Beyond list brokerage, AccuList can support direct marketing clients with “predictive analytics,” meaning scientific analysis that leverages customer and donor data to predict future prospect and customer actions. It will scientifically “cherry-pick” names from overwhelming “big data” lists and other files. For example, AccuList’s experienced statisticians build customized Good Customer Match Models and Mail Match Models to optimize direct mail results for prospect lists, as well as one-on-one models for list owners to help acquire more new customers or donors. Plus, predictive models aid other marketing goals, such as retention, relationship management, reactivation, cross-sell, upsell and content marketing. Below are some key ways predictive analytics will harness data for better marketing ROI.

More Swift, Efficient and Effective Lead Scoring

Lead scoring is too often a sales and marketing collaboration, in which salespeople provide marketers with their criteria for a “good” lead and marketers score incoming responses, either automatically or manually, for contact or further nurturing. Predictive analytics will remove anecdotal/gut evaluation in favor of more accurate scoring based on data such as demographics/firmographics, actual behavior and sales value. It also speeds the scoring process, especially when combined with automation, so that “hot” leads get more immediate contact. And it allows for segmentation of scored leads so that they can be put on custom nurturing tracks more likely to promote conversion and sales.

Better List Segmentation for Prospecting, Retention and Messaging

With predictive analytics, list records can be segmented to achieve multiple goals. The most likely to respond can be prioritized in a direct mail campaign to increase cost-efficiency. Even more helpful for campaign ROI, predictive analytics can look at the lifetime value of current customers or donors and develop prospect matching so mailings capture higher-value new customers. Predictive analytics also can tailor content marketing and creative by analyzing which messages and images resonate with which customer segments, identified by demographics and behavior, in order to send the right creative to the right audience. Finally, analytics can develop house file segmentation for retention and reduced churn, looking at lapsed customers or donors to identify the data profiles, timing inflection points and warning signs that trigger outreach and nurturing campaigns.

Optimizing for Channel and Product/Services Offer

Data analysis and modeling can also be used to improve future marketing ROI in terms of channel preferences and even product/services development. By studying customer or donor response and behavior after acquisition, analytics can identify the most appropriate promotion and response channels, communication types, and preferred contact timing by target audience. Plus, a customer model can match demographics, psychographics and behavior with product and offer choices to tailor prospecting, as well as upsell or cross-sell opportunities, to boost future results.

Committing to a Good, Clean Customer Database

Reliable predictions require a database of clean, updated existing customer or donor records, with enough necessary demographics/firmographcs and transactional behavior for modeling. So, to prevent garbage-in-garbage-out results, AccuList also supports clients with list hygiene and management, including hygiene matching for DO NOT MAIL, NCOA and more, data appending of variables from outside lists, merge-purge eliminating duplicates and faulty records, response tracking with match-back, and more advanced list screening options.