Empowering Retail Success with AI/ML-Driven CLV Insights

Client Background

A leading luxury fashion retailer headquartered in the United States, our client boasts a vast customer base exceeding 6 million individuals and annual sales surpassing 4.5 billion USD. The client wanted to apply a systematic data-driven approach demonstrating the future value businesses can generate from their sales and marketing initiatives. CLV models enable predicting customers’ future purchase power and scope of profitability resulting into design and evaluation of sales strategies that are more sustainable value creation for long-term.

The Challenge

In navigating the dynamic landscape of the retail industry, our client has faced various challenges in their pursuit of sustainable growth. Key among these challenges has been the need to enhance customer segmentation methodologies to gain deeper insights into diverse customer behaviors and preferences.

Additionally, they've aimed to develop predictive models capable of accurately forecasting customer spending patterns, empowering targeted marketing efforts. Moreover, streamlining acquisition spending to maximize returns on investment and cultivate long-lasting customer relationships has been a crucial focus. These efforts collectively represent our client's endeavor to adapt to the evolving retail environment and drive meaningful growth by better understanding and serving their customer base.

Key Solutions

In response to these challenges, Xoriant proposed a comprehensive solution leveraging advanced AI/ML algorithms and robust statistical methodologies. Our solution entailed:

Segmenting the client's diverse customer base into distinct categories, including premium, loyal, and other segments, to enable granular understanding of customer behavior.

Developing predictive CLV models capable of projecting customer spending for the upcoming fiscal quarter and the subsequent 12 months, thereby facilitating precise revenue forecasting.

Implementing personalized targeting strategies in marketing campaigns to drive higher sales conversion rates and foster improved customer retention.

Our key contributions included:

Developed and implemented AI/ML based models to assess CLV to optimize sales strategy and marketing budget.

Developing a consistent methodology for both customer segmentation and customer lifetime value prediction.

Pre-processing 60 million transactions involving 6 million unique customers, employing clustering techniques to create an optimal number of customer segments.

Producing 6,000 of the latest set of customer attributes and applying SVD-based feature extraction for noise reduction through feature engineering.

Implementing a modular modeling approach allowing the addition of new attributes in the future.

Rolling out version-controlled and well-maintained codes for production-ready models.

Supporting an end-to-end pipeline and ensuring fully automated implementation.

Business Benefits

The implementation of our solution yielded significant business benefits for our client, including:

Enhanced Decision-Making Capabilities: Streamlined customer segmentation and CLV prediction methodologies enhanced decision-making, leading to more targeted sales and marketing strategies.

Improved Operational Efficiency: Efficient pre-processing of vast transactional data improved operational efficiency, enabling quicker insights into customer behavior.

Tailored Offerings for Enhanced Customer Satisfaction: Production of comprehensive customer attributes facilitated better understanding of customer preferences, resulting in tailored offerings and improved customer satisfaction.

Agility and Adaptability to Evolving Needs: Implementation of a modular modeling approach ensured adaptability to evolving business needs, fostering agility and responsiveness.

Optimized Resource Utilization: Version-controlled, production-ready models optimized resource utilization, contributing to cost savings and improved ROI.

Streamlined Processes for Enhanced Productivity: End-to-end pipeline support and full automation streamlined processes, reducing manual effort and accelerating time-to-insight, thereby enhancing overall productivity and competitiveness.