Problem – Without the right product number, online shoppers have trouble finding a part to repair a plumbing issue. The only frame of reference the customer has is a picture of the part they have uploaded.
Solution – The build artificial intelligence (or deep neural network) image matching module compares the given image from the customer with images of all cataloged products, identifying the most accurate match for their consideration.
Benefits – Improves the end-user experience and customer satisfaction, and customer care agents don’t need to assist consumers in identifying the right spare part. Hassle free self-service empowers consumers with tools they need.
Problem – The pressure to make a sale in a limited amount of time can be problematic for customer service representatives (CSRs).
Solution – Imagine a system that predicts the buying behavior of a consumer, including preferred services or products. Prompts advise agents to make recommendations at the point of call, saving time and stress for both parties.
Benefits – Average Handle Time (AHT) is greatly reduced. CSRs confidently and accurately pitch recommendations. Non-buyers are filtered out from their channels. Buying trends and overall Convesion Rate Optimization (CRO).
Problem – Large organizations require new employees to access different applications, networks, and databases. The onboarding process may take weeks to provide accessibility.
Solution – Based on the job role, a list of recommended applications, networks, and database access is generated for the new hire.
Benefits – ML processes generate system-initiated access requests, reducing the need for manual intervention. Employees can start achieving professional goals faster, creating a feel-good, stress-free environment.