How to Triple Conversions 3x with Syntasa [Webinar]

by Michael Finn

Today’s consumers are hungry for personalized experiences — and brands are eager to deliver. According to Accenture, 91% of consumers are more likely to shop with companies that provide relevant recommendations, while 89% of digital businesses are actively investing in personalization, according to Forrester.

It’s clear that personalization is a top priority for digital professionals. But are you tackling this goal in the most effective way? Personalization isn’t what it used to be. Now it’s about using your first-party behavioral data to build your own personalized recommendations.

We recently hosted a webinar to share how leading consumer electronics and mobile electronics retailer Dixons Carphone used Syntasa to implement data-driven personalized recommendations — increasing conversions 3x in 3 months.

Watch the full webinar here, or read on for the highlights.

The Results

Dixons’ two primary goals were to a) improve attach rates for online purchases and b) increase add-to-basket rates using data-driven product recommendations. Previously, Dixons had used manual product recommendations, based on suggested product bundles created by members of their online merchandising team.

By using Syntasa, Dixons could now provide algorithmically personalized recommendations that took into account recent related purchases for all consumers, as well as individual behavioral data (e.g., context, brand, and price affinity). The results included:

  • 3x increase in add-to-basket rates
  • +50% growth in bundle coverage (the share of product views on the website where a recommended bundle was displayed)
  • +5% units per order when any personalized bundle type added to basket

How Syntasa Works

There are four key capabilities any company needs to deliver algorithmic personalization to customers at scale:

  1. Data: The ability to unify your first-party behavioral data and enterprise data at the individual level is crucial. This can be difficult since data typically lives in different silos and can be massive in volume.
  2. AI/ML models: Building custom AI/ML models will help you achieve your specific personalization goals. This is where combining your data really pays off you — training your algorithms with your company’s data means they’ll perform better than off-the-shelf black box models from personalization vendors.
  3. Activation: You need the ability to activate these recommendations across all your channels.
  4. Private cloud: Behavioral data (which is key to personalization) is difficult to process with legacy enterprise technologies. The massive volumes and complexity of this type of data requires the use of cloud services. And the more customer data you include, the more important it becomes to keep that data private and secure – your best bet is to use your own virtual private cloud.

Historically, these capabilities have proved prohibitively costly and time-consuming for most companies. Syntasa’s self-service web application enables digital teams to implement cost-effective (and just plain effective) personalization strategies in less time.

In just three months, Dixons used Syntasa to build a data pipeline to ingest behavioral data from Adobe Analytics, combine with other customer data, and stitch together this data using our ID graph. Dixons used Syntasa to create AI/ML recommendations and activated these across their channels to provide customers with algorithmically personalized product recommendations.

For more on how Syntasa can help your team, watch the full webinar here.


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Tags:  Personalization

Michael Finn

VP of Product Marketing at Syntasa

Mike is VP of Product Marketing at Syntasa (a Marketing AI Platform loved by Marketers, Data Scientists, and Data Engineers for its ability to unlock real value from their enterprise and clickstream data).

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