Customer processes call center generated tickets, and makes decision to issue credit or discount and approves benefits extended to the customer. The team wants to increase their Return on Investment (ROI) by managing three core practices of their business; reduce cost of handling a ticket, manage resources to produce highest efficiency and being effective by reducing Mean Time to Respond (MTTR).
Due to increased demand of new product launched by customer, and consumers are buying products and offerings. Consumers are calling customer support to get credit or discounts and these calls are logged as ticket for a resolution. This has become paramount problem such as operational cost, team efficiency and Mean Time Response for each ticket (MTTR), all these factors cost is increased year over by year.
C2S Technologies worked with customer to solve client problem.
We have leveraged several modules to solve core problems. These include Event Tree Module, Markov Chain Module, and Nodal Analysis.
We primarily did evaluate customer data by classifying several headers such as Product Category, Region, Agent Type, and cohort buckets.
Using Monte Carlo Markov Chains (MCMC) assessing the customer data and prescribe a methodic sequence of steps for lessening the Mean response time (MRT) is being evaluated with realistic example. A nodal analysis was instrumental to evaluate this analysis.
Enabled Markov Chain Monte Carlo methods to increase ROI by managing reducing cost, managing resources for highest efficiency and being effective by reducing Mean Time to Respond (MTTR).
Customer operational cost has been reduced by 30%, team efficiency went up by 20% and Mean Time to respond for any ticket down by 38%.
• 6% prescriptions claims are the patients who do not exists, which save significantly around $500K.
• Reduced team operational cost by 30%.
• Increased team efficiency by 20%.
• Mean Time to Response by down by 38%.