When IoT Breaks Your Mobility Cost Model: Why Traditional Trend Analysis Fails

Most mobility cost strategies were designed around smartphones and tablets: one device, one user, fairly stable usage patterns. You trend each line's usage over time, pick the right plan, and revisit periodically. It's not perfect, but it's predictable.

IoT is different enough that this approach can actually mislead you.

In our Verizon IoT environment, we saw something that looked completely backwards at first glance: total data usage went down, but monthly recurring charges went up, and overage charges went up too. If you only look at usage trends, that result makes no sense. Once you look at how the plans are structured and how the devices are used, it becomes much clearer.

This blog walk through why traditional trend-based capacity management doesn't work well for IoT, and how we used retroactive plan changes and dynamic pooling to control cost instead.

The Pricing Mix: Cheap Plans, Expensive GB

In our setup, there were essentially two types of IoT data plans.

The first type was a low-MRC, "pay-by-the-drink" model. The monthly recurring charge per device was very low, which looked attractive from a fixed-cost perspective. The tradeoff was that the effective cost per GB was high, something like a $5-per-GB world where you are clearly punished if usage spikes.

The second type was a more expensive pooled data plan. These plans had a higher MRC but a much lower marginal cost per GB when usage was high. If you know a device will consistently consume larger amounts of data, putting it into the pool makes sense.

In a smartphone world, you can usually guess who the heavy users are and keep them on higher-allowance or pooled plans, while lighter users stay on cheaper options. The key assumption is that usage "belongs" to the person carrying the device.

IoT breaks that assumption.

The Real Problem: Usage Moves Between Devices

IoT devices are tied to business processes, locations, and projects, not individuals. A device might be heavily used this month because it is attached to a particular machine or site, then barely used next month when that work shifts somewhere else and a different device becomes the heavy user.

That's exactly what happened. Devices that were assigned to pooled plans suddenly showed very low usage, while devices on low-MRC, pay-by-the-drink plans experienced unexpected spikes. The total fleet usage went down, but a larger share of that usage landed on plans were every GB was expensive. The result was higher MRC and higher overage at the same time.

If you try to manage this like smartphones by trending usage by IMEI or SIM and right-sizing each plan individually, you get lost very quickly. The data is telling you a story about the device, but the real pattern lives in how the company is using the devices for different work.

Turning Retroactive Plan Changes into a Strategy

One important feature of Verizon's IoT billing model is the ability to change plans retroactively to the beginning of the bill cycle. Instead of treating this as a small administrative convenience, we treated it as a core optimization lever.

The traditional mindset would be: at the start of the month, decide which devices should be on pooling plans and which should be on low-MRC plans based on historical usage, and then live with the results. In IoT, this is almost guaranteed to misallocate cost, because the device that was quiet last month be the one doing all the work this month.

So we flipped the timing.

We started the month with many devices on the low-MRC plans to keep fixed cost as low as possible. During the month, we monitored usage to see where the data was actually accumulating. Near the end of the bill cycle, we identified the devices that had accumulated enough usage that the pay-per-GB cost was becoming painful. Those devices were then reassigned into a pooled plan retroactively, all the way back to the start of the cycle.

In practice, this meant trading a higher MRC for those specific devices in exchange for a much lower effective cost per GB, and a significant reduction in overage. Devices that stayed low-usage remained on the cheaper plans and never paid for pooling they didn't need.

This approach tuned a static plan assignment problem into a monthly after-the-fact optimization process.

Why Conventional Trend Analysis Fails Here

The usual playbook, trend usage, assign the right plan, and occasionally tune things, is built on several assumptions that simply don't hold for IoT.

First, per-device trending breaks down. A device can swing from near-zero usage to heavy usage and back again depending on where it is deployed and what it is connected to. A six-month history for a SIM doesn't tell you much about what that SIM will do next month, because the business process behind it might change.

Second, static plan assignment does not match the dynamic nature of IoT workloads. With smartphones, it's often fine to re-evaluate plans once or twice a year. With IoT, if you don't use end-of-cycle re-rating when it's available, you leave a lot of money on the table. The capacity decision needs to be made with full knowledge of where the usage actually landed, not based on guesses at the beginning of the month.

Third, fleet-level usage trends can be misleading. You can see total GB go down and still see cost go up, because the billing structure cares about which bucket that usage landed in. If more of the traffic hits the high-cost "pay-by-the-drink" plans, your total bill can easily rise while overall consumption falls.

In short, conventional trend analysis assumes stability at the device level. IoT usage is often unstable at that level, and that's the heart of the problem.

The "All in the Pool" Temptation

There is a very simple way to doge this complexity: put everything into a pooling plan. That can make forecasting extremely straightforward. Your MRC is relatively predictable each month, and you don't have to worry about spikes landing on the wrong plan.

The downside is that your are now paying a high fixed MRC for every device, including all the small, intermittent, or seasonal users. For large fleets, that can add up to a lot of money spent on capacity that is never actually used.

In our case, that tradeoff wasn't acceptable. We needed something between chaos and "all in the pool." The combination of low-MRC plans as the default and selective, retroactive pooling for devices that turned out to be heavy users provided that middle ground: enough flexibility to follow the workload, without carrying unnecessary fixed cost on very SIM.

What This Means for Decision Makers

If your organization is used to managing smartphones and tablets and is now expanding into IoT, it's important to reset expectations about how capacity and cost should be managed.

You can't assume that per-device historical trends will tell you which SIMs will be heavy users next month. You can't assume that total usage going down will automatically translate into cost savings. And you probably can't get optimal results by setting plans once and leaving them alone.

Instead, the operating model has to shift. IoT cost management becomes a monthly, data-driven process where you:

• Monitor usage patterns over the cycle.

• Take advantage of any carrier features that allow retroactive plan changes.

• Compare the "as-billed" cost to an optimized scenario.

• Apply plan changes near the end of the cycle to re-align cost where the usage actually occurred.

For finance and IT leaders, the key message is that IoT cost behaves differently from user-assigned devices. The tools, reports, and processes that worked well for smartphones and tablets are not enough on their own. IoT needs a more dynamic approach, especially when the carrier offers flexibility that can be used to reshape the bill after the fact.

If you're designing your IoT strategy, build this reality into your planning from the beginning. The technology side is only part of the story; the billing model and how you manage it can make the difference between a stable, efficient cost structure and a monthly surprise.