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Luxury Ecommerce Retailer Improves Promotional Offers and Increases Customer Loyalty with Advanced Analytics
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Machine Learning
Applicable Industries
- Retail
- E-Commerce
Applicable Functions
- Sales & Marketing
- Business Operation
Services
- Data Science Services
- System Integration
- Training
The Challenge
A luxury e-commerce retailer based in Singapore was facing challenges in garnering repeat business and inspiring customer loyalty. Despite having a successful business operation across 8 neighboring countries, the company struggled to earn repeat business from customers, which is a common issue in the luxury retail sector where purchases are often discretionary and infrequent. The company had a wealth of customer data available through account creation and Facebook login, but this data was not being effectively utilized. They needed an analytics program to leverage this data for personalized customer engagement, a recommendation engine, and tailored offers to boost customer loyalty and optimize revenue.
About The Customer
The customer is a Singapore-based e-commerce luxury retailer specializing in high-end designer brands. The company has grown its business operations across 8 neighboring countries and enjoys success in the region. However, like many luxury retailers, they face challenges in earning repeat business from customers due to the discretionary nature of luxury purchases. The company collects extensive buying and activity data from customers who create free accounts or log in using Facebook credentials. Despite having this data, it was not being effectively utilized to engage customers and drive repeat business. The company sought to implement an advanced analytics program to address this issue and improve customer loyalty.
The Solution
Antuit was engaged to design and deploy a marketing analytics framework and predictive model to improve customer engagement and loyalty. They began by segmenting the retailer’s customers using Recency-Frequency-Monetary (RFM) scoring, which ranks customers based on the time spent on the site, frequency of visits, and money spent. From these RFM models, Antuit identified four distinct customer clusters and created Purchase Propensity models to understand the purchasing behavior of each segment. They also set up a Customer Migration Matrix to pinpoint customers worth retaining. Antuit then implemented a test and control framework to monitor the effectiveness of the analytics solution. Once the segmentation and profiling were complete, Antuit collaborated with the client to create new marketing campaigns with tailored offers and promotions for the targeted segments. They advised the company on the types of promotions to engage their most active customers, including exclusive previews of select items for the most valuable customers.
Operational Impact
Quantitative Benefit
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