Digital Employee Experience Management Solutions

Most technology problems inside a company never become a support ticket.

The laptop takes four minutes to boot. The VPN drops twice a day. The finance application hangs for eight seconds every time someone saves. None of this generates a call to the service desk, because none of it is broken exactly, and everyone has quietly built a workaround. They make coffee during boot. They reconnect without thinking. They save less often, which is its own problem.

An IT department measured on ticket volume and uptime will report that everything is fine. The employees know otherwise, and the gap between those two views is the entire reason this category exists.

Where the category came from

This did not start as an employee experience play. It started as virtual desktop troubleshooting.

Organisations running VDI had a specific and painful problem: when a user complained that their session was slow, nobody could tell whether the cause sat in the hypervisor, the storage layer, the network, the profile, or the application. The infrastructure monitoring showed green because, from the infrastructure's point of view, everything was green. A generation of tooling grew up to answer that question by instrumenting the session itself rather than the servers underneath it.

Two things turned that niche into a category.

The first was the shift from managed desktops in controlled offices to laptops on home broadband. Once the endpoint left the building, the entire diagnostic model that assumed a known network stopped working. Forrester published its first evaluation of this market in Q4 2020, which is not a coincidence.

The second was that executives started asking a question IT had no instrument for. Employee experience became a board-level topic, HR started running engagement surveys that scored technology badly, and somebody eventually asked whether the technology complaint was true. Nobody could answer with data. The tools that had been sold to VDI administrators turned out to be the closest thing available to an answer, and the category repositioned upward accordingly.

That heritage still shows. Several products in this market carry deep end-user computing DNA and comparatively shallow experience-design thinking, and a few have the opposite problem.

What the tooling actually does

These platforms put an agent on the endpoint and collect telemetry the service desk never had. Boot and shutdown duration, application crashes and hangs, device health, memory and CPU pressure, network performance, and the behaviour of the specific machine belonging to the specific person.

That data becomes an experience score, which is the mechanism that makes the invisible visible. Instead of a ticket count, IT gets a number per device, per application, per team, per office, and can watch it move.

On top of that sit four capabilities that separate the serious products from monitoring dashboards.

Qualitative feedback, gathered through in-context surveys, outreach campaigns, and sentiment analysis, because telemetry tells you the application hung and only the employee can tell you whether that ruined their afternoon.

Root cause analysis, reducing the time between noticing a pattern and understanding what causes it. This is where the volume of collected data becomes a liability as much as an asset, and where vendors increasingly point at AI.

Remediation, ideally automated, running fixes across thousands of endpoints without a human touching each one. The mature version of this is self-healing, where a known bad state is detected and corrected before anyone notices.

And personalisation by persona, since a developer, a call centre agent, and a field engineer have genuinely different definitions of a working laptop. A machine that would be unacceptable for video editing may be entirely adequate for data entry, and treating both against the same standard produces either wasted hardware spend or a lot of frustrated designers.

A category with two names

Worth clearing up, because the vendors use both and the analyst firms disagree.

Forrester's category is end-user experience management, and its evaluations are published under that name. Digital employee experience, or DEX, is what the tooling measures rather than what it is called. Forrester defines DEX as the sum of everything employees perceive about working with the technology they use for daily work and for managing their relationship with their employer across their whole employment.

That definition is broader than it first appears. It covers the laptop, but also the HR portal, the expenses system, the onboarding process, and the tooling someone touches on their last day. Most products in this market instrument a fraction of that scope, which is worth remembering when a vendor claims to measure digital employee experience rather than device and application performance.

Gartner names the tooling itself digital employee experience management, and publishes a Magic Quadrant for DEX management tools under that name.

So the same product gets evaluated by one firm as EUEM and by the other as DEXM. In practice the industry has largely adopted the Gartner vocabulary, and Forrester's own analysts now use DEXM in commentary even while the formal category name stays EUEM. If you are searching for research, search both.

Three evaluations, one very stable result

Forrester has run this market three times, and the trajectory is unusual.

The inaugural New Wave in Q4 2020 covered eleven vendors against ten criteria. Two Leaders: Nexthink and Lakeside Software. Strong Performers were Aternity, Catchpoint, VMware, and 1E, with NetMotion, Tanium, and ThousandEyes as Contenders and Liquidware and vast limits as Challengers. Forrester noted at the time that Nexthink led on holistic experience management and scored among the highest on strategy.

