Emotion Analysis case study

The power to create buyer personas based on received data

Emotion Analysis

Driver CTR


Rider CTR


Rider Engagement


We will share another success story from one of our clients, company N. This company deals in creating navigators for all methods of transportation. Their main focus is to generate the greatest available detail, accuracy, and real-time updates for their customers. The company aims to provide the best available navigation systems. In an attempt to research on what more they could be doing for their clients, company N approached Wonderflow.

Before they started working with Wonderflow’s all-in-one solution, they handled their customer feedback analysis by collecting specific data from a few sources and analyze them manually. Although it gave certain insights, this wasn’t enough to meet their goals of truly understanding their customer. After a pilot program, where Wonderflow analyzed and reported on a set amount of reviews, the results were satisfying to company N.

However, that was just the beginning. In the case of the Wonderboard, the more you use it, the better it gets. The AI behind the data collection and analysis improves with each use, providing better reports and insights, in terms of quantity, quality, and accuracy. Before long, Wonderflow was able to collect, analyze and report on more than 112,000 reviews for 41 products, from 11 channels, in 6 different languages.

How it works

With all this data, the analysis generated crucial insights for company N. The most significant change in the insights was that Wonderflow segmented the reviewers based on which vehicle they were using. Company N already knew that their navigator users were split into two main categories: drivers (cars) and riders (motorcycles). The reviewers, though, did not identify themselves as either by themselves.

Through the Wonderboard’s Emotion Analysis, we were able to segment the reviewers, based on how they felt when reviewing the navigators. For example:

What I liked most was that it could plan interesting routes for me to go out for a joyride. It meant that I got to discover new places and new ways to go that were great fun. Hands down this is my favorite part.

The above would be a review by a rider. More than 75% of riders-reviewers used words like “joyride, interesting, discover, tour”, which described a sense of adventure. It is safe to assume then that riders were interested in using their motorbikes for more than commuting. What is more, riders were analyzed to have long-winded reviews (average length of 280 words), since they are eager to share their emotions and a sense of adventure. Finally, riders used strong, emotional adjectives, like “significantly, excellent, fantastic”. Riders were proven to be more romantic and sensitive in their reviews. Most of them were eager to use the navigator in order to take on adventures of all kinds with their bikes. Traveling while going through beautiful scenery or taking an interesting route was more important for them than how long the ride would take.

On the other hand, we have this review:

I now use my N daily on my commute to work just in case there are road works or an accident I can divert.

You can already guess that belongs to a driver. More than 82% of the driver-reviewers used words like “business miles, guide, road closures, plan a route”. These words are mainly used to describe day-to-day processes (i.e. daily commute). Drivers were found to be more pragmatic and concise than the riders. Their reviews had an average of 200 words, which is a significant difference from the riders’ average review length. Finally, driver-reviewers used unemotional phrases and neutral adjectives, like “simple to use, accurate directions” and “it works ok”. With that being said, the main aspects that they cared about were the ease of use and the best way to commute.

How to apply this analysis for MARCOM?

Having this kind of information and acting upon it, in a way that will be beneficial are two different things. The Wonderboard prompted through actionable insights that those words can be used in Marketing Communications (MARCOM). That way, the wording from the analysis can increase CTR (click-through rate) and Engagement. Here’s how: before company N puts money behind campaigns and advertising, they test and experiment.

“You can always have an opinion about a headline and visual, but if you haven’t tested it with your audience, you don’t know how it will be perceived. In order to test you need different variations and here the Wonderflow analysis gives our copywriter great input by recommending which words and tone of voice to use.”, said Sophie, N’s Head of Marketing.

A proof of concept, however, it is always necessary. For this reason, they created three facebook ad variations for both groups: same visual, but different messaging. One of them was the copy they used in previous campaigns, and the others two were based on the wording from the analysis. The outcome proved their assumption: the engagement with the ads was significantly higher for the new text. More specifically, Click Through Rate increased by 26% for drivers and 67% for riders.

The names in this case are anonymized.


Overall, the analysis done by Wonderflow helped company N to dive deeper into their customers’ needs. They were able to identify their customers’ personas enabling them to provide products that will be more accurate to their needs.

After a new marketing campaign with different messages targeting both riders and drivers, company N was able to present some impressive results. More specifically:

Driver Results:

  • +26% CTR
  • +79% Engagement

Rider Results:

  • +67% CTR
    +143% Engagement

Get in touch

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