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Using Machine Learning to Turn Data Into Insights


A well-established professional association had a membership database containing a wealth of information on their 33,000+ members. The association wanted to use this data to help understand why some members were more engaged than others and boost membership attendance at events held by the association. However, with such a large database, the association didn’t know where to start when it came to turning all of that data into insights they could actually use to help accomplish their goals.


Our Analytics + Decision Science team recognized right away that this was the perfect type of problem to solve with machine learning. Broadly speaking, machine learning is the use of statistical algorithms to find patterns within large amounts of data. Machine learning has been the key to recent breakthroughs in artificial intelligence, and there are also lots of common applications that we encounter in our everyday lives, like when Netflix recommends something to watch or Google autocompletes a search query.

In order to solve our client’s problem, we used machine learning to analyze their membership database and organize the members into different groups, or clusters, based on a variety of attributes, including professional experience, membership tenure, past engagement, and others. After the machine learning algorithm broke the database into different clusters, our analysts drilled down on the results. Using tools like principal component analysis, we could see what attributes the computer weighted most heavily when dividing up the clusters.

The next step was to work with our Digital Marketing + Strategy team to identify which clusters had the most potential for growing engagement. In the end, we found five clusters that had clear potential based on their attributes. Then, we took those five clusters and created five distinct member personas, just like the customer personas we’re familiar with as marketers. Now, using these personas, the professional association can create personalized marketing campaigns with specific content tailored to each persona.

On top of this, we even worked on a proof-of-concept machine learning algorithm that would identify which individual members would be most likely to sign up for a new event. This tool could help our client avoid fatiguing members who are not likely to attend an event, as well as determine which members to potentially target using more costly marketing like direct mail.


Our client was thrilled! 

With machine learning, we were able to parse and analyze a massive amount of data in ways that would have taken human analysts months or even years to do.

Our client was able to transform a large, unwieldy database into five actionable member personas built from real data.

We helped our clients develop sophisticated email campaigns that included 5 workflows and 20 unique paths, targeting each persona with personalized content to guide them toward registering for an event. This new structure is allowing our client to leverage their large database in a strategic, measurable way with a focus on engagement rather than volume of messages sent—a huge improvement of user experience for the organization’s membership.

If you have a big, difficult dataset that you know holds potential but you’re not sure how to get started working with it, give us a call. We’d love to help your team leverage machine learning to tap into the potential of your data.



About the Author

Jenny Bristow is the CEO and Founder of Hedy & Hopp. Prior to starting Hedy & Hopp, Jenny launched, grew and sold a digital agency in Seattle and worked at Amazon. She was named one of St. Louis Business Journal’s 30 under 30, won a Stevie Award for Female Entrepreneur of the Year in 2018 and speaks regularly at healthcare marketing industry events.

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