Business is going soft—it wants to know how you really feel. Turns out, there’s hard data in those feelings, and companies are mining it to glimpse how customers are reacting to things like product launches and promotions—so they can adapt with the right type and quantity of product in the right place, and not lose opportunity sales or stockpile excess goods.
It’s not only sales and marketing, the original social listeners—design, manufacturing, and supply chain are also taking a keen interest in social media signals that provide early indicators of customer sentiment. In an age of product proliferation and wild demand swings, they need to sense, in near real time, what people want, how much they may order, and through which sales channels.
People mull things over after a buy, often sharing their thoughts and feelings with others. Business is turning to social listening and sentiment analysis to capture these potential demand signals on social media, then chew over the “unstructured” input with analytics and machine learning to gauge the customer’s product experience—and maybe the need for a design tweak, or added incentives.
Everyone wants to stay on top of the market; absent that knowledge, it’s nearly impossible to respond as quickly as customers expect with rapid delivery. Your manufacturing facilities and supply chain may be agile, but if you haven’t staged enough inventory of the right type during a launch or promotion, you’re never going to be able to issue enough procurement orders to get a supply chain response that keeps pace with market demand—especially competing with juggernauts like Amazon, which is offering 1-2 hour delivery in major cities and 1-2 day delivery everywhere else.
Social listening tunes in to social channels to capture consumer sentiment. It crawls tweets, Instagram, Facebook, and other social networking sites, then uses natural language processing (a subset of machine learning) and statistical processing to interpret the communications wrung from the electronic text. It focuses on things like whether the sentiment is positive or negative, amount of activity, influence and reach of the communicator, trends, emotional gyrations, potential problems, and so forth.
This is particularly crucial in swift-moving retail. Fast fashion, for example, places a strong emphasis on social media (especially key influencers) and the Internet (number of page visits, dwell time on a particular page) to assess the customer experience. Analytics and machine learning top off this data with historical trends—from sales of previous merchandise with similar attributes—to help predict demand, and then “learn” customer behaviors during the launch or promotion to uncover order patterns.
Social listening and sentiment analysis gives a boost to Sales & Operations Planning (S&OP), where sales, finance, and operations collaborate to generate and adjust the forecast and business plan. Each of these functional areas needs predictive data to issue forecasts for volume and revenue projections and to ready production and supply chain for order fulfillment.
That’s why everyone’s suddenly so sociable.
