serviceMob
The Answer Engine for Customer Service.

A record of a call that never should have happened twice

Somewhere inside a large company right now, a customer is calling for the second time about the same problem. The first agent was polite. The average handle time was fine. The satisfaction survey - the one the customer never opened - would have scored well. Every dashboard says the operation is healthy. And yet here is the second call, proof that none of those numbers noticed the thing that mattered: the customer still didn't have an answer.

serviceMob is a company built on that gap. It looks at the enormous, scattered exhaust of customer service - the logs sitting in the CRM, the contact-center platform, the workforce-management tool, the ticketing queue - and argues that the most valuable information is the part nobody is reading. The industry has a name for data it collects but never uses. serviceMob has a name for what it does with it: Ontolytics.

The word is a mash-up of ontology and analytics, and it is doing real work. An ontology is a map of how things relate - point of view, channel, phase, component, actor, resolution. serviceMob's bet is that if you model every customer experience as structured, behavioral data rather than a loose pile of transcripts and tickets, you stop describing what happened and start seeing why it keeps happening. That is the difference between a report and an answer.

Retiring the metrics that measure the wrong thing

The provocative part of serviceMob's pitch is what it throws out. The classic contact-center scorecard - average handle time, customer satisfaction surveys, first contact resolution - rewards activity and speed. serviceMob replaces it with behavioral measures that try to capture whether the customer actually got resolved, tracked algorithmically across every channel rather than asked about in a survey.

Note the shift. The left column counts things the company did. The right column measures what the customer experienced. It's a small philosophical move with a large operational tail: once you rank the root causes of contact, you can start eliminating them - what serviceMob calls demand prevention, as distinct from the deflection most of the industry sells.

Betting a behavioral model beats a century-old formula

Contact centers have staffed themselves with the Erlang-C formula for roughly a hundred years. serviceMob's claim is that a behavioral ontology, fed with repeat-demand signals, forecasts more accurately than the standard approach. Here is how the company frames the gap (figures are serviceMob's own):

Approximate, vendor-reported. Treat as serviceMob's marketing figures, not independently audited benchmarks.

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The second call that no longer happens

Return to that customer dialing in for the second time. In serviceMob's version of the story, the second call is not an anonymous data point that vanishes into a healthy-looking average. It's a signal - logged, structured, traced back to a root cause, and ranked against every other reason people are reaching out. Somewhere upstream, a product team or an operations lead gets told: this is the thing generating the repeat contacts, and here is its price.

Fix that upstream thing, and the second call never gets placed. Multiply it across a service operation and you get the numbers serviceMob likes to quote - hundreds of thousands of contacts that simply stopped happening, and the savings that trail behind them. Whether the industry adopts the vocabulary of Ontolytics or not, the underlying idea is stubborn and hard to argue with: the most useful data in customer service is the data you're currently throwing away. serviceMob's whole job is to pick it up.