Data Analytics – How to Use Data More Effectively in Financial Marketing
With the growing field of data analytics – as well as the growing number of data resources available to financial marketers – it seems that we have access to more information about our customers than ever before. While the availability of data is no longer the problem, the ability to mine, analyze, and leverage that data to make better business decisions still can feel like a mystery.
To help break the data code and demystify “data analytics,” here are some data basics. The good news? Odds are you’re already using data to refine your marketing strategy. If that’s the case, you can make little changes to translate that data into actionable steps – even without a full-time marketing technologist on your team.
Descriptive, Predictive, and Prescriptive
In the analytics world, there are three types. In a basic continuum of complexity, they are descriptive analytics, predictive analytics and prescriptive analytics.
Descriptive analytics, quite simply, tell us what happened. More than 90 percent of organizations use descriptive analytics, which can be a valuable starting point for the greater data conversation. A simple dashboard like Google Analytics on a website can offer insight about website engagement and user behavior, which can lead to bigger questions. “We saw more hits on our landing page. Did this result in more direct loan inquiries?” or “We shared a credit card promotion on our social channels that had strong engagement. How did that translate into online applications?” Descriptive analytics are a first step and useful to track real-time as well as historical data for context or benchmarking.
Predictive analytics help us ask the question, “What might happen if…” If a credit union runs a HELOC campaign every year, marketing may look at analytics and start building a promotional calendar that takes advantage of the lessons learned. This data becomes the raw materials for building out the marketing strategy based on trends and patterns identified in the past. It forecasts potential reactions by members and is inherently probabilistic in nature. We can’t say something will happen, but predictive analytics help us share what we believe could happen based on the data available. More Farmer’s Almanac than Magic 8-Ball, predictive analytics helps make more informed decisions.
Prescriptive analytics are the holy grail of analytics. Using prescriptive analytics, organizations can use data to “prescribe” the best course of action. As an example, financial institutions use credit scores – a prescriptive analytic – to determine the probability of a customer paying a bill on time, which in turn informs policy about to whom to make a loan. Many prescriptive analytics require complex algorithms and robust data sets to run simulations, making this the hardest type to acquire.
Using Data Better
So, how can you use data, even basic data, better? Here are some quick tips we’ve shared with our financial clients to help shape marketing strategy.
Align your data needs to business goals.
This might be obvious but start asking questions that align with business goals. If you want to enhance a loan portfolio, dig for data about the types of loans that perform well, ask questions about the types of customers that engage, and examine data relationships between marketing tactics and responses.
Look for data that tells more of the story.
Think like a journalist and look for data that helps fill in more of your story. Who is our ideal customer? What products do they carry with us? When do we see peaks of engagement? Where and how do they interact with us? Why do they make the choices they make? Asking these, “Who? What? When? Where” and How?” questions will help you identify where you have data sets available, where there are gaps, and how you can fill in some of those missing pieces.
Take data as far as you can but don’t disregard your gut.
While many of us have access to some data, small credit unions or community banks likely don’t employ a team of data analysts who love to uncover the mysteries buried in our core systems. With the data easily accessible to marketing, and maybe an occasional assist from our IT team, we can still get information that is really helpful in making and measuring marketing decisions. When the data runs out, trust your gut to weigh the information you have available with your experience and insight as a marketer to make decisions that drive results. Marketing has always been a soft science, blending data and information with the art of motivating human behavior. No matter your data starting point, you can improve your analytics game by determine what data would be relevant to your business goals, who is responsible for collecting it, and how you will monitor it over time to identify trends and gather useful insights. Happy data crunching!
