Transforming Healthcare Marketing with Voice Analytics

Researcher Spotlight
April 25, 2022
Ashley Avarino & David Ferris

JUMP AHEAD

The quality versus quantity tradeoff is timeless. In research, this tradeoff manifests in the differences between quantitative and qualitative methods. Quantitative methods (e.g., surveys) use large sample sizes, but are restricted to quantitative questions that don’t typically generate detailed insights. On the flip side, qualitative methods (e.g., interviews, focus groups) provide high-quality insights with small sample sizes.

A typical solution to this conundrum is to send out a quantitative survey to a large number of participants, and supplement these findings with qualitative data from small focus groups or one-on-one interviews (i.e., a mixed-methods approach). But what if you could collect both quantitative and qualitative measures without the sacrifice?

Adrian Sansone is the founder of DOCREPLAY.ai, an innovative healthcare marketing firm that offers a mixed-methods solution to this classic quantitative vs. qualitative problem. Originally a Biology major, Adrian has 25 years of Pharmaceutical Marketing experience. During this time, he realized that Pharmaceutical Marketing is “behind the times”. Leveraging AI-enabled technology, DOCREPLAY.ai breaks through the limitations of traditional marketing research and analytic strategies by doing qualitative research at scale.

"Phonic.ai is awesome… it’s the most advanced and intuitive survey function platform with broad domain application. Moreover, the reliability and service are meaningful differentiators." - Adrian Sansone, Founder of DOCREPLAY.ai

Giving Healthcare Providers A Voice

In healthcare settings, the primary consumer is the healthcare provider that recommends pharmaceuticals (e.g., physicians, nurse practitioners, physician assistants, nurses). Healthcare providers have limited time and resources to participate in marketing research, which can be a challenge when aiming to collect authentic and reliable insights. Adrian decided that they best way to maximize participants’ time while still collecting qualitative data, would be to integrate audio responses into effortless surveys.

“Phonic is simple, easy, and fast. And in healthcare, that is critical. That’s why Phonic is the perfect partner for us.”

While audio surveys do reduce the time and effort needed to partake in healthcare marketing research, they also allow researchers to collect insights at scale. That is, data for a much larger sample size can be collected, while reducing (or maintaining) the resources required from the researcher. One potential caveat to collecting qualitative data at scale is what to do with that amount of data. DOCREPLAY.ai has a solution for this: AI-driven data analytics.

AI-Driven Voice Analytics

The keystone of DOCREPLAY.ai is an AI-enabled algorithm, developed by a team of PhD graduates from MIT and Berkeley. Audio responses are fed into the AI model and analyzed using a complex language quantification system to identify:

  1. What the respondent is saying and how they are saying it
  2. How this compares with brand messaging
“The Holy Grail of marketing is when your customers can incorporate your brand idea - the emotion and the story - when they share it with their peers.”

The model is adaptable to brands at any stage in development; insights can be collected and analyzed for products that are in pre-launch and those that are already on the market. After data is run through the model, Adrian’s team provides feedback so that brands can shape or re-shape their marketing strategies and messages.

Better Together

Quantity and quality are better together. Like red velvet and cream-cheese icing, voice data and AI-driven analytics just make sense. Why compromise sample size or data quality when you can have your cake and eat it too?

Check out DOCREPLAY.ai for more information on how they are changing Pharmaceutical Marketing research.

Interested in how you can collect qualitative data at scale with Phonic? Get in touch at contact@phonic.ai.

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