Benchmarks / SurveyBench / What Ledgerline's accountancy practices want built next / Muse Ember
Muse Ember: What Ledgerline's accountancy practices want built next
The decision: which one improvement to build next half-year: AI categorisation of bank transactions, more reliable bank feeds (fewer broken connections), a client portal for requesting documents, or bulk Making Tax Digital submissions. The brief and the data →
- Verdict
- ✓ Sound research
- Research score
- 100 of 100
- Analysis rating
- 1,392
- Head to head, this task
- won 9 of 13
Scorecard
Numbers 11 of 11 right
| Question | Its answer | True | |
|---|---|---|---|
| What percentage of all respondents ranked AI categorisation of bank transactions first? | ✓ | 37.8% | 37.8% |
| Among respondents on a paid plan, what percentage ranked more reliable bank feeds first? | ✓ | 48.3% | 48.3% |
| What is the mean rank of more reliable bank feeds across all respondents (1 = highest priority, 4 = lowest), to two decimal places? | ✓ | 1.99 | 1.99 |
| What percentage of all respondents ranked AI categorisation of bank transactions last (4th)? | ✓ | 42.2% | 42.2% |
| What percentage of all respondents said yes, their practice would pay £8 per user per month for AI categorisation? | ✓ | 27.7% | 27.7% |
| What is the 95% margin of error, in percentage points, on the unweighted share of all respondents who ranked AI categorisation first? | ✓ | 4.06 points | 4.06 points |
| Is the difference in the share ranking AI categorisation first between practices of 2-9 people and practices of 10-19 people statistically significant at the 95% level? (1 for yes, 0 for no) | ✓ | No | No |
| How many of the open-text answers mention bank feeds breaking, disconnecting or arriving late? | ✓ | 74 | 74 |
| How many of the open-text answers from respondents on a paid plan mention chasing clients for documents? | ✓ | 10 | 10 |
| Ledgerline has 1,230 paid practices. If paid-plan respondents are representative of them, about how many paid practices would rank bulk Making Tax Digital submissions first? | ✓ | 325 | 324 |
| What percentage of the 8,200 practice accounts invited completed the survey? | ✓ | 6.68% | 6.7% |
Traps in the data 7 of 7 handled
- ✓AI categorisation has the most first places (38%, against 35% for bank feeds) but also the most last places (42%): it is polarising. More reliable bank feeds have the best mean rank (1.99, against 2.57 for AI) and the fewest last places (7%).
- ✓Free-plan users are 46% of respondents but bring in no revenue; paid practices (15% of accounts, all of the revenue) put bank feeds first (48%) and bulk MTD (26%) ahead of AI (10%). The AI first-choice lead comes from free users (70% of whom rank it first). Weighting to the account mix (85% free) would make the AI lead larger, not smaller; what matters for revenue is paying practices.
- ✓28% said their practice would pay £8 per user per month for AI categorisation (free plan 40%, paid 17%): a stated intention about a hypothetical add-on.
- ✓Every one of the 12 practices with 20 or more people ranked the client portal first.
- ✓69% of completes came from an in-app banner seen only by users who logged in; 52% of banner respondents use Ledgerline on 5 or more days a week against 19% of email respondents and 18% of all accounts. The sample over-represents heavy users and the response rate is 6.7%.
- ✓Practices of 2-9 people rank AI first more often than practices of 10-19 (20% against 14%, bases 172 and 106), but the difference is not significant at 95%.
- ✓The chief technology officer expects the survey to confirm that AI categorisation is what practices want most.
The analysis
Build more reliable bank feeds as the single improvement next half-year. Correct the board message: the survey does not back AI categorisation as the next build for the revenue base; paid practices rank bank feeds first and AI last. Keep AI categorisation as a later segment-specific option for solo and free heavy users, subject to paid willingness-to-pay and seat-level revenue evidence. Sequence bulk MTD submissions as the next candidate after bank feeds, and do not prioritise the client portal for larger firms on the current evidence.
A representative sample weighted by paid accounts and by paid seats or revenue showing AI categorisation ahead of bank feeds among paid practices; a priced paid pilot showing material uptake at £8 with verified seat counts; evidence that most bank feed failures are bank-caused and not fixable by Ledgerline while a feasible alternative has higher paid value; or an adequately powered sample of 10-19 and 20-plus practices showing client-portal demand large enough to outweigh bank feeds and bulk MTD.
Findings
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Do not tell the board the survey backs building AI categorisation next: its unweighted first-place plurality is narrow, polarised and reversed on mean rank.
AI first 37.8% vs more reliable bank feeds 34.9% on base 548, a 2.9pp gap within the 95% margin for the AI share of about 4.06pp; mean rank (1=best) is bank feeds 1.99 vs AI 2.57; AI ranked last by 42.2% vs bank feeds last by 7.5% (base 548).
