#profitect

While there are other solutions that can address this problem for retailers, few of them can accurately capture true intent the way sentiment analysis does. Understanding intent is critical to understanding the true meaning behind a comment and getting accurate insights.

For example, consider these two fictional comments about a handbag:

“I bought this cheap purse.”

“I bought this purse cheap.”

Notice that despite having the exact same words, the order in which the words appear changes the meaning. #1 has a negative intent, while #2 is more positive, highlighting a great deal. Having an effective way to manage and understand reviews and their meaning helps retailers collect accurate insights into consumers’ shopping experiences or product issues.

A second benefit of leveraging a sentiment analysis solution is that it provides an effective and efficient way for retailers to act on this type of data. Instead of compiling all of the data from reviews into mind-numbing spreadsheets (which often take hours to digest) — or even worse, scouring online reviews every morning — prescriptive analytics provide store associates or brand owners with prescribed actions that are based on the data sets collected.

For a real-world client example, a retailer introduced a new line of warmer-weather clothes last fall in anticipation of a cold, dreary winter season. After a few days, there was a significant uptick in negative reviews around one of the jackets in the new line. Consumers were complaining about how the material was not holding up well after washing and drying, and were showcasing why other consumers should not buy the product. The prescriptive action from sentiment across all of the reviews brought this detail to the retailer’s attention quickly, highlighting that the jacket material needed to be re-evaluated. This insight allowed the retailer to react quickly and add a “Dry Clean Only” tag to the remaining product, which solved the issue and saved sales for that particular jacket.

Prescriptive analytics and sentiment analysis solutions are not only helping retailers save time by combing through massive amounts of data and reviews, but also by highlighting only the reviews that truly need addressing: price, cleanliness, satisfaction or profiling. When you combine all the information you need — the unstructured data, the scores and intents assigned to it by sentiment analysis, and the corresponding KPIs (i.e. sales reports, return rates, merchandise quality, damages, etc.) — you have everything you need in order to drive operational efficiency and improve the customer experience.