DAC Blog Authors Why Sentiment Analysis is Key to Effective Reputation Management
Filter By
Healthcare Content Strategy Customer Relationship Management Data Analytics Design Digital Media Local Presence Management News SEM SEO Strategic Insights Web Development COVID-19 Series See all our authors
Digital moves fast.
Subscribe to our monthly newsletter to get ahead of the curve with new articles, videos, white papers, events, and more. Unsubscribe anytime. For more information, see our Privacy Policy.
Why Sentiment Analysis is Key to Effective Reputation Management

Why Sentiment Analysis is Key to Effective Reputation Management

Tuesday, April 02, 2019

It’s no surprise that a business’s review score can affect its success. The most basic understanding is that high scores are good and low scores are bad. Many businesses focus on keeping their star ratings above a certain threshold, which on the surface sounds reasonable. However, blindly following star ratings without understanding why those scores rank the way they do can lead to fixing problems that aren’t there and ignoring issues that you didn’t see.

The challenge of sifting through reviews

Google and other platforms increasingly encourage written reviews, in which users can detail specific experiences and leave useful information for potential customers. What businesses need to capitalize on is that this information—left for consumers by consumers—can also offer valuable business intelligence for the owners themselves. The customer experience is the most important part of your business and what customers are saying to each other is often a lot more sincere than what they would say to the business when asked.

This, however, creates a problem for any large-scale business. Reading the text of a couple dozen reviews is manageable, but how about a few hundred—or maybe even thousands? For brands with 1,000+ locations, there is no reasonable way for a person to analyze the content of reviews at a high level. Managers of individual locations may find it feasible to stay on top of their reviews, but that will only help a single location rather than the company as a whole.

The solution to this problem is to have fewer human eyes manually reading these reviews and allow machine learning to help. Our Sentiment Analysis tool does just that. Using machine intelligence, the tool uncovers the most important keywords, topics of conversation, and general sentiment of your reviews.

How Sentiment Analysis digs deeper

The larger your business, the more crucial a tool like Sentiment Analysis is to better understand your brand’s reviews. On the surface level, there are clear benefits to knowing that your restaurant has positive keywords often come up such as “filling”, “authentic”, and “good service”. It can be troubling, however, if within that same business your negative keywords come up as “expensive” or “unsanitary”. These problems are simple and clear to address—and can be uncovered with just a quick glance at Sentiment Analysis.

The solutions that come from Sentiment Analysis can go far deeper. By comparing the brand as a whole to specific regions or even individual locations, you can spot trends that a star rating alone would not. For example, if your locations in one city or state are faring far poorer than those in another region, the lower general star ratings can give you an indication that it is a geographic trend, but not tell you why.

With Sentiment Analysis, the keywords that appear can tell you the whole story. Words that appear positive in some locations can appear negative in others. If you are a restaurant that sells seafood, your lobster being thought of as positive in the south but negative in the north can show that your brand uniformity isn’t as standard as you’d like it to be. If you see a competitor’s name appearing in the north but not in the south, they are clearly a competitor worth much greater consideration in the north.

Sentiment Analysis can even provide valuable insights on an individual level. Two locations with the same star rating can have totally different sentiments. If a location with 4 stars sees generic terms such as “price”, “value”, or “convenience”, it has an entirely different sentiment as a 4-star location that mentions “service”, “helpful”, or even frequently repeats the names of specific employees. Not all star ratings are made equal. Sometimes the machine learning of Sentiment Analysis is what’s needed to point out the human element of your business that’s having the greatest impact.

Star ratings are only one piece of the reputation management pie and Sentiment Analysis will give you another key piece. Those two elements, combined with the final piece of Pre-Populated Review Responses, will deliver everything your business needs to effectively manage its reputation. Once you partner with DAC to help with your reputation management, you can focus on all the other things you need to grow your business. Contact DAC!

Subscribe to our monthly newsletter to get ahead of the curve.
Get exclusive access to new articles, videos, white papers, events, and more. Unsubscribe anytime. For more information, see our Privacy Policy.