Direct marketers constantly wrestle with the never-ending challenge of maximizing campaign response rates to generate more revenue. According to recent research from the Direct Marketing Association, direct mail response rates currently stand at 2.18%, while e-mail response rates are slightly higher at 2.45%. In an era of continuously-rising postal rates and clogged e-mail in-boxes, marketing professionals spend inordinate amounts of time and effort endlessly dreaming up new creative, tweaking campaigns, and procuring different mailing lists from myriad providers in efforts to boost response percentages.
In reality, the solution to this problem may not lie in finding a better mailing list, or developing a better collateral piece. The answer may be found in more effective customer segmentation and precision targeting — a critical component of direct marketing strategy that’s made easier by the latest advancements in predictive analytics.
Predictive analytics has now been widely adopted by businesses across several industries, including by direct marketers, for whom it is a vital asset. Why? Because predictive analytics allow direct marketers to easily identify individuals most likely to respond to certain offers, and hopefully, become long-term customers. As a result, direct marketers are able to increase response rates and maximize campaign return on investment (ROI) through the use of statistically-derived targeting strategies. Let’s take a look at this technology in greater detail.
Predictive analytics is a process, based on statistical and data mining techniques, that models current and historical customer performance data and traits to make “predictions” about future outcomes and customer behaviors. These predictions can be expressed as numerical values, or scores, that correspond to the likelihood of a particular occurrence or behavior taking place in the future. In corporate America, predictive scores are typically used to determine the risk or opportunity associated with a specific customer or transaction. These evaluations assess the relationships between many variables to estimate risk or response.
As a result, many firms rely on predictive scoring to make important customer decisions. For example, credit scoring, used throughout the financial services sector, is one of the most familiar and well-known predictive analytics applications available today. Credit-scoring models, such as the well-known FICO Score, analyze an individual’s credit history and other financial information, and assign a relevant score that indicates the customer’s likelihood to make future credit payments on time. Predictive analytics are also used in many other industries, including retail, travel, healthcare, pharmaceuticals, telecommunications, and more recently, online marketing.
In consumer direct marketing, predictive analytics is used to segment prospect lists by identifying consumers more likely to respond to certain offers as well as to reactivate past customers and prospects. Acquiring new leads and converting them to paying customers can be rather costly, though the Internet has in recent years proven a great new source of high volumes of low-cost leads. The quality of those Internet-based leads can vary widely, however. Reactivating old leads that failed to convert can be one of easiest and most cost-effective means of generating incremental sales.
How can predictive analytics reactivate old leads…and create added revenue? It is rather straightforward, really. A predictive analytics solution provider uses an existing file of unconverted consumer leads, updates, and augments the leads with external data sources to ensure the lead is authentic, and that the prospect can be contacted. Next, a set of custom affinity scores is applied to each lead in the file to determine the scope of offers each prospect is most likely to respond to during a given campaign. Then, those scored leads, and each lead’s opt-in criteria, are mapped to the company’s available offerings to ensure the marketing program is targeting the right people with the most relevant goods or services. Time and time again, this approach has proven to generate enhanced response rates, leading to added revenue, because of this improved, data-driven targeting.
Direct marketers can harness the power of advanced predictive analytics solutions to increase campaign response rates, create added revenue streams, and elevate overall marketing ROI. While the technology is quite sophisticated, the rationale behind it is not: more effective targeting of customers leads to more sales. Moreover, companies can fully leverage existing assets, such as unconverted online leads, that are otherwise of no value. Predictive analytics can truly solve a number of persistent direct marketing problems, while delivering on many different objectives.
Jeff Liebl is vice president of marketing and business development at St. Cloud, MN-based eBureau, a company that provides precision marketing, risk management, and fraud prevention scoring and information solutions to multichannel marketers. He can be reached at firstname.lastname@example.org.