2018 Year End Overview of Business Reviews

Connectivity has long been the leader in local data having aggregated and monitored data throughout the web for almost a decade and a half.  We have nearly a billion reviews that we have not only discovered but gone back to check many times over to verify any catch any updates.  The reviews and other content have been provided in Reputation Monitoring tools, APIs, and several other products both directly sold and through partners.

We decided to review some of the data that we have to provide you with information on these reviews including information on reviewers, how they are rating businesses, what they are reviewing at the business, and what words they are using to describe those businesses and their products & services.

We would like to encourage readers to review this data and provide us with feedback in terms of what data they would like to see going forward (i.e. data by vertical, an annual review of the year’s data, listings information, etc).  Please provide your comments, questions, or requests in the comments below.

As always, please reach out to us in the comments, via email, or through our website if you would like Connectivity to help you with your business.

What we found:

  1. This data is based on all reviews across all US business review sites.
  2. Connectivity looked at the number of stars each review was given by the reviewers. There are more 5 star reviews than all the others combined.
  3. Connectivity looked at the date the reviews were published. Users are slightly more likely to post a review Monday through Wednesday and slightly less likely to post a review on Sundays.
  4. Our friends at Identity Labs helped us evaluate user accounts and identified gender based on an algorithm that factored in knowledge of the account, name of the users, content of what they wrote, and Computer Vision/AI analysis of user profile photos. Reviewers are a bit more likely to be a female.
  5. Connectivity processed the reviews utilizing Natural Language Processing to identify what reviewers were talking about and how they described them. This is interesting because we often see a 4-star review of a restaurant where users talk about excellent food and poor service. Another example is a doctor or a dentist that get a 1 or 2-star rating but the reviewer only complains about waiting in the waiting room for too long. The NLP gives us far more insight than the star rating alone. No surprise here that there are lots of reviews discussing restaurant/food-related information since the number of restaurants/food trucks/etc is high and they are the most frequently reviewed category. More insights on this by vertical may be coming in the future.

So do you have anything of interest you would like to research? Please let us know in the comments.

Remember, Connectivity is available to partner or provide data or monitoring services. Please contact us if you are interested!


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