Industries
Twelve industries. 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 products are the same. The proof below is anonymized and specific.
Where the proof is
What the experience model found, sector by sector.
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Fortune-50 telecommunications provider
Telecom and cable
Opportunity identified. Over 7,500 accounts drove more than $10.5M a year in avoidable demand. Effort escalated above 600% across repeat contacts (AMPRx 21 to 148 minutes) while CSAT fell 47% and NPS fell 98%, at R² above 0.99.
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Healthcare unicorn and a major U.S. health insurance payer
Healthcare
Member and provider support, about 25,000 contacts a month: 98.3% forecast accuracy, 110 FTE reduction, occupancy up 30%, abandonment down 70%. For a major U.S. health payer, an eight-dimension diagnostic identified $14.5M in revenue optimization.
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Travel and hospitality company
Travel and airlines
The incumbent service model was on a trajectory that would have required about 230 more FTEs than needed. Resolution times fell 52% on voice, 36% on chat and 54% on SMS.
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Global shipping-insurance provider
Insurance
Cost optimization delivered across six business units with no CRM backbone. The seven-stage policy lifecycle mapped, 845 requirements validated, RPA stood up in underwriting.
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Energy solutions company
Power and utilities
A Microsoft Access front end over Azure SQL redesigned as 8 core objects and one workflow on AWS: 71 requirements, 313 tickets, 25 architecture decision records, 16 weeks, a seven-person squad.
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Home services SaaS decacorn
SaaS
Contact rate down 78%, abandonment down 80%, contacts per customer from 3.2 to 1.8 on the phone and 3.5 to 1.9 on chat, $13M in cost deflection and a 60 FTE reduction.
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Specialty biosciences manufacturer
Life science and biotech
Five integration defects root-caused on the order-to-cash pipeline and eight exceptions governed to resolution, with a four-tier contact model and the middleware migration from an interim bridge to a real-time ERP connector.
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Premium consumer learning platform
EdTech
268 functional requirements derived from 47 scenarios across six towers, more than 125,000 support tickets analyzed over a trailing twelve months, and a three-year NPV case behind the roadmap.
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.
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.
Why the model travels
The ontology is built per industry. The method is not.
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.
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.