Online reviews & AI: how to get insights into trends and create professional responses?

by | Jun 20, 2024 | Business, Features, Technology | 0 comments

Use AI to understand online reviews

In today’s digital age, online reviews are not just feedback but a powerful tool that you can leverage to understand customer sentiment and improve services. As you accumulate thousands of reviews across various platforms, manually analysing them becomes an unfeasible task.

This is where Artificial Intelligence (AI) steps in to transform raw data into actionable insights and professional responses, enabling businesses to stay ahead of trends and maintain positive customer relationships. But how do you get started? What analysis can you do? How do you handle online reviews?

In this blog, I’ll answer those 3 questions and will help you get started yourself by giving live samples and prompts! Next week, we’ll cover how to respond on reviews to improve customer relationships.

Getting started: How do you get all your online reviews in an easy export?

So let’s assume you have gathered a few thousand reviews over the years on your Google profile. Analysing or copy/pasting them manually would take ages, so how can you download them in CSV or similar format? One option is to go to and request an export of your Google Business Profile. Via Google Takeout, you can also download a lot of other information but all we need in this case are your reviews.

After creating the export, just look up the “Reviews” file. It’s in JSON format, which is not understandable if you’re not a developer, but that doesn’t matter—we’ve got AI to guide us through the process, don’t we?

Of course, you can export reviews from other platforms like Facebook, Amazon and Yelp to include in your analysis. Our dataset for this blog includes 2.744 reviews for 1 garden centre, and the oldest one is from 2017. That should be sufficient to work with!

Sentiment analysis and trends

Since all reviews have dates and times, we can do a sentiment analysis and ask Chat GPT to highlight recent trends, positive or negative. It would be good to understand if ratings go up and down, so let’s ask for a graph with the average rating per quarter, starting in Q1, 2020, so we ask a clear question to Open AI:

– Can you make a graph with the average rating per quarter, starting in q1 2020?

The output is a nice graph showing the trend of all reviews from Q1, 2022, till Q2, 2024:

Graph showing online reviews
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You can see the reviews are pretty stable, around 4.4 stars, but there’s a decline in ratings in the 3rd quarter of 2023, which, of course, makes us wonder what happened. But before we zoom in on this period, we have to see if the number of reviews during that period is in line with the average over the years. Otherwise, we would draw conclusions based on just a few negative reviews.

The numbers:

· Average per quarter from 2020 & onwards: 94,3

· Reviews in Q3, 2023: 58

So, the number of reviews in that specific quarter is pretty low compared to the average. If we ask Open AI to give us the negative ones and to summarise the complaints:

· 1-star (September 26, 2023): Beautiful flowers but very unfriendly at the counter.

· 2-star (September 24, 2023): Ordered pancakes at 2:56 PM, and despite an initial delay, service was slow.

· 2-star (September 14, 2023): Refused to replace a dripping bouquet at the cashier.

· 2-star (August 24, 2023): No comment provided.

So, 2 of the complaints originate from bad customer service – perhaps something to improve with some training via the HTA or GCA? We can conclude that the drop in reviews is caused by only 4 customers so it’s not a trend to worry about.

Segment issues per product group or area

Three negative reviews having an impact on the overall rating in Q3, 2023 were related to bad customer service, and 1 hasn’t had any context. But how do customers rate other departments and areas of the garden centre? For this store, we identified the following main areas:

– Café

– Christmas

– Flowers

– General

– Plants

We can ask our Chat GPT assistant to make a breakdown of reviews in these 5 categories. To make sure AI doesn’t make the wrong assumptions, I usually add lines like this to the call I make:

– If it’s not clear if the review relates to one of these 5 areas, flag it as “General”

– If a review is about 2 or more areas, flag it as “General”

If you don’t do that, an AI tool might follow its own path and give you false information. It’s important to give strict guidance and set your expectations. The outcome based on the 500 most recent reviews:

– Café: Average rating of 4.31 based on 13 reviews.

– Christmas: Average rating of 4.00 based on 14 reviews.

– Flowers: Average rating of 4.00 based on 6 reviews.

– General: Average rating of 4.39 based on 345 reviews.

– Plants: Average rating of 4.27 based on 15 reviews.

The difference between Christmas and the rest of the reviews would mean there’s room for improvement in the Christmas department. We can ask to analyse the Christmas reviews to get an idea of what to improve.

Actionable feedback on online reviews

Data and stats are interesting, but it’s better to figure out what actionable feedback your customers have given in their reviews. This query will result in a list of things you can discuss with your team since it’s a practical list with improvements for the Christmas department:

– Lack of variety and stock of Christmas items

– Customer service too busy during weekends

– Suggestion to have more events and workshops

Through the interaction with AI, we don’t have to read 14 online reviews for the Christmas area manually; we just get a summary of what to improve.

Of course, we can do that for the entire centre and all reviews as well, so we asked to summarise the last 100 1—and 2-star reviews and give actionable feedback.

The outcome:

– There are repeated mentions of unhelpful or unfriendly staff. Prioritise training to enhance customer interaction and ensure staff are approachable and knowledgeable.

– Some customers have complained about limited variety and perceived stagnation in product offerings. Regularly refreshing the stock and ensuring high-quality products could address these concerns.

– Issues with pricing, including overcharging, have been noted. Review pricing strategies and ensure pricing transparency to build trust and avoid customer dissatisfaction.

– Comments about facility-related issues such as cleanliness and organisation have surfaced. Improving the upkeep and presentation of the store could enhance the overall shopping experience.

– There are complaints related to specific operational aspects like beverage temperature at the cafe. Paying attention to these small details can significantly impact customer satisfaction.

The owner of this store can use this to discuss improvements with his team. The customer is always right (even if he isn’t), so the feedback is very valuable. And by using Chat GPT to analyse all data, we don’t have to spend hours on making summaries and report.

Who doesn’t want to shop at a store with helpful & knowledgeable staff, a wide variety of products with correct pricing, in a clean environment and getting served a hot coffee or cold coke?

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