Most enterprises believe their mobile programs are efficient because bills are predictable, plans are pooled, and nothing appears broken. That confidence is misplaced.
When you examine how mobile lines are actually used, rather than how they are priced, a clear pattern emerges: a large share of enterprise mobile lines behave like data connections, not phones.
These lines use mobile data regularly, but make little to no voice calls and send almost no messages. Despite this, they are billed under voice-inclusive, flat-rate plans that were designed for a different era of usage.
The result is structural overspending that is difficult to see on invoices and can quietly compound into millions of dollars per year across large fleets.
This paper draws on real, line-level usage data across multiple countries and shows:
- How voice and messaging usage has become incidental
- Why current pricing assumptions no longer match real behavior
- Where the hidden cost comes from and why it remains invisible
- How enterprises can validate the same conclusions using their own data
The analysis does not rely on benchmarks or surveys. It relies only on what the lines actually do.
If you are responsible for technology or spend, the correct reaction after reading this paper is simple: “We should check our own numbers.”
TL;DR
- Part 1: Enterprise mobile plans are priced for behavior that most lines no longer exhibit.
- Part 2: In a real multi-country organization, 59%+ of mobile spend disappeared once pricing was based on actual data usage.
- Part 3: A data-first, usage-based model allows enterprises to correct this without changing how anyone works.
Part 1 – What Enterprises Actually Have Today
1. How Enterprise Mobile Is Still Priced
Enterprise mobile programs are still priced around an assumption that has not meaningfully changed in years: that a mobile line is primarily a phone.
Plans are built to include voice calling by default, with data layered on top. Costs are typically fixed per line per month, and optimization efforts focus on discounts, pooling, and contract terms.
This structure only makes sense if voice usage is a regular, meaningful activity for most lines.
The data shows that this assumption no longer holds.
2. Why Invoices Look Reasonable Even When Waste Exists
One reason this issue persists is that it does not present as a failure.
Bills are stable. Services work. Nothing spikes unexpectedly. From an operational standpoint, everything appears under control.
But paying consistently for something you do not use still looks stable.
3. Why Line-Level Usage Matters More Than Plans
Most enterprises review their mobile programs at the plan or pool level. That view answers whether the organization is on the right plans.
It does not answer a more fundamental question:
What does each individual line actually do?
When usage is examined line by line, a very different picture emerges.
4. What Voice Usage Actually Looks Like
When looking at voice usage, it helps to translate statistics into everyday terms.
Across countries:
Median voice usage: 0 minutes per line per month
More than half of all lines did not make or receive a single call.
75th percentile: approximately 1–2 minutes per month
Three out of four lines used two minutes of voice or less in an entire month.
95th percentile: single-digit minutes per month
Even among the most voice-active lines, usage rarely exceeded a few minutes.
Put differently:
If you lined up 100 enterprise lines, 95 of them would use less than 10 minutes of voice in a full month.
This level of usage is incidental, not functional.
5. What Messaging Usage Looks Like
Messaging usage is even more concentrated.
Across countries:
- Roughly two-thirds of lines used zero messages
- Over 90% used three messages or fewer
Messaging is not a regularly used service for most enterprise lines.
6. What Data Usage Looks Like by Comparison
Data usage tells a different story.
Across the same lines:
- Data appears every month
- Usage repeats over time
- Almost every line consumes data
Operationally, data is the only consistently used service.
7. The Core Mismatch
Despite this shift in behavior, pricing remains anchored to legacy assumptions.
Enterprises are paying for services most lines barely use. The waste does not appear as an error. It appears as a quiet structural inefficiency.
Part 2 – Real Case Study
8. Case Study Overview
This case study is based on real enterprise mobile usage and billing data across multiple countries.
All identifiers have been removed. Only observed behavior and resulting cost relationships are presented.
Scope:
- Thousands of active enterprise lines
- Multiple countries
- 12 months of itemized usage
- Domestic and international data usage
The question examined was simple:
How does cost change when data is priced based on actual usage?
9. Countries Included
- United States
- United Kingdom
- Germany
- Canada
- Spain
- Singapore
Each country was analyzed independently, then aggregated.
10. Voice Usage by Country
Across all countries:
- Roughly half or more of lines used zero voice
- Three-quarters or more used five minutes or less
No country showed voice usage consistent with traditional pricing assumptions.
