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Getting in customers' faces

Evan Schuman | Dec. 10, 2014
When retailers use IT analytics to get close to their customers, they need to do it the right way.

Credit: RyanMcGuire via Pixabay, CC0 Public Domain

Retailers love thinking about how they can use IT analytics of social media to get close to their customers. But when a retailer breaks through the invisible social media wall and reacts to an online post with a very personal in-store interaction, it may not reap the desired increased-sales outcome.

Let's back up for a moment. Because so much of social media happens in the public sphere, retailers have the theoretical ability to capture and analyze millions of customer interactions and make sense of them. But moving from theory to reality is where the fun kicks in.

One example of how sophisticated the analytics is becoming comes from, which this year tried to quantify the social traffic around Black Friday. Its analysis also illustrates the hurdles that remain before retailers can extract meaningful and useful data from the oceans of conversations made available on social media.

The vendor came up with a lot of hard numbers. For example, Kohl's led social conversations on Black Friday, with 46,000 posts, a 1,384% year-over-year increase. Some other findings: "Walmart (34K posts, -42 percent YOY), Apple (26K posts, +192 percent YOY), Target (16K posts, +60 percent YOY) and Amazon (15K posts, +134 percent YOY) were among the other most-talked about brands and stores. Conversations peaked around 4am ET (125K posts), 5pm ET (168K posts) and 11pm ET (102K posts), indicating consumers were shopping from the wee hours of the morning until the late evening."

All quite interesting, but not as valuable as Salesforce's attempts to figure out what people were saying. The breakdown: 63,000 posts concerned Black Friday brawls, 19,000 chronicled experiences people had waiting in line, and 60,000 were from consumers who were planning to skip the mayhem of Black Friday in favor of Cyber Monday.

Still, the real value for retailers requires an even deeper dive. They want to know just what was being said about them, favorable and unfavorable. Even better would be the ability to differentiate between thoughtful complaints -- specific and based on a legitimate grievance -- and kneejerk hate posts, like "Walmart sucks." They'd love to have an analytics system that could recognize sarcasm and therefore move a comment like "Just saw paper napkins at BigBoxStoreX selling for $58. What a great deal!" from the favorable to the unfavorable column.

Best of all would be software that could match social media usernames with real accounts on the chain's CRM system and could overlay their comments with actual actions. That would tell them how many complaining shoppers ended up returning products and how much time elapsed between the comment and the return. With information like that, the retailer might be able to save the sale. The longer the gap between comment and return, the better the chance of changing the outcome.


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