What it is
Analytics is the practice of making your numbers true before you spend money against them. It suits anyone about to make a budget decision, and any business whose figures end up in board papers, investor updates or advertising claims. The work covers a measurement strategy, tracking built and tested, an audit of what your current data is actually counting and a reporting rhythm that matches how decisions get made. Expect the audit to be uncomfortable. Duplicate tags, broken events and attribution that counts one sale three ways are common and usually invisible until somebody looks. We do not promise that better data will grow your business, because data does not decide anything. We promise you will know which half of your budget is working, and then we help you fix the other half. Deciding on numbers you have not checked is not a decision. It is a guess with a chart attached.
Most marketing decisions are made on numbers nobody has tested. An event that has fired twice since a redesign. A conversion that stopped counting when a form changed. An attribution model quietly crediting the same sale to three channels at once. None of it shows up in the dashboard. It shows up in a year of spending against a fiction, which is an expensive way to find out and a completely avoidable one. The dashboard will never tell you it is wrong. It will keep producing confident figures until somebody tests them, which is what the audit is for.
We build measurement the way an auditor would. Every metric defined in plain words, every event named deliberately and every source documented, so any figure can be traced back to where it came from. The point is not more dashboards. It is fewer arguments about what the numbers mean. When two departments report different figures for the same thing, the problem is rarely the maths. It is that nobody ever agreed what was being counted, and both sides are working from their own definition. Defining the metrics is unglamorous work, and it is where most of the value in this service actually sits.
What analytics cannot do is worth saying plainly. Attribution is not a solved problem and probably never will be. No tool can tell you with certainty which advert caused a sale three weeks and four devices later. We will show you what the data can support, mark the conclusions that are educated guesses and suggest the test that would get closer to the truth of it. That line matters, because budget decisions hang off it and pretending otherwise costs money. You will always be able to tell which of your numbers are solid and which are working assumptions. Very few reporting setups can say the same.
What we do
Measurement strategy
What to measure, why it matters and which decision each number supports. The strategy turns a long list of metrics into a short set of definitions everyone agrees on, so two departments stop reporting different figures for the same thing and arguing about it in meetings every month.
Tracking implementation
Events and conversions built properly and tested, across web and app, with the quality assurance done in daylight rather than discovered in a board meeting. Every tag is checked against its definition before it goes live and checked again after every release, because code changes and tracking breaks quietly.
Data quality audits
The truth about what your current numbers are counting. We find duplicate tags, ghost referrals, broken checkout events and the gaps between platforms, then rank each problem by how much of your budget decision it is quietly distorting. The findings come with evidence, not opinions.
Reporting cadence
Weekly signals and monthly verdicts, pitched at the level each audience needs. The board gets the decision and the reason behind it. The team gets the detail it can act on that week. Nobody gets a forty-tab workbook and a hopeful smile from across the table.
How we work
Audit
We trace how your data gets collected and where it breaks. Tags firing twice, events missing, consent blocking more than it should and the gaps between what each platform claims. Then we tell you which numbers to trust today and which to stop quoting.
Plan
A measurement plan written in plain language. The metrics, their definitions, the events behind them and the decisions each one informs. You approve the definitions before anyone builds a tag against them, which is where most of the value sits and where most projects skip.
Build
Tracking gets implemented, tested and documented. We work alongside your developers or do the tagging ourselves, and we validate every event against the plan before it is allowed to count as real data in a real report that anyone reads.
Prove
Reporting that shows the number and the caveats beside it. Where attribution cannot answer a question we say so, and offer the test that would get closer. The measure of the work is whether your decisions changed, not whether the dashboard looks busy this month.
What it costs
Analytics work is priced by the scope of the tracking and the state of what already exists, and what moves the number is how many platforms, forms and systems have to agree with each other. Auditing an existing setup costs less than building a measurement model for a business with apps, offline sales and a CRM to reconcile. The audit is usually the best money in the first month, because it tells you what to fix first and what to stop trusting. Retainers come after the foundations and shrink once the setup is stable. We would rather leave you with something your team can maintain than a permanent dependency on us, and we build it that way from the start.
Who it is for
For anyone about to make a budget decision, and for businesses whose figures end up in board papers, investor updates or advertising claims. In regulated markets such as iGaming, fintech, healthcare and law, numbers get examined and they have to withstand it. For local service businesses the question is simpler and just as important. Which of the things you pay for actually produce phone calls, and which merely appear to work because the tracking says so. Both need the same thing, which is data they can defend without a caveat in every other sentence. The size of the business changes the scale of the work, not the principle behind it.
Two kinds of business come to us: local service businesses and companies in regulated markets. See everything in Be proved.
Questions we get asked
We already have analytics. Can you check it?
That is the most common way we start. An audit tells you which numbers to trust before anything is built on them, and it usually turns up at least one figure the business has been quoting with confidence that is not measuring what anyone thinks. Finding that out early is uncomfortable. Finding it out during a budget round, with a decision already made, is far worse. Either way the answer to your question is almost always yes, and the check takes less time than you fear. Nobody has ever told us they wish they had not looked.
How long does implementation take?
Two to six weeks for most sites, including testing. Complex platforms, apps and strict consent requirements extend that, and we would rather take an extra week than publish numbers we cannot stand behind. We give you the timeline before we start rather than discovering it halfway through, and the plan includes the testing, because tracking that has not been tested is a rumour with a timestamp on it. If something needs a developer release, we build that into the schedule instead of being surprised by it.
What is the difference between analytics and GA4?
GA4 is one tool. Analytics is the discipline around it, deciding what to measure, building the tracking and keeping the data honest over time. A perfectly configured GA4 property still answers the wrong questions if nobody ever agreed what the questions were, which is why the plan comes before the platform. Plenty of businesses have the tool and none of the discipline, and their reports show it in ways that are hard to spot from the inside. The tool is the cheap part.
Can analytics tell me which marketing made the sale?
Not with certainty, and anyone who says otherwise is selling a model. Attribution shows what it can observe and quietly assumes the rest, which is fine as long as everyone in the room knows where the observation ends. For the questions it cannot answer we use tests, and we are explicit about which conclusions are solid and which are educated guesses worth another look before the budget moves. Being honest about the limits is what makes the rest of the numbers believable to a sceptical board.
Do you work with our existing reporting tools?
Yes. Looker Studio, Power BI, spreadsheets or whatever the board is used to seeing. The measurement layer matters more than the front end, so we fix that first. Once the data is sound, almost any dashboard will tell the same story and the arguments about whose numbers are right tend to disappear. Replacing the reporting tool is rarely the answer, and it is a good way to spend a quarter changing nothing that matters. Start with the data and the presentation becomes an easy problem.
Will better analytics actually change what we spend?
It should, and that is the entire point. Good measurement is uncomfortable because it ends arguments with numbers and takes away the comfortable fudge everyone had learned to live with. If the figures never change a decision, something is wrong with either the reporting or the willingness to act on it, and we would rather name that early than keep producing charts nobody uses. Measurement only pays when someone is prepared to act on what it says. That part is a management question rather than a technical one.
More Be proved services
- GA4 & GTMSetup and governance that stands up to an audit.
- Looker StudioOne dashboard, plain language, the figures that decide things.
- Digital strategyThe plan that ties all four together, and the budget with it.
Tell us what you need from analytics. We will tell you what we would do about it.