The Software People Are Leaving: 136,000 Switching Events Analysed

There is a genre of article that has dominated software commentary this year. SaaS is dead. The SaaSpocalypse. Companies are taking their software back. Every one of them argues that businesses should leave their software vendors, usually citing the same handful of pricing statistics.

Not one of them shows who actually is. That is a strange gap, because the data exists. Software review platforms ask buyers what they switched from and what they switched to, and those answers accumulate into a map of where users are moving.

We counted them. Across 136,000+ recorded switching events covering 6,954 products as of September 2026, the picture is far more specific than the narrative, and considerably less apocalyptic.

Why nobody publishes this

The absence is explicable. Writing "SaaS is dying" requires a point of view and a few licensed statistics. Writing "here are the products people are leaving, counted" requires a corpus of software reviews with structured switching fields, which most publishers do not have and most vendors would not publish if they did.

The result is that the entire conversation operates one level of abstraction above the useful one. A founder deciding what to build does not need to know that software spend is rising 14% a year. They need to know which markets contain people actively leaving a product they already pay for. Those are different questions and only one of them is answerable with evidence.

How we measured it

When a buyer reviews software on Capterra, they frequently state what they used previously and what they considered instead. Aggregated across a category, that produces a directed graph: for any product pair, how many people reported moving in each direction.

Net switching flow is the simplest possible reading of that graph. For each product, count the times someone reported switching away from it, subtract the times someone reported switching to it. A large negative number means far more people described leaving than arriving.

One structural fact is worth pausing on. The corpus holds 91,932 reported exits against 44,134 reported arrivals. That asymmetry is not evidence that software is collapsing. It is evidence that people narrate escape more vividly than selection, which is a useful thing to know about review data generally and a reason to read the direction of these numbers rather than their absolute level.

Who loses the most people

By raw net outflow, the list is dominated by exactly the products you would expect: large, mature, broadly deployed platforms with enormous user bases and correspondingly enormous review volumes.

Resist the obvious conclusion. None of these companies are in trouble, and this table is not a mortality list. A product with millions of users and tens of thousands of reviews will accumulate more exit mentions than a product with a thousand users, regardless of how well it retains. Raw net flow measures absolute movement, and absolute movement tracks size.

What the table does tell you is where the volume of dissatisfaction lives. If you want a market where a meaningful number of people are actively looking for something else, these categories contain them. That is a different and more useful claim than "HubSpot is losing".

Who leaks worst for their size

Normalising fixes the size problem. Outbound share asks: of all the switching activity that mentions this product at all, what fraction is people leaving rather than arriving? A product at 50% is in balance. Anything meaningfully above that is losing the exchange.

The cluster is tight, between 70% and 73%, which is itself informative. Rather than a few catastrophic outliers, there is a band of established enterprise platforms all leaking at a similar rate. Digital signature, event management, expense reporting, SEO tooling, survey research, HR and CRM are structurally different markets, and they are all shedding users at roughly the same ratio.

That pattern looks less like product failure and more like a category of software reaching the stage where it is bought by committee, configured heavily, and tolerated rather than liked. Our analysis of why SaaS customers churn finds the same shape from the complaint side rather than the movement side.

Where switching concentrates

Aggregating by category rather than product shows where the churn volume actually sits.

Two entries deserve attention for opposite reasons. Applicant tracking shows 852 exits across only 36 products, the densest churn per product in the set. Digital signature is tighter still at 553 exits across 28 products. Both are consolidated markets where a small number of incumbents absorb an unusual amount of dissatisfaction, which is a materially better setup for a new entrant than a fragmented category with the same total churn spread over 130 products.

For the adjacent view of which markets have room rather than which have churn, see the most underserved software markets and low competition SaaS ideas.

Why people actually leave

The corpus records what the destination product did better. After consolidating near-duplicate labels, the ranking is stark and it contradicts most commentary on this subject.

Interface quality outweighs the next cause by roughly six to one. That is an uncomfortable finding for the prevailing narrative, which frames software switching as an economic decision driven by price inflation and build-versus-buy arithmetic. In the stated reasons of people who actually moved, price is not the headline. Usability is.

It is also an encouraging finding if you are building. Pricing advantage is difficult to defend and trivially matched by an incumbent with more capital. Interface quality in a specific workflow is defensible precisely because large platforms cannot optimise for one workflow without degrading forty others. That is the structural reason the wedge strategy in single feature micro SaaS ideas keeps working.

"We spent three months trialing every major tool combination. The verdict: switching tools doesn't fix disorganization, but having fewer tabs open actually helps more than we expected." — r/SaaS

"Typeform looks great but gets expensive fast. SurveyMonkey feels stuck in 2012." — r/SaaS

What the SaaSpocalypse narrative misses

The dominant story this year holds that AI has collapsed the cost of building software, so companies are replacing their vendors with custom builds. The supporting figures are real: software prices rising around 14% annually against general inflation near 3%, and a widely cited enterprise survey finding that roughly a third of large companies have replaced at least one tool with something built in-house.

