Every practice area has the same twelve articles. What to do after a car accident. How long you have to file. What damages you can recover. Your firm has a version, and so does every competitor within two hundred miles, and so does every content mill selling the same template to forty firms at once. That library is table stakes. It is not a moat. Anyone with a budget and a weekend can rebuild it, and increasingly anyone with a language model can rebuild it in an afternoon.
There is exactly one category of content that cannot be copied that way, and it is the category almost no firm publishes. Numbers nobody else has. If you pull three years of filings from your state court system, code them, and publish the resulting counts, a competitor cannot replicate your article without doing the same work. They cannot paraphrase it. They can only cite it. That is what a moat looks like in practice, and it is also, not coincidentally, the thing reporters actually want when they call.
The catch is that most firms hear the word research and picture a nine month project with an economist attached. That is not what this is. A useful study is often one dataset, one question, six weeks, and a spreadsheet. What follows is how to scope one, where the data legitimately comes from, and how to package it so it gets picked up instead of sitting on your blog with eleven pageviews.
Why a Number Behaves Differently Than an Explainer
An explainer competes on quality and freshness against everyone writing the same explainer. A number competes against nothing, because it did not exist until you produced it. Three things follow from that.
- It is quotable. A journalist can lift a statistic and attribute it in one sentence. They cannot lift your explanation of comparative negligence, because their outlet has its own writers for that. Quotability is what turns coverage into a link.
- It ages into an asset instead of a liability. A 2024 statistic is still a 2024 statistic in 2029, and it becomes a data point in a trend the moment you run the same pull again. Your explainers, meanwhile, need audits every time a statute moves.
- It survives being summarized. When an AI answer engine compresses your page into two sentences, an explainer loses everything that made it yours. A finding keeps its attribution, because the number needs a source and the source is you.
None of this means original research ranks better on its own. Plenty of studies get published and go nowhere. The value shows up when the study feeds the rest of your marketing, which is a distribution problem more than a research problem, and we will get to it.
What a Small Study Actually Looks Like
The most common failure is scope. Firms decide to study something enormous, discover the data cleanup alone is three months, and quietly abandon it. Constrain hard at the start using four filters.
- One question, phrased as a headline. If you cannot write the headline before you start, you do not have a study, you have a data browsing session. Write the sentence you hope to publish, then find out whether the data can support it or refute it. Either outcome is publishable.
- One dataset, or two that share a clean join key. Every additional source multiplies reconciliation work. County plus year is a workable join. Free text party names are not, unless you enjoy fuzzy matching.
- A geography your firm actually serves. National numbers are already published by people with more staff than you. Your advantage is caring about the three counties nobody has bothered to break out.
- A cell count you can defend. If slicing by county and year leaves you with eleven cases in a box, do not report that box as a percentage. Collapse the categories or widen the window.
Realistic examples that a two person marketing team has finished before. Median days from filing to disposition for a case type across five counties, three years. Share of a state’s fatal crash reports that occurred on a single corridor. Growth in a specific filing category since a statute changed. Distribution of OSHA citation types in one industry across your state. None of those need a statistician. They need a clean pull, a pivot table, and the discipline to say only what the numbers say.
Where Legitimate Data Comes From
You do not need to scrape anything gray. The volume of properly public, properly licensed data is larger than most firms realize, and using the official channel is what makes the finding citable later.
Federal Statistical Agencies
These are the cleanest starting point because the documentation is real and the methodology is already peer reviewed. The Bureau of Justice Statistics publishes collections with full codebooks, questionnaires, and downloadable microdata, including the National Crime Victimization Survey, which has been running since 1973 and covers roughly 240,000 respondents across 150,000 households a year. Sample sizes that large let you cut by demographics without falling off a statistical cliff. Also worth knowing are the federal traffic fatality files, the workplace injury and illness series, and the agency inspection databases, all of which support state and sometimes county level breakouts.
State Court Administrative Offices
Nearly every state has an office of court administration that publishes annual caseload statistics, and a good share will provide extracts on request that never appear in the published report. The published PDF gives you the shape of the data. A polite email to the research or statistics contact, explaining what you want and why, gets you the underlying table often enough to be worth the twenty minutes. Ask for the format you can actually use, which is CSV, not a locked PDF.
County Clerks and Docket Systems
This is where the genuinely uncontested numbers live, and also where the work is. Some counties expose bulk data or a records request path. Others give you a search interface and rate limits. Read the terms of use before you automate anything, and when in doubt file a public records request instead of scraping, because a request produces a paper trail and a scrape produces a cease and desist. Budget for the fact that county data is messy, inconsistently coded between offices, and sometimes just wrong. Sampling two hundred dockets by hand is a legitimate method if you disclose it as one.
Regulatory and Agency Datasets
Enforcement actions, licensing boards, inspection results, complaint logs, insurance department filings. These are underused because they are boring to browse, and underused is the point. If your practice touches a regulated industry, the regulator is publishing something about it.
Sourcing It Without Creating a Problem
A study that embarrasses the firm is worse than no study. Five rules keep you clean.
