Optimization Modeling Helps Marketers Get Prospects’ Attention

Segmentation and predictive modeling are great for improving individual customer interactions. But there are analytic offerings that can help marketers with the broader issues they face when considering their bigger-picture strategies.

For example, how can they determine the impact on campaign response rates were they to promote a new product within an email promotion, as opposed to an established product or a combination of the two? What would be the effect of reducing the marketing budget for a specific campaign by 10%? What would be the impact on revenue of sending out twice as many emails as are currently being sent?

Marketers have tools for determining which campaigns to send to which customers in a multi-product organization. Up to a point, marketers can use experience and intuition to create the highest returns, enforce contact policies, and stay within budget (even when a company offers multiple products via hundreds of campaigns to millions of customers). But turning to optimization brings hard science to gut-level decisions.

Optimization is a technology-based approach which relies on mathematical techniques to help marketers maximize economic outcomes. The goal is to make the most of individual customer communications. But optimization lets marketers take into consideration business variables such as resources, budgets contact policies and the likelihood that given buckets of customers will respond to specific offers.

The basis for marketing optimization involves performing what-if analysis. The results offer insight into the impact of changes before they are made. This allows marketers to target customers with an eye toward maximizing profitability, response rates, asset levels, or any other chosen parameters, all while taking into account customer preferences, propensities, profitability, costs, contact policies, and other business objectives.

Case History: Telecommunications

To grow in a highly saturated mobile phone market, one telecommunications company needed to increase market share as well as revenue per acquired customer. Using optimization, the company sought to reduce campaign costs—which added up to millions of dollars—by 30%; significantly improve its return on investment in marketing campaigns; and raise customer satisfaction by eliminating uncoordinated and conflicting marketing communications.

Through optimization analysis, the company was able to calculate which elements of its campaigns were the most successful. Campaign reports indicated which channels going forward would be effective in a given situation and which customers should be targeted with what types of approaches and when.

The company’s marketing department then used the analyses to refine and optimize future campaign design and execution by:

· Scoring and modeling to help segment user categories and behaviors.
· Managing channel propensity.
· Addressing customer loyalty and churn.
· Improving cross-sell and up-sell rates.

For this company, optimization has resulted in a response rate three to four times better than before and, in some cases greater than 10 times better.

Enhanced Contact Strategy

Optimization can also fine-tune contact strategies so marketers don’t over-saturate customers or violate corporate governance requirements. For example, optimization enables marketers to eliminate uncoordinated and conflicting communications. Nobody wants to receive a 10% online discount and a 15% in-store offer for the same product, or a credit card offer from the same bank every day.

With optimization, relationship factors such as customer risk, advertising exposure and householding ensure that valuable customers are receiving the best possible set of communications across every channel at the frequency the customers determine.

Finally, optimization can help increase organizational efficiency. For example, analysis can quantify where changes in staffing and budget will really pay off, identify whether marketers are leaving money on the table, or where they have unused capacity.

Larry Mosiman is global product manager for SAS Customer Intelligence.