# Market Research Report: [Market or study title]

> Full market research report: executive summary, objectives and research
> questions, methodology with sample-size reasoning, market sizing,
> segmentation, customer needs, competitor findings, trends, implications,
> recommendations, limitations and an appendix with the questionnaire
> outline. Based on common market research practice. Replace every
> `[bracketed]` value and every row marked *Example*.

<!--
Guidance: How to use this template
- Agree the research questions (section 2) with the decision owner before
  fieldwork. Every finding should answer one of them.
- Separate what the data shows (findings) from what you think it means
  (implications) and what you propose (recommendations).
- Report sample sizes, dates and margins of error next to every
  percentage, so readers can judge how much weight it bears.
- State limitations plainly; a report that hides them loses credibility
  the first time a reader spots one.
-->

## Document control

<!-- Guidance: Identify the commissioning team, the research agency (if any) and the fieldwork dates. -->

| Field | Value |
|---|---|
| Report ID | [MR-YYYY-001] |
| Title | [Study title] |
| Version | [0.1] |
| Status | [Draft / Final] |
| Commissioned by | [Team, @sponsor] |
| Research lead | [@author] |
| Agency / panel provider | [Name or "In-house"] |
| Fieldwork dates | YYYY-MM-DD to YYYY-MM-DD |
| Confidentiality | [Internal / Restricted] |
| Last updated | YYYY-MM-DD |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Research lead | [@name] | [Final] | YYYY-MM-DD |
| Sponsor | [@name] | [Accepted] | YYYY-MM-DD |
| Data protection reviewer | [@name] | [Respondent data handling confirmed] | YYYY-MM-DD |

## Revision history

<!-- Guidance: Record changes to data, weighting or conclusions. -->

| Version | Date | Author | Change summary |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@name] | Topline results |
| 0.2 | YYYY-MM-DD | [@name] | *Example:* Added segmentation and weighting by company size |
| 1.0 | YYYY-MM-DD | [@name] | *Example:* Final report with recommendations |

## Contents

<!-- Guidance: Keep the list aligned with the headings below. -->

1. [Executive summary](#1-executive-summary)
2. [Objectives and research questions](#2-objectives-and-research-questions)
3. [Methodology](#3-methodology)
4. [Market sizing](#4-market-sizing)
5. [Segmentation](#5-segmentation)
6. [Customer needs findings](#6-customer-needs-findings)
7. [Competitor findings](#7-competitor-findings)
8. [Trends](#8-trends)
9. [Implications](#9-implications)
10. [Recommendations](#10-recommendations)
11. [Limitations](#11-limitations)
12. [Appendix](#12-appendix)

## 1. Executive summary

<!-- Guidance: One page: the decision the research supports, the three to five key findings with numbers, and the recommendation. -->

This study was commissioned to inform [decision]. We surveyed [n]
[respondent type] and interviewed [N] [respondent type] between
[dates].

**Key findings**

1. *Example:* 62% of finance managers at firms with 50 to 250 staff
   still close the month with spreadsheets (n = 412, margin of error
   plus or minus 4.7 points).
2. *Example:* The top unmet need is automated bank reconciliation, rated
   "critical" by 48%.
3. *Example:* Willingness to pay clusters between 150 and 300 per month.
4. [Finding]

**Recommendation:** [one or two sentences].

## 2. Objectives and research questions

<!-- Guidance: Tie each research question to the business decision it informs. Unanswerable or decision-free questions should be cut. -->

**Business objective:** [for example, decide whether to launch a
month-end close product for mid-size firms in [region]].

| ID | Research question | Decision it informs | Method |
|---|---|---|---|
| RQ-1 | *Example:* How large is the addressable market? | Investment size | Desk research, survey |
| RQ-2 | *Example:* Which tasks cause most pain in month-end close? | Product scope | Interviews, survey |
| RQ-3 | *Example:* What do buyers pay today and expect to pay? | Pricing | Survey (price sensitivity questions) |
| RQ-4 | *Example:* Which vendors are considered and why? | Positioning | Survey, interviews |
| RQ-5 | [Question] | [Decision] | [Method] |

## 3. Methodology

<!-- Guidance: Describe the design so another researcher could repeat it: methods, population, sampling frame, sample size, fieldwork, weighting and analysis. -->

### 3.1 Research design

| Phase | Method | Sample | Dates |
|---|---|---|---|
| *Example:* Exploratory | Desk research: industry statistics, reports, filings | n/a | YYYY-MM-DD |
| *Example:* Qualitative | 45-minute semi-structured interviews | 18 finance managers | YYYY-MM-DD |
| *Example:* Quantitative | Online survey, 14 minutes | 412 completes | YYYY-MM-DD |

