Analytics is the lifeblood of modern marketing. This is true in every industry, but its significance is amplified in banking. Changing economics, multi-product relationships, and expanding channels require that financial marketers have timely, data-driven insights to optimize return on investment and drive growth.
Unfortunately, banking execs often get bogged down with “the way we’ve always done things,” or find themselves mired in never-ending projects that were poorly scoped or misunderstood. So how should financial marketers tackle data analytics?
With the right strategy, you can drive more insights more quickly, address long-standing analytic gaps/needs, and invest in newer advanced methodologies to drive deeper customer and business insights. Follow these steps and the results will be both tangible and measurable, producing significant business outcomes that include increased account production, deeper customer engagement, and ultimately, better return on your marketing investments.
1. Catalogue Your Analytic Needs
The first step is to understand and create an inventory of the breadth and depth of your organization’s analytic needs. Today’s analytic requirements can be categorized in a hierarchy from simple reporting needs to cross-campaign audience analyses to modeling and advanced attribution. Each of these requires different skill sets and tools to complete.
Before you do anything else, you must identify and understand everything that will be needed from an analytic perspective. It is wise to complete this exercise during an annual planning process in order to ensure your analytic needs are aligned with your strategic and business goals for the next year (and beyond).
You are likely to find that an infinite number of interesting insights and analytic tools could be developed, but you must focus on those that are crucial to delivering against your plan. Draft a manageable list of specific analytic outputs needed to better understand program performance, segment your audience, and inform ongoing marketing decisions. This list could run the gamut from improvements to your existing business intelligence tool to deep dive analyses of past campaigns to new predictive models supporting targeting and segmentation.
By now, analytics is not completely new to your business, but this deliberate approach will allow you to take a step back to ensure that you understand the analytic requirements underpinning your annual plan.
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2. Establish a Hierarchy of Priorities
Like all business functions, analytic resources are not unlimited, so choosing what projects get priority requires discussions, assessment and trade-offs. Begin this prioritization process by aligning all analytic needs under your key strategic and business goals for the year. If there is a task that doesn’t support a strategic goal, then that item can be set aside for later consideration.
Your next step is to evaluate remaining items and place them on a matrix of impact vs. effort. While it sounds like a simple construct, this approach requires you to develop a high-level estimate of the value of each task and necessitates deep discussions with your analytic partners to determine the time and resources required for each task.
While the approach is mostly quantitative, a qualitative component can also be included, depending on your goals and objectives. This ultimately leads to a clear understanding of top priorities and the resources required to complete them.
3. Assess Resources That Will Be Required
The third step in the process is to ensure that your business has access to the right resources to fulfill your analytic priorities. This is easier said than done, given the increasingly specialized skill sets and advanced capabilities needed for efforts like predictive modeling, attribution, and decisioning. For many banks, investing in specialists is not cost efficient. Thankfully, a robust pool of third parties exists that can support a specific project or commit to supporting your people-based marketing strategy on a long-term basis.
After analyzing your priorities, objectives, and workload, you may realize that your analytic needs are pyramid shaped: a large volume of regular, predictable campaign reporting and diagnostics, a smaller volume of customer insights, and a few specific advanced analytic needs, such as modeling.
In order to resource against this “pyramid,” you may choose to deploy a core team of in-house analytic resources to support predictable and repeatable projects. This team can be supplemented with third-party resources for specialized and/or one-time needs. This model gives you both the certainty that your ongoing, core needs will be met, and the flexibility to nimbly adapt to shift priorities.
4. Ongoing Feedback Loop to Track Progress and Reprioritize
The final step in the process is not really a step, but rather an enduring commitment to evaluation and reprioritization. There are several approaches that can work for this final step, but the keys are to track progress against priorities, consistently reassess prioritization, confirm that priorities align with available resources, and discuss learnings and improvements.
On this last point, the goal is not to examine the results of the analytics, per se, but rather to discuss whether a given project produced the desired output in the desired time frame. Comparing results back to the original effort, versus the impact analysis conducted in step two, will help to inform future evaluations and identify process gaps and/or needs for supporting analyses.
It is good practice to conduct a bi-weekly prioritization session, which allows you to ensure progress, align on priorities, and discuss findings and insights. While in most cases, prioritizations tend to be relatively steady, some pressing business needs or senior management “fire drills” may require you to shift priorities on the fly. But a clear process allows you to clearly understand the tradeoffs these changes entail.