The Friction Severity Matrix
February 17, 2026 • framework
Identifying friction in digital products is a relatively straightforward task. Predicting and quantifying their impact on user retention, churn, and ultimately revenue — before the damage is done — is a more complex challenge.
To address this, we have developed the Friction Severity Matrix, a framework that helps us estimate the potential churn risk associated with different levels of friction.
Friction in digital products terminates customer relationships. Contentsquare's 2025 Digital Experience Benchmarks Report, drawn from over 90 billion user sessions across 6,000 websites, found that 40% of all online visits were affected by user frustration in 2024, and that overall conversion rates declined 6.1% year-over-year as a direct consequence [1]. As the report's chief marketing officer noted, in 2025 "almost half of online visits will continue to suffer from preventable friction, which is, at best, cutting journeys short, and at worst, driving customers away entirely" [1].
Forrester's 2025 Global Customer Experience Index, which surveyed over 275,000 customers across 469 brands in 13 countries, reinforces this at the brand level: a sustained deterioration in product experience quality erodes customer loyalty across every industry studied, with even incremental improvements translating into measurable churn reduction and share-of-wallet gains [2].
The methodology presented here synthesises these and related benchmarks into a practical framework that estimates monthly churn risk by mapping the breadth of product impact against the severity of the friction experienced. The goal is to make churn risk legible and rankable before it is realised in revenue loss.
Before engaging with the matrix, it is worth being explicit about what the cell values are and are not. No published study has measured monthly churn rates across a controlled population segmented by friction scope and severity simultaneously. The figures presented here are modelled estimates, calibrated against three categories of evidence:
The cells become progressively less empirically grounded as severity and scope increase. The Isolated row has weaker direct evidence for the Medium and High cells, because isolated friction is difficult to separate from platform-wide signals in most published research. The Platform-wide / High upper bound represents a theoretical ceiling for complete product failure; actual figures will be strongly modulated by switching costs, contract lock-in, and competitive alternatives. These caveats are reasons to treat the matrix as a prioritisation scaffold rather than a forecast, and to calibrate outputs against your own product's churn history wherever possible.
Before applying friction multipliers, an appropriate baseline monthly churn rate must be established. According to data aggregated across more than 2,800 SaaS companies tracking 47.3 million customer accounts between 2023 and 2024, average SaaS monthly churn sits at approximately 5.3%, with median B2B SaaS monthly figures in the 3.5–4.2% range [3, 4]. Segmentation matters substantially: SMB-focused products typically run at 3–7% monthly, enterprise products at 1–2% monthly, and early-stage companies may see 10% or higher in their first months before product-market fit stabilises retention [5, 6].
For the matrix below, a working baseline of 3.5–5% monthly is assumed, representative of a mid-market or SMB-facing digital product in a competitive category. This is consistent with Recurly's 2023 subscription benchmark data [7] and UserMotion's 2024 analysis [4].
Behavioural signal and engagement evidence
Contentsquare's 2024 Digital Experience Benchmarks Report, analysing 43 billion sessions across 3,590 websites, found that frustration affected 39.6% of all sessions in 2023, rising from 38.1% the prior year, and that the cumulative effect of frustration factors reduced engagement value per visit by 15% [8]. By 2024, that frustration rate had stabilised at approximately 40%, declining -1.8% year-over-year, though the problem remains pervasive [1]. Across the 2025 dataset, sites achieving the highest retention rates averaged 17% fewer rage-click events per page and earned 18% more page views per visit, establishing a direct quantitative link between frustration suppression and retention outcomes [1].
Friction signals and conversion impact
Amplitude illustrates the effect of checkout friction with the observation that sessions featuring rage clicks may convert at rates as low as 0.9%, compared with 4.1% for smooth-experience sessions, a gap that, if representative, implies friction is suppressing conversion by more than three-quarters at the most critical product moment [9]. While this figure is illustrative rather than a controlled measurement, it is directionally consistent with the engagement-value degradation documented by Contentsquare [8] and provides a conservative anchor for the High Severity multipliers in the matrix.
