Our client wanted to understand current DPS purchasing practices in order to set pricing and contracting strategies that most strongly resonate with their customer base.
We began with an initial deep dive session with the client’s market research team and marketing/product teams to explore their understanding of the current landscape. This session enabled us to confirm research objectives and understand the analysis needs.
Following this, web-enabled interviews were conducted with Pharmaceutical Executives (respondents were recruited from a list of companies provided by the client). All respondents were screened based on their involvement in, and ability to speak to the commercialization of diagnostic algorithms within their organization. Each interview covered:
- Warm-up and mind-set framing – Capturing an overview of the respondents’ role at their pharmaceutical company in relation to AI algorithms
- Broad based value of Product X – Presenting respondents with Product X solution. Respondents worked through an exercise to reveal what the value of Product X is for their company
- Pricing and value to company – Capturing likelihood to invest in Product X
- History of partnerships – Understanding what kinds of partnerships, if any, the respondent has engaged in / is familiar with for AI algorithm development and deployment
Interviews were conducted via the Civicom platform which allowed the client to dial in live. It also provided a chat feature so that the team could converse with the moderator during the interview.
Final strategic recommendations were developed by considering all elements of the research. We provided our client with a report comparing pricing options, helping them to understand:
- All pricing exercises were linked to inform the client of the most attractive business model and price point for the DPS
- The value stakeholders assign to the DPS. This was identified through Perceived Pricing (Perception of the value of the product via an open-ended question), Van Westendorp (assessing price preferences, including costs that are perceived as ‘too expensive’ and ‘too cheap’) and Gabor Granger (likelihood of purchase is examined at pre-specified price points on a 0-10 point scale)