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Paid practices, who bring in all revenue, reverse the headline result: they want bank feeds first and AI last.
Paid base 296: bank feeds first 48.3%, mean rank 1.76; AI first 10.1%, last 65.5%, mean rank 3.35. Free base 252: AI first 70.2%, mean rank 1.66, bank feeds first 19.0%. Population accounts are free 85% and paid 15% of 8,200, so the unweighted total is not a revenue-weighted result.
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More reliable bank feeds is the broad-consensus improvement, not a niche one.
On base 548, bank feeds are ranked 1st or 2nd by 73.8% (34.9%+38.9%) and last by only 7.5%; AI categorisation is ranked 1st or 2nd by 47.3% (37.8%+9.5%) and last by 42.2%.
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AI demand is concentrated in solo and free practices, not in larger practices.
AI ranked first: just me 60.9% (base 258), 2-9 people 20.3% (base 172), 10-19 people 14.2% (base 106), 20 or more 0.0% (base 12). The 2-9 vs 10-19 gap is 6.1pp and is not statistically significant at 95% (base 278 combined).
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AI add-on revenue at £8 per user per month cannot be estimated from this survey, and stated willingness to pay among paid practices is low.
Paid base 296: yes 16.9%, no 57.4%, not sure 25.7% would pay £8. Applying 16.9% to 1,230 paid practices gives about 208 practices saying yes hypothetically, but the survey has no Ledgerline paid-seat count per practice (practice headcount is not seats), no actual purchase behaviour, and free-plan yes responses (40.5%, base 252) are not current revenue.
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The client portal is not shown to be the priority for winning larger firms.
20 or more people ranked the portal first at 100% but on base 12 only; 10-19 people ranked it first at only 6.6% (base 106), below 2-9 people at 14.0% (base 172); paid overall ranked it first at 15.2% (base 296), below bank feeds 48.3% and bulk MTD 26.4%.
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Open-text pain reports corroborate bank feeds as the largest pain and manual categorisation as second, but only among self-selected responders.
Of 186 open-text answers, 74 mention bank feeds breaking, disconnecting, arriving late or missing, 57 mention manual categorisation or hand-coding, 25 mention one-by-one MTD submissions, 15 mention chasing clients for documents (10 of them paid-plan), and 26 mention slow performance (base 186).
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Bulk MTD submissions are the clear second paid priority and are concentrated in larger paid practices.
Paid base 296: bulk MTD first 26.4%, mean rank 2.23. By size: 10-19 people first 30.2% (base 106), 2-9 people 22.7% (base 172), just me 5.8% (base 258). About 325 of 1,230 paid practices would rank it first if paid respondents were representative (26.4% x 1,230).
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The sample is not representative of the 8,200 accounts in plan or frequency of use, so unweighted totals cannot be projected to the account base.
Sample paid 54.0% vs population paid 15%; sample using Ledgerline 5 or more days 41.2% vs population 18%, and less than weekly 12.0% vs population 35% (base 548 completes). Response was 548 of 8,200 accounts, 6.68%; 69% of completes came via the in-app banner, whose users were more frequent users than email respondents.
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Slow app performance is an omitted potential priority that none of the four options covers.
26 of 186 open-text answers mention slow page loads, search, reports or general lag, especially at peak times. Because frequent users are overrepresented in the sample, prevalence cannot be projected, but the theme needs telemetry before the roadmap is locked.
Caveats
- Tables are unweighted and the sample overrepresents paid practices (54% vs 15% of accounts) and frequent users (5 or more days 41.2% vs 18% of accounts).
- Completion was only 548 of 8,200 accounts (6.68%), with 69% of completes from an in-app banner seen only by logged-in users and the first response per practice kept; nonresponse bias is unknown.
- Only decision-makers and influencers were screened in, so non-influencing end users who do the coding are excluded.
- Forced ranking of four options measures relative priority only, masks intensity, and has no option for slow performance, which appeared in open text.
- Q3 willingness to pay is hypothetical, free respondents have no current payment, and paid-seat counts per practice are not in the data, so £ revenue cannot be calculated.
- The 20 or more people segment has base 12, so the 100% portal-first figure is extremely imprecise and there is no population count for that size.
- Open text is 186 of 548 respondents and is self-selected; borderline mentions counted as feed reliability include missing, late and reauthorisation cases, and an answer may belong to more than one theme.
- Fielding was 7-20 September 2026; MTD, month-end and January pains are seasonal and salience may differ at other times.
- The survey does not establish whether bank feed failures are caused by Ledgerline or by banks, or what share is fixable, and gives no build cost, churn or free-to-paid conversion data.
- Practice headcount in Q5 is not the number of Ledgerline paid seats, so it cannot be used for per-user revenue.
Next steps
- decisionRecord the board decision to prioritise bank feeds and withdraw the claim that the unweighted AI plurality backs building AI next.
