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In the ever-evolving landscape of online retail, Marks & Spencer (M&S) found themselves facing a crucial challenge: lagging behind industry leaders in online revenue generation. Enter a new mission team, assembled to breathe new life into M&S's digital strategy. In this case study, I delve into how strategic design, fuelled by research, behavioural science, technology, and data, sparked a revolution in M&S's recommendations approach, propelling them to newfound heights of revenue growth and customer engagement.
M&S faced a dual challenge: low online revenue from customer recommendations and the growing dominance of online shopping. This underscored the urgent need for personalised, engaging digital experiences and relevant product recommendations tailored to each customer's preferences.
Despite the substantial potential of recommendations, M&S was only generating £60 million in revenue annually, a mere 3% compared to Amazon's impressive 35%.
Usage of recommendations was limited and fragmented across channels, highlighting untapped potential.
Remarkably, customers spending additional time interacting with recommendations yielded an extra £2.21 to £11.02 per order, showcasing the significant impact of effective recommendation strategies.
It was important to put the right product in front of the right customer at the right time and M&S was missing out on the opportunity to show our customers more products they might love. Seeing this gap in the customer experience and rapid growth in number of customers choosing to complete their shopping online, the Phoenix Recommendations team was born.
The team's process involves generating ideas through innovation & behavioural science sessions, prioritising them based on complexity and value, and testing them through UX design prototypes and A/B tests. With the aim to run at least 15 tests per quarter, iterating based on real-time customer feedback and data insights.
Leveraging behavioural design principles, we delved into understanding user problems and motivations. Drawing from insights gleaned from behavioural science, psychology, cognitive science, and neuroscience, we aimed to predict user behaviours and decision-making processes.
Our focus was on ensuring that M&S presented the most relevant products to each customer, addressing missed opportunities in the customer experience and capitalising on the growing trend of online shopping.
We adopted behavioural science in recommendations by taking a gradual and iterative approach which required consistent learning, experimentation, and adaptation. It began with understanding our user’s behaviour, followed by the application of those insights in our design process, and finally, validating our design decisions through user testing and feedback.
"How can we enhance the product carousels in order to drive engagement?"
I would like to see big & clear product images which will enable me to see more details and be confident to buy straight awayI want to see all the relevant information clearly
Update recently viewed carousels which can incorporate quick ATB buttons.
Carousels that could work on Home Page, DLP, PLP, PDP, Basket.
Deliver consistency through an updated suite of product carousels to be used across page types and channels
Customers feel more confident to ATB from the new enlarged product cards as it allows them to view items more clearly
Interaction rate once seen 10%
Revenue Per Visit increased by 2.1%
Recommendations Add-to-Basket (ATB) rate improved by 6.1%
Estimated annualised incremental revenue benefit amounted to £3.15 million
As a customer
I want to pick my size easily, especially when there are multiple options
So that it’s clear what size is in stock and that I’ve added the right size
TEST RESULT
MOBILE
Click-through rate: 15.8% Add to Bag rate: 36.5%
DESKTOP
Click through rate 11.9% Add to bag rate 34.5%
REVENUE
Acknowledgement across desktop and tablet
Improve the ‘dead-end’ journeys where the size is out of stock By providing similar recommendations filtered by their size we make it easy for our customers to continue their shopping journey.
Improve conversion by providing visually similar recommendations which are available in their size.
Make it easy to discover relevant products in the offer
How might we give our customers inspirational shopping experience
The Phoenix team is focused on two main objectives for the future: making recommendations in real-time and using recommendations to inspire customers. Additionally, plans are underway to implement real-time recommendations in collaboration with the M&S Decisioning teams.Conclusion: The Phoenix Recommendations team's innovative approach to personalised recommendations has significantly impacted M&S's online revenue and customer engagement. Through a combination of data-driven strategies, experimentation, and cross-functional collaboration, they continue to drive innovation and set new standards for personalised shopping experiences in the retail industry.
The Phoenix Recommendations team's innovative approach to personalised recommendations has significantly impacted M&S's online revenue and customer engagement. Through a combination of data-driven strategies, experimentation, and cross-functional collaboration, they continue to drive innovation and set new standards for personalised shopping experiences in the retail industry.
Since its inception, the Phoenix team has served over 1.1 billion product recommendations carousels to customers, nearly doubling the total online revenue from recommendations within 18 months.
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