The Q3 2022 Wave covered nine vendors against thirty two criteria, a threefold increase in evaluation depth in two years. Three Leaders: 1E, Lakeside Software, and Nexthink. ControlUp, Riverbed Aternity, and ThousandEyes were Strong Performers, with Ivanti, Tanium, and VMware as Contenders.

1E's profile in that evaluation is worth quoting because it captures how differentiation worked at the time. Andrew Hewitt described the company as "a remediation powerhouse that's building its experience credibility", crediting a lightweight distributed architecture capable of fixing problems in real time even on offline endpoints, along with mass automated self-healing. Strong on the fixing, still proving the measuring.

The Q3 2024 Wave covered eight vendors against thirty four criteria, with two Leaders: Lakeside Software and Nexthink.

Read those three together. The criteria count went from ten to thirty four, meaning the evaluation got far more demanding. The vendor count fell from eleven to eight. And the same two names led all three times.

That combination is rare. Most categories in this series show churn at the top. This one shows a market where the specialists established a lead early and the larger platform vendors, several of whom have every structural advantage in endpoint management, have not displaced them.

What happened to everyone else

The names that dropped out of those lists mostly did not fail. They were bought.

Aternity had already moved inside Riverbed, which is why it appears under that name in 2022. NetMotion was acquired by Absolute Software. ThousandEyes sits inside Cisco. VMware went to Broadcom. vast limits, the German vendor behind uberAgent, was acquired by Citrix.

The pattern is consistent with what happened in cloud cost management and in AIOps: capabilities that begin as standalone products get absorbed into the platforms that already own the adjacent territory. What makes this category interesting is that absorption has not yet decided it. Two independents still lead, which suggests the specialist depth is worth something the platforms have not replicated.

The experience score is a construct

Every vendor in this market produces a headline number, and buyers treat those numbers as though they were measurements. They are not. They are composites, weighted according to decisions the vendor made, and two platforms scoring the same estate will not agree.

This matters practically in three ways.

Scores are not portable between vendors, so switching platforms resets your baseline and destroys your trend data. Anyone who has built a two-year improvement narrative on one vendor's score understands why this raises the switching cost considerably.

Scores are configurable, which means they are gameable. If the weighting can be adjusted and the same team owns both the adjustment and the target, the number will improve whether or not anything else does. Deciding early who owns the scoring model, and separating that from who is measured by it, avoids an awkward conversation later.

And scores flatten distribution. An average experience score of eighty across ten thousand devices can describe a healthy estate or one where nine thousand people are fine and a thousand are miserable. The second case is the one that generates attrition, and it is invisible at the headline level. Look at the tail rather than the mean.

The half nobody instruments

Sentiment is the part of this category that separates it from infrastructure monitoring, and it is routinely underweighted in evaluations.

Telemetry establishes that an application crashed eleven times last week. It cannot establish whether anyone cared. Some crashes are catastrophic and some happen in a tool nobody uses. Conversely, a system performing perfectly against every technical metric can still be experienced as awful, because the workflow it enforces is stupid, or because it logs people out every ninety minutes for a security policy nobody explained.

The vendors that lead this market pair quantitative telemetry with structured qualitative feedback for that reason, and the pairing is the product. Buying the telemetry half alone gives you a very precise account of a problem you have not confirmed exists.

There is a survey fatigue risk on the other side. In-context feedback prompts work because they arrive at the moment of friction and take five seconds. Used carelessly they become another interruption in a working day already full of them, and response rates collapse in a way that is hard to recover. Treat the sentiment capability as a limited budget to be spent rather than a feature to be switched on everywhere.

The conversation that is not with IT

Endpoint agents collecting per-employee behavioural data sit close to territory that works councils, unions, and European data protection regimes take seriously.

The technical reality is that these platforms can see which applications a named individual used, for how long, and when. Vendors provide anonymisation and aggregation controls precisely because that capability makes people uncomfortable, and in several jurisdictions the deployment cannot proceed without formal consultation.

The organisations that handle this well decide the position before procurement rather than after. Will data be identifiable or aggregated by default? Who can see individual-level detail and under what circumstances? Is there an explicit commitment that this data will not be used in performance management? Answering those questions in a works council meeting without having prepared is a reliable way to stall a rollout for two quarters.

