{"id":107304,"date":"2019-04-02T20:00:00","date_gmt":"2019-04-03T01:00:00","guid":{"rendered":"https:\/\/www.dacgroup.com\/why-sentiment-analysis-is-key-to-effective-reputation-management-2\/"},"modified":"2024-12-20T09:22:50","modified_gmt":"2024-12-20T14:22:50","slug":"why-sentiment-analysis-is-key-to-effective-reputation-management-2","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-gb\/insights\/blog\/local-search\/why-sentiment-analysis-is-key-to-effective-reputation-management-2\/","title":{"rendered":"Why Sentiment Analysis is Key to Effective Reputation Management"},"content":{"rendered":"

It\u2019s no surprise that a business\u2019s 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\u2019t there and ignoring issues that you didn\u2019t see.<\/p>\n

The challenge of sifting through reviews<\/h2>\n

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 capitalise on is that this information\u2014left for consumers by consumers\u2014can 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.<\/p>\n

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\u2014or maybe even thousands? For brands with 1,000+ locations, there is no reasonable way for a person to analyse 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.<\/p>\n

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.<\/p>\n

How Sentiment Analysis digs deeper<\/h2>\n

The larger your business, the more crucial a tool like Sentiment Analysis is to better understand your brand\u2019s reviews. On the surface level, there are clear benefits to knowing that your restaurant has positive keywords often come up such as \u201cfilling\u201d, \u201cauthentic\u201d, and \u201cgood service\u201d. It can be troubling, however, if within that same business your negative keywords come up as \u201cexpensive\u201d or \u201cunsanitary\u201d. These problems are simple and clear to address\u2014and can be uncovered with just a quick glance at Sentiment Analysis.<\/p>\n

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.<\/p>\n

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