A large US publisher wanted to know more about their subscriber base in order to develop actionable marketing solutions using segmentation.
Mather Economics developed data transfer protocols in order to become fully immersed in the publisher’s data. Mather was able to incorporate detailed information about the subscriber, including interests, tenure, digital engagement, et al., to create a holistic view of the subscriber. Using these detailed data, Mather employed econometric techniques such as hierarchical clustering, iterative k-means clustering, and principal components analysis to scientifically segment the subscriber base.
Mather took a hands-on approach to implementing and defining the segments within the publisher’s marketing databases. Results revealed certain segments were prone to deals, such as upsells, while others were influenced informationally by simply giving retirement advice. Insights such as these enabled the publisher to tailor marketing campaigns to groups of like-minded subscribers, which allowed for a more customized experience for the subscriber. This also led to reduce costs through eliminating communication to subscribers who had previously shown disinterest in the topic.