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How to use critical guest experience reviews alongside quantitative data

Guest experience reviews are critical to sales performance, but by nature they are more qualitative, which can make those reviews tough to incorporate into or alongside more quantitative data, such as sales reports. Despite their difficulty, these customer reviews can be a critical piece of information for any marketing leader who wants an unvarnished perspective regarding the guest experience their store or restaurant is offering.

Traditional methods that capture guest experience

  1. Traditional customer satisfaction survey: This could be something you sent via email; offer a 10 percent discount coupon for filling out the survey, for instance. A great example of this is The Home Depot, where a store employee circles the area on the sales receipt as the customer pays and mentions the chance to win a $50 gift card.
  2. Survey services/apps: There are also companies that provide a post-sales survey, like a kiosk with red, yellow and green buttons that customers can hit on the way out to provide some indication of the experience. Maybe it can be as simple setting up a comment box with pencils and paper provided.
  3. Anecdotal feedback: Customers share feedback directly with store managers and/or employees. It’s a great way to understand the experience they’re having, as well as potential pitfalls.

The universal challenge with all of these methods is that it’s tough to capture the perspective and perception of the customer experience in the proper context. If a customer goes to a restaurant that’s clean, has fast and courteous service, but an order gets messed up, that one negative could impact the customer’s perception of the experience.

A new approach

With that challenge in mind, we’ve actually found a lot of value in grabbing online reviews, from Yelp and Google. The ones we typically look for are reviews that are named, where there’s some legitimate citation back to a person, as opposed to an anonymous review. This kind of data requires a different acquisition mechanism than the more traditional methods, but the insights within these comments can reveal much deeper insights.

It’s easy to assume that the majority of these kinds of comments are from haters, trolls or people who just want to vent, and those certainly exist. But if you look at variances over time and your percentage of negative comments rises over time, you wouldn’t expect that to be caused solely by the fact that there’s a perceived higher rate of simply angry people on the platform.

The other way to evaluate these kinds of responses is to look at your comments compared to competitors. For example, let’s say half your store’s reviews are negative and half are positive. If you pull your next competitor and see that 80 percent of their reviews are positive and 20 percent are negative, that tells you that maybe something’s wrong or different with your own guest experience. If someone is striving for excellence in their customer experience, even the people who are just negative for the sake of doing so, should be taken seriously from a data collection standpoint.

Turn comments into data

To take feedback from customers and turn that into measurable data, it’s important to think of and start to bucket the collected comments by sentiment. Positive and negative reviews over time and the percentage of different star ratings. When you do this broadly, it will tell you one thing, but when you start parsing by store location, it starts to get interesting very quickly. Even still, that will only get you so far.

The real value in parsing all that data is in the text of those reviews–with a good analytics team using good software, you can start to parse that text into more categories. For example, a restaurant may have a cleanliness category, a food quality category, and a takeout experience category. The way we define comments for those is by looking for different phrases and sentences inside the reviews.

Then, we run all that categorized feedback through a natural language processing algorithm that tells us the sentiment associated with that data. By leveraging technology for this step, we can determine quantitatively what the cleanliness rating is, as well as discern a sentiment rating of every review we’ve input. That then gives you customer feedback in a format that aligns with your other more naturally quantitative data.

Defining guest experience and why it matters

So, does guest experience really have anything tangible to do with the cash register? Briefly, yes. For example, if you notice that food quality experience sentiment has been declining over time, and you can see that it lines up with declines in sales, that is an indicator that something is going on that needs to be addressed at the store or restaurant.

This can help you over time identify locations that are thriving and providing quality guest experiences. You will also be able to identify locations that may be underperforming, determine the cause(s) and take corrective action. It’s important and necessary to understand how customer experience can effect your sales, and this is one way to turn that feedback into measurable data points.