A large healthcare network, serving over 120,000 patients a year, wished to quantify the effects of a social network influencer campaign promoting their treatment of postpartum depression. This can be a difficult subject for patients to open up about, and we needed to figure out how to communicate to the target audience.
Tracking the topics of natural consumer conversations on social media provides an invaluable metric for gauging the performance of marketing initiatives.
Using natural language processing techniques, and our proprietary I.S.A.C.C. (Intelligent System Analysis of Consumer Content) conversation modeling, we categorized organic social media posts based on their content over three months leading up to and following the campaign. Using the volume and proportion of the most relevant topics, we then measured the effect of these campaigns on new patients and referrals.