Updated: June 2026
During our recent ISP Virtual Summit, we held a technical webinar on how to measure QoE and how it can help operators manage their networks proactively. Hosted by Preseem CEO Dan Siemon, the session also included a detailed look at how Preseem is deployed within customer networks. You can view the full webinar here or read on for a recap of the QoE portion.
(If you’re wondering what QoE is, we define it as simply the Quality of Experience a subscriber has when using the internet.)
Why Regional ISPs Should Care About QoE
Providing superior QoE is more important than ever for a couple of reasons. First, more people are now able to work from home and need a reliable internet experience to ensure important video calls don’t buffer or drop.
Second, our homes now have many internet-connected devices. One person may be watching Netflix while another is playing Fortnite and another shopping online, etc. QoE optimization ensures that each person’s internet experience ‘feels fast,’ no matter what plan they have, how busy the network is, or how many people in their home are online at the same time.
Good QoE translates to happier customers, and this means less churn and fewer support calls for operators. It can also help increase subscriber capacity on the network, though this needs to be done in a way that isn’t artificially reducing demand or using similar techniques that are a) bad for subscriber QoE and b) make your service less competitive against fiber.
Network Congestion Can Cause Poor QoE
So what are some of the problems that lead to poor quality of experience for subscribers? Network congestion can certainly be an issue, as a busy network means less throughput for customers. As well, there’s the subtle related issue of how QoE can really degrade rapidly in congested networks.
For example, many operators will have seen a busy link operating just fine at 90% but once it gets to 92-93% suddenly the experience is bad. This is called a congestion collapse. Essentially a threshold is reached where suddenly latency goes way up and “good put” (useful bytes transferred by the network) goes way down.
What’s interesting is that this is not fundamental, it’s basically the result of oversimplified queueing. This can be avoided by using better queueing techniques like AQM.
It’s also important to remember that these links are not just access points or backhaul links. When you deliver a plan to a subscriber, e.g. shaped to 10Mbps, they have a virtual link of 10Mbps, and this exact same problem happens within that virtual link. This self-congestion within their own plan speed can be a major cause of poor QoE.
Proactive Management is Difficult Without the Right Tools
Knowing how to measure QoE and manage it proactively has traditionally been extremely difficult. Identifying problems in the network and prioritizing fixes is not easy to do, especially at scale. This is especially true in multi-vendor networks or for operators with a limited number of RF experts on staff. Some of the problems that can restrict proactive management of the experience include:
- Multiple vendor tools and lots of charts
- RF expertise (can be very manual and time-consuming)
- Correlating information from different sources (e.g. subscriber usage, RF conditions)
- Vendor tools with point-in-time snapshots are insufficient (instead it would be better to look at the worst minutes of the day and compare with real-world performance—this is what Preseem does). AP management tools are useful for viewing link rates and debugging Ops problems, but point-in-time metrics are not useful in diagnosing the overall quality of the network. This requires much more of an ‘over-time’ analysis.
Also, measuring QoE can’t really be done using traditional network management solutions. A network monitoring system is focused on elements (e.g. SNMP) and not subscribers. Obviously, you’ll always need to know when network elements are up or down, but measuring the quality of experience is a different thing entirely. This requires different metrics than those you’d use when monitoring network elements.
It’s also impossible to scale because a commercial NMS is not priced in such a way that you can get down to the individual subscriber level. For example, you wouldn’t want your NMS pinging 5,000 customers constantly—that would become cost-prohibitive in a hurry.
QoE is More Than Just Traffic Management
You can’t necessarily deliver a great experience by arbitrarily limiting traffic. ISP QoE is about understanding and improving the service delivered to customers. To have a complete QoE solution, you need to have:
- Subscriber QoE information, measured in a way that’s indicative of their actual experience.
- Deep integration with the underlying access network. For example, the first thing Preseem does is map the subscriber topology and the access elements to which they’re attached.
- Collect a ton of information. We understand the real-world behavior of APs because we see hundreds of thousands of APs with very fine-grain data on how they’re performing.
With this information, you can drive proactive quality improvements, which means you don’t actually need to do much traffic management other than to occasionally mitigate congestion.
