This Automated Bot refers to assisting different users in finding the best products at the best prices based on factors such as user reviews, ratings and price points. The Bot can be integrated with various E-commerce platforms like AMAZON to provide personalized product recommendations to users. The bot will use the ChatGTP chatbot API to find the best options across all items based on the price and rating. And then send the required results via email.
Shopping Online can be overwhelming, especially when there are numerous products to choose from, and users often face the challenge of finding the right product that meets their needs and budget. For example, it takes a lot of time on Amazon to read customer reviews and ratings.
The solution provided by this bot is to use advanced algorithms to analyze user data and provide personalized product recommendations based on user reviews, ratings and price points. This Bot helps the user to find the right product that meets their needs and budget. The bot also helps users to avoid low-quality products or overpriced items. This bot aims to increase user satisfaction and drive sales for an e-commerce platform. The solution can be implemented by integrating the bot into the E-commerce website using ChatGPT Chatbot API. It will then allow users to receive tailored product recommendations as they shop.
Without automation: On average organizational resources are spending around 1-3 hours per week to analyse 10 product and if your business requires your resource to analyses 15 products per week then it takes 15-45 hours per week of your resource to perform these tasks.
With Our Product Recommendation bot: perform all these activities with high accuracy in a matter of just 2-3 minute pers product and 30-45 minutes a week for 15 invoices.
On Average 29.5 hours/per week saved through our RPAaaS solutions.
Average per hour salary of a resource is between $15 to $45 USD.
ROI: 29.5 hours saved a week at 52 weeks = 1534 hours, at $30/hour = $46020 annually saved.
Note: Please note that these numbers are approximate and can vary depending on specific circumstances, such as the complexity of the product and the search criteria to be used.
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