Yes sure, so we were working on improving conversion for one of the CPG client and their problem was churn rate was very high, they were not getting personalized attention on their e-commerce site, they used to browse product and they are not able to find right product or possibly engine was not able to suggest, recommend them the alternate right product or upsell the right product to increase average order value. The personalization was weak, they were trying to achieve that and then that's where generative AI came, wherein we were able to assess prediction based on predictive analytics using integrating with data points of customer behavior, segmenting that customer into particular categories, then his behavior browsing behavior, other analytics touch points on pages, his history, his or her history and then how soon he is making his mind. There are a lot of parameters that went into and we started doing predictions of how customer would end up in this e-commerce site. Now those predictions can lead to conversions or can at least reinforce in customers mind that this is the right product, so we enabled integrated that prediction with personalized content, personalized products, for example if we are identifying intent of customer, now that intent can be mapped with the product also internally. Through generative AI we used to capture that intent, take that intent as input and then map those intent with the products that are there on e-commerce and then showcase those products on the pages. We could even upsell those, so we were able to you know increase average order value by ten percent. The challenges we faced was definitely in fine-tuning the model with the right prompt engineering, with the right data set, quality data, that was the challenge that we faced. Initially with the results were not exciting but when as and when we started doing better prompts engineering, better fine-tuning, we started improving the results. Yeah that's it.
