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The direct approach: Using strategy and data analysis to guide direct marketing


Many banks approach account generation as a gamble: Spend enough money on marketing and existing customers will hopefully sign up for a new product, or new customers will open accounts. But this approach creates needless risk that can waste significant money on ineffective marketing campaigns.

So hold the dice: There’s no reason a financial institution’s marketing efforts have to be a gamble. Meaningful growth can come through marketing that takes advantage of proper data use to support strategic priorities. By successfully linking key areas of strategic focus with comparison data from peer financial institutions, banks can more accurately project progress that can bolster product goals and objectives in 12-month planning cycles.

Statisticians are not magicians

Using statisticians to fill the role of strategists can indicate that financial institutions have failed to create an overarching strategy for their marketing efforts. That’s because they think analytics are all that’s needed to drive efficiency. You can boast the most efficient marketing campaign in the world. But if it fails to have a meaningful impact on your institution’s top goals and objectives, it’s an empty victory at best. 

Data is an enabling component of strategy—but not a strategy in and of itself. It should always work to serve key objectives.  In other words: An overall marketing plan, supported by individual campaigns, needs clearly defined strategy to steer the analytics. Strategy should clearly outline the key goals of each campaign. Analytics can assess whether goals are realistic on the front end and the progress achieved on the back.

Put in proper perspective, an informed growth strategy creates the goals; data analysis shows the optimal path to achieve those goals.

Broken down further, two components are essential for effective data analysis: first, the ability to access and organize the bank’s data; and second, a meaningful reference point for data comparison. Understanding the unique composition of a financial institution’s customer base—achieved by overlaying product distribution on key customer relationship categories—can provide a basic starting point.  This approach offers insight into relevant statistics that all institutions should look at, regardless of individual growth targets.

Analysis of the analysis: Seeking smart data insights

Core data analysis should seek answers to the following five sets of questions:

  • How many customers were new to the bank in the last year—and how does that compare to peers?  Drilling deeper, how effective has the bank been in deepening new household relationships?
  • What insight does the number of households per branch and new households per branch give the bank into whether the branch network should grow or shrink?
  • How many customers only use one service per service category and how does that tally compare to peer institutions? What does this say about attrition risk and cross-sell potential?
  • How concentrated are deposits in small groups of households?  What does that say about deposit portfolio risk?
  • How does the overlap between deposit and loan product usage within a bank’s customer base compare to peers?  What does that say about growth opportunities on both sides of the balance sheet?

Understanding the distribution of accounts and balances in context of the bank-customer relationship holds the key to assessing the organization’s basic strengths and weaknesses.  Also ask yourself: Where do I have elevated attrition risks?  Where are my most immediate growth opportunities? Ultimate answers to questions like these require a data benchmark. Comparing a range of key metrics to a comprehensive data set of your peers can provide a uniquely intuitive, effective tool to assess opportunity and risk by product category, over defined time windows.

Lights, data, action: Launching the campaign

Benchmarking can also spot gaps to attack, strengths to more fully leverage and risks to address. But a final question remains: How do we put a plan into action?

Here, bringing in an additional category of peer data can prove invaluable. By examining marketing campaign performance by product, offer and segment, banks can pinpoint which tactics most effectively capture opportunities specific to individual institutions.  When combined with broader benchmarking, campaign data creates a highly accurate model that projects campaign impact.

Combining strategy, national peer financial institution data and campaign-specific data creates a ready-made action plan—one that accurately and efficiently turns opportunities into growth. But to ensure success every time, it’s paramount that peer group datasets are accurate. And to be most accurate, a dataset of peer institutions must rest on thousands of datasets over many years. This allows trend analysis, month to month and year to year, to validate the stability of the data. The dataset must also be standardized in a way that yields relevant comparisons. This way, financial institutions will have confidence that they’ll get the insights they need to plot successful campaigns.

Data-driven marketing can also succeed because it enables customers to respond to individual messages by self-identifying their financial needs at a point in time. It can make customers more aware of relevant products to improve their financial lives. When offering new services, the right information, in the hands of the right customer, at the right time, can make the difference between a negative reaction and positive one. 

So what should financial institutions do if they’re interested in moving away from gambling with their marketing budget? The answer: Connect your strategic priorities with top opportunities through data-driven marketing.  Marketing dollars should only be spent on opportunities with the greatest chance of success and the highest return on investment. An informed strategy combined with data-enabled marketing can keep financial institutions from betting with their marketing dollars and reward them instead with predictable results.

High-hopes gambling, on the other hand, compares to a typical casino proposition—and an entirely different data set would reveal that spinning the wheel on a whim yields odds that are rarely if ever in your favor.

Tim Keith is co-founder of Infusion, a Memphis-area provider of data-driven direct marketing campaigns that generate strategic growth for community and regional financial institution. He also works as the company’s strategy leader. Tim can be reached by email at [email protected]