Keywords: Natural Language Processing, Deep Learning
Our e-commerce client has approached us with a goal to streamline and optimize their customer support operations. With a monthly influx of hundreds of customer support tickets, the current workload has become a substantial challenge. The tickets predominantly revolve around inquiries concerning order and delivery statuses, as well as product-related information. A notable portion of these tickets exhibit remarkable similarities, essentially necessitating the customer support team to address recurring questions repetitively. This recurring task not only consumes valuable time but also hinders efficiency.
Utilizing our chatbot technology, we've created a solution that suggests responses for their customer support team. These proposed answers are crafted to match the team's writing style, allowing for easy integration with minimal adjustments. Our system draws from various data sources such as Shopify, customer information, tracking data, and website details to ensure the responses are contextually informed. Furthermore, the system continuously learns from our support team's interactions, with each response contributing to its knowledge base. This approach streamlines the support process, enhancing accuracy and efficiency in customer interactions.
As part of our solution, we introduced a bi-weekly report that highlights the prominent themes and pertinent customer inquiries from the preceding two weeks. This report offers actionable insights on addressing these queries, either by augmenting website information or enhancing details in the order confirmation email. Notably, for certain specific queries, the support team successfully incorporated approximately 50-60% of our recommended responses with minor adjustments, resulting in a substantial decrease in their workload.
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