Clinical Researcher—August 2024 (Volume 38, Issue 4)

SITES & SPONSORS

Chris Bryant

Recently, an industry driven by metrics admitted it’s probably not making the most of its data. This conclusion—noted in the marketing sector—offers both a warning and sound advice for the pharmaceutical industry, which collects data from its investigator meetings, speaker trainings, and ad boards, but often doesn’t have all necessary resources to convert this information into meaningful metrics and actionable insights that can impact business strategy.

More than 500 business-to-consumer marketing, media, and advertising executives were surveyed about challenges to measuring their return on ad spend (ROAS), an important indicator of their return on investment (ROI). Wakefield Research, in partnership with LiveRamp, conducted the survey and released the resultant report, “Looking to Improve ROAS, Organizations Shift Focus from Data Collection to Measurement Optimization in 2024.”

Among the findings, it was stated that 97% of executives in marketing, advertising, and brand management had challenges using data to measure impact. In fact, 90% admitted they invested heavily in data collection, but “not enough in the measurement and analytics capabilities they need to use the data to its full potential.” More than three-quarters of them, then, are making improving their measurement rationale and analytics interpretation capabilities a priority this year.

Making Better Use of Existing Data

Pharmaceutical companies have always collected data in some format from their investigator meetings, speaker trainings, and ad boards. Many use the information for compliance purposes as well as to assess the ROI or educational impacts of the meeting itself. As meeting technology has become more sophisticated, more and different types of meeting data have become accessible.

While the intention is to gather more actionable insights, often the result is that organizers are either overwhelmed by raw data they don’t have time to correlate, or simply hand-pick the same few datasets they’ve used for years. This is understandable but also a huge, missed opportunity. Data used correctly can yield a story about the meeting that is easy to understand and easier to act upon by both sponsors and trial leads.

The focus needs to switch from collection to purposeful metrics optimization to make converting data to insights more seamless. This is not as daunting as it seems; the right technology partner should be able to help.

Bridging the Gaps

Ultimately, you need to bridge the gaps between study teams, life science stakeholders, and technology providers. The first step, then, is to work with your technology partner in the meeting planning stage to identify data that would help you answer important questions, test theories and preconceptions, and provide context for insights the stakeholder could easily act on. Your partner should be able to develop a plan for how to acquire those data as part of the meeting using their technology (or engineer engagement)—ideally, by working directly with study teams on their goals and obtaining buy-in for metrics optimization.

The goal is to gather demographic information that will provide context when correlated with all other meeting metrics. Correlating demographics with pre- and post-test data that show the level of knowledge on the topic could, for example, highlight differences in understanding of patient profiles or enrollment procedures among different sites. If your technology partner can provide you with content engagement insights at the site level, post-investigator meeting follow-up can be more targeted and, ultimately, yield faster results.

Going Above and Beyond

To really make the most of measurements and analytics though, you need to go beyond the demographic, contextual information and develop datasets around information that can impact your business.

Consider the information you’ve never received from a meeting before, but which would impact how you develop or market a drug, or even just improve future meetings. Collect the same data across a series of meetings on the same topic (multiple regional investigator meetings for a global clinical trial, for example). Then measure and analyze those data to see what patterns emerge in the meetings themselves or in the attendees’ understanding or behavior. Benchmarking across all meetings and events enables you to look at meeting performance for ways to improve programming to meet your goals in the future.

In many cases, pharmaceutical companies and their meeting partners don’t have the extensive databases, data warehousing, and analytics tools in-house to make sense of the raw data in this way. To some extent, this is a challenge the marketing industry shares (compounded by the fact marketers have data coming in from multiple sources for one ad campaign).

The right technology partner should be able to help you with transforming data into insights so you don’t need to build out that infrastructure. As the level of insights you can obtain from data collected during life science meetings continues to increase dramatically, lean on your technology partner to make sure your data are used to their fullest potential.