Issues surface only when they get frequency heavy

Challenge in customer problem solving is to stay alive to situations that may be getting reported for the first time. Natural propensity of agents and engineers is to bucket every new situation into a problem class that system is comfortable living with and has SoP on. And there lies the real problem.

Tons of work has been done on identifying and building early warning devices for human body.Banks and Financial services players, chiselled by multiple black swans, have spent enough time and money to build systems that study customer behavior via time gaps between risky actions, usage of risk alerting texts and many other such measures.

Listening to social media, telematics and IIoT based capability for machine listening in Industry 4.0 are all creating data that can be leveraged for spotting anomalies and problems early. While product management and design departments have started baking in this feedback, a big gap still remains in our ability to understand day to day customer reported problems and “call it".

Customer Support leaders are therefore back to frequency measurement and fighting fires much after they are started. A problem hitting the top of charts in a small country offers more chances of getting spotted than another which is spread nationwide but remains below the threshold bar everywhere.

If only there were a method to cut through this frequency clutter and spot the disease well before it gets frequency heavy

Sainapse listens to customer problems in full context - reading attachments from XL files, PDFs to even engineering drawings and is not limited by set words, text mining rules or error codes.Sainapse Theme Discovery leverages this holistic understanding of customer queries and reported problems to spot budding themes that could potentially even be a black swan.

Borrowing from Billy Joel, ‘we didn't start the fire, no we didn't light it, but we tried to fight it'.