MEMBER MARKETING INSIGHTS FOR JUNE

ENSURING ACTIONABLE RESOURCE ALLOCATION DECISIONS THROUGH BETTER DATA

BY GARY D. STEIN, PARTNER, CAPITAL PERFORMANCE GROUP, LLC

Whether banks are in a growth or cost-cutting mode, marketing executives are expected to utilize their budgets judiciously and effectively. Marketers, thus, require an objective, data-driven approach to determine how to allocate marketing resources, including marketing spend or direct mail volume, across geographic regions or between branches or other business units.

Many banks struggling to implement data-driven approaches get derailed by having too much information or the wrong types of data to achieve clear insight. Here are nine keys to ensuring that the information you collect helps you make actionable decisions:

1. Clearly define the decision-making objectives. The objectives will determine information requirements and should dictate the analysis framework. Typically, more specific objectives yield more insightful analyses. For example, a marketing initiative aimed at driving more prospective customers to the branches would suggest a need for determining where the greatest populations of prospects reside. However, knowing that the same initiative is ultimately intended to drive home equity growth, one would also want to consider home ownership rates and the propensity of market households to use home equity products.

2. Consider the overall strategy and positioning of the bank when defining analysis metrics. For example, while dollars usually determine opportunity, higher household income and wealth do not necessarily translate into a more attractive market for banks that target the mass market. Income and wealth measures can be very helpful, but only if used to identify markets with dense populations of households falling within the bank's target criteria. Likewise, while competition is an important factor in many resource allocation decisions, an institution may not consider all competitors to be equally formidable. Often, community banks can more easily distinguish themselves from larger banks than from fellow community banks, thus complicating efforts to compete with them. In such instances, competitive analyses and metrics might exclude large bank competitors or weight them lower.

3. Involve all key stakeholders early in the analysis process to ensure acceptance of results. No one wants to spend time and effort on a well-thought analysis only to have executive management reject the results and question the input. Involving key senior managers early in the exercise and obtaining approval on approach and information selection can be critical to achieving consensus on recommended decisions.

4. Limit the number of metrics. Too much information can lead to the dreaded "analysis paralysis" and delay decision making and ultimate action. Dependent and highly correlated variables do not typically shed greater insight on analyses. Consider whether the extra time and expense for collecting additional information will bring new clarity to the implied results. For example, the incremental cost and effort may not be worthwhile to gather consumer population data if household counts for the same geographic area have already been collected.

5. Consider outside data. A corollary to limiting the number of metrics is to question the requirement for homegrown information when relevant publicly available data already exists. There may be no need to conduct focus groups or market surveys to understand the financial services needs of commonly defined segments such as college students when results of broader and likely better constructed national studies can be purchased for a fraction of the cost.

6. Take care in selecting data sources. Outside data can be useful and cost effective, but quality varies. Understand how information is derived. Third-party survey results may appear to provide deep, insightful information, but the results may be invalid if the respondent base was small or the survey approach was poorly structured. For example, an online survey of retirees would likely be biased and not provide an accurate assessment of the mature market's comfort level with technology.

7. When modeling, be careful not to combine diametrically opposing measures into a single ratings formula. Doing so can reduce the granularity of the results and overall effectiveness of the decision-making process. Higher potential markets typically attract more competitors, and a ratings approach that rewards markets for population but penalizes them for competition might end up illuminating small, mediocre markets with few competitors. A matrix that plots market composition against competitive density can help to identify markets that excel on both dimensions while suggesting more specific strategies for entering or targeting competitive, attractive markets.

8. Leave time to audit data. Even the finest models can falter and spew misleading results if input data is incorrect. Review both internal and purchased data and question outliers.

9. Don't rely on numbers alone. As important as quantitative opportunity metrics and a sound approach are in making critical decisions, step back and consider whether there are any key decision drivers that cannot be so neatly measured. For example, many third-party data firms provide good estimates for market growth. However, these projections may not consider recent announcements of planned developments or of new businesses relocating to the market.

Take care in your analysis, as the results and the decisions that stem from them will likely have significant impact. The nine steps listed above can go a long way to ensuring success.

Gary Stein is a Partner with Capital Performance Group, LLC, a management consulting firm providing advisory, planning, analytic, and project management services exclusively to the financial services industry. Please visit their website at www.capitalperform.com. Gary can be reached directly at gstein@capitalperform.com or 202-337-7876.