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Friday, October 30, 2015

Example Use of Watson for Social Benchmarking

Always looking for good, simple examples of the use of cognitive methods, and thus also Watson. Just recently connected with Eric Santos, of Benchmark Intelligence,  and he writes how they use Watson for their social intelligence bench marking and trending. A good example of what can be done.

" ... Benchmark is a product suite that helps retail chains understand why certain locations perform better than others. Benchmark discovers the factors (customer service, product quality, cleanliness, etc) that affect unit performance. Benchmark Intelligence is a proud IBM Watson ecosystem partner. 

Currently Benchmark collects its data through various ways which includes social media listening, SMS comments, surveys and field audits. A good portion of this data is qualitative and unstructured. We needed a way to run analysis on this data and identify trends, that’s why we turned to IBM Watson. 

Benchmark is leveraging Watson’s Alchemy languages, specifically their sentiment analysis and keyword extraction technologies. We are using these cognitive technologies to analyze this unstructured data and discover the variables (customer service, cleanliness, etc.) that affect performance at each location. 

Watson looks at thousands of open-ended data points (social media reviews, SMS comments, etc.) on our platform for any given chain. For each data point Watson defines whether the statement as a whole is positive, negative or neutral. Watson also identifies the key words that make up the statement. That way as locations gather more data points, we can identify the trends that are going on at each location in the chain. 

Example use cases of this include knowing that customers complained about cockroaches at a specific location 5 times in one week and customers at another location in the same chain complained about a cashier named Bryan 4 times in one week.

Once Benchmark understand what these trends are, we can surface actionable insights that retail chains can use to improve the performance across their portfolio of locations.   .... " 

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