Two important aspects to effective traffic management are 1) optimize for latency and not just throughput, and 2) don’t arbitrarily limit traffic—it’s not really necessary in most cases and there are better ways to do it. For us, analytics and driving improvements are much more important to delivering good QoE than limiting traffic.
How to Measure QoE Effectively
At Preseem, we measure the traffic for each individual IP address in the system—latency, loss, throughput, and combine that with our topology knowledge that we have for the network (discovered from the network directly)—and we use that to understand the quality of experience delivered in various parts of the network. This allows you to understand and uncover things like:
- Overloaded backhauls and APs
- Poorly-performing APs
- In-home Wi-Fi problems
- Whether an issue is network-wide or specific to the customer
Also, because this is measured at the traffic level, it can also find interesting things like:
- Bad bonded links
- Overloaded routers that are causing packet loss
- Bad optical cables
Because it’s not looking for any specific cause, it can find some surprising things. We believe this is the only way you can understand the experience, e.g. subscriber metrics tied back to the topology (which then makes it actionable).
What Metrics Should ISPs Collect for QoE Monitoring?
Understanding QoE at the subscriber level requires a different set of metrics than those used to monitor network infrastructure. The following are the core measurements that give ISPs an accurate picture of what subscribers are actually experiencing — not just whether equipment is online.
Throughput, measured per subscriber against plan speed. The relevant question is not what the link can carry, it’s whether each subscriber is receiving the throughput they’re paying for, and how consistently. A subscriber on a 50 Mbps plan who regularly receives 20 Mbps during peak hours is having a measurably different experience than their plan implies, and that gap won’t show up in link-level monitoring.
Loaded latency, not just unloaded latency. Unloaded latency, measured on an idle connection, tells you very little about the experience a subscriber has when their household is actually using the internet. Loaded latency, measured while the connection is under active use, reveals bufferbloat and queue management problems that are invisible on an idle link. The gap between a subscriber’s unloaded and loaded latency is one of the most reliable indicators of whether their plan enforcement is creating a poor experience at the speed ceiling. Learn more about how to reduce latency here.
Packet loss and retransmission rate. Packet loss at the subscriber level directly causes degraded experience for real-time applications. It also serves as a diagnostic signal: loss that tracks with throughput utilization typically indicates self-congestion or poor queue management, while loss that appears independent of utilization often points to RF link degradation or CPE issues. That distinction matters before a truck ever leaves the yard.
Jitter. Variation in latency is particularly damaging for voice and video applications. A subscriber whose latency averages 20ms but fluctuates between 5ms and 150ms will have a noticeably worse experience on video calls than one whose latency is stable at 30ms, even though the average looks acceptable.
RF signal quality and link rate (fixed wireless only). For fixed wireless operators, the radio link rate, i.e. how fast the CPE radio is actually communicating with the AP under current signal conditions, is a critical QoE input that throughput and latency measurements alone won’t surface. A subscriber whose radio can only sustain a 15 Mbps link rate while on a 25 Mbps plan will never reach their plan speed, regardless of what the network is doing. Collecting this metric alongside subscriber experience data enables support teams to distinguish between a network problem and a hardware limitation without dispatching a truck.
Plan utilization percentage. Knowing how often a subscriber hits 80%, 90%, or 100% of their plan ceiling, and at what times of day, is the leading indicator for both upsell conversations and churn risk. Subscribers who consistently saturate their plan during evening hours are experiencing self-congestion, whether or not they call to report it.
AP and sector utilization. Subscriber-level metrics become fully actionable when they’re tied back to the network topology. An AP approaching capacity affects every subscriber behind it, and that pattern—multiple subscribers simultaneously showing degraded QoE on the same AP—is the signal that distinguishes an infrastructure problem from an individual subscriber issue. Collecting AP utilization alongside subscriber metrics is what makes that correlation visible before complaints arrive.
The Business Cost of Not Measuring QoE
For most ISPs, the absence of subscriber-level QoE monitoring doesn’t show up as a single identifiable failure. It shows up as a pattern of costs and decisions that each feels normal until they’re looked at together.
