On November 13, Kohl’s announced a Q3 drop in sales that caught investors and analysts by surprise. The iconic retailer had long been viewed as one of the best-run regional retail businesses in the United States and had actually outperformed the S&P index over the previous year. So what happened in the waning months of 2014?
Using social data to observe changes in Kohls’ brand health (a metric based on consumer conversations in social media), the disaster should not have come as a surprise. Rather, Kohl’s might have been able to avoid its decline.
We queried our database for 15 months of social data from December 8, 2013 to February 1, 2015, extracting consumer discussions about Kohl’s. There were over 500,000 unique entries. From this data set, we used our classifiers to collect specific mentions affecting brand health. By observing changes in Kohl’s brand health instead of using conventional methods like costly and time-consuming periodic surveys, we mined organic consumer conversations across social networks and other digital properties to determine how consumers felt about Kohl’s and to correlate the real-world impact of online conversations.
As Figure 1 indicates, the decline in brand health toward the end of 2013 and into the middle of 2014 is obvious. Despite some upticks in late spring of 2014, declines continued or leveled off. Brand health correlates quite closely to same-store sales.
Here, brand health derived from digital consumer data could have served as the canary in the coal mine and alerted Kohl’s to the issue early on, allowing more time to correct course and avert disaster. If you’d like to learn more about how retailers can use social data to avoid revenue surprises, fill out the form below to download the free report.