11. Domestic vs International Data Usage
This analysis is not about travel-only usage. It covers all enterprise data usage.
Across countries:
- Domestic data represented the clear majority of volume
- International data represented a smaller share of volume, often low single-digit percentages
Despite this, international usage accounted for a disproportionately large share of cost under traditional pricing.
12. Why International Usage Distorts Cost
Traditional enterprise pricing treats international usage as entitlement risk.
That risk is priced into every line, regardless of whether the line ever uses international data.
As a result, small amounts of international usage inflate total spend across the fleet.
13. Reclassifying the Fleet by Behavior
Two nested groups were defined based on observed usage.
Group A (subset):
- Zero voice
- Zero messaging
- Data usage present
Group B (superset):
- Up to five minutes of voice
- Up to three messages
- Data usage present
Group A is a strict subset of Group B.
14. Phase 1 – Data-Only Lines (Group A)
When Group A lines were priced using usage-based data pricing:
- Between 75% and 99% of total cost was removed, depending on country
- At the aggregate level, approximately 95% of cost associated with these lines disappeared
No behavior changed. The savings came entirely from removing unused assumptions.
15. Phase 2 – Data-Dominant Lines (Group B)
When Group B lines were priced with data as the economic anchor:
- Between 59% and 99% of cost was removed, depending on country
- At the aggregate level, total cost dropped by approximately 94% for this group
Savings came from pricing actual usage rather than bundled possibility.
16. What Actually Drove the Savings
The savings were not driven by eliminating international usage.
They were driven by:
- Pricing data by consumption
- Removing bundled assumptions
- Eliminating embedded international risk
Once cost followed usage, excess spend disappeared.
17. What This Case Study Demonstrates
- Most enterprise lines are data-dominant
- Domestic usage drives volume; international pricing drives cost
- Usage-based data pricing delivers very large percentage savings
- No behavior change is required
Part 3 – From Analysis to Execution
18. What the Case Study Makes Clear
Enterprise mobile waste is not caused by misuse.
It is caused by pricing models that no longer match reality.
Fixing it requires changing how connectivity is priced, not how people work.
19. Design Principles Required to Fix the Problem
Any viable solution must:
- Treat data as the primary economic unit
- Price domestic and international usage uniformly by consumption
- Work continuously, not just during exceptions
- Allow incremental adoption
- Make usage and cost directly traceable
20. Bcengi WorkPass Model at a High Level
Bcengi WorkPass is built around a data-first, usage-based model.
- Persistent eSIM per line
- Data metered and charged by actual use
- Pricing applied by country of consumption
- No fixed bundles or pre-purchased allowances
Enterprises pay for what their lines actually use.
21. How Bcengi WorkPass Aligns With Phase 1
Phase 1 addressed lines that behaved entirely as data connections.
WorkPass allows these lines to:
- Move to pure usage-based pricing
- Eliminate costs tied to unused services
- Change nothing operationally
It is a pricing correction, not a workflow change.
22. Domestic and International Data Under One Model
WorkPass prices data the same way everywhere:
- Based on where it is consumed
- Scaled by usage
This removes the need to embed international risk into every line.
23. What Enterprises Can Actually See and Control
With a usage-based model, enterprises can clearly see:
- How much data each line used
- Where that data was used
- The cost associated with that usage
At the fleet level, this makes it possible to:
- Identify data-only vs data-dominant lines
- Track cost drivers directly
- Validate savings using existing exports
There are no pooled allowances or hidden offsets.
Cost follows usage directly.
Bonus – Optional Controls for Tighter Budgeting
The case study did not require controls or limits.
Savings came from pricing alignment, not restriction.
For enterprises that want tighter budgeting, WorkPass supports optional controls such as:
- Usage thresholds
- Country-specific policies
- Real-time visibility
These controls are optional, not required.
24. Why This Is Different From Traditional Optimization
Traditional optimization works within legacy pricing models.
WorkPass changes the model itself by making cost proportional to behavior.
That is why the savings observed are structural, not incremental.
25. Final Observation
Most enterprise mobile lines no longer behave like phones.
Enterprise pricing still assumes they do.
When pricing reflects reality, waste disappears.
