Advanced analytics is a type of analysis that uses sophisticated statistical techniques and tools to predict unknown future events, segment audiences, and generate recommendations.
Advanced Analytics in Action
Certainly, that’s a “lofty” definition. A far easier way to explain and understand exactly what advanced analytics is all about is by scenario.
Here’s an example of how one association used its data to foresee the future.
ABC Association hosts several in-person courses throughout the year. In the past several years, the association had decided to add additional courses in geographic areas where there are large populations of members. The goal was to increase overall attendance at the courses.
However, attendance has not improved. Meanwhile, expenses have increased due to the logistical costs of hosting so many in-person events.
What should the association do?
Instead of making decisions based solely on gut instinct, politics, or tradition, ABC Association decided to use its data to better understand its members and their needs.
ABC Association used:
- Behavior analyses to examine online behavior to identify member needs. This helped its Education Department develop and enhance content based on members’ interests.
- Segmentation to group members, based on behaviors and then used a propensity model to identify those segments with the greatest propensity to register for courses and directed marketing efforts to those segments.
- Predictive analytics model to determine which existing and new course locations would attract the members most likely to attend.
- A second predictive analytics model to inform personalized recommendations to target segments to cross-sell courses.
Because of this analysis, the association consolidated courses, picked optimal locations, developed valuable content, and reached out to the right people.
The result? The association tripled the number of attendees, while simultaneously reducing the number of events! As you can imagine, this dramatically improved profitability and increased member satisfaction.
Making Sense of Advanced Analytics
So how can your association start using advanced analytics?
Advanced analytics is more accessible than you might think, but it requires high quality data, clearly defined business questions, and expertise.
The first step is to understand your data and ensure its quality. A strong data governance program will help define the meaning of data and document how you manage, collect, clean, use, share, and store data. A good data governance program includes a cross-functional team, a defined set of procedures, and a plan to execute and monitor those procedures.
Next, you’ll want to consider your business questions or hypotheses. As Stephen Covey recommends, “Begin with the end in mind.” Think about what success looks like and then identify what’s keeping you from your desired future state. That thought process often leads to well-crafted business questions that explore opportunities and risks.
Finally, unless you have internal capabilities for advanced analytics, you will probably require the assistance of a data scientist familiar with statistical techniques and programs like R or SPSS. The key is to find a partner who is knowledgeable in data science, but also understands business practices.
For additional information, watch the recent webinar in which Debbie King, CEO of Association Analytics®, and Carlos Restrepo, Senior Director of Customer Accounts at Abila, discuss how associations can leverage advanced analytics to proactively engage, recruit, and retain members.
About the Author
Tori Miller Liu is Vice President at Abila Product Partner, Association Analytics®.