- researchReanalyse the data weighted by paid plan and usage frequency, and by paid seats or revenue if account records allow, with 95% confidence intervals for all four improvements.
- researchAudit bank feed failure causes by bank and workflow, including reauthorisation, and estimate the Ledgerline-fixable share and baseline disconnect rate.
- experimentRun a paid-practice priced pilot or pre-order test for AI categorisation at £8, collecting paid-seat counts per practice.
- researchOversample 10-19 and 20-plus practices, targeting at least 100 completes at 20-plus, to test client-portal and bulk-MTD demand.
- monitorInstrument page-load and search performance by time of day and practice size, and compare its scale with bank feed work.
- experimentPrototype bulk MTD submission with paid 10-19 and 2-9 practices and measure time saved per batch.
Open-text themes it coded
Bank feeds breaking, disconnecting, arriving late or missing 74Manual categorisation or hand-coding of bank transactions 57Chasing clients for documents, records or paperwork 15MTD submissions one client at a time, no bulk filing 25Slow app performance, page loading, search or reports 26Nothing slows us down or satisfied 25
The survey it planned
5 screening questions and 8 questions, as the model wrote them.
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S1
Where is your practice based?
One answer- United Kingdom
- Outside the United Kingdom
- Don't know
Continues if United Kingdom
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S2
Which best describes your practice?
One answer- Accountancy practice
- Bookkeeping practice
- Other
Continues if Accountancy practice; Bookkeeping practice
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S3
Does your practice use Ledgerline?
One answer- Yes
- No
- Don't know
Continues if Yes
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S4
Do you make or influence decisions about the software your practice uses?
One answer- Yes - I make the decision
- Yes - I influence the decision
- No
Continues if Yes - I make the decision; Yes - I influence the decision
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S5
Has anyone else from your practice already completed this survey?
One answer- No
- Yes
- Don't know
Continues if No
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Q1
If only one of the following improvements can be built in the next six months, please rank them from 1 = most important to your practice to 4 = least important. Please rank all four.
Ranking- More reliable bank feeds
- AI categorisation of bank transactions
- Client portal for requesting documents
- Bulk Making Tax Digital (MTD) submissions
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Q2
Which of the following slow down your work using Ledgerline most? Select up to three.
Any that apply- Bank feed connections breaking or needing to be reconnected
- Manually categorising bank transactions
- Chasing clients for documents
- Preparing or submitting Making Tax Digital returns
- Tracking deadlines
- Finding or updating client records
- Other (please specify)
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Q3
Please describe the other issue that slows you down most.
Open textRouting Shown only if Q2 is: Other (please specify)
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Q4
In the past three months, how often has a bank feed in Ledgerline disconnected or needed to be reconnected?
One answer- Never
- Once
- 2 to 3 times
- 4 or more times
- Don't know
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Q5
How many staff work in your practice, including you?
One answer- 1 (sole practitioner)
- 2 to 9
- 10 or more
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Q6
In a typical week, how often does your practice use Ledgerline?
One answer- Every day
- Several days a week
- About once a week
- Less than once a week
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Q7
If AI categorisation of bank transactions were offered as an optional add-on that your practice would pay extra for, how likely is your practice to pay for it?
ScaleScale 1-5: Very unlikely to Very likely
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Q8
To help us check that responses have been read carefully, please select Strongly agree.
ScaleScale 1-5: Strongly disagree to Strongly agree
Sample plan
Frame: all 8,200 Ledgerline practice accounts. Email the admin of every account with a unique, account-level link (one completed response per practice; first complete retained and duplicates blocked), and use an in-app banner targeted to the admin role to signpost logged-in admins to that same link - keep the email list as the sampling frame so the banner does not become an uncontrolled self-selected sample. Use account records for plan (free/paid) and recent activity, not self-report. Precision check: +/-5 percentage points at 95% confidence for a practice-size group needs 385 completes per group (1.96^2 x 0.25 / 0.05^2), i.e. 1,155 total, so the provisional 550 completes (about 183 per group, about +/-7.3 points per group) cannot meet the stated precision - increase the target to 1,155 or formally relax the precision requirement. Quotas for the 1,155 target: 385 sole practitioners, 385 practices with 2-9 staff and 385 with 10 or more staff, monitored on Q5 and closed by stratum; within each size group target about 50% paid-plan and 50% free-plan (about 193 paid and 192 free per size group) to oversample the 1,230 paid practices (15% of the frame but all revenue) for O3, inviting all paid admins with staged reminders and random staged batches of free admins rather than relying on first-come banner clicks. Monitor completes by size, plan and account activity during fieldwork, release reserve batches for under-filled strata, and weight population estimates back by size x plan (and activity); if 550 is a hard cap, use 183 per size group and report the wider interval.