What Do Consumers Really Feel About Autonomous Vehicles?
What do consumers feel about the coming era of autonomous transportation? On the face of it, the advantages seem evident - the freed up personal time, relieving driving stress, making the journey more productive and enhanced safety for drivers addicted to their cell phones. But are there worries about robots taking over the world? Would people trust an autonomous vehicle (AV} to ferry their young kids to parties or disabled/aged relatives to medical or social appointments? Are they filled with dread about the prospect of a 18 wheeler AV truck with chemical cargo barreling down the highway at high speeds?
J.D. Power is a global leader in consumer insights, advisory services, consumer data and analytics for helping business clients understand consumer behavior and trends. The Mobility Confidence Index Study has been published since 2021, in collaboration with the MIT Advanced Vehicle Technology (AVT) Consortium. Based on on-line survey responses from 3,000 vehicle owners in the U.S. age 18 and older and balanced to reflect basic census demographics across the U.S, it offers important insights on consumer sentiment towards AVs. Interestingly, their studies indicate that consumer confidence in AVs has been decreasing from 2021-2023, with concerns ranging from data privacy and AV system hacking to confusion about what the different levels of autonomy mean (conditional vs fully autonomous, the exact definition of the ODD or operational design domain, etc.) However, confidence soars among the respondent group that has experienced a fully autonomous ride hailing experience from providers like Waymo in San Francisco or Phoenix.
Crewasis.ai, a startup out of TechStars L.A. accelerator , aims to use "publicly available social media content with a B2B marketing/business intelligence platform based on proprietary data science and artificial intelligence (AI) models to predict customer trends". (Disclosure: I am an advisor to Crewasis.ai). The focus is on healthcare, cosmetics, food, beverage and other consumer-centric sectors. I asked CEO Sharon Joseph, a Brooklyn, NY based native (Crewasis.ai is based here) how she came up with the rationale for her start-up:
"I'm passionate about using data to help businesses make smarter and faster decisions. Our solutions save business and brand owners up to 90% of their time with an all-in-one platform. We differ from survey based market analysis companies in terms of costs, speed, use of richer and unbiased data sets, and analyzing multi-dimensional customer inputs".
Crewasis' platform collects social media, news, and any other publicly available data (based on keywords) , filters it, uses commercially available Large Language Models (LLMs) to interpret intent and uses these as inputs to a trained AI engine to uncover insights and identify trends. The AI system is industry agnostic and has been trained using input-output data curated by human experts.
Hugo Van Den Biggelar is Director of Consumer Memberships at Nike and one of Crewasis's customers. "Crewasis helps with narrowing down data to get to insights fast".
Advantages of this approach include:
There are disadvantages of course - for example, a large fraction of the customer population may not be active on social media, which means that their opinions are not factored in. Additionally, a more accurate drill-down on demographics (age, gender, geographical location, etc.) is not possible. But it does provide quick insights and a basis for designing a more focused and survey campaign.
When I broached the idea of examining the transportation sector, Ms. Joseph was excited, and we engaged on a 4 week sprint with her data science team to understand consumer sentiments towards two segments of transportation autonomy: autonomous ride hailing and personal ownership of AVs.
"Autonomous Vehicles" as a keyword and limiting the date range to January - July 2024 generated 72K data points (social media posts). These were analyzed further for trends and insights into ride-hailing and personally owned AVs (see Figure 1).
A sampling of posts is shown in Figure 2.
Using AI based LLMs to extract consumer sentiment in different market sectors is a game changer to traditional market and brand analysis and an excellent avenue to provide customers with business intelligence. Crewasis' current applications have been trained on using text inputs. The product roadmap includes using imaging, video and audio inputs. These different information inputs have the potential to compare brands, study consumer interest, and provide deeper insights into consumer likes/dislikes. It will be interesting to see how this impacts and shapes decisions on AV deployment in the near future.