What the switching data adds is direction. People are not mostly leaving software for no software. They are leaving software for other software, and the corpus records 44,134 arrivals to prove it. The movement is lateral, not exit.

That distinction changes what it implies for a builder. If companies were genuinely abandoning categories, building in those categories would be foolish. If they are churning between vendors inside a category, then the category has both demonstrated willingness to pay and demonstrated dissatisfaction, which is the most favourable combination available. Our analysis of what software AI can't replace reaches the same conclusion from the market-structure side: AI-native competition is concentrated in ten categories and effectively absent from seventeen others.

One founder described the commercial value of this intent directly:

"The person searching for a competitor alternative is in an active buying decision. They've already decided to leave their current tool. They're not browsing. They're buying." — r/SaaS

"Wrote 3 how-to-switch blog posts. Combined, these 3 posts generate about 40% of our organic signups. Everything else we've published combined generates the other 60%." — r/SaaS

If you are building

Three practical readings, in order of how much weight the data supports.

First, treat high-churn categories as validated demand, not as open goals. A category with heavy net outflow proves people pay and are unsatisfied. It does not prove they will buy from you. The products bleeding users here have distribution, brand and sales teams. The realistic entry is one workflow done conspicuously better, not a replacement suite. Check the gap is documented before committing, via documented pain points and mining Capterra reviews for SaaS ideas.

Second, prefer consolidated categories to fragmented ones. Applicant tracking concentrates 852 exits across 36 products. Asset tracking spreads 1,003 across 130. The first is a market where dissatisfaction has a small number of addresses. The second is a market where it is diffuse and hard to reach. Cross-reference with SaaS market saturation and the state of micro SaaS competition before assuming a crowded category is contestable.

Third, build the interface, not the feature list. The stated-reason data is unusually clear on this. Roughly 566 mentions of interface quality against ~95 for support and ~80 for reporting. If you are choosing between matching an incumbent's feature matrix and making one workflow dramatically less irritating, the switching data says the second wins moves. That is consistent with what buyers say in small business software pain points and negative reviews on G2 and the App Store.

For category-specific starting points, the B2B SaaS ideas and niche SaaS ideas collections sort by market rather than technology, which is the right axis here. Legacy system API wrapper ideas covers the integration-shaped version of the same wedge, and boring industries begging for micro SaaS plus industries still running on spreadsheets cover the markets where the incumbent is not software at all.

The switching wedge, worked through

Abstractions are easy to nod at and hard to act on, so take the densest opportunity in the dataset and walk it properly. Digital signature shows 553 reported exits against 212 arrivals across just 28 products. That is the tightest concentration of dissatisfaction per product anywhere in the corpus, and DocuSign alone accounts for 728 of the exits recorded against it across all categories, at a 72.2% outbound share.

Step one, confirm the money is real. Digital signature is not a category people use for free and abandon. It sits inside contracting, HR onboarding and procurement workflows where the software is a line item with a budget owner. The presence of 212 arrivals matters as much as the 553 exits: people are not leaving the category, they are shopping within it.

Step two, find the specific irritation. This is where the stated-advantage data earns its place. Across the corpus the winning product is described as more intuitive roughly six times more often than it is described as better supported or better at reporting. In a signature workflow that translates to concrete things: how many clicks to send, whether the recipient needs an account, whether templates survive a field change, whether the audit trail is legible to someone who is not a lawyer.

Step three, size the wedge honestly. Twenty-eight products already serve this category and the incumbent has brand recognition strong enough that its name is a verb. You are not replacing it. You are taking one workflow, for one kind of buyer, where the general-purpose tool is conspicuously annoying. That is a smaller ambition and a much higher probability of revenue, which is the trade the micro SaaS examples collection documents repeatedly.

Step four, check nobody already did it. Concentration cuts both ways. A category with 28 products and heavy churn may be concentrated because it is genuinely hard to serve, or because the obvious wedges are taken. Run the same category through customer pain point research before assuming the opening is real, and look at what micro SaaS actually charges to sanity-check whether the price point supports a business.

Applicant tracking rewards the same treatment for a different reason. It shows 852 exits across 36 products, and hiring software has an unusual property: the person who selects it is rarely the person who suffers it daily. Recruiters inherit a tool chosen by HR leadership for compliance and reporting reasons, which is precisely the setup that produces interface complaints at volume. When the buyer and the user are different people, usability debt accumulates without generating churn until it becomes unbearable, then generates a lot of it at once.

What changed in how software is sold

There is a second shift running underneath the switching data, and it affects how a new entrant should price rather than what they should build.