Feature adoption and workflow integration as retention anchors
McKinsey's SaaSRadar analysis of nearly 200 growth-stage SaaS businesses with revenue between $10M and $200M found that top-quartile-growth performers show 10–30% lower customer churn and 40–50% lower gross-revenue churn than mean performers, a gap McKinsey attributes primarily to their ability to protect the base through product stickiness and deep customer success [10].
A 2025 analysis in the International Journal for Multidisciplinary Research synthesising McKinsey, Pendo, and Forrester data identified product adoption as the primary leading indicator of B2B SaaS retention, and foregrounded failure to deliver value, rather than pricing or competitive pressure alone, as the dominant driver of voluntary long-term churn [11].
ProfitWell's Integrations Benchmark study found that products with at least one integration have 10–15% higher retention, rising to 18–22% for products with four or more integrations, demonstrating that depth of workflow integration directly modulates a customer's tolerance for and sensitivity to product friction [12].
Friction removal case evidence
Mouseflow's Friction Score tooling, applied by e-commerce operator Supacart to diagnose and resolve checkout friction (reducing an 8-step flow to 2 steps), produced a 6% reduction in churn within three months [13]. Contentsquare's work with Harrods identified a single hidden error state on the checkout page that was suppressing approximately 1,000 monthly conversions; resolving it produced a 50% reduction in checkout rage-click rates and an 8% reduction in cart abandonment [14]. A UX redesign of the DNA Payments merchant portal (simplifying onboarding and eliminating cross-device inconsistencies) produced an 18% increase in new user growth alongside a 2.3× reduction in churn within the first two weeks post-launch [15].
Experience failure and churn at the brand level
PwC's Future of Customer Experience Survey, conducted with 15,000 consumers across 12 countries, found that 32% of respondents would stop doing business with a brand they loved after just one bad experience [16]. Core service failure, in which a product cannot deliver its primary function, is identified alongside price perception and ease of switching as one of the most consistent drivers of B2B churn decisions in the academic literature [17]. These brand-level studies represent ceiling-level churn scenarios rather than per-feature estimates and are used as upper-bound anchors, not direct inputs, for the High Severity cells.
The matrix is structured across two dimensions.
Scope: the breadth of the product experience affected by the friction event:
Severity: the functional and behavioural impact on the affected user:
Monthly churn estimates per cell are produced by applying observed friction multipliers to the 3.5–5% working baseline. Multipliers are derived from:
Scope acts as an additional scaling dimension: isolated friction primarily affects users of that specific feature; workflow-level friction affects all users attempting a core task; platform-wide friction degrades the experience for the entire active cohort.
Low Severity multiplier: 1.0–1.5× baseline. Persistent annoyance, but task completion remains intact.
Medium Severity multiplier: 2–4× baseline. Frustration signals (session abandonment, support volume, engagement drops) are established churn precursors in the behavioural analytics literature.
High Severity multiplier: 4–10× baseline. Task failure corresponds to the conversion gap data and brand-level abandonment evidence; the range reflects the difference between isolated task failure (lower end) and platform-wide functional collapse (upper end).
The following table reports estimated monthly churn risk for the cohort experiencing the friction, calibrated to a 3.5–5% SMB/mid-market baseline. Cells marked with an asterisk have weaker direct evidentiary support and should be treated with additional caution.
| Platform Scope / Friction Severity | Low (Confusing) | Medium (Frustrating) | High (Unusable) |
|---|---|---|---|
| Isolated (single feature) | 3–5% | 5–9% (*) | 8–14% (*) |
| Workflow-level (core journey) | 5–8% | 10–17% | 20–35% |
| Platform-wide (core functionality) | 8–13% | 15–25% | 35–60% |
On the Isolated row
The Low cell (3–5%) is at or near baseline by design. A mildly annoying peripheral feature should not measurably elevate cancellations for a product that otherwise delivers value.
The Medium and High cells both carry a (*) because isolated friction is difficult to cleanly separate from platform-wide signals in published research.
A 1.5–2.5× multiplier above baseline for the Medium cell is consistent with the engagement-value degradation evidence from Contentsquare [8], but is not directly evidenced for isolated scope specifically.
