Response: Certainly! One of the projects I worked on involved improving conversion rates and reducing churn for a CPG client’s e-commerce platform. The primary challenge was the lack of personalized recommendations, which led to customers struggling to find relevant products and ultimately abandoning their carts.  

To address this, we leveraged generative AI and predictive analytics. We began by integrating multiple data points, such as customer browsing behavior, purchase history, and interaction patterns, to create detailed customer segments. Using this data, we built predictive models to identify customer intent in real-time. For example, we analyzed how quickly a customer navigated through the site, the types of products they viewed, and their past preferences to predict their likelihood of making a purchase.  

The next step was integrating these predictions with personalized content and product recommendations. Generative AI played a key role here by dynamically generating tailored product suggestions and content that aligned with the customer’s intent. Additionally, we implemented upselling strategies by mapping customer intent to complementary or higher-value products, which were prominently displayed on the e-commerce pages.  

One of the biggest technical challenges we faced was ensuring the quality and relevance of the data being fed into the models. Initially, the predictions were inconsistent due to data gaps and suboptimal prompt engineering. To overcome this, we focused on fine-tuning the models, improving the quality of the training data, and iterating on the prompt engineering process to enhance the accuracy of the outputs.  

As a result, we achieved a 10% increase in average order value and a significant improvement in customer engagement. This project not only addressed the client’s immediate challenges but also demonstrated the transformative potential of generative AI in driving business outcomes.