The Power of Analytics to Drive Loyalty – Part 2: Predictive Analytics
Last week, we gave you an overview of David Rosen’s webinar focusing on The Power of Analytics to Drive Loyalty. This week, we would like to dive a little deeper into one of the most interesting and pressing topics he discussed: segmentation models that marketers have the opportunity to apply when managing and analyzing their loyalty campaigns. In the Webinar, Rosen breaks down segmentation into three approaches and highlights their benefits:
1. Profitability Segmentation
- Prioritizes and manages members in order to increase profits and retain customers
- Focuses on a bottom-up value calculation model that takes into account actions and accruals
- Drives program activity, targeting APIs
2. Needs and Behavioral Segmentation
- Understands members and customizes how we communicate personal, relevant offers
- Offers multi-variable cluster analysis with manageable sense of segmentation factors and actionable segments that will form outward-facing personality of the program
3. Predictive Analytics Segmentations
- Provides day-to-day analytics approach for offers and continuous learning
- Allows for multi-variable regression for using past performance and ongoing testing to best predict the outcome of ongoing offers
Rosen particularly focuses on the power of Predictive Analytics. This approach can give marketers a new kind of insight into their customers and allows them to analyze any campaign down to its ROI. By comparing a group of consumers, stores, or products subject to a campaign versus a control group that did not participate in the campaign, marketers are able to achieve the kind of in-depth analysis they desire, and make it a relatively seamless experience as well. So in addition to learning about the behavior of individual customers, marketers can get a true sense of the effectiveness and productivity of certain campaigns.
TIBCO’s predictive analytics starts with a propensity model, which looks at a number of variables that reflect a customer’s past purchases and future shopping baskets. Looking at these factors allows marketers to ask, and answer, the most pressing questions of any and all loyalty programs: how do we match the right offer, the right incentive, the right motivator, to the right consumers at the right time and place? How can we maximize the effectiveness of that incentive, and the overall productivity of the campaign?
These analytics allow you to tailor your campaigns down to the specific consumer. You now have the ability to understand why one consumer is more likely to buy one item, while another is more likely to buy something completely different, while a third may not be likely to buy anything at all, unless a 20% discount was offered.
What does this mean for you, the loyalty marketer? Unprecedented insight into your customers’ behavior, the effectiveness of your tactics, and a clear-cut path of next steps to grow your ROI.
In our next post, we will focus on ‘Offers Looking for People, and People Looking for Offers.’
You Might Like:
- Webinar On-Demand: Success with Social Loyalty
- Webinar On-Demand: The Power of Analytics to Drive Loyalty