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Showroomprivé: Putting ML-Powered Targeting in the Hands of Marketers
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Predictive Quality Analytics
Services
- Data Science Services
The Challenge
Showroomprivé, an e-commerce retailer specialized in flash sales, faced challenges in targeting their marketing emails. Until 2016, the team selected the target audience for these marketing emails manually based on what they know about the brand. However, this approach presented several challenges. Brands often have overlapping or broad audiences, which meant touching some prospective buyers multiple times, while others not at all. This also meant casting out a wide net, potentially sending emails to people who were not interested in that particular brand. The ultimate goals of the project was for the marketing team to be completely autonomous in targeting and sending these emails.
About The Customer
Showroomprivé is an e-commerce retailer specialized in flash sales. The company has approximately 950 employees and is present in 7 countries across Europe. The company has a data team of 16 members across 4 data teams. Showroomprivé has been at the cutting-edge of leveraging data for improvements both in the product and in customer service as well as on the operational and business side. The company has been working from the ground up to develop its capacity to use data for these improvements.
The Solution
Showroomprivé leveraged Dataiku to develop a machine learning-based targeting system for their marketing campaigns. The system, known as Targetor, allows marketers to generate their own machine learning-powered targeting recommendations. The company went through three iterations of Targetor to perfect the system and add additional features over the course of four years. The marketing and data teams were even able to make improvements for a second iteration of Targetor that provided a ranking of users instead of just a homogenous group, allowing marketers to better prioritize their campaigns. After about a year and a half of use by the marketing team, the data team at Showroomprivé was adding so much functionality and so many features that the model was becoming complex. They decided to undergo a project that would streamline the model, allowing them to continue to scale and add functionality for many years to come.
Operational Impact
Quantitative Benefit
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