Recommendations AI helps IKEA Retail adapt to customer needs

IKEA Retail (Ingka Group) has increased global average order value for eCommerce by 2% with Recommendations AI

The pandemic brought a lot of new challenges and one of these was that it altered customer behaviour and needs as well.

Companies had to make decisions on how to move forwards, changing ways of working and still being able to deliver high-quality customer service. At IKEA they deemed it necessary to take a more scientific approach to handle the operational complexities of delivering product recommendations at scale, improving their level of personalisation, and having a holistic understanding of our customers.

Albert Bertlisson, Head of Engineering - Edge at IKEA Retail, explained how the first step was to improve their ability to get high-quality quantitative information to understand how their ‘recommendation’ solutions affected personalisation. 

They did this through high volume A/B testing on customer behaviour and after initial experimentation, they had a few key learnings:  

  1. The mix of both UX and algorithms are really important for a cohesive customer experience. 
  2. The quality of personalisation can’t be measured in silos. Statistical significance can be attained by testing several groups of recommendations at once.

 

Data-driven decisions

 

Their teams turned to Recommendations AI, a google product to help improve and deliver what they wanted. “While too much fine-tuning and customisation could lead to subpar performance, in general we found that it was a great strategy to give us several versions of ML powered recommendations to work with. The further you personalise the experience, the more options you have to likely pick the best one for the customer,” said Bertlisson. 

Recommendations AI models like  ‘Recommended for you’, ‘Frequently Bought Together’ and ‘Others you may like’; are coupled with business goals like optimising for conversion rate, click-through rate and revenue. They experimented with many different model combinations and custom rules. One of the simplest custom configurations they used was to only recommend items that were in stock, and when items were out of stock they looked at similar items that were available to augment the experience. 

Our collaboration with Google Cloud accelerated our learning process during experimentation. We worked closely together early in the product development. Additionally, their model provided flexibility to change direction and allow for more options than we had previously. Ultimately, this provided us a way to drastically improve our time to market with a product that produced tremendous results that we could not have accomplished on our own.” added Bertlisson. 

 

Did AI help improve customer experience?

 

IKEA found that with more personalised and real-time recommendations available they saw great success. They were able to increase the number of relevant recommendations displayed on a page by +400%. 

Recommendations AI algorithms helped customers in two ways: 

  1. Customers were able to find products that they liked quickly and establish their preferred choice among other options more quickly as well, giving them confidence to make a purchase through much fewer clicks. Even though we previously already had well tuned recommendations of several types, with Recommendations AI we measured +30% improvement in click through rates. 
  2. Average order value saw a +2% surge with numerous examples of how Recommendations AI could help customers find both attractive and directly complementary products, expanding the customer purchase from a single product to an entire home furnishing solution.

In the future, we see opportunities of improving the customer journey through a more visual experience that inspires the customer rather than relying on customers to use their imagination to visualise groups of products together. Vision Product Search provides that and is something we’re looking into deploying next,” concluded Bertlisson.

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