- Use the official access route. Bulk download, documented API, or records request. If a site’s terms prohibit automated collection, that prohibition applies to you specifically, and you are the party least able to argue it did not.
- Never publish anything that identifies a person. Aggregate to a level where no individual is recoverable. Suppress small cells. Court records being public does not mean republishing the plaintiff’s name in a marketing asset is a good idea.
- Keep your own client data out of it entirely. Firm case results feel like a tempting dataset and they are a confidentiality and advertising rules problem wrapped in a selection bias problem. Public data only.
- Run it past whoever handles your advertising compliance. State bar rules on comparisons and results based claims are unforgiving, and a statistic can imply a claim you did not intend to make.
- Save everything. Raw file, the date you pulled it, the exact query, the cleanup steps, and the final table. When a reporter asks how you got the number, six months later, you need to answer in an hour.
Analysis That Will Not Fall Apart Under Questioning
You are not trying to publish in a journal. You are trying to publish something that a skeptical editor cannot poke a hole in. That bar is lower than academia and much higher than most marketing content clears.
Report counts alongside percentages, always. A 40 percent increase reads dramatically until the reader learns it was five cases to seven. Describe what changed in the data collection, because agencies revise definitions and a jump in your chart is frequently a coding change rather than a real world event. Say correlation when you mean correlation. Resist the causal verb, because that is where studies get taken apart, and once one number is wrong the whole piece is discounted.
Write the limitations section before the findings section. It forces honesty while you still have time to fix the design, and it is the section reporters read to decide whether you are careful. Three or four plain sentences about what the data cannot tell you will do more for your credibility than a fifth chart.
Packaging It for Pickup
Most original research fails at packaging, not at research. The findings are fine and the page is built like a blog post, so nobody can use it.
- Lead with the number. The headline and the first sentence carry the finding. Methodology goes near the bottom. A journalist on deadline should be able to decide in eight seconds whether this is useful.
- Publish the data. A downloadable CSV or a clean HTML table signals that you are not hiding anything and gives other sites something to link to. Tables are also the version an answer engine can actually parse.
- Make charts that stand alone. Title, axis labels, source line, your firm name in the corner. Charts get screenshotted and travel without the article, so the image has to carry attribution on its own.
- Write a real methodology section. Source, date range, geography, record count, inclusion rules, cleanup decisions, known limitations. Six paragraphs at most.
- Give it a permanent home. A stable URL you never change, updated in place each year rather than replaced with a new slug, so that every link earned in year one still counts in year four.
- Include a citation line. Spell out how you want to be credited. People use what you hand them.
Distribution Is Half the Project
Budget as much time for getting it seen as for building it. The people most likely to cite a state level legal statistic are local reporters covering courts, trade publications in the affected industry, and the state bar or association newsletters. That is a list of maybe thirty contacts, built by hand, and thirty targeted emails outperform a wire release by a wide margin.
The study also makes every other outreach channel work better. When you already have a finding attached to your name, responding to journalist queries stops being an opinion and becomes evidence, which is the difference between getting used and getting ignored on the source platforms attorneys use to reach reporters. It sharpens the pitches you were already entitled to make, since most firms are sitting on digital PR angles they already own and just lack a hook with a number in it. And the coverage it produces is the kind of earned, editorially chosen link that behaves differently from anything you can buy, which is the entire argument behind link building that actually moves rankings.
Reuse the asset relentlessly. The study becomes a slide in your CLE deck, a section of your annual practice area review, a chart in a pitch, and the source you cite in the next three explainers you write. One dataset should generate a year of material.
The Honest Tradeoffs
This is not a cheap tactic and it is not fast. Expect forty to eighty hours of real work for a first study, most of it in data cleanup rather than writing, and expect the first attempt to run over. Someone at the firm has to own it, and that person needs to be comfortable in a spreadsheet, which is a rarer skill in legal marketing departments than anyone admits.
The data may also refuse to cooperate. You will scope a question, pull the records, and find that the effect you expected is not there. Publish it anyway when the null result is interesting, or shelve it and move on, but plan for maybe one in three ideas dying at that stage. That is a normal hit rate, not a failure of process.
Pickup is genuinely uncertain. A solid study with good outreach might earn a handful of citations rather than a wave, and no one can promise you coverage or rankings from it. What you can count on is the durable part. The page exists, the number is yours, and nobody can write around it. Compared with the twelfth version of an explainer that forty firms already published, that is a better place to spend the same budget.
Start with one question, one public dataset, and one county you know well. Ship it, learn what the data cost you in hours, then decide whether year two is a repeat pull or a bigger swing.
Talk to Rubiks Before You Pick the Dataset
The place these projects go wrong is scoping, and scoping happens before anyone opens a spreadsheet. Rubiks scopes original research studies for law firms, identifies which public datasets can actually answer a question in your practice area and geography, handles the pull and the analysis, and builds the study page and outreach list so the finding reaches the people who cite things. If you have a hunch about your market that nobody has ever put a number to, that hunch is usually the study. Talk to Rubiks and we will tell you within a call whether the data exists to prove it.