Ford set its sights on overtaking Tesla in the electric car market when it announced in May its competitive $40,000 price point for the new F-150 Lightning electric pickup. Scanning consumer sentiment and determining whether it succeeded in winning over popular opinion is typically a difficult process. But new research from Maryland Smith found a way.
Maryland Smith’s Kunpeng Zhang and P.K. Kannan, along with Yi Yang of the Hong Kong University of Science and Technology, employed AI techniques to analyze large-scale social media engagement to provide insights into how competition changes as a result of new technological advancements.
According to the research, published in the Journal of Marketing, Ford’s push into the electric automotive market shows how firms compete to satisfy specific consumer needs. Ford and competitors like Tesla make up a “product-market,” defined by certain characteristics from within a market. In Ford and Tesla’s case, accessible electric vehicles.
“Identifying the product-market boundary and examining the strength of competition between brands within the product-market have long been important issues for managers,” the researchers write. “That’s why we’ve developed a deep network representation learning method to develop competitive market structure visualization using large-scale social engagement data that helps managers identify opportunities and threats to their brands from outside the product-market boundaries.”
Rapid changes to the competitive environment, including technological advancements and company acquisitions, have blurred the boundaries of product markets in recent years, the researchers write. That’s especially important considering the implications for next-generation product design, product positioning, new customer acquisition, and pricing and promotion decisions.
To provide more clarity, the researchers turned to social media and studied millions of users’ brand engagement data to create “a deep network representation learning model to capture latent relationships among thousands of brands across many categories.”
Using an AI-based deep Autoencoder technique, the data is compressed into a map that visualizes the market structure of learned representations of brands. The map, they write, helps users glean insights into relationships between brands and the ability to spot potential competitors and cross-promotion strategies.
By also using case studies such as Amazon’s purchase of Whole Foods and Tesla’s introduction of the Model 3, the researchers say the study suggests how managers can make connections between brands outside of a specific product-market that are similar to other brands within it.
Amazon’s acquisition, for example, put it closer to other supermarket retailers such as Target and Walmart. Tesla’s Model 3 announcement, on the other hand, moved the company slightly away from the luxury car brand market and displayed greater appeal from mass-market car buyers.
“Such findings can help a brand to target users of proximal brands, cross-promote with those brands or launch coalition loyalty programs or identify potential threats from other brands,” the researchers write. “The research highlights the value of user engagement data on social media platforms, unmatched by any other source. Analysis of such data should become part of regular monitoring of competitive landscape and opportunity identification.”
Even airlines such as Southwest would benefit from cross-promoting through semi-related companies like Disney Cruise Line and Hyatt based on user engagement.
“Our research reveals that managers can obtain very useful insights from user engagement data on social media platforms, at a scale and breadth of scope that cannot be easily matched by any other source,” the researchers write. “Analysis of such data should become part of regular monitoring of competitive landscape and opportunity identification.”
Read More: “Identifying Market Structure: A Deep Network Representation Learning of Social Engagement,” in the Journal of Marketing.
Media Relations Manager