Call Centres – Next Best Move
- Decision taking in call-centres is notoriously problematic
- Central to the problem is the huge difference between decision skills from worker to worker
- This leads to a random customer experience ranging from excellent with a well-skilled operator to terrible with a poorly skilled operator
- Training and ramp-up time is expensive as it uses both a skilled and an unskilled employee and both do not produce during training
Brain Everywhere Solution
- Top operators trained 11 Sue models for the most common screens on the call-centre software. Once novice users typed in the details of the call, they were sent to Sue and the model advised the operator on next best move.
- Each of the input fields for the most commonly used screens in the software and the ones considered most important
- The input of the top call-centre operators
Success and Improvements
call-centremetrics improved – the average length of a call dropped, customer perception improved, the longest call dropped, hang-ups reduced
- Workshop to determine which models to make (2 days) and then 3 half-days per model
More Use Cases
Sue can convert any telemetry from a reactive rule base to a predictive model that will avoid unplanned stoppages and nip problems before they become critical. Sue is the ideal AI for IOT. With the vast amounts of telemetry being generated by IOT, there are not enough experts and engineers to read it all. Sue can read every telemetry reading and email / autocall / refertohuman when action is needed. IOT, Mining, High Precision Farming, Telcoms, Vehicle Monitoring
Sue can approve renewal of risks and bills risks within fuzzy thresholds. Underwriting, Utility Bills
Sue can predict missing data points in a set using the other data.
In any transactional environment, Sue can read every single transaction and flag outliers and exceptions for human review including fraud, money laundering or pricing. Sue is able to imitate an expert that reads a page of transactions and spots the fraudulent one. Except that Sue can read ALL the transactions in a large organization and never needs sleep. Telecoms, Banking, Finance, Triage of Insurance Claims
Call Centres Next Best Move
Sue can recommend the next best move based on what is put in on each screen, standardizing service and optimizing flows.
Sue can learn any individual decision in the medical field and replicate it
Sue is being used to analyse specific metrics, economic indicators, weighted risk parameters and soft input to take shareholding decisions ending in Buy/Hold/Sell. Transaction Pricing, Shares, Currency Trading, BlockChain Smart Contracts, Actutarial Pricing Methodology, Fitment of Statistical curves, Reserve Forecasting
Sue can act as a help desk able to run set scripts 24/7 anywhere in the world, detect data breaches and protect database by running scripts and smart approving every db/os action
You don’t need Cambridge Analytica to target advertisements or profile clients. With a few pieces of information Sue can segment your whole population and send each person the right message. Investment, Retail Deals, Politics
Most AI engines need 80 pieces of information to fire, leading to client fatigue. Sue can give an initial approval after as few as 3 parameters. Credit cards, Employee applications
Unlike humans, Sue can review every single item offered and give an initial unbiased opinion Underwriting, Risk Selection, Recruitment Selection, Holiday Authorizations, Exploration Predictions
Sue can read the psychometric results of a candidate and give you the probability of success in a division based on the results and performance assessments of other candidates.