It’s true – information is power. And more information? Often, more power.
Social media listening, often referred to as social media monitoring, is a way to identify and analyze how people are talking about your brand online. ‘Listening’ services provide some valuable information including facts, figures, metrics and measures about reviewer activity and ratings. Sounds great, right? But think about it this way – without context, many of these measures are at best mysterious, at worst – virtually meaningless.
The data collected in social media listening is valuable; but the next steps – transformation, exploration and analysis – provide your brand with the most accurate and valuable findings. Social Data Analytics provides the same basic foundation and metrics as social media listening, but takes the analysis much further. By using sentiment analysis and topic modeling as critical components, social data analytics provides layers of context to the rows of data so that restaurants can begin answering the all-important “why?” behind the numbers and trends. Here are a few insights we uncovered in 2017:
An analysis of the Colorado location of a national Italian restaurant showed much lower food ratings and contained words such as “mushy” in reference to pasta. We discovered that the entire chain was following the same prep recipe, without taking into consideration that in many of the stores in the state were at such high altitude that water boiled faster – leading to overcooked pasta.
In an analysis of actual text pertaining to a beverage brand, we discovered that customers were complaining about their soft drinks being ‘flat’ or “watery.” By matching the text to the exact dates and addresses, the analysis enabled our client to pinpoint the stores that were not properly serving their product.
An analysis of grocery chains found that gourmet grocers’ customers in certain parts of the country were “pickier” than in other parts, ultimately bringing down the entire brand score. By overlaying psychographic data to the ratings and then doing in depth topic modeling and analysis of the review text, we were able to see that very minor critiques drove ratings – i.e., a shopper would report that everything was in order, service was good, the location was convenient, but a minor observation (i.e. “my blackberries weren’t sweet enough”) garnered a 3, rather than a 4 or 5, simply due to the type of shopper in that trade area.
Brands taking advantage of Social Data Analytics will continue to find a wealth of answers to important questions. With a thorough understanding of ratings-drivers, operators can begin to take actions to improve ratings, reviews – and ultimately traffic ($$). This blog was written by Fishbowl’s Katharine Dalton, Analyst, Primary Data.
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