### 3.2 Population and sampling

- **Target population**: [for example, finance decision makers at firms with 50 to 250 staff in [country]]
- **Sampling frame**: [panel provider, customer list, business register]
- **Sampling method**: [quota sample by company size and sector / random sample]
- **Screening criteria**: [for example, involved in choosing finance software]

### 3.3 Sample size and margin of error

For estimating a proportion $p$ at confidence level with critical value
$z$ and a desired margin of error $e$, the required sample size is:

$$
n = \frac{z^2 \, p(1-p)}{e^2}
$$

Using $p = 0.5$ (the most conservative value), $z = 1.96$ for 95%
confidence and $e = 0.05$ gives $n \approx 385$. Conversely, the margin
of error for an achieved sample is $e = z\sqrt{p(1-p)/n}$; with
$n = 412$ it is about plus or minus 4.8 points at $p = 0.5$. Subgroups
have wider margins and are flagged where $n < 100$.

### 3.4 Fieldwork, quality control and weighting

[Describe fieldwork dates, incentives, attention checks, removal of
speeders and duplicates, and weighting targets.]

*Example:* "438 responses were collected; 26 were removed for failing
attention checks or completing in under 5 minutes, leaving 412. Results
were weighted to the business register by company size band."

### 3.5 Analysis methods

[Describe how the data was analysed so readers can judge the strength of
each finding.]

- **Descriptive statistics**: weighted percentages and means, with bases
  shown for every figure.
- **Significance testing**: differences between subgroups tested at the
  95% level; only significant differences are described as "higher" or
  "lower" in the text.
- **Segmentation**: [for example, cluster analysis on needs and
  behaviour variables, four-cluster solution chosen for interpretability].
- **Qualitative analysis**: interview transcripts coded by two analysts
  against a shared codebook; themes reported when raised by at least a
  quarter of interviewees.

### 3.6 Ethics and data protection

[State how consent was obtained, how personal data was minimised and
stored, retention period and whether results are reported only in
aggregate. Follow applicable privacy law and industry research codes.]

## 4. Market sizing

<!-- Guidance: Show the method, the inputs with sources and the result. Use bottom-up sizing where data allows and reconcile with top-down estimates. -->

| Measure | Method | Inputs | Result | Source |
|---|---|---|---|---|
| *Example:* Total addressable market | Firms x annual spend | 24,000 firms x 2,800 | 67.2M per year | Business register YYYY; survey Q12 |
| *Example:* Serviceable market | Firms using cloud accounting | 24,000 x 55% x 2,800 | 37.0M per year | Survey Q5 |
| *Example:* Obtainable (year 3) | 5% share of serviceable | 37.0M x 5% | 1.85M per year | Sales capacity plan |

Top-down cross-check: [compare with published market estimates and
explain any gap].

## 5. Segmentation

<!-- Guidance: Describe segments that differ in needs or behaviour, not only in demographics. Give size, attractiveness and how to reach each. -->

| Segment | Share of sample | Defining traits | Key need | Attractiveness |
|---|---|---|---|---|
| *Example:* Spreadsheet loyalists | 34% | Small team, low software budget | Low cost, simple import | Low |
| *Example:* Growing multi-entity firms | 27% | 2 or more legal entities, rapid hiring | Consolidation, audit trail | High |
| *Example:* Tool-heavy optimisers | 21% | Already use 4 or more finance tools | Integrations, automation | Medium |
| *Example:* Outsourced finance | 18% | Bookkeeping outsourced | Collaboration with accountant | Medium |

[For each attractive segment, add a short profile: who they are, how
they buy, who influences the decision, which channels reach them and
what would make them switch. Profiles are more useful to product and
sales teams than percentages alone.]

*Example:* "Growing multi-entity firms are typically 120 to 250 staff,
have a finance team of three to six, and add a legal entity every 12 to
18 months. The financial controller leads the evaluation; the chief
financial officer signs. They discover tools through their external
accountant and peer communities, and two in three said a failed audit
or a missed reporting deadline would trigger a switch."

## 6. Customer needs findings

<!-- Guidance: Report needs with importance and satisfaction, so the gaps stand out. Combine survey numbers with interview quotes. -->

| Need | Rated critical | Satisfied with current tools | Gap | Base |
|---|---|---|---|---|
| *Example:* Automated bank reconciliation | 48% | 19% | High | n = 412 |
| *Example:* Close checklist with owners and dates | 37% | 31% | Medium | n = 412 |
| *Example:* Variance commentary for management pack | 29% | 22% | Medium | n = 412 |
| [Need] | [N%] | [N%] | [H / M / L] | [n] |

> *Example quote:* "The close takes eight working days and half of it is
> chasing people for numbers." Finance manager, manufacturing, 140 staff.

