By: Kenna Smith, VP, B2C Marketing, PSCU
During the last few years, the buzz about data analysis for financial institutions has been around predictive analytics. This trend accelerated when the country was shut down and it became increasingly necessary for cardholders to interact digitally. While financial institutions have been generating campaigns targeting cardholders within a specific segment and based on certain criteria for many years, ultimately, campaigns are predictive in nature and should leverage data that is current and accurate. By including data from various digital channels in campaign segmentation, financial institutions will be better prepared to meet the cardholder at their most preferred place to drive engagement.
There are four types of data analytics, with each methodology using data to answer different questions. All of these data-driven answers can be leveraged for marketing campaigns.
Descriptive analytics look at data statistically to tell us what happened in the past, providing context to help organizations interpret information. The majority of financial institutions are already using data at this level of analysis. However, results from descriptive analysis have a short shelf life, becoming outdated depending upon shifts in the market and the changing life events and preferences of cardholders.
As the amount and types of data an organization collects grow, using diagnostic analysis expands upon the descriptive analysis to help us understand trends and correlations between variables. This type of analysis aims to answer questions like “Why did it happen?” and “How can an adjustment improve the outcome?” Microsoft Excel, the tried-and-true spreadsheet tool that has been around for decades, is still a primary choice for ciphering the data to answer those types of questions. Many financial institutions have mastered Excel and are using it to do their data analysis at the diagnostic level.
Over the past couple of years, a renewed focus has been put on predictive analysis — making predictions on what is most likely to happen in the future, based on historical behaviors from many sources of data segments. This level of analysis has been available for a while through the segmentation models that financial institutions use to roll out credit and debit promotional campaigns, although not all financial institutions are utilizing this level of analysis. There are machine learning algorithms that provide automation capabilities for this analysis that consider key trends and patterns to predict what will happen next, or the analysis can be done via manual segmentation.
As digital channels have become the way of the future for managing spend for card transactions, predictive analysis, especially when done utilizing a machine learning model, has proved to be a valuable asset. Striving toward the use of predictive analysis is a great goal if your financial institution is not already at this stage.
Prescriptive analytics takes predictive data to the next level. This analysis helps organizations to determine optimal marketing strategies, allowing campaigns to become more personalized and accurate in engaging consumers—reaching them where they are, with relevant offers, at precisely the right time.
However, prescriptive analysis requires access to large data sets, which means financial institutions need partnerships with all of their vendors to collect all of their data into one centralized location. The more data you are able to bring together, the more precise your analysis and, ultimately, your marketing can become. Marketing teams are at the beginning stage of understanding how prescriptive analytics can be developed to allow more robust digital marketing in a cost-effective way. With the right technology, partnerships and expertise, it can be a very powerful approach to moving your financial institution to the next level. We can expect to see analysis moving more toward this direction in the future.
Data is key
Summer is almost here and before we know it, it will be time to start planning your promotional campaigns for the 2022 holiday season. The second half of the year is when consumer spending increases, so it will be important for financial institutions to choose campaigns that provide the right level of segmentation to meet cardholders where they are, while minimizing cost.
Today, fintechs often partner with vendors and financial institutions to deliver more relevant and impactful approaches to their marketing campaigns. This is typically handled through predictive models, personalized emails, texting and multi-messaging journeys. PSCU, for example, has established the first step by laying the foundation for campaign automation that will sync up to our digital channels in the near future, enabling a full B2C digital marketing experience to cardholders for financial institutions.
As financial institutions continue to assess their digital marketing strategies, keep in mind it just may not be practical to accomplish everything on your own. Partnering with a fintech or CUSO and leveraging their relationships across all your applications can help accomplish your goals more quickly.
Data helps to shape knowledge – and knowledge is power. Your data is the key to unlocking the power to accomplish the level of spend engagement you are seeking. By using one or a combination of descriptive, diagnostic and predictive analytics, you can achieve the level of segmentation needed so your cardholders’ experience will drive spend at the level you are intending.
Kenna Smith is Vice President of B2C Marketing at PSCU. In this role, she oversees B2C marketing, automation, and operations for PSCU. Smith has over 25 years of marketing, product and operations experience in the financial services industry.