The Five Most Useful Types of Analyses for Account Acquisition03.05.2018
The Five Most Useful Types of Analyses for Account Acquisition
Marketing today is all about targeting prospective customers in direct mail, email, and even through digital channels like social media and online advertising. Sure, there are still financial institutions using the old spray and pray techniques of the 80s and 90s, but relevance is key to effective marketing today. Just like ineffective onboarding leaves a new customer feeling unvalued and lost, ineffective acquisition efforts leave your prospects wondering if you really understand their struggles or know how to help them.
To be clear, “spray and pray” marketing isn’t helping your ROMI either.
It’s not just about finding people. It’s about finding the right people and delivering the right products at the right time. But it’s not easy. Therein lies the challenge of customer acquisition. Here’s the trouble, broken down:
1. Finding the right people (prospects with the highest potential for profitability).
Banks have long viewed account holders who are established in their careers, communities, and personal lives as the ones most likely to generate maximum profit for the financial institution. But that needs to change. It’s shortsighted, especially as millennials enter the financial growth phase of their lives. Financial institutions now need to take a much longer-term perspective on customer profitability and find ways to not only identify the customers with the greatest potential but to help them fulfill that potential as well.
2. Identifying the right products and services for each prospect.
Thinking outside of the box is key. Baby boomers aren’t the only ones looking for retirement accounts. Newlyweds aren’t the only consumers seeking first-time mortgages. Established families may be shopping for refinancing deals at the same time they’re hunting for auto loans and retirement advice.
The modern customer journey is diverse, and as a result, financial institutions can find it difficult to match product messaging to prospects’ needs and lifestyles.
3. Communicating with prospects the right way at the right time.
In today’s multichannel marketplace, it’s not enough to simply match the message to the prospect. Banks must also match the communication mode, and initiate contact with prospects not only through the consumer’s preferred channel but also through every channel the consumer is using.
Data and Analytics
What is the key to overcoming these challenges? Data and analytics.
If banks want to reach the right people at the right time with the right offers, then properly analyzing and interpreting the massive amount of data available to us today is the key to doing it.
Below are some of the most useful types of analytics for customer acquisition, and some examples of how each can be used by the financial industry. Think of this as FinData 101.
- Descriptive analytics. What happened? Descriptive analysis attempts to answer that question. This type of analysis focuses on describing things, like the number of prospects that originated a mortgage, the associations of these prospects with recent life events such as getting married or relocating to a new town, and a generalized look at the attribute profiles before and after these consumers pursued their new mortgages.
- Diagnostic analytics. Why did it happen? Diagnostic analysis wants to understand the whys of a situation and takes the number crunching a step further. It uses multiple variables and known quantities to explore signals of behavior, eliminating the noisy randomness that detracts from the events you are trying to understand. How is it used in practice? Diagnostic analytics enters the equation when you see a dip in customer satisfaction scores or a decrease in ROI.
- Inferential analytics. How is it different? To draw conclusions (e.g. around segmentation or A/B tests) inferential analysis compares a well-defined sample set of data against a broader data set. The analysis objective creates estimates with a substantial degree of confidence. For example, inferential analysis might examine new client deposits at a specific branch and split the data by initial product or opening balance to learn about their correlation with profitability.
- Predictive analytics. What will happen in the future? By looking at current and past data, such as banking behaviors, predictive analysis looks to forecast future outcomes. We often use this kind of analysis to create marketing signals that you use to offer account holders something they need but have yet to pursue. The benefit of this kind of analytics is that you can find yourself in a non-competitive situation by being the first to contact them.
- Prescriptive analytics. What should we do? Prescriptive analytics uses both descriptive and predictive analytics to suggest possible courses of action. This level of analysis reveals what you need to do by optimizing your business to reflect the sweet spots discovered in the historical data. Many businesses are expanding their business intelligence teams and building dashboards to find what is the best recipe for achieving performance or growth objectives. This is the most valuable kind of analysis and usually results in some of the strongest ROI since direct access to this kind of data can result in swift pivots or changes that immediately address a problem. Not only is this kind of analysis good for the business, it could also be used to advise your account holders on the best investment options or products that will solve their unique needs.
This data science is becoming the norm, but many organizations don’t have a trained team of data scientists at their beck and call. Yet, not making the most of the data at your disposal is a grave mistake, so you’ve got two choices. Hire someone (more likely, a team of someone’s) or outsource.
If you choose to bring someone who is skilled in data analysis on board, be ready for a challenge. Demand is sky high for data analysis pros, and you’ll be competing with the entire financial industry for the best of the best. Outsourcing data modeling can be a way for banks to extract the greatest value from their data with the lowest investment of time and resources.
For a more in-depth look at this topic, download our white paper, “Supporting Customer Acquisition through Effective Data Modeling.”
Reprinted with permission from Deluxe Corporation, a consulting firm that works with financial institutions to create programs and services that drive revenue and build long-lasting relationships with customers.