Industries

Any industry. One model of the experience.

Contact demand is a derivative of the business that creates it, so the ontology is built for each sector: its channels, its phases, its points of view. The three dimensions do not change, which is why the model travels to sectors that are not on this page. The proof below is anonymized and specific.

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Where the proof is

What the experience model found, sector by sector.

Where the model also runs

Same ontology, different units of growth.

The Brain models contact demand from the units a business grows in. These sectors have their own drivers, their own phases and their own vocabulary in the ontology. They are examples, not the list.

  • Financial services

    Cards, deposits and lending servicing. A proof engagement on one portfolio baselines the real cost to serve and the revenue silently at risk, then quantifies revenue at risk, cross-sell upside and cost savings in dollars. The evidence, before the commitment.

  • FinTech

    Subscriber growth and billing cycles create contact demand on a schedule: every cohort calls when its statement lands. WFMaaS forecasts from those cohorts, and the Brain ranks the issue types each release and each billing change creates.

  • Private equity

    One ontology per holding, one scale for cost to serve, repeat demand and churn across the portfolio, so operating partners compare like with like and know which company's service data hides margin.

  • Gaming

    Contact demand tracks the player curve, patch by patch. WFMaaS models traffic from daily active users so staffing follows the release calendar, and the Brain shows which issue types each patch introduced.

  • Consumer packaged goods

    Demand arrives from the shelf and the doorstep at once: order status, damage, substitution, returns. The ontology counts the experience across retailer, marketplace and direct channels, so a packaging or fulfilment change is visible in contacts rather than argued about.

  • Automotive

    Owner, dealer and connected vehicle each generate their own demand, and a recall or an over-the-air update lands on all three. Modeling the experience across them shows which issues are the vehicle, which are the dealer, and which are the software.

Why the model travels

The ontology is built per industry. The method is not.

The sectors above are where we have run it, not the boundary of where it works. If your industry is not on this page, the question is never whether the model fits. It is which units your business grows in, and those become the ontology.

Each client receives an ontology mapped to their industry and business segments. A telecom's phases are activation, billing and repair; a payer's are enrollment, claims and prior authorization; an airline's run from booking to the day of travel; a bank's from application to servicing. The three dimensions never change: point of view, channel and phase. Pick one of each and it should map to a source system. Where it does not, the NULL is the finding.

Because the dimensions hold, the metrics hold too. AMPRx, CPRx, 1CX Rate and DTRx mean the same thing in a claims queue, a repair queue and a player-support queue, which is what lets the proof above be compared at all.

Questions

What sector leaders ask.

Do you have references in my industry?

The proof on this page is anonymized because the clients ask us to keep it so. In a working session we can walk through the engagement shape and the numbers in more depth, and arrange conversations where the client agrees.

My industry is not on this page. Does the model still work?

Yes. The three dimensions are point of view, channel and phase, and every service operation has all three. What changes per sector is the vocabulary inside them: a payer's phases are enrollment, claims and prior authorization, a carmaker's run from purchase through service and recall. A working session maps your phases in the first hour.

How long before the model says something useful?

Proof engagements run on a single portfolio, scoped to your numbers, over 12 to 16 weeks: connect and model, baseline the real cost to serve and the revenue at risk, then quantify the case in dollars. The evidence comes before the commitment.

Is regulated data a problem?

Client workloads run in serviceMob's managed AWS environment, with controls aligned to the SOC 2 Trust Services Criteria, on AWS infrastructure covered by AWS's own SOC 2 Type 2 report. Every tenant's data is segregated in its own instance. Details are on the security page.

Prove it on one portfolio. Decide on the evidence.

Bring one line of business to a working session. We will show which of these programs is closest to your operation and what a 12 to 16 week proof would measure.

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