How Footasylum used AI to drive conversion

The Retail Hive was pleased to have James McGhee, Head of Customer Experience at Footasylum, join us for our 2019 Customer Experience & Engagement meeting.

James is an advisory board member and participated as a storyteller on the CX and Customer Journey Mapping table. He spoke with our Editor, Hannah Dolan, to discuss how Footasylum successfully incorporate AI into their business model.

James, please tell us about Footasylum’s journey to implement AI to enhance customer experience

At Footasylum we’ve always prided ourselves on being hyper-personal. Our buying team spend a huge amount of time in and around our stores, looking at what customers are wearing and using those findings to provide our consumers with what they wanted at a localised store level.

However, we wanted to get to a position where we’re sending out almost 1:1 personalised marketing – based on what we know about our customers, not just culture and local trends.

Our intention as a business is to stay relevant to our customers, so our digital marketing approach is to produce lower volume, higher quality content. We knew it was necessary to grow our eCommerce business. We’d had success in growing the bricks and mortar business and wanted to translate that within the eCommerce environment.

We don’t sell single pieces and we’re not a designer brand, so we wanted to make sure we’re putting the right products in the right stores to grow as a business. That’s always been a key principle for us.

In order to grow our eCommerce operations, we had to understand customer behaviours and tie the habits of our online traffic with our store purchases.

Recognising trends and segmenting our customers is key. It’s this understanding that enables us to push better marketing to the correct people at relevant times of the year allowing for massive returns on investment.

How did you go about measuring the results?

Firstly, we used our old marketing strategies as a test space. Additionally, we used A/B testing to small groups of people to see initial conversion rates.

The more we experiment, the more we increase the base of people we can trial the personalised marketing on.

It’s about having that intention to hyper-personalise, and then understanding how you can achieve that through data-led decisions.

The AI needed a lot of data to learn from, so our loyalty scheme has been really useful for data capture. Even though we are pushing already popular products, we’re able to increase the frequency of purchases.

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