Use Predictive Analytics to Harness Big Data Power

Predictive analytics, meaning scientific analysis that leverages customer and donor data to predict future prospect and customer actions, can scientifically "cherry-pick" names from overwhelming "big data" lists and other files. For example, at AccuList, 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 can aid other marketing goals, such as retention, relationship management, reactivation, cross-sell, upsell and content marketing. One of the benefits of analytics is improved lead scoring, for example. 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. In fact, 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. Data analysis and modeling can also be used to improve future marketing ROI in terms of channel preferences and even product/services development. Of course, reliable predictions require a database of clean, updated existing customer or donor records, which AccuList also supports via its list hygiene and enhancement services. For helpful links, see https://www.acculist.com/predictive-analytics-harnesses-data-for-marketing-roi/