Resources

Use Cases:

Measuring and Optimizing Campaign Impact using Drug-GPT

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At a Glance

Challenges

Time consuming analysis
Inadequate message alignment
Limited behavioural insights

Benefits

A robust quantitative measurement of alignment at a population and HCP/KOL level
Share of voice
Decision making tool for evaluation and planning of HCP/KOL campaigns

Customer Objective

Healthcare Advertising Agency A wanted to test their Clients’ assertions about their drug, against real world conversations in relation to HCP/KOL campaign impact. Agency A were manually trying to understand how KOL messages on oncology related sites aligned with key brand messages from their Client relating to efficacy, safety, tolerability, quality of life and accessibility. Manual interpretation of alignment to key brand messages by HCPs was extremely time consuming and difficult to track over time.

Brief

The brief given to Talking Medicines was that Agency A wanted an AI-generated score to minimize errors in the computation of alignment between HCP/KOL messages and the set of key brand messages. The Agency wanted scores for the entire population as well as for each individual HCP/KOL to allow for the grouping of individuals with similar alignment.

Solution

Talking Medicines analyzed the Agency’s key brand messages and HCP messages, assigning an AI-generated alignment score between each key message-HCP/KOL message pair. This enabled the Agency to see the general trends in alignment for the population of data as well as a score per HCP/KOL.

This allowed the Agency to see:

Which HCPs had similar alignment vs those that did not.
The HCPs that were aligned with key brand messaging and those that the were not.

This information was used to calculate share of voice vs messaging, and to strategically plan on an influencer program to bolster alignment with key brand messages.