Qualitative themes from the interviews add the reasons behind the
numbers:

| Theme | Interviewees raising it | What it means |
|---|---|---|
| *Example:* Chasing inputs from other departments | 13 of 18 | Delays come from people, not calculations |
| *Example:* Fear of errors in manual journals | 9 of 18 | Audit trail and approvals are buying criteria |
| *Example:* Distrust of "black box" automation | 6 of 18 | Automation must explain each match it makes |
| [Theme] | [N of N] | [Interpretation] |

## 7. Competitor findings

<!-- Guidance: Awareness, consideration, usage and reasons for choice or rejection by vendor. -->

| Vendor | Aware | Considered | Using | Main reason chosen | Main complaint |
|---|---|---|---|---|---|
| *Example:* Vendor A | 71% | 33% | 18% | Integration with accounting system | Price increases |
| *Example:* Vendor B | 45% | 14% | 6% | Strong reporting | Complex setup |
| *Example:* Spreadsheets | n/a | n/a | 62% | Familiar, no cost | Error-prone, slow |

## 8. Trends

<!-- Guidance: External forces likely to change the market in the next two to five years, with evidence and expected effect. -->

| Trend | Evidence | Expected effect | Time horizon |
|---|---|---|---|
| *Example:* Mandatory e-invoicing rollout | Government timetable published YYYY | Forces digitisation of payables | 1-3 years |
| *Example:* AI-assisted reconciliation | 3 major vendors launched features in YYYY | Raises baseline expectations | 1-2 years |
| [Trend] | [Evidence] | [Effect] | [Horizon] |

[Summarise in a short paragraph how the trends combine. Note which
trends strengthen the opportunity, which weaken it, and which signals
the team should monitor after the report is published, with a named
owner and a review date for each.]

## 9. Implications

<!-- Guidance: Interpret findings for the business decision. Reference the research question each implication answers. -->

- **RQ-1**: [for example, the serviceable market supports a product line but not a standalone company in this region alone]
- **RQ-2**: [for example, reconciliation is the wedge feature; checklists are table stakes]
- **RQ-3**: [for example, a price point of about 200 per month fits most of the high-attractiveness segment]
- **RQ-4**: [for example, Vendor A is the reference competitor; price increases create switching intent]

[Add a short paragraph on what the research does not settle. For
example, the survey shows strong stated demand for reconciliation, but
it cannot show whether buyers will trust an automated match without a
trial. Name the follow-up research or experiment that would close each
open question, such as a concierge pilot, a pricing test or a further
round of interviews with a specific segment.]

## 10. Recommendations

<!-- Guidance: Specific, prioritised and owned. Each recommendation should trace to a finding. -->

| Priority | Recommendation | Based on | Owner | Next step |
|---|---|---|---|---|
| 1 | *Example:* Target growing multi-entity firms first | Sections 5 and 6 | @product-lead | Recruit 10 design partners |
| 2 | *Example:* Launch with reconciliation plus close checklist | Section 6 | @pm | Scope MVP |
| 3 | *Example:* Price test at 180 and 240 per month | Section 9, RQ-3 | @pricing | A/B test on waitlist |

## 11. Limitations

<!-- Guidance: Be specific about bias, coverage and timing, and how they might change the conclusions. -->

- **Sample bias**: [for example, online panel under-represents firms without a dedicated finance role]
- **Self-reported data**: [stated willingness to pay usually overstates actual payment]
- **Timing**: [fieldwork ran during year-end, which may inflate pain ratings]
- **Subgroup sizes**: [segments under n = 100 are directional only]

## 12. Appendix

<!-- Guidance: Include the questionnaire outline, discussion guide and data tables. -->

### 12.1 Questionnaire outline

| Block | Topics | Questions | Type |
|---|---|---|---|
| A. Screening | Role, company size, involvement in purchase | Q1-Q4 | Single choice, terminate if not qualified |
| B. Current practice | Tools used, close duration, team size | Q5-Q9 | Multiple choice, numeric |
| C. Needs | Importance and satisfaction for 12 needs | Q10-Q11 | 5-point scales |
| D. Spend and pricing | Current spend, price sensitivity | Q12-Q15 | Numeric, price ladders |
| E. Vendors | Awareness, consideration, use | Q16-Q18 | Grid |
| F. Profile | Sector, region, growth | Q19-Q22 | Single choice |

### 12.2 Other material

- Interview discussion guide [link]
- Weighted data tables with bases [link]
- Codebook for open-ended answers [link]

### 12.3 Glossary

| Term | Meaning in this report |
|---|---|
| Base (n) | Number of respondents who answered the question shown |
| Margin of error | Range within which the population value is expected to lie at 95% confidence |
| Weighting | Adjustment so the sample matches known population proportions |
| Close | The month-end process of finalising the accounts for a period |

## Standard and licence note

Based on common market research practice for study design, sampling,
reporting and disclosure of limitations. This template is original text
written for this collection. Template licence: MIT.