C$-12$ Case Studies

USING AUTOMATED TEXT ANALYSIS FOR MARKETING INSIGHTS

What do books, online reviews, social media posts, earnings calls, financial reports, customer service calls, packaging, consumer forums, movies, and popular music all have in common? They are a treasure trove of potential marketing insights. As technology advances$-$automated textual analysis, the computer$-$assisted measurement, tracking, and interpretation of information contained in text$-$has the potential to quickly extract insights from reports, lyrics, scripts, call transcriptions, and other text$-$heavy documents to help marketers better understand firm, consumer, and market behavior. ${?}^{18}$

Imagine, for instance, being able to quickly mine online reviews to understand which criticisms and praises customers are most commonly writing about a brand. Imagine further being able to not only extract the key phrases of criticism or praise associated with specific products or product features, but also the corresponding reactions that these phrases are generating among readers of these reviews. These insights would be invaluable to marketing teams and their companies more broadly. They could help marketers better understand the goals and feelings of their consumers and the attitudes those consumers hold towards products. They could also help marketers understand how customers are positioning their products against those of competitors and what unmet consumer needs could be addressed to secure an advantage over the competition. They could even help marketers predict how customers are likely to react to product changes, thus helping marketing teams more quickly devise strategies to better connect with their target markets and preempt complaints. By using automated textual analysis, marketing teams could position themselves to more quickly identify and react to changes in their environment so that their companies can more effectively innovate, preempt competition, and thus attract and serve customers.

There are several ways marketers could use automated textual analysis to analyze their customers. First, marketers could leverage this technology to scan digitized text from news, magazines, podcasts, and books for words that may hint at upcoming trends. For instance, a marketing team in the energy bar industry may mine for words associated with taste or spice preferences to better inform the flavor of new product launches. Second, marketers could scan online reviews to better recognize innovation opportunities that may increase customer satisfaction. An apparel company, for instance, could scan for service problems or phrases referencing shapes or sizes to better gauge demand for plus$-$sized clothing. Third, marketers could analyze customer call transcripts to gain insight into higher potential product or service improvements that could better serve customers. For instance, an online brokerage could review its call center transcripts to better understand the pain points customers are having with its online portal or mobile applications when trying to place transaction orders. The company could leverage this information to improve the design of its applications to promote a more intuitive user experience that facilitates a more streamlined ordering process.

Beyond customers, companies can also use automated textual analysis to collect competitive intelligence in several ways. First, companies can analyze the transcripts of competitors' quarterly earnings calls for phrases that may indicate these competitors are shifting strategy or are about to make a major strategic move, such as an acquisition, in the near future. This can allow companies to more proactively assess the threats and opportunities that such shifts may trigger. Second, marketers can analyze the online reviews of competitors' products for phrases hinting at any form of dissatisfaction that might be leveraged in the company's advertising or sales activities to increase customer switching.