Dynamic Segmentation and Next Best Offer for a Leading Nicotine Product Manufacturer

Business Challenges

An India-based nicotine product manufacturer sought to improve their ability to personalize offers for both new and existing customers. While the client had a vast hyperlocal store network, collecting reliable sales data from these stores posed significant challenges. Despite previously attempting to leverage advanced analytics, the results did not quite align with the client’s expectations as they derived low improvement in sales.

Understanding Customer Journey

Customer Stage-Specific Offers

Increase Existing Customer Revenue

Beyond that, the client was finding it difficult to fully grasp their customer journey and create offers aligned with each stage of the purchasing lifecycle. This lack of insight hindered customer engagement and the effectiveness of their marketing efforts. Additionally, they aimed to boost revenue from existing customers by encouraging repeat purchases, requiring a data-driven approach to address these challenges effectively.

Let us handle the data analysis, while you watch your revenue grow!

Quantzig developed a comprehensive strategy to tackle the client’s data collection challenges and optimize dynamic segmentation.

To overcome the data collection hurdle from the hyperlocal store network, Quantzig conducted extensive market research. This data was then integrated with POS, marketing, sales, and social media data to create an aggregated data framework, enabling a comprehensive view of customer behavior.

Quantzig utilized advanced statistical techniques, including K-means and C-means clustering, to effectively segment the existing customer base. This segmentation allowed for targeted strategies, and likelihood analysis was employed to design personalized product bundling and discounting strategies, ensuring maximum relevance for each customer group.

To address the challenge of understanding new customer behaviors, Quantzig developed an autonomous prediction engine. By combining insights from multiple machine learning algorithms, this engine classified new customers into existing segments, continuously recalibrating propensity scores based on each new purchase, ensuring the most accurate predictions and optimized offers.

Results & Impact

By collecting and integrating data from hyperlocal stores with POS, marketing, sales, and social media data, Quantzig established a robust data management framework that addressed key data challenges. The customer segmentation strategies using K-means and C-means clustering allowed the client to identify actionable segments and implement tailored bundling and discounting strategies, driving a 5% increase in revenue.

The autonomous prediction engine played a pivotal role in boosting new customer acquisition by 15%. Additionally, the targeted product recommendations led to a 12% improvement in cross-selling opportunities. This holistic and innovative approach helped the client achieve their business objectives and maximize customer value.

Ultimately, Quantzig’s solutions unlocked new growth avenues and maximized the client’s business potential.