By capturing and analyzing social-media conversations, companies can improve their offerings and margins.
Gaining even a slight edge in today’s tightly contested, rapidly shifting product markets can help companies reap sizable gains in share and margins. In this game of inches, the required capabilities are zeroing in on what consumers really want and will pay for, unearthing chatter about product deficiencies that undercut sales, and knowing where product designs should be tweaked to shave costs. Yet doing all this at scale—over tens (if not hundreds) of product categories and hundreds (even thousands) of SKUs—confounds most consumer and retailing businesses. Quantitative and survey-laden tools, such as conjoint analysis, can help companies focus on whether their customers value specific features and on possible trade-offs among them. But companies rarely apply these time-consuming, costly exercises to a broad cross-section of products. They must often rely on best guesses.
There is, however, another avenue to gain the necessary insights: buzz analytics, which reads burgeoning signals from social media and can help companies in many industries to identify and prioritize actions across broad product lines. Buzz analytics captures consumer insights by mining the abundant and free information from online conversations, such as comments about product features on company websites and external platforms like Facebook and Twitter. It then assesses these positive and negative sentiments and converts them into meaningful metrics at the product-feature level. Companies can also run such analyses on their competitors’ offerings to benchmark their strengths and weaknesses. While not rigorously scientific, this is a rapid, cost-effective way of gathering data and testing hypotheses that can guide product-design tactics and strategy.
Buzz analytics has many uses. Companies can deploy it to develop insights on product features that could add value and increase market share through better pricing or better marketing and merchandizing options. It can also help them determine which features are less important to consumers and thus suitable for elimination or modification to optimize costs. In our experience, companies have successfully used buzz analytics, over a year, across a broad range of product categories and SKU variations. That’s helped these companies to nudge products to leadership positions within their categories, to correct quality issues, to raise margins, and to target marketing expenditures more effectively. The exhibit provides a visual case study on how buzz analytics suggested changes to the design and features of a bicycle pump.
About the author(s)
Dave Fedewa is an expert principal in McKinsey’s Atlanta office, where Guillermo Lopez Velarde is a senior expert and Brian O’Neill is an associate principal.