实例探究 > Luxury Ecommerce Retailer Improves Promotional Offers and Increases Customer Loyalty with Advanced Analytics

Luxury Ecommerce Retailer Improves Promotional Offers and Increases Customer Loyalty with Advanced Analytics

公司规模

Large Corporate

地区

Asia

国家

Singapore

产品

Antuit Marketing Analytics Framework
Antuit Predictive Model
Antuit Customer Migration Matrix

技术栈

Machine Learning
AI
RFM Models

实施规模

Enterprise-wide Deployment

影响指标

Customer Satisfaction
Revenue Growth
Productivity Improvements

技术

分析与建模 - 预测分析
分析与建模 - 机器学习

适用行业

零售
电子商务

适用功能

销售与市场营销
商业运营

服务

数据科学服务
系统集成
培训

关于客户

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.

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挑战

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.

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解决方案

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.

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运营影响

The implementation of the Antuit solution led to an improvement in marketing ROI in the range of 5-20% across the company's portfolio in the first market that went live in Singapore.
The newly designed, analytics-backed campaigns helped improve customer stickiness and engagement.
The solution enabled the company to measure the true lift of its promotional campaigns, providing valuable insights for future marketing strategies.
The segmentation and profiling of customers allowed for more personalized and effective marketing efforts, enhancing customer satisfaction and loyalty.
The use of RFM models and Purchase Propensity models provided a deeper understanding of customer behavior, enabling more targeted and impactful marketing initiatives.

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数量效益