Sweaty Betty taps Dynamic Yield technology to target and engage online shoppers more effectively
UK activewear retailer, Sweaty Betty, says it has seen a 62% revenue uplift in six months after using Dynamic Yield’s data driven, artificial intelligence (AI) approach and marketing technology to make better recommendations and online quizzes to get to know the customer better.
The company wanted to improve the website experience and boost conversion rates by gaining valuable insights on key audience segments.
Sweaty Betty needed to improve its ability to accurately target customers at the right time, at the right place, with the right product — no easy feat with such diverse needs across its customer base and the brand’s ambitious international growth.
A retailer best known for clothes that last a lifetime, Sweaty Betty’s products come with higher price points and require more product education than many other sportswear brands.
This can be overwhelming for shoppers, so one of its goals was to simplify and guide the product discovery process with quizzes, informing customers about the types of products to buy and questions they should consider when making a purchase.
Using Dynamic Yield Experience OS, these approaches could be tailored, tested and adjusted with ease, increasing the viability by filtering the most popular products - like leggings and bras - based on the answer profile of each user.
In the UK, Sweaty Betty saw an uplift in revenue after using more personalised recommendations, including a +52% increase in the number of items per transaction and 57% higher order values.
In a trial aimed at measuring the effectiveness of personalised, time sensitive messaging during peak selling periods - notably Black Friday - Sweaty Betty found that pop-ups flagging “limited stock”, or the amount saved if checked-out at that moment, would lead to a 20.4% increase in purchases from new EU customers (compared to a control group with no personalised messaging).
They also led to speedier checkouts and more conversions from site visitors.
Savings were based on each individual’s shopping cart and differed for each user. The pop-up was shown to 95% of those users who added an item to the shopping basket and proceeded to the following two more pages (showing reasonable intent to buy).
The remaining 5% of visitors served as the control group, in order to measure uplift.
Following testing, Sweaty Betty determined that AI powered recommendations drive better conversion rates than manually chosen ones.
So, it used the algorithms in Experience OS to add a product recommendation using contextual information from other users’ behaviour to serve similar browsed products in their results.
Tests showed a 3% uplift in average order value (AOV) in the UK, and 8% rise in the US, after using recommendation widgets displayed on the product details page.
Based on this, Sweaty Betty has since deployed these widgets site wide and significantly reduced the number of team hours spent on manually providing recommendations.
Dynamic Yield’s “affinity mapping” enables it to create audience segments, hone in on potential opportunities and pursue them with precision. Its digital team isn’t huge, so it was important that the solution was scalable and economical.
The project took less than six months to implement and see the rewards. The audience data in Dynamic Yield turned out to be invaluable, given it had previously made assumptions about certain audience segments that turned out to be inaccurate with testing.
A number of other potential revenue avenues are also being tested and explored.
“With Dynamic Yield, we can build amazing experiences with huge agility and speed across digital channels. Since we’re no longer heavily reliant on big technical integrations and merchandising, we can now move faster and can deliver more experiences for our customers,” says Helen Martin, Lead Digital Product Manager at Sweaty Betty.
Sweaty Betty plans to ingest CRM data into Dynamic Yield to build more sophisticated audience segments. It also has plans to expand the personalisation programme and use the same AI technology to help make more recommendations via email.
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