With the U.S. economy hovering at a near recessionary level of growth over the past several years, many companies large and small are forced to do more with less. Marketing managers, especially, are finding it increasingly necessary to justify their expenses – and ultimately their jobs – as companies tighten their belts.
One of the most effective ways businesses and marketers can improve profitability and identify the marketing function's contribution to the bottom line is through the use of controls to measure and improve the performance and accountability of ongoing sales initiatives.
Controls are an integral part of any accountable, data-driven marketing strategy. They typically involve selecting and isolating a segment of the customer population, which is then used as a basis of comparison to the rest of the customer base.
This allows marketers to isolate the impact of a specific action, event or marketing approach on a promotional activity, whether it is direct marketing, advertising, packaging or messaging. Controls are especially effective at making assessments across multiple campaigns, which otherwise can be difficult.
With the insight managers gain from control comparisons, they can make better, more quantifiable decisions on current and future marketing campaigns that will positively impact customer engagement, sales, retention and profitability.
In fact, the act of planning for a control comparison in and of itself is beneficial as it requires managers to think ahead about what they want to measure and why. Is my marketing working? Am I marketing to the right customers? Is this new offer more effective than the one I am currently using?
The answers can lead to new insights and options never before considered. They also will allow the marketer to determine which type of control measure would best fit their situation: treatment controls, selection controls or universal controls.
Treatment Controls
Treatment controls allow marketers to evaluate the impact of a specific marketing treatment on profitability by isolating its effect within a marketing campaign.
For example, before committing to a full-scale rollout, a retailer wants to test the efficacy of a new holiday leader catalog on boosting December in-store sales. To isolate the effect of the catalog mailing, the retailer selects 10% of all active customers to receive the catalog (treatment group), with the other 90% of customers not getting the mailing.
Once the December sales figures are in, the marketer finds that the average sale among the treatment group is $80, whereas the rest of the customer population averages $75. The result of the mailing was a $5 or 6.67% increase in average sales among those who received the catalog.
If the company's entire customer population is 500,000 and the treatment group is 50,000 of that total, the estimated impact on sales would be $2.5 million – had the catalog been distributed to the entire eligible population. If the cost per each mailing were $3, then a $1.5 million investment in a catalog rollout would yield $2.5 million in sales, with a net revenue gain of $1 million.
Selection Controls
When evaluating the impact of a specific targeting methodology, marketers are best served by selection controls that isolate the impact of the targeting on customer response. This approach compares the response rates from a randomly selected customer population versus a targeted customer population (selected from the same overall customer base).
Not only will marketers be able to gauge the impact a targeting program has on sales, they also can test the targeting in advance of a marketing rollout, to ensure they spend their marketing dollars in the most effective way.
To illustrate, take the example of a retailer that wants to test the effectiveness of a new customer targeting program on a promotional mailer highlighting a specific product. Utilizing a selection control, the retailer would send the promotional mailing to both the targeted population and a randomly selected group, and look for any meaningful difference in the response rates.
If the average response rate of the targeted group is higher than that of the non-targeted group, the retailer will know that the targeting is indeed effective at boosting sales. The retailer could then continue with a larger rollout based on the targeting methodology, and use the selection control as an ongoing tool to evaluate and improve the targeting program.
Universal Controls
Universal controls are best at evaluating the impact of a broader marketing strategy. In this instance, a marketer would choose a select group of customers who would be excluded from all marketing activity (or a certain series of marketing treatments) for a period of time to compare their behavior to those customers who received marketing.
As an example, a retailer expects to increase sales from new customers by sending them a welcome and incentive mailing, but is unsure of how much or whether the amount of the increase justifies the costs of the mailing. To find out the answer, the retailer could select 10 percent of newly acquired customers who would receive the incentive mailing at periodic intervals. A comparison of sales among the selected segment to the rest of the new-customer population will reveal the impact of the campaign as a whole.
A Worthwhile Endeavor
Even when an ideal control-based design is not possible with one of these approaches, marketers can still use these principles and some creativity to come up with a reasonable basis for comparison that will provide quantifiable results.
Yes, control comparisons require additional time and costs to develop and implement, but they provide actionable results that marketers can use to analyze current campaigns, better allocate marketing dollars and continuously improve the return on future sales efforts.
Jennifer Cooley is a vice president at Analytic Innovations LLC, a provider of data-driven marketing software and consulting services.
