Summary

We recently completed a project for a $4.6B retail company, creating a call assistant that helped them save over $500k in customer support costs. The company asked us to create an automated customer service solution that could handle inbound customer calls, so they could reduce their customer service costs while maintaining a high level of customer satisfaction.

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The company asked us to help

The company was looking for a way to reduce customer service costs while still providing a high level of customer satisfaction. They had previously been using a traditional customer service system that required manual customer service agents to answer customer inquiries, which was costing the company a significant amount of money.

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What we've done

In order to create a call assistant for this company, we integrated machine learning and natural language processing (NLP) technologies into their existing customer service system. The call assistant was designed to be able to understand customer inquiries and respond to them in a natural, human-like manner. We also built a back-end system to track customer interactions, which allowed the company to track customer feedback and satisfaction levels.

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Results

The call assistant was a success, and the company was able to save over $500k in customer support costs. The customer satisfaction levels remained high, and the company was able to provide a better customer experience while still reducing their costs.

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