Radicle Science to explore the intersection of marketing and research at Expo West

By Claudia Adrien

- Last updated on GMT

AI and machine learning allows for the exploration of large, heterogenous data sets to find intersections. @ andresr/Getty Images
AI and machine learning allows for the exploration of large, heterogenous data sets to find intersections. @ andresr/Getty Images

Related tags Artificial intelligence Machine learning personalised nutrition

The future health and wellness market will be transformed by the intersection of areas such as artificial intelligence, clinical trials and marketing to deliver personalized supplement regimens and messaging to consumers.

Proof-as-a-service company Radicle Science will discuss this and other themes affecting the future of the supplement industry next month during a presentation and two panel discussions at the Expo West natural products trade show in Anaheim next month during an education session titled "Navigating the Future of Supplements: Integrating Science, Technology, Marketing and Compliance to Drive Customer Lifetime Value"​.

Speaking with NutraIngredients-USA ahead of the show, the team at Radicle Science explained that when it comes to clinical trials, many companies often fail to relate scientific data across various levels of marketing.

“For finished product companies, they may not recognize how much consumers care about the clinical evidence behind the product,” said Jeff Chen, co-founder and CEO at Radicle Science. “Moreover, brands may be uncertain as to how to translate clinical trial data into messaging that is scientifically accurate and understandable to consumers.”

Global consulting and research firm McKinsey & Company​ found that efficacy and scientific credibility are the top two factors consumers seek when selecting in a wellness product. 

Consumer sentiment does not always align with a company’s ability to relay the science. There is the potential risk of communicating explicit or implied claims. Additionally, brands can be unsure of how to leverage clinical data in a manner that is compliant with regulators, acceptable to retailers and that won’t draw the attention of class-action litigation, Chen added. 

“These regulatory rules govern what is the necessary scientific data to substantiate explicit and implied claims,” he said. “Brands that can prove their products effectiveness in rigorous clinical trials have substantiation for an incredibly wide range of strong explicit and implied claims. They can creativity use statements and themes related to ‘clinical proof’ or ‘scientific validation’.” 

Compelling science

For Radicle Science, its scientific gravitas rests on a business model that is backed by an AI-driven, virtual, direct-to-consumer clinical trial approach that provides large-scale and affordable predictive health data. The company, which is a B-corp., seeks to foster both precision medicine and better public health policy.

“We're leveraging AI and machine learning and looking at health outcome data to really drive that precision analysis and understanding the variations between the subgroups,” said Pelin Thorogood, co-founder and executive chairwoman at Radicle Science. “The point is AI and machine learning are what we need to effectively analyze large data sets.”

This means meeting consumers where they are. The company collects data on consumer smartphones and computers, for example, so that data is not collected the old-fashioned way, such as having a participant drive to a clinical site.

Keeping the data collection engagement easy and less time consuming also assists researchers in their efforts to include diverse populations in studies, ensuring not only different genders and ethnicities but also other demographics such as disabled people, single parents and people who live in rural communities.

“We want to be really inclusive, and technology enables that. It means we actually have data from all of us, which means we can generalize it to the entire population,” Thorogood said. “It’s this richness that powers us to explore hidden correlations between formulations, ingredients, dosages and their effectiveness for different conditions for different populations and eventually drive toward providing personalized insights for consumers based on gender, ethnicity, life stage and lifestyle.”

This can translate into more targeted marketing campaigns, she added.

AI and machine learning allows for the exploration of large, heterogenous data sets to find intersections. Thorogood noted that much of what humanity has seen in the course of history are health innovations that emerged thanks to serendipity.

“We are now increasing the odds of manifesting serendipity because we have much larger data sets,” she said. “We're able to actually explore them to find correlations and really see how different products may affect different people differently.”

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