Fall 2019 - Inside Outdoor Magazine

Tech Savvy Inside Outdoor | FALL 2019 50 T he crystal ball – oft spoken of in folkloric terms but never avail- able when you need it – has entered the realm of possibility. In our switched-on world, where digital interaction is present vir- tually every moment of an individual’s life, we as marketers now have the tools to peer into the future using data, rather than crystalline, to gaze forward at where our businesses are going.  Predictive analytics, or the process of using new and historical data to foresee the result, activity, behavior and trends of our consumer base, is the key that’s making successful busi- nesses, well, successful. Enterprises primed for growth in today’s hyper- competitive marketplace are using predictive analytics to gain a deeper understanding of their customer bases to maximize revenue, efficacy of mar- keting budgets and, of course, profits.  So how can you unlock the ben- efits of predictive analytics for your business? Let’s look at some of the key predictive tools and how they can be deployed to help your business. 1. Predictive modeling of cus- tomer behavior: Using data points gleaned from previous campaigns (par- ticularly those data that help us under- stand what worked and what didn’t) plus all demographic information known about your customer base, you can build predictive models to draw correlations to link past behavior and demographics. This model endeavors to score each customer according to their likelihood to buy certain products and projects when and how to best ap- proach this individual. In the wild, you may have seen tactics such as sug- gested products being offered to you during your online purchase checkout. This is a basic example of how this model works in execution. 2. Qualification and prioritiza- tion of leads: Chasing a lead that is not likely to convert can be expensive. Applying predictive analytics to lead modeling can get you more bang for your lead investment buck. It uses an algorithm to score leads based on known interest, authority to buy, need, urgency and available funds. The al- gorithm – using public and proprietary information – analyzes, compares and contrasts customers who converted with those who did not and then find “alikes” among the incoming leads. The higher the score, the more quali- fied the lead. The highest-scoring pros- pects should be directed to sales or of- fered immediate incentives to convert; medium scores deserve a drip cam- paign; low scores … forget about it. 3. Customer targeting and segmen- tation: Among the most common use of predictive analytics, customer target- ing and segmentation takes three basic forms. “Affinity analysis” refers to the process of clustering/segmenting the cus- tomer base according to attributes they have in common, facilitating “fine tune” targeting. “Response modeling” looks The Digital Crystal Ball By Adriana Lynch Three ways to use predictive analytics to grow sales