia Tech.
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