Silent churn is the largest and least visible cost. Most subscribers who have a bad experience never call; they simply find another option. At $50–$70 ARPU, losing 10 to 20 subscribers per month to experience problems that nobody knew existed costs between $6,000 and $17,000 in annual recurring revenue, with no support ticket, no complaint, and no data trail connecting the cancellation to a cause. By the time that silent churn shows up in the numbers, the problem has typically been running for weeks or months. The ISP responds by adjusting pricing, running promotions, or upgrading infrastructure, often solving the wrong problem because the right data was never collected.
Truck rolls dispatched without QoE context are frequently avoidable. When a subscriber calls in about slow speeds and the support team has no subscriber-level visibility, the decision to dispatch a technician is made without knowing whether the problem is on the network, at the CPE, or inside the subscriber’s home. At $150–$500 per dispatch, and with industry estimates suggesting 17–20% of field visits return with no fault found, a significant portion of that cost is a direct consequence of making dispatch decisions without the right information. Each no-fault-found visit also means the subscriber still has the same problem after the truck leaves.
Support contacts are more expensive without early warning. Telecom support calls average $20–$25 per ticket in North America before any field costs are considered. A meaningful share of those calls are about problems that have been building for days or weeks—degrading AP performance, a radio approaching its link rate ceiling, self-congestion on an entry-level plan—that proactive QoE monitoring would have surfaced before the subscriber picked up the phone. The cost of the call is not just the handle time. It’s the NOC escalation that follows when the support rep can’t diagnose the issue, the skilled engineering time spent investigating a problem that has nothing to do with the core network, and the subscriber’s eroding confidence in the ISP through each interaction.
Infrastructure investment decisions made without QoE data are expensive guesses. Every quarter, ISPs allocate their budgets to network upgrades, equipment replacement, and capacity expansion. Without knowing which parts of the network are generating churn, which APs are affecting the most subscribers, and whether churn is a network problem or a competitive one, those decisions are made on incomplete information. An ISP that spends a quarter upgrading infrastructure to reduce churn, only to have the real cause turn out to be a competitor offering a lower price, has not just wasted that investment. It has also delayed the response that would have actually worked.
The data required to answer these questions exists inside every ISP’s network. QoE monitoring is the practice of systematically collecting it at the subscriber level before damage is done.
Solving the Self-Congestion Problem
Operators will often get calls or tickets from customers complaining that their internet feels slow, or that multiple devices and activities in the home are slowing each other down (e.g. online gaming is causing Netflix to buffer). If you look at the relevant AP and don’t see anything wrong, that’s usually a sign that the subscriber is self-congesting. This is generally caused by poor queuing management techniques in your plan enforcement platform.
Rather than use standard FIFO queue shaping, Preseem uses FQ-CoDel shaping which divides traffic across virtual queues per subscriber. This means flows can be isolated and keep latency low, even when multiple devices are online. This solves the problem unless the network has other underlying problems, e.g. overloaded APs.
Manage Your Multi-Vendor Network Proactively
One of the major ways that Preseem helps operators manage their networks proactively is by providing simple scores that quantify QoE and show where issues are occurring. The inputs for these scores include the behavior of your APs, behavior of CPEs in the network, combined with a model of data collected from all Preseem customers that tells us what APs are capable of and how they should perform.
For example, our Business Value score is a combination of subscribers with a) poor modulation and b) that are active when other subscribers in your network are active. This tells you those subscribers that are having the biggest impact on your airtime, so you can fix those first to increase capacity on your network.
This gives your team one spot to understand the quality of experience being provided across multiple vendors and gives them an action list to execute.
Similarly, our Subscriber Capacity score uses the same data-based approach. Preseem takes in billions of metrics across our customer base and this tells us how subscribers typically behave in a given network and what specific APs should be capable of. We’re then able to convert that into a score that tells you how many subscribers you can have on a given AP in the current RF conditions. This allows your sales and marketing team to proactively target specific areas where you know you can add more customers.
Automatic AP Shaping
Dan also gave a preview of some upcoming features for Preseem designed to help operators further improve QoE performance and embrace proactive management, including our Automatic AP Capacity Management tool that helps solve overloaded AP issues. Update: This feature is now live in Preseem.
