Adore Me Case Study: Lingerie Brand Utilizes Machine Learning for One-to-One Marketing

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.

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Technology Provider: Optimove

User Company: Adore Me

Industry: Consumer Retail

Application: Marketing / Advertising

Problem

Adore Me required a more personalized and targeted method for customer communications and marketing. The company required better attribution on marketing spend, and more personalization and automation across multi-channel campaigns.

Actions Taken

Adore Me used Optimove’s segment modeling technology to find an initial set of “personas” (customer segments with distinct qualities or behaviors) to be targeted for specific offers or incentives. Over their first year of collaboration, Adore Me was able to regularly target over 60 unique personas.

Adore Me’s work with Optimove allowed them to expand beyond email marketing (previously, their only mode of customer contact) into push notifications, Facebook Custom Audiences, Google Display Network, and more. Optimove also aided in validating marketing tests for impact on conversion rates and financial impact, states Josselin Petit-Hoang, Adore Me’s Head of CRM.

Results

Adore Me reported a 15% increase in monthly revenue generated by Optimove-driven campaigns, as compared with control groups, in addition to a 22% increase in average order amount. The company credits Optimove with fully automating 85% of customer campaigns.

Transferable Lessons

Customer segmentation and targeting is difficult work, and nearly all companies can make more informed decisions about their targeting and their split-tested offers and communication.

Pattern recognition is one of the most outstanding capabilities of machine learning. In addition to finding patterns in streaming metrics (business intelligence) or stock market data (finance), patterns can be recognized in CRM data that allows for potentially useful “clustering” that might have evaded human perception (particularly at scale).

Year-over-year, marketing continues to shift towards improved personalization (so-called “one-to-one marketing”) and improved attribution. With more marketing moving into digital channels, quantifiable data will likely only speed up this trend of personalization. Particularly for companies with a substantial volume of lead and customer data, split-testing will also become much more mainstream in industries where it has not been the norm.

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