Companies can learn a great deal about customer sentiments regarding their products or their reputations by using social analytics.
For instance, Pottery Barn does an effective job of using social analytics and listening to and reacting to customer concerns minutes after those concerns are posted to Facebook, Twitter, and other social media channels, according to an article in Business 2 Community.
One of the more infamous examples involves Nestle. In 2010, the environmental watchdog group Greenpeace launched a social media attack against Nestle’s Kit Kat brand.
Greenpeace was concerned that the candy bar contained palm oil from Sinar Mas, an Indonesian supplier it claimed was engaged in unsustainable forest clearing to collect and produce its palm oil.
In a video Greenpeace posted on YouTube, a bored office worker bites into a Kit Kat, which then appears to be the bloody finger of an orangutan, one of the various species threatened by unsustainable forest clearing for palm oil. The video – and Nestle’s response by forcing its removal citing copyright issues – quickly went viral and created a public relations nightmare for the company.
Despite the potential fallout that can occur from these types of incidents, many experts caution companies not to rely too heavily on social customer sentiment since the information can sometimes be skewed by sarcasm or tone.
However, technological advances in the use of natural language processing (NLP) are making it possible for business leaders to be able to look much more closely at the relationships between words as they are expressed in social media to better infer their meanings.
A recent study by PwC bears this out but its authors offer cautious optimism.
“The best NLP tools can provide a level of competitive advantage, but it’s a challenging area for both users and vendors. It takes very rare skill sets in the NLP community to figure this stuff out’,” says Jeff Auker, a director in PwC’s customer impact practice. “It’s incredibly processing and storage intensive, and it takes a while. If you used pure NLP to tell me everything that’s going on, by the time you indexed all the conversations, it might be days or weeks later. By then, the whole universe isn’t what it used to be.”
Several industry thought leaders, including McKinsey & Company, recommend blending social media sentiment data with other types of customer feedback, such as online surveys, text analytics that are applied against customer-company emails and SMS exchanges, voice of the customer programs, and other types of customer forums.
Doing so can provide decision makers and front-line employees with a more multi-layered and complete view of customer sentiment and intentions.
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