We also touched on how Preseem is moving to a multi-access technology approach as our traditionally fixed wireless customers adopt fiber into their networks. He also spoke about the goal of making Preseem a layer of network quality assurance that sits atop whatever access technology operators use. Watch the full video below!
Frequently Asked Questions
What is QoE?
QoE (Quality of Experience) is a measure of how well an internet service actually performs for the end user, as opposed to how it performs at the network infrastructure level. For ISPs, QoE captures the full subscriber experience: whether the connection feels fast during peak hours, whether video calls stay clear when multiple devices are online, whether gaming is responsive, and whether the service delivers on advertised speeds. QoE is distinct from traditional network performance metrics. A network can pass every infrastructure health check while subscribers behind it experience bufferbloat, packet loss, or throughput well below their plan speed. For regional operators, QoE is the metric that connects network decisions to business outcomes: subscriber satisfaction, churn rate, support call volume, and revenue retention.
What is QoE monitoring?
QoE monitoring is the continuous measurement of the actual experience subscribers have on a network. This is not just whether devices are up or down, but whether subscribers are receiving the speeds they are paying for. It also measures whether customers are experiencing latency that makes video calls feel broken, or losing packets in ways that cause applications to stutter. For ISPs, QoE monitoring is distinct from traditional network monitoring because it measures at the subscriber level rather than the infrastructure level. A network element can be fully healthy by every SNMP metric available while a subscriber behind it is buffering, dropping packets, or unable to reach their plan speed.
What is the difference between QoE monitoring and network monitoring?
Traditional network monitoring (SNMP, element management systems) tells you whether network infrastructure is up or down. QoE monitoring tells you what subscribers are actually experiencing on that infrastructure. The two can show completely different pictures: a link can be operating within normal parameters while dozens of subscribers behind it are experiencing self-congestion, bufferbloat, or degraded throughput due to poor queue management. ISPs that rely only on network monitoring are measuring the health of their equipment, not the health of their subscribers’ experience.
What metrics should ISPs collect for QoE monitoring?
The core metrics for subscriber-level QoE monitoring are throughput measured per subscriber against plan speed, loaded latency, packet loss and retransmission rate, jitter, plan utilization percentage, and AP and sector utilization. For fixed wireless operators, RF signal quality and radio link rate are also critical, as they reveal whether a subscriber’s CPE radio can actually support the plan they’re on, independent of what the network is delivering. These metrics become fully actionable when tied back to network topology, so that a pattern of degraded QoE on the same AP can be identified as an infrastructure problem rather than a series of unrelated individual issues.
What is self-congestion and how does it affect subscriber QoE?
Self-congestion occurs when a subscriber’s own connection fills up their virtual plan link, causing the same queuing and bufferbloat problems that occur on a congested AP or backhaul link, but entirely within that subscriber’s allocated bandwidth. A subscriber on a 25 Mbps plan who starts a large download while on a Zoom call can self-congest their own virtual link, causing the Zoom call to drop even though the network itself has capacity to spare. This is one of the most common causes of “slow internet” complaints on lower-tier plans, and one of the most frequently misdiagnosed, because it does not show up as congestion in AP or link-level monitoring tools.
How can ISPs use QoE monitoring to reduce churn?
Most subscribers who have a bad experience never call to report it, they simply cancel. Without subscriber-level QoE monitoring, ISPs have no way to identify which subscribers are struggling before they make that decision. QoE monitoring surfaces degrading subscriber experience in real time, allowing ISPs to intervene proactively, whether that means fixing an AP, addressing a plan mismatch, or initiating an upsell conversation with a subscriber who is consistently hitting their plan ceiling.
Can QoE monitoring work across multi-vendor networks?
Yes, provided the QoE monitoring solution is designed to operate independently of vendor platforms. Measuring QoE at the traffic level by analyzing actual subscriber traffic inline, rather than polling vendor-specific APIs or management interfaces, produces normalized metrics that apply consistently regardless of whether the underlying equipment is Cambium, Ubiquiti, Calix, Nokia, or any other vendor. This is particularly important for regional ISPs running hybrid fixed wireless and fiber networks, where subscriber experience needs to be understood across access technologies without requiring separate tools for each vendor.