The High cell's range (8–14%) reflects the realistic split between users who cancel, and those who contact support, find a workaround, or wait for a fix rather than churn immediately.
On the Workflow-level / High cell
A range of 20–35% represents a 5–8× multiplier above baseline, grounded in the Amplitude conversion gap data [9], the PwC one-bad-experience abandonment rate (32%) [16], and the DNA Payments case, where a 2.3× churn reduction following a workflow redesign implies pre-fix churn was elevated to a corresponding degree above healthy levels [15].
On the Platform-wide / High cell
A range of 35–60% represents a severe monthly churn spike consistent with near-total functional failure. It is not a prediction that 35–60% of users will cancel on day one. Instead, it represents the monthly churn rate likely observed in the first billing cycle following a catastrophic product regression.
The upper bound assumes minimal switching friction and available competitive alternatives; enterprise products with annual contracts and high switching costs would sit at the lower end or below this range.
This is consistent with core service failure being identified as a primary voluntary churn driver [17] and with ProfitWell's finding that failure to deliver value is the dominant driver of cancellation.
Worked example
A workflow-level friction event in an onboarding flow (broken validation messages preventing profile completion, affecting all new users) maps to the Workflow/High cell: an estimated 20–35% monthly churn for the affected cohort.
Resolving the issue is consistent with recovering a substantial fraction of that elevated churn; Harrods and Supacart case evidence suggests that point interventions on high-severity flows normalise conversion and abandonment rates within 60–90 days of deployment [13, 14].
Prioritise the Platform-wide and Workflow-level, High-severity cells first
These are the events where friction is not incidental but definitional to the user's experience of the product. The difference between an Isolated/Low event and a Platform-wide/High event is not linear; it is closer to an order of magnitude in churn risk. McKinsey's finding that top-quartile SaaS performers show 40–50% lower gross-revenue churn than their peers [10] reflects the compounding effect of systematically addressing these cells over time, not any individual intervention.
Distinguish UI friction from product functionality failures
Rage-click signals and navigation confusion are important leading indicators, but they are a subset of a broader problem. Core service failures (features that do not work, workflows that cannot be completed, functionality that diverges from user expectations) are consistently identified in the churn literature as equal or greater drivers of cancellation [17]. A product health audit should encompass both behavioural analytics (session replay, frustration scoring) and functional testing against core user journeys. The two are complementary, not interchangeable.
Use the matrix to frame ROI, not just to rank problems
A 5% increase in customer retention can increase profits by 25–95%, according to Bain & Company research published by Harvard Business Review, a ratio that makes friction remediation one of the highest-leverage investments available to a product team [18]. Against a working baseline of 3.5–5% monthly churn, moving a cohort from a Workflow/High scenario (20–35%) back to baseline represents a retained churn delta of 15–30 percentage points for that segment. At any meaningful user count, that is a material ARR protection exercise. The DNA Payments case (a 2.3× churn reduction from a single workflow redesign) demonstrates that even a Workflow/Medium scenario, when corrected systematically, produces disproportionate retention returns [15].
Audit core user journeys against the scope and severity dimensions above. Identify the cell each friction event maps to. The matrix then produces a prioritisation order that is more defensible than intuition and more granular than a flat severity list.