"The share of SaaS companies running seat based pricing dropped from 21% to 15% in twelve months, while hybrid models went from 27% to 41% over the same window." — r/SaaS

Per-seat pricing is losing ground to usage and outcome models. That matters for switching because seat-based contracts create a specific kind of resentment: the customer pays for capacity they do not consume, notices it during every budget review, and starts shopping. Several of the heaviest leakers in the outbound table price per seat or per contact, and the complaint pattern is consistent enough across categories to be worth naming.

The practical implication for a new product is that pricing structure is part of the wedge, not a detail to settle afterwards. If the incumbent charges per seat and the buyer's pain is that half their seats are dormant, then usage-based pricing is a feature. It is also one an established competitor finds difficult to copy, because changing pricing model on an existing book of business is a revenue event their finance team will block.

The caution is that outcome pricing introduces its own definitional arguments. Deciding what counts as a resolved ticket, a completed signature or a successful match turns out to be contentious, and customers notice quickly when the definition is generous to the vendor. Pricing innovation is a real wedge and a real liability at the same time.

None of this is visible in the switching counts themselves. It comes from reading the categories that leak hardest and noticing what they have in common commercially. Treat it as a hypothesis the data is consistent with rather than something the data proves.

Nobody wins in this data, and that matters

Having ranked the products losing the most people, the obvious next question is who is gaining them. We ran it. The answer is nobody, and the reason is instructive enough to publish rather than quietly drop.

Sorting every product with 100 or more switching events by net inflow, the single best performer in the entire corpus is net -7. The next is -19, then -20, then -22. There is no positive number anywhere in the ranking. Every product in this dataset, without exception, has more people reporting that they left it than arriving at it.

That is not a finding about software. It is a finding about reviews. The corpus holds 91,932 reported exits against 44,134 reported arrivals, a ratio of roughly two to one. When someone writes a review they reliably narrate what they escaped and only sometimes narrate what they chose. The asymmetry is baked into the collection method, so it propagates into every net figure.

The practical consequence is that net flow in this dataset is only ever a relative measure. HubSpot at -715 is not losing 715 customers, and a product at -7 is not nearly break-even in any real sense. What the numbers support is ranking: which products absorb disproportionately more exit narration than their peers. Read as a league table it is informative. Read as an absolute churn figure it is simply wrong, and anyone citing it that way, including anyone quoting this article, has misunderstood the source.

This is also why the outbound share column exists. Expressing exits as a proportion of all movement involving a product cancels most of the collection bias, because the same reporting asymmetry applies to the numerator and denominator alike. It is the closest thing to a fair comparison the data allows, which is why the 70% to 73% cluster is the most trustworthy number in this entire analysis.

We are stating this plainly because the temptation with a dataset like this is to publish the dramatic version and let the caveat sit in a footnote. The dramatic version would be "HubSpot lost 715 net customers". That sentence is false, it is the sentence most likely to be quoted, and a research page that invites its own misreading is not worth much. The honest version, that a tight band of mature enterprise platforms absorbs around seven in ten exit mentions in their categories, is less quotable and considerably more useful if you are deciding where to build. For more on reading evidence like this without over-claiming, see how to find SaaS ideas.

Where this could be wrong

• Self-reported, not observed. These are switches people mentioned while writing a review. Silent churn, the majority of all churn, is invisible here.
• Exits outnumber arrivals roughly two to one (91,932 against 44,134). That is a property of how people write reviews, not evidence that software is emptying out. Read direction, not level.
• Raw counts favour large products. Net flow and outbound share tell different stories deliberately, and the second is the fairer comparison.
• Category ordering is partly a taxonomy artifact. Capterra's categories are granular and this sample skews alphabetically at the top. Treat category rank as indicative rather than exact.
• The "why" is one-sided. The corpus records what the destination did better, not what the origin did worse. Those are mirror images, but they are not identical, and the stated advantage is written by someone motivated to justify their choice.
• Pricing is not measurable here. The price comparison field is effectively empty across the corpus, so this analysis makes no claim about cost as a switching driver either way.
• Every product in the corpus is net negative. The best performer anywhere in the data is -7. That is a collection artifact, not a market condition, and it is the single most important thing to understand before quoting any figure above.
• Product naming is imperfect. A small number of entries carry placeholder labels from anonymised reviews, which we excluded from the tables but which inflate the raw distinct-product count of 6,954 slightly.

None of those caveats move the central finding. A concentrated band of established platforms is losing roughly 70% of its switching exchanges, the dominant stated reason is interface quality rather than price, and the movement is lateral rather than an exit from software. For how to turn that into a specific product decision, see how to validate a startup idea and how to find problems worth solving, or start from revenue intelligence if you want the money side first.

Frequently asked questions

BigIdeasDB Research. (2026). The Software People Are Leaving: 136,000 Switching Events Analysed. BigIdeasDB. Retrieved from https://bigideasdb.com/software-people-are-switching-away-from-2026