DenmarkRetail and e-commerce2022 – ongoingPandora
Personalized product recommendations in marketing emails
Pandora's personalized emails lifted click-to-open rates by 50% after it moved its customer data to one platform
Problem
Pandora's customer profiles, online and offline orders and web activity sat in a legacy system that was hard to scale. Marketers had no single view of consumers to target campaigns with.
Approach
Pandora consolidated its customer data on the Databricks lakehouse. Marketers use a Master Consumer View that groups consumers into 11 segments. Recommendation models, which the page describes as AI and machine learning, feed personalized product suggestions into the emails. Marketers also run their own SQL queries and Power BI reports on the same platform.
Results
Takeaway
Pandora's email gains came after customer data was brought together and consumers were grouped into segments. The company did not say how it separated the effect of personalization from other changes.
Categories
- Size
- Large enterprise
- Industry
- Retail and e-commerce
- Business function
- Marketing and sales
- Innovation type
- Process innovation
- AI technology
- Recommendation and personalization
- Adoption
- Off-the-shelf tool or platform
- Evidence
- Vendor case study
Sources
Source summaries
Databricks describes how jewelry retailer Pandora moved customer, order and web data onto its lakehouse and used it for personalized emails and consumer segmentation. The page reports a 50% rise in click-to-open rate across 65 million emails a year, 80% growth in email marketing and DKK 255 million in quarterly revenue driven by personalization. It also reports that data now moves from source to endpoint in hours rather than more than a day.
Full details are in the original sources.
