Improves customer service satisfaction and average handling

About

The third largest network in the world serving around 8-10 crore travel annually which translates to more than 2.2 crore travel per day. With a massive user base of customers and with their ever-increasing expectations, the enterprise gets a traffic if around 10,000 to 15,000 tweets per day on Twitter only. These concerns range from service complaints (e.g., non-functional fans, toilets, charging-points; quality of food and beverages), emergency calls (e.g., medical emergencies, eve teasing) to feedbacks (e.g., how to make them more efficient).

Problem

To handle this large volume of customer concerns, enterprise have a Twitter cell that works day and night to address these concerns. These concerns are manually read, filtered and assigned to appropriate DRM’s or units for appropriate actionability. This being an intense and time taking process, restrict the enterprise to address to only a portion (approximately 40%) of the concerns and that too with an average lag time of 2-2.5 hours impacting the overall customer satisfaction of the customers.

Solutions by Rezo

Rezo recently ran a pilot where Rezo was attempting to bridge the gap between the customer and the enterprise. Rezo started to address only a certain set of issues around Fans and Lights, Toilets, Water Facility, Quality of Food, Rates, and around Train Delay. Rezo was able to automatically identify the issue category, identify if the tweet had missing information and request back for additional information from customer (e.g., missing PNR number if asking for fixing of fans or lights), map customer concerns to the correct DRM/unit in near real time.

Achievement

Rezo mapped the queries to the relevant officials almost real time and resolution started to happen in less than 30 mins.

twitter comments automation