In a recent InternetRetailing webinar, Tripling conversions at Dixons Carphone: supercharging personalisation in three months, we heard from Apoorv Kashyap, known as AK, head of customer advisory and success at Syntasa, Michael Finn, VP, product marketing at Syntasa, and Shawn Zargham, Syntasta co-founder and CTO.
Here’s a bulletpoint overview of the event:
• The webinar started with a poll.
• POLL: how satisfied are you with your personalisation efforts today? Very satisfied: 17% Somewhat satisfied: 17% Not satisfied: 42% No personalisation solution in place yet: 25%
• AK then opened the webinar with overviews of customer intelligence specialist Syntasa, which works with clients including Dixons Carphone, Samsung, Sky, Adidas, and Tesco, and of Dixons Carphone, which has 1,500 stores and 16 websites in eight countries.
How strategic priorities are changing with digital transformation
- Online retail: large untapped growth potential.
- Mobile: growing 100% year-on-year.
- Ease of use: growing tenfold at the same time.
- Product coverage: “Having a lot of products on different websites it makes more sense to provide the right recommendations to users so you have the best chance of providing the right products to the right users.”
- Experience: how to use data and tech to deliver the most relevant experience with the right message at the right time as customer moves from prospect to purchaser and eventually a promoter of the product
How Syntasa delivered recommendations for Dixons Carphone
- Goals and objectives: first initiative with Dixons Carphone was to help them improve the attach rate – selling add-on products – and to improve add to basket rates on two of their 16 websites.
- Project launched three months ahead of Black Friday.
- How it worked from kickoff to build, productionise and activate. Focused on two specific websites.
- Took less than three months, went live well in advance of Black Friday. Dixons Carphone later won an award for best use of AI in ecommerce in 2019 for their personalised recommendations.
- How it happened: how Syntasa combined Adobe Analysis and customer data and built custom AI/machine learning recommenders to offer personalised recommendations before activating on site.
- The customer experience: different approaches for different seasons, including varied approaches to discounting. “The idea was to provide a strong enough platform and a scaffolding that can be used again and again to try [different] recommendations.
- Project overview
- The results: including increases in add-to-basket rates (3x for personalised bundles, 1.3x for basket bundles)
Michael Finn followed with an overview of why changing shopper behaviour has changed the way recommendations happen.
- How and why personalisation is important for retailers.
- Digital transformation as top corporate priority.
- Opportunities in customer experience.
- Which retail leaders perform best in personalisation?
- Delivering algorithmic personalisation: 1. data 2. Build AI/ML models to fit the situation 3. Activate across channels so “you don’t get your customers getting one offer on a website, another on email and another on an ad.” 4. The importance of private cloud
- Self-service digital applications for digital teams
How Syntasa works in practice
Shawn Zargham explained how Syntasa uses data to improve the journey, context and intent, starting from the home page
- Examples of how products can be personalised – and why it’s important. “If you don’t know what their intent is it’s hard to decide how to interact.”
- Encouraging brand loyalty is important for consumer-facing brands, and personalisation can help boost that. Increasing basket sizes also critical
- Existing personalisation solutions: how they typically work now – and how they are developing.
- Real world complications: “in a real world environment you have to be considering the product releases, house brands, seasonal activities etc.”
- Building a framework for designed experiences, connecting to algorithms and more.
The webinar closed with a summary of key learnings and a Q&A