How Businesses Can Get Inside the Minds of Their Competitors

If we could, perhaps we could make a better use of game dynamics.     Or just simulate their behavior under multiple contexts.

How Businesses Can Get Inside the Minds of Their Competitors

Wharton's Anoop Menon and Jaeho Choi discuss their research on using natural language processing to analyze competitive strategy.

Every business would love to know the minds of its competitors, and what they are likely to do next. Strategy analysts have thus far used simple tools that employ mostly financial and other structured data to try and predict competitors’ moves. But new research at Wharton has shown how natural language processing techniques could be used to parse tomes of unstructured data such as text buried in conference calls or annual reports to more accurately anticipate competitor strategies.

The research opens new pathways to measure and test assumptions firms make in their competitive strategies, and to “visualize how firms are positioned with respect to each other, and then map that on to performance consequences,” says Wharton management professor Anoop Menon. His research paper, “What You Say Your Strategy Is and Why It Matters: Natural Language Processing of Unstructured Texts,” is co-authored with Jaeho Choi, a Wharton doctoral student, and Haris Tabakovic, an associate at The Brattle Group, a Boston-based international arbitration services firm.

For their study, the researchers used natural language processing (NLP) techniques to measure “strategic change, positioning, and focus,” across their sample of 50,506 business descriptions of publicly held companies contained in their 10-K annual reports, from 1997 to 2016.

Menon and Choi shared the main takeaways for business strategy analysis from their research with Knowledge@Wharton.

An edited transcript of the conversation follows.

Knowledge@Wharton: Anoop, could you tell us what led you to explore this topic in your research? What was your objective?

Anoop Menon: The notion that there is a lot of information that is buried in unstructured text has been around for a while. We know that strategy is very complicated, but we tend to measure it using very, “simple metrics” like a few financials here and there. But we all agree and understand there is a huge amount of information that is buried in text like conference calls and annual reports that gets at the meat of the strategy, how the strategists are thinking about competition and product market choices.

Sadly, we currently don’t have a really good technique or set of techniques to get at that information. So that was the starting point. About six or seven years ago, my co-author Haris [Tabakovic] and I came across this burgeoning line of research in computer science about using natural language processing techniques to extract text, but in very different fields – not ours. [There were] some applications to political science but not at all to strategy. We said we should be able to take some of those techniques and get at the information that is buried in the text..... "