Many marketing reports in UK businesses are still built by hand. Someone exports figures from Google Ads, Meta, GA4 and Search Console on the first working day of the month, pastes them into a spreadsheet, fixes the dates that did not line up, and emails a PDF that is out of date by the time anyone reads it. I replace that routine with a pipeline: the data is collected on a schedule, checked, combined with what happened to each enquiry, and delivered to the people who need it without anyone touching a CSV file.
What changes when the report builds itself
The hours go back first. Copying, pasting and reformatting stop entirely; what is left is a few minutes of checking that the figures look right, and the person who used to build the report can spend that time reading it instead.
The numbers also become consistent. A manual report is only as reliable as the last export, and small mistakes creep in: a filter left on, the wrong date range, a currency column summed with a count. An automated pipeline makes the same calculation the same way every time, so a change in the figures means something changed in the business rather than in the spreadsheet.
Finally, you hear about problems sooner. When data arrives daily, a tag that stopped firing on Tuesday shows up on Wednesday, not at the end of the month after three weeks of spend went unrecorded.
Who this suits, and who it does not
- In-house marketers who rebuild the same monthly or weekly report for a director, a board or a finance team.
- Owners spending across several channels who want one weekly email showing spend, enquiries and cost per enquiry without logging into each platform.
- Businesses with several locations, brands or ad accounts, where combining the data by hand is where most of the errors come from.
- Teams that record lead outcomes in a CRM and want sales and revenue reported next to the marketing spend that produced them.
It does not suit everyone. If you run one channel and its own reporting screen answers your questions, automation adds cost without much benefit. If your tracking is unreliable, automating the report just delivers wrong figures on time, so check that your conversion tracking records real enquiries first. And if what you actually want is a well-designed dashboard you open yourself, the design matters more than the plumbing, and the scope is smaller.
What gets automated
Every build is scoped to the report you already produce, or the one you wish you had. Most cover the same five parts.
Collecting the data on a schedule
Each platform is connected through a connector or the platform’s own API, and the data is pulled at a set time into one store, usually Google Sheets for smaller set-ups or BigQuery when the volume or history justifies it. Google’s own products connect to Looker Studio at no extra charge; Meta Ads, LinkedIn Ads and most CRMs need a third-party connector with its own subscription, which I price into the scope so there are no surprises.
Joining marketing data to business outcomes
Platform figures stop at the click or the form. The useful part of a report is what happened next: whether the enquiry was qualified, whether it became a quote, and what it was worth. Where your CRM or booking system records that, I bring the outcome in and match it to the source campaign, usually through the UTM tags on the original visit, so the report can show cost per sale as well as cost per lead.
Checks before anything is sent
Automated reports fail quietly. A connector loses its login, an API changes, or a campaign is renamed and drops out of a filter. I add checks that look for the usual symptoms (a day with no data, spend with zero conversions, totals that do not match the platform) and flag them before the report reaches anyone. Watching data freshness matters most here, because a report that silently stopped updating last Thursday looks exactly like a quiet week.
Delivery to the people who read it
Reports go where people already look: a scheduled email from Looker Studio with a PDF attached, a summary posted to Slack or Microsoft Teams, or a refreshed sheet the finance team already uses. A director usually wants five numbers and a comment; a channel manager wants the detail. I build one source and shape the output for each reader rather than sending everyone the same twelve pages.
Alerts between reports
Some things cannot wait for Monday’s email. Spend running well over the daily budget, a landing page returning errors, or enquiries falling to zero are worth a message the same day. I keep alerts to a handful of thresholds you agree with, because a channel that pings every hour gets muted within a week.
What I deliberately leave out
I keep personal data out of reports. Names, email addresses and phone numbers have no reason to appear in a marketing summary, and leaving them out keeps the pipeline simpler under UK GDPR. Where outcome data comes from a CRM, I report counts, values and rates, not people.
I also leave out metrics nobody acts on. Impressions, sessions and follower counts can stay in the source platforms; the report carries what changes a decision.
How a build runs
- Start from the current report. You send me the last two or three versions and tell me who reads them and what they decide from them. That shows me which numbers matter and where the manual effort goes.
- Agree the definitions. We write down what counts as a lead, a sale and a channel, and which date each one is counted on. Most arguments about reports turn out to be arguments about definitions.
- Audit the sources. Before connecting anything, I check the tracking and naming in each platform. Inconsistent campaign names and missing tags are fixed here, often with a shared UTM link builder so new links follow the same rules.
- Build and run it alongside the old report. For one reporting cycle, the automated version runs next to the manual one. Any difference gets explained before the old process is switched off.
- Hand over. You receive the documentation and a walkthrough, and the person who used to build the report learns how to check and adjust the new one.
Problems I see most often
Platforms disagree, and the report hides it. Google Ads, Meta and GA4 each count conversions with their own attribution rules and windows, so the totals never match. I show each platform’s own figure alongside one agreed source of truth, with a note on why they differ; this explanation of why GA4 and Facebook conversion numbers disagree covers the main causes.
GA4 figures shift depending on how they are pulled. Thresholding can withhold rows for small groups of users when the property’s reporting identity draws on Google signals, and data pulled through the API can differ slightly from what the interface shows. I choose the method that stays stable and note its limits in the documentation.
Consent reduces what analytics records. On UK sites that load analytics only after a visitor accepts cookies, GA4 does not observe the visitors who decline. The report should say so rather than present those figures as complete, and should lean on platform and CRM counts where completeness matters.
Connector vendors handle your data. A third-party connector processes your account data on its own servers, so it needs a data processing agreement and a check on where that data is stored. I include this in the scope rather than leaving it for someone to find later.
What you receive
- The working pipeline in your accounts, with every connection authorised by a login your business controls.
- The scheduled reports and alerts, set up for each group of readers.
- A written definitions sheet: every metric, how it is calculated and where it comes from.
- A runbook covering what each check means, what to do when one fails, and how to add a new campaign or channel.
- A walkthrough session, recorded if you want it.
If I also run your ads, the same pipeline feeds the reporting for monthly Google Ads management or Facebook and Instagram ads management, so there is one version of the figures rather than mine and yours.
What it costs to build and to run
The build cost depends on the number of sources, whether CRM outcomes are included, how many different reports are needed and how clean the existing tracking is. I write each quote in GBP after a free audit or first call, and we agree it in writing before work starts.
Running costs are separate and usually small, but they are real. Third-party connectors are charged monthly, often per data source or per account, and BigQuery is charged on storage and queries once usage passes its free allowance. I list these in the scope so you can compare them with the staff time the manual report currently takes.
Next step
Send me your last monthly report, with any personal details removed, and a line on who reads it. I will tell you which parts can be automated, which sources need fixing first, and whether the time saved justifies the build. You can do that, or book a free 30-minute call, through my contact page.