Predictive modeling has opened new avenues of opportunity to generate personalized insights from your member data that you can act on now to scale your association.
We used to rely on our intuition and industry knowledge to craft our outreach communications and develop our product portfolio and events calendar. This often meant generalizing the behaviors of our membership base and crossing our fingers. Or, it meant tagging members based on profile information and behavioral engagements and segmenting them into groups of “similar members.”
The problem with generalization and even segmentation is that every member in your organization is on a different journey. They have their own story, level of hurdles, and head full of questions. And they each have a unique set of goals that they want to fulfill through their membership.
With predictive data modeling, you can now personalize your marketing, products, and services. Injecting personalization allows you to connect with the human behind the faceless Member ID to make more of an impact, save time, and improve ROI.
Today, we’ll explore how you can use member data in predictive modeling to:
Predictive modeling (sometimes called propensity modeling) forecasts the likelihood of future outcomes so that you can act now to shape that result in your favor. It may sound intimidating, but the truth is you’re already using AI-driven predictive analytics techniques today.
Google shows you predictive text based on your previous searches and search intent. Amazon shows you ads based on previous purchases and searches and what it thinks you’ll need next.
Your association can use predictive data modeling as well to identify risks, emerging opportunities, and even new products, events and campaigns based on your individual member data.
It works by plugging your past and current member datasets (including specific profile, transactional, and behavioral data of individual members) into a machine learning-based predictive model.
Predictive modeling can help you predict and prevent churn (i.e. people leaving or not renewing their membership). In essence, it works by pinpointing the members who aren’t likely to renew and which of these at-risk members could be steered back on the path to renewal through targeted outreach.
Based on historical trends, you may know that about 75 percent of your 10,000 or so members up for renewal are actually planning to renew. With predictive analytics, you can identify which of your members make up the 25 percent that are at risk of not renewing. A predictive model can even give you the renewal likelihood of each individual member—and the reasons for this prediction.
Your marketing team can then decipher where to focus outreach efforts and who to leave alone. So instead of wasting valuable resources, you can use that time and money to focus on low-scoring members. In particular, the at-risk members who are likely to respond positively to a certain campaign.
You achieve dollar savings and better ROI by identifying members who are most likely to be interested in a message or product, while avoiding those who are unlikely to respond positively.
Connecting with each member on an individual level leaves them engaged, coming back for more, and contributing to your organization beyond just paying their annual dues. In other words, it increases their lifetime member value.
Predictive modeling allows you to take a more personalized approach to member engagement and develop unique member journeys for unique members.
For each of your members, you can figure out which products, events, or courses they’re most likely to buy as well as how and when to engage them to prompt a conversion.
You can respond to what people are doing online, how products are used, and what people are interested in by evaluating and developing member-specific outreach approaches through predictive analytics modeling.
Over time, predictive modeling allows you to match an outreach approach that’ll be most effective to that individual member on a 1:1 basis.
Monetizing your member data through predicting analytics techniques is a powerful way to diversify your revenue streams.
It solves the problem of identifying unique product and pricing configurations to satisfy underlying market niches and emerging trends hidden in your data.
With phased predictive modeling, you can collect individual input on what members desire in a product or campaign. Each time the model is run, it gets more engagement insight and adjusts accordingly so that you can better understand what each individual member likes and dislikes.
With this invaluable data about member data (i.e. metadata) in hand, you can then group members into segments who have similar needs and preferences. Each segment indicates a market niche that can be satisfied by a tailored product.
By grouping markets based on needs uncovered from predictive data modeling, you can then develop more than one product or campaign uniquely suited to the needs of a cluster of individuals in the marketplace. You can also expand a la carte purchase opportunities and other non-dues revenue sources with these personalized insights.
We hear this question all the time. Tons of associations fret about the quality of their data, which can hold them back from transforming digitally and embracing change-makers like predictive modeling.
Truth be told though, all data is good information.
Granted, the more data you have and the better it is, the quicker you’ll get actionable strategies from a predictive model. But that’s not to say that you can’t get great insights from what you have now. It’s simply about making your data work for your association based on your organizational needs.
Really, the type of member data you’d want to feed into a predictive analytics model depends on the questions and challenges you’re looking to solve. If you want to forecast churn, for example, then you’d ideally want at least a thousand members in your database, their renewal history, and at least five years of transactional history.
In a perfect world, you may also have associated chapter enrollment history, committee engagements, publications purchased, and event attendance. But again, you don’t need this level of integrated member data to start extracting value from predictive analytics right now.
Running member data through a predictive model saves time, money, and produces a greater ROI on outreach and new product and campaign launches. It’s all made possible by collecting personalized insights about member data.
With the level of personalization that you can receive from predictive modeling, anything is possible.
Want to see if you have the right kind of member data to plug into a predictive model in order to hyper-personalize your engagements and maximize ROI? Check out our Data Quality Assessment to learn how you can make your member data work.