A few years ago, whenever anyone mentioned Big Data, Artificial Intelligence (AI), or Machine Learning, I always thought of Data from Star Trek: The Next Generation. You remember him? Data was a sentient being with gold eyes and deathly pale skin-wrapped super-processing computer.
If you asked Data to calculate how many ship crewmembers were likely to order hot chocolate at the commissary within seconds the answer would be calculated. You didn’t know how Data came up with this answer, but it didn’t matter because Data would always be right.
When I sat in the leadership chair for a federally insured credit union in Pennsylvania, I would have given anything to have access to “Data” with his all-knowing brain available upon command to ask questions that would result in growth in assets and revenue by using the actionable AI from his information. I could ask Data who was ready to buy a new car. Would they rather buy a used one? Which members of my credit union had credit cards or loans from other lenders that I could refinance for them? How do we get all those folks who have raised their hands to wanting to refinance their mortgage with us, to give us their contact information?
As Data learned the habits of the crew through measurable information, there was an increase in machine learning through predictability. If Data was working in the marketing department, it could help me match consumers with the right product and I could place the correct marketing content in front of members at the right time. We could predict member’s buying probabilities by using the resources available through information points in our data system at a speed beyond human capacity.
If you think of using AI as augmenting the knowledge you already have of your credit union members, you can perfectly time their next banking opportunity with direct marketing to them. The combination of enhancing your own data with Artificial Intelligence provides you with one-to-one communication efforts and actionable results because, who knows your credit union members better than you? Plus, the more you use AI, the faster the machine learning platform learns your members’ habits and helps you predict trends, which make planning for the future much easier.
Many marketers are scared of integrating AI into their marketing strategy because it’s a rapidly evolving field with seemingly endless applications. You may have had relatively little experience with AI to date; however, you have likely used data-driven marketing in the past when working with credit bureaus to find potential new members who fall into certain age, income, and credit score categories.
If you are integrating AI in your marketing strategy for 2021 and beyond, here are three things to consider.
- What are your goals and what is your strategy to reach those goals? What outcomes do you want to grow? Are you more focused on making member acquisition pay for itself or do you want to turn one-time borrowers into repeat customers?
- Is your data AI-ready? You will want to know that your in-house data has enough quantity and quality. Here are a couple of ways to ensure your data is AI-ready: Clean your existing data by eliminating inconsistent, incomplete, or duplicate records. Also, you can add depth to your data source by working with a vendor to provide third-party demographic and behavior-based data.
- Finally, you will need to conduct implementation of the platform and integrating it into your marketing team. What approach makes the most sense for your team? Your office is probably maxed out with daily operations of a marketing department, so a third-party service might be a good fit for you.
If you have a robust IT department you might want to build capabilities that are suited to your credit union’s needs by bringing coding, testing, and implementation in-house. Each approach has its pros and cons, so it’s important to carefully consider benefits versus implementation time and costs.
As you do your due diligence for finding and engaging an AI service bureau-based platform or “AI Alliance,” keep in mind that this enables your credit union marketing to be primed to deliver all sorts of robust data science without the high cost of building it in-house.