Customer Sentiment

How award-winning retailer, Schuh, uses AI analytics to make sense of large volumes of VoC data in seconds

How award-winning retailer, Schuh, uses AI analytics to make sense of large volumes of VoC data in seconds
SentiSum CEO & Customer Service Expert
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How award-winning retailer, Schuh, uses AI analytics to make sense of large volumes of VoC data in seconds

Schuh's one of those companies that lives and breathes customer experience.

They’ve won retailer of the year more times than we can count and are swift in their adoption of new technologies that help ease friction in the buying journey.

Collecting voice of the customer data is a key part of the story of how Schuh stays reactive to their customer.

However, in 2019, Schuh stumbled into a familiar situation:

They had too much customer feedback to actually analyse it all and take action on it.

On top of a significant number of monthly customer service queries, Schuh sits on more than 720,000 customer reviews.

The high volumes of customer voice data needed a significant manual analytics outlay to make sense of, and the resulting insight was often so delayed it was no longer actionable.

The Challenge Title

This led Sean Mckee, the Director of eCommerce and customer experience, to come to us with these challenges:

1/ How can we analyse all our feedback channels without relying on small samples and significant, and possibly biased, manual analytics?

2/ How do it in a timely way so that the issues mentioned don’t continue to impact customers?

3/ How can we prioritise which issues to fix first?

If those questions could be answered, the web optimisation and customer experience management teams would save a lot of time, be responsive to the customer’s voice, and be able to focus on high impact issues first.


With SentiSum, Schuh has been able to turn all their customer conversations and feedback into granular insights without any manual effort.

Sean particularly valued the additional objectivity our AI insights engine provided.

“We've saved hours of manual sorting of free customer text while simultaneously achieving an objectivity which previously eluded us”.

The analytics of customer feedback is completely automated, so the Schuh team never have to worry about subjectively sifting through feedback in the pursuit of reducing customer friction.

Schuh can now trust the insight to inform the roadmap of the web optimisation team.

“We now have a clearer impact "to do" list for the Web Optimisation team. They use the insights to reduce customer friction along the digital buying journey”.

Machine-learning based analytics brings greater detail and granularity, surfacing issues the team weren’t yet aware of and telling them how many customers were facing this issue, too.

For clarity, confidence, and boosting sales, AI-based analytics has been a powerful.

“I now get to quickly "take the temperature" of significant numbers of customers as I review trade. Now I better understand patterns over time which will lead the team to taking action on customer friction. Less friction means more sales”, Sean says.

SentiSum has been Schuh's customer insight analytics tool of choice since 2019.

Review the topic and sentiment of all your feedback/ customer support interactions in one easy-to-use dashboard:

 We have an extensive 30-day free trial where you can see our AI in action on 12 months of your historical data. Learn more here.

Reach out to Sean Mckee on LinkedIn to get his direct feedback on working with SentiSum.

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