Contentsquare. 2025 Digital Experience Benchmarks Report. January 2025. Analysis of 90 billion sessions, 389 billion page views, across 6,000 websites globally (Q4 2023 vs Q4 2024). https://contentsquare.com/press/2025-digital-experience-benchmarks/
Forrester Research. 2025 Global Customer Experience Index Rankings. June 2025. Survey of 275,000+ customers across 469 brands, 13 countries. https://www.forrester.com/press-newsroom/forrester-global-customer-experience-index-2025-rankings/
Dollarpocket. SaaS Churn Rate Benchmarks Report. January 2026. Analysis of 2,847 SaaS companies tracking 47.3 million accounts, 2023–2024. https://www.dollarpocket.com/saas-churn-rate-benchmarks-report
UserMotion. SaaS Churn Rate Benchmarks 2024. November 2024. Analysis of 1,000+ subscription-based businesses. https://usermotion.com/saas-churn-rate-benchmark-2024
Churnfree. B2B SaaS Benchmarks: A Complete Guide 2026. https://churnfree.com/blog/b2b-saas-churn-rate-benchmarks/
Adam Fard Studio. SaaS Churn Rate Benchmarks: Key Factors and What Makes a Good Rate. September 2024. https://adamfard.com/blog/saas-churn-rate-benchmark
Recurly. Customer Churn Rate Benchmarks. 2023. Study of 1,200+ subscription sites over 12 months. https://recurly.com/research/churn-rate-benchmarks/
Contentsquare. 2024 Digital Experience Benchmarks Report. February 2024. Analysis of 43 billion sessions, 200 billion page views, across 3,590 websites globally (Q4 2022 vs Q4 2023). Frustration affected 39.6% of sessions in 2023, up from 38.1% in 2022; cumulative frustration factors reduce visit engagement value by 15%. https://contentsquare.com/blog/digital-experience-benchmark-report-2024/
Amplitude. What Are Rage Clicks: Detect and Fix User Frustration. November 2025. Illustrative benchmark: checkout sessions with rage clicks convert at ~0.9% vs ~4.1% for smooth-experience sessions. https://amplitude.com/explore/analytics/rage-clicks
McKinsey & Company. Grow Fast or Die Slow: Focusing on Customer Success to Drive Growth. October 2016. SaaSRadar analysis of ~200 growth-stage SaaS businesses ($10M–$200M revenue). Top-quartile performers show 10–30% lower customer churn and 40–50% lower gross-revenue churn than mean performers. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/grow-fast-or-die-slow-focusing-on-customer-success-to-drive-growth
Sharma, A. A Data-Driven Analysis of the Primary B2B SaaS Retention Drivers. International Journal for Multidisciplinary Research (IJFMR). 2025. Synthesis incorporating McKinsey, Pendo, and Forrester benchmarks. https://www.ijfmr.com/papers/2025/6/59593.pdf
ProfitWell Integrations Benchmark, as cited in: Paragon. Reducing SaaS Churn with Integrations. Study of 500,000 companies: products with 1+ integrations show 10–15% higher retention; 4+ integrations show 18–22% higher retention. https://www.useparagon.com/blog/reducing-saas-churn-with-integrations
Mouseflow. 6 Ways to Fight Customer Churn with UX (Supacart case study). March 2025. Checkout friction reduction from 8 steps to 2 steps; churn reduced 6% within three months. https://mouseflow.com/blog/fight-customer-churn-with-ux/
Contentsquare. Harrods Customer Story. Single hidden error state on checkout suppressing ~1,000 monthly conversions; resolution produced 50% reduction in checkout rage clicks and 8% reduction in cart abandonment. https://contentsquare.com/customers/harrods/
Euvic. Top 5 Banking & Fintech UX Design Case Studies (DNA Payments case study). Merchant portal UX redesign: 18% new user growth and 2.3× churn reduction within two weeks of launch. https://www.euvic.com/us/post/banking-and-fintech-design-examples
PwC. Future of Customer Experience Survey 2017/18. Survey of 15,000 consumers across 12 countries. 32% of respondents said they would stop doing business with a brand they loved after just one bad experience. https://www.pwc.com/us/en/advisory-services/publications/consumer-intelligence-series/pwc-consumer-intelligence-series-customer-experience.pdf
Bhattacharyya & Dash, as cited in: Aytekin, B. et al. A Novel Methodological Approach to SaaS Churn Prediction Using the Whale Optimization Algorithm. PMC / PLOS ONE. 2025. Core service failure identified alongside price perception and ease of switching as a primary driver of voluntary B2B churn decisions. https://pmc.ncbi.nlm.nih.gov/articles/PMC12074543/
Reichheld, F. Loyalty-Based Management. Harvard Business Review. March–April 1993. A 5% increase in customer retention produces profit increases of 25–95% depending on industry. Widely reproduced in retention literature; figures reflect specific industry modelling and should be treated as directional rather than universal. https://hbr.org/1993/03/loyalty-based-management
© 2026 userNebula.com All rights reserved.