The commitment not to use experience data for performance management is worth making explicitly and in writing, because the temptation arrives later. A platform that can tell you which employees have the worst technology experience can, with a small change of framing, be read as telling you which employees are least productive. Those are different claims and conflating them will destroy trust in the programme permanently.

The population nobody measures

Almost everything in this category assumes a knowledge worker with a managed laptop.

A large share of the workforce in retail, logistics, manufacturing, healthcare, and hospitality does not have one. They have shared terminals, handheld scanners, kiosks, or a personal phone with one corporate application on it. Their technology experience is frequently worse than the head office population's and considerably less visible, because agent-based instrumentation on a shared device tells you about the device rather than about any of the twelve people who used it.

If a meaningful proportion of your workforce is frontline, ask specifically how the platform handles shared and non-standard endpoints before you evaluate anything else. Several products in this market will give you a confident score for the twenty percent of your workforce that already complains loudest and nothing at all for the rest.

Where it overlaps with what you already own

Three adjacent categories claim part of this territory, and the overlap is real rather than marketing.

Endpoint management suites already have an agent on every device and increasingly ship experience dashboards. If you run one at scale, the honest question is whether its native capability is sufficient rather than whether a specialist is better, because a specialist is better and may not be better enough to justify a second agent.

Observability platforms instrument applications and infrastructure, and some now extend to the endpoint. The distinction is perspective: observability answers whether the service is healthy, this category answers whether the person using it is having a bad time. Those diverge more often than you would expect.

Service management platforms own the ticket, and the integration between experience data and the service desk is where a lot of the practical value sits. A proactive fix that closes a ticket nobody filed only shows up as value if the two systems talk.

How these actually get funded

Since the benefit is diffuse, the business case usually gets assembled from four places.

Service desk cost, through reduced ticket volume and faster resolution, which is the easiest to measure and usually the smallest number.

Hardware refresh deferral, by identifying which devices genuinely need replacing rather than replacing on a three-year cycle. This is frequently the largest single line and the one finance finds most persuasive.

Onboarding and productivity time, particularly the hours lost in the first week of employment to technology that does not work.

And attrition, which is the number everyone wants to claim and nobody can defend. Technology frustration contributes to people leaving. Quantifying that contribution credibly is beyond what any of these platforms can prove, and building a business case on it invites the finance scrutiny that kills the project.

Where the category is heading

Forrester's recent commentary points at expansion. The uses organisations put these platforms to are broadening beyond IT operations, and AI dominates current vendor roadmaps, mostly aimed at the root cause problem rather than at collection.

The device manufacturers are circling too. HP has been positioning itself as a future-of-work platform connecting devices, software, security, and workflows into an AI-orchestrated employee experience rather than as a hardware company. That is a serious strategic claim from a vendor that already ships the endpoint.

Which raises the obvious question for buyers. If your endpoint vendor, your management suite, and your specialist platform all claim the experience layer, you are being sold the same capability three times. The specialists have held their lead through three evaluations on the strength of data depth and remediation. Whether that holds as the platform vendors keep pushing is the open question in this category, and the Q3 2024 evaluation is now old enough that the next one will be worth reading closely.

What to test

Instrument before you buy, if you can. Most vendors will run a proof of value on a subset of devices. The useful test is whether it surfaces a problem you did not already know about, because a tool that confirms your existing ticket data has told you nothing.

Ask how long until first insight, not how long until deployment. Agent rollout is straightforward. Getting to a defensible experience score with meaningful baselines takes longer, and vendors differ sharply here. Forrester's own read on the Leaders in the current evaluation put time to value at the centre, crediting out-of-the-box templates, AI-based root cause analysis, and customer enablement rather than raw capability.

Test the agent's own footprint. A platform that measures experience by consuming noticeable CPU and memory on every device is a genuinely funny failure mode and not a rare one.

Check remediation authority before capability. Automated self-healing is only useful if someone will let it run in production, and that permission conversation belongs to security and endpoint teams rather than to whoever is buying.

Ask what happens to your score history if you leave.

And decide what you will do with a bad score. The category produces uncomfortable numbers about specific teams, offices, and applications. If there is no budget or mandate to act on those numbers, the platform becomes an expensive way to document a problem you have already decided to live with.

Forrester does not endorse any vendor named here, and tier placement should not be read as a recommendation to buy.