Read: The Retail Hive Digital Boardroom: Using AI and Machine Learning

AI, Machine Learning, Robotics

How can retailers utilise Artificial Intelligence (AI) and Machine Learning (ML) to improve online performance, without diluting brand identity and customer experience? This was the main thread of discussion during our Digital Boardroom, in association with Apptus that took place at the end of last year.

The Retail Hive invited an intimate group of members to join a secure forum where they could talk openly about their experiences with AI and ML.  The group comprised of those who had already successfully integrated AI into their online operations, as well as those at the very beginning of their journey. The discussion, which examined two major trends in the use of AI and machine learning within retail (1. The application of technologies offsite; and 2. applications onsite to personalise and enhance customer journeys) brought some interesting points to the table from a wide range of perspectives and helped to overcome some scepticism toward the application of these technologies, whilst simultaneously unearthing top tips for overcoming the challenges many participants were facing.

Here are some of the highlights:

  • Retailers need to think about why a product is being recommended rather than simply accepting “that’s what the algo said” – an answer that will simply not be acknowledged when communicated with the wider business. Read the full report for how to do this!
  • When it comes to relinquishing control of merchandising to a machine, some retailers have real concerns. There are many variations in machine learning types, from fully supervised to fully unsupervised. Major issues can arise if algorithms are set off and left to learn and make decisions without the intervention of human intelligence; especially if the machine is making decisions based on poor site performance.
  • Test, test, test! Retailers should look at their site holistically and set an overall objective from the start, and then test different recommendation engines against one another.

We are delighted to publish the full post-meeting report, which is available to download here

A huge thanks to our valuable partner, Apptus and to our Retail Hive members for their candid contributions.

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