What AI and 100M Social Media Interactions Reveal About Product Markets

Readers can view and interact with the market structure map here: https://market-structure.github.io/index.html

Firms compete to satisfy consumers’ specific needs. The market and the competing products comprise a “product-market” with a boundary. Identifying the product-market boundary and examining the strength of competition among brands within the product-market have long been important issues for managers. It has implications for product design, product positioning, new customer acquisition, and pricing and promotion decisions. Rapid changes to the competitive environment, however, have made identifying product-market boundaries increasingly challenging. The traditionally defined SIC and NAICS classification codes may not be adequate, and especially not for capturing consumers’ perceptions of and preferences for brands. 

Technological advances change product-market boundaries. For example, film cameras gave way to digital cameras, then the digital camera product-market was upended by technological developments in smartphones. Similarly, Ford recently introduced its F-150 Lightening electric pick-up truck at a low price of $40,000 to remove a major barrier for customers thinking about making a switch from gasoline engines and Tesla introduced its electric Model 3 starting of $39,500 to broaden its appeal to mass-market car buyers. Both moves thereby changed competition within these lower-end vehicle product-markets. Companies also increasingly enter product-markets they previously did not compete in. For example, Amazon, hitherto an online platform, essentially crossed product-market boundaries when it acquired Whole Foods, thus presenting traditional grocery brands with a new and innovative competitor. Similarly, Whirlpool Corporation, the world’s largest home appliance maker, acquired Yummly, a recipe search engine with 20 million users, getting itself closer to how its potential consumers cook.

The reality is that product markets are more fluid than ever. Given the potential for new and unforeseen relationships between brands, managers need deeper insights into the fluid product-market boundaries. How can managers accurately identify potential threats and opportunities, especially those in different product-markets? How can managers derive these insights using easy-to-obtain and publicly available data? A new Journal of Marketing study addresses these questions and derives marketing insights using big data (over a hundred million social media user engagement “likes” and “comments”), spanning several thousands of brands in different product/service categories. Our research looks at product-markets from a different perspective by focusing on consumers’ perceptions of brands based on social media engagement data, which unveils more of the dynamics at play, rather than using purchase data that are locked within pre-specified product-market boundaries. We can show that two brands are very close to each other, even though they are in completely different SIC categories. In other words, by examining brand-user relationships, we generate a more inclusive and current representation of brands and the competitive/complementary relationships among them.

Using brand engagement data involving millions of social media users, we capture latent relationships among thousands of brands and across many categories to reveal a highly precise market structure. We build a brand-user network using the data and then compress the network into a market structure map that visually represents the brands. Readers can view and interact with the map at https://market-structure.github.io/index.html.

For example, with Amazon’s acquisition of Whole Foods, our analysis reveals that Amazon moves slightly away from Lowes Home Improvement and closer to other super-market retailer brands. Whole Foods’ proximity to other retail brands such as Target and Walmart increases, while proximity to supermarket brands such as Goya Foods and HelloFresh decreases slightly. In the case of Tesla, after the Model 3 announcement, the brand moves away slightly from luxury car brands and closer to non-luxury car brands, showing that it is gaining appeal from mass-market car buyers. 

Our market structure map helps managers identify brands outside of the product-market that are close to a specific brand. For example, Disney Cruise Line and Hyatt are two brands outside the airline product-market, but are identified as proximal brands to Southwest but not for United. Such findings can provide opportunities for Southwest to target users who like Disney Cruise and Hyatt in social media. Southwest can cross-promote with Disney Cruise and/or Hyatt on each other’s websites or launch coalition loyalty programs. From the viewpoint of other hotel chains competing with Hyatt, gleaning such insights early on may help them take proactive actions. 

Our research reveals that managers can obtain very useful insights from user engagement data on social media platforms at a scale and scope that cannot be easily matched by any other source. The power of our method lies in its ability to capture the dynamic changes in market structure. Since the maps are based on the analysis of big data that can be collected in a relatively short time, our methodology can track changes in brands’ relative position when firms introduce new products, new promotions, and new marketing initiatives. Firms can deploy our method to enhance their social network-based marketing efforts by better targeting specific potential customers.

Read the full article.

From: Yi Yang, Kunpeng Zhang, and P. K. Kannan, “Identifying Market Structure: A Deep Network Representation Learning of Social Engagement,” Journal of Marketing.

Go to the Journal of Marketing.