Past performance does not guarantee future results

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Raisa Suhir
Raisa Suhir

Marketing success depends on matching the product to the market at the right time. In financial services, target marketing is typically based on analysis of response lists, customer data and campaign data combined with compiled data. In general, at the beginning of a campaign, the target selection criteria are mainly driven by knowledge gained from analysis of past performance.  

Relying solely on historical data can affect the performance of your next campaign

Campaign history doesn't reflect data regarding who is responding to or buying from competitors. Nor does it include intelligence on who intends to buy the product, their channel preferences, or their attitudes. You can't know when your direct mail piece drives consumers to your competitor's Web site to buy the product you promoted.

There are two ways to improve this nearly universal targeting practice. First, target the best prospects in today's market, and second, reduce the impact of historical targeting bias on future performance.

The fastest and most cost-effective way to achieve these improvements in your next campaign is to integrate objective, market-based metrics driven by market research data.

To illustrate how market research data can be integrated in a target marketing application, imagine using a syndicated market research survey has to develop intelligence, including channel preference, on recent and prospective term life insurance buyers. By combining survey data with demographics, the demographic factors that differentiate potential or recent buyers from non-buyers can be identified. Statistical models driven by these demographic factors can assess the propensity to buy term life insurance overall and the propensity to buy term life insurance when a triggered by direct mail offer.

These research-driven buying propensities provide a universal metric that can assess the current market potential, concentration of potential buyers, best target markets for direct mail and a benchmark of potential performance.

Assess the current market. Consider two counties in New Jersey: Essex and Passaic. Essex has 295,177 households; while Passaic has172,505. For the term life insurance marketer, these two counties have virtually identical markets: 12,282 for Essex and 12,244 for Passaic. Passaic has a higher concentration of term life prospects (propensity) than Essex: 7.1% vs. 4.2%.

Target today's market. The research-based metrics show that 9% of US households bought multiple financial products and services through direct mail. The affinity for the direct mail channel propensities can be used with the term life propensities. In Essex County, the best direct response segment comprises 17.8% of prospects; Passaic, 19.1%. These prospects have a propensity to buy term life through direct mail at three times the US average. To increase ROI, direct campaigns in high response segments should mainly be driven by direct mail, supplemented with touches in other media. In low direct response areas, ROI should be driven mainly by other media and supplemented by direct mail.

Benchmark your next campaign. Prospect concentration rates of 4.2% in Essex County and 7.1% in Passaic are ultimate product-specific market benchmarks that are used in setting revenue objective, marketing resource and media allocation, and targeting.

Developing robust and complete information about customers and prospects requires looking beyond historical and internal data. Transforming market research into buying propensity metrics can support and unify the strategic and tactical dimensions of financial marketing.

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