Luxury Ecommerce Retailer Improves Promotional Offers and Increases Customer Loyalty with Advanced Analytics
公司规模
1,000+
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
- Productivity Improvements
- Revenue Growth
技术
- 分析与建模 - 数据挖掘
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 电子商务
- 零售
适用功能
- 商业运营
- 销售与市场营销
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.
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.
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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