Ometria Labs: Manière De Voir
Fashion brand boosts its newsletter revenue by 40% using AI-based predictive segmentation by Ometria
At Ometria, we know our most significant product milestones have been achieved by closely collaborating with our amazing clients. Ometria Labs is our innovation stream, where we test out new and innovative ideas for potential features that solve retail marketers’ biggest day-to-day challenges, and share the results with you.
Manière De Voir (MDV) is a fast-growing sports luxe fashion brand, based in Manchester.
Treating all its customers as an extension of the MDV family, the retailer wanted to move away from a one-size-fits-all approach to its newsletters, making them more relevant to each recipient’s tastes and interests.
MDV’s marketing team wanted to personalise the contents of each newsletter depending on factors such as category and style affinity, and whether the recipient is interested in mens- or womenswear.
But determining customer tastes and interests relies on making sense of the complex interactions between multiple data points, and with a small marketing team, the prospect of manually carrying out the extensive customer segmentation needed to personalise its newsletters prevented the retailer from moving forward with its strategy.
So MDV called upon Ometria’s Predictive Segmentation functionality to build customer audiences that the marketing team could use to personalise its newsletters.
Using Ometria’s predictive segmentation engine, a series of mutually exclusive customer segments were created combining gender and category preference (for example, ‘womenswear preference and shorts’) for MDV’s regular ‘new in’ email.
Instead of using manual segmentation, the audience for each of these segments was automatically created, using machine learning to determine each individual’s affinity towards certain categories.
The algorithm takes into account factors such as:
- The individual’s purchase and browsing behaviour.
- Relationships between similar people and products.
- Temporal correlations, based on the buying cycle of each product.
- Reinforcement learning, based on whether a shopper responds to machine-generated predictions in the expected way.
Emails with personalised content and subject lines, tailored to the category affinity of the recipient, were sent to each segment.
As well as saving MDV’s marketing team a significant amount of time by eliminating manual work from the segmentation process, the use of Predictive Segmentation resulted in a:
- 40% uplift in revenue per email sent
- 24% uplift in open rate
- 43% uplift in click rate to open rate