CASE STUDY

AI-Enhanced Customer Analytics Raises Indian Optical Retailer’s Customer Retention by 28%

AI-powered hyper-personalized campaigns resulted in 147% ROI.

147%-ROI-from-Upsell-Campaigns

Wanted: Performance-Improving AI

This company, one of India’s largest eyewear retailers, manages over 60 stores and has an active e-commerce presence. However, their analytics efforts were producing lackluster results. They needed to find a partner who could use AI and ML to analyze their existing data structure, deploy an effective marketing tool, and uncover richer insights, and drive creative, data-based campaign strategies.

They decided to pilot Absolutdata’s NAVIK MarketingAI during an eight-week period around India’s festive season. With core capabilities focused on customer segmentation, centralized and store-level promotion intervention, and personalized product recommendations, this tool is designed to power up marketing effectiveness. The company hoped it would generate a significant ROI as well as help them retain and grow customers.

Tighter Targeting, Improved ROI with MarketingAI

During the pilot, better offer personalization and optimized campaign timing led to the following great results:

147% ROI from upsell campaigns

28% retention of high- and medium-risk customers

In addition, favorable customer segment migrations from the campaign provided an 83% uptick in ROI.

AI and ML Personalization Engine Powers Improved Customer Loyalty

Driving ROI and maintaining customer loyalty during a festive season is quite a feat, one that requires careful personalization and offer timing. Five types of predictive analytics were used to achieve this result: Customer Life-Stage, Customer Micro-Segment, Behavior and Intent Prediction, Response to Marketing, and Marketing Recommendation. The last process, Marketing Recommendation, combined information gleaned from the previous four analysis to suggest appropriate programs for each customer segment; these were further tailored to achieve different business objectives.

To determine each action, several behavioral models and data types were combined, including information on demographics, bankability, affordability, communication timing, channel preferences, and propensity to purchase. Additionally, conversion factors like timing, channel, and product fit were used to determine how to customize each offer and when to deliver it.

AI-ML-Personalization-Engine

This comprehensive tool allowed the company to experience the benefits of AI across a wide variety of use cases, including customer acquisition and win back, churn prevention, loyalty drivers, and cross-sell/upsell opportunities.

MarketingAI- Data-Backed-Suggestions

Navigating Seasonal Marketing with AI

Thanks to AI, this company now has the powerful analytics needed to successfully navigate marketing during the holiday season – and the rest of the year. With data-backed suggestions, they can move forward confidently with their messaging and personalization. And they can improve customer churn figures while boosting ROI and upsell/cross-sell opportunities.