Analytics & BI
End-to-end analytics: from ads to profit
I connect ad spend to real sales and build end-to-end analytics that shows the payback of every channel and campaign.
What is end-to-end analytics
End-to-end analytics traces the customer journey from the first ad click to payment and repeat purchases, then links that journey back to acquisition spend. Instead of scattered ad accounts, web analytics systems and CRMs, it gathers data into a single source where you can see the real path from impression to money. This lets you measure payback by profit — ROI, ROMI, CAC and LTV for each channel and campaign — rather than settling for clicks and leads. As a result, budget decisions rest on each source's actual contribution to sales, not on intermediate metrics.
When you need end-to-end analytics
What's included
Collecting ad spend
I set up the export of spend, impressions and clicks from ad accounts and channels into the warehouse. The data is normalized to a single format and refreshed regularly, with no manual downloads.
Web and mobile analytics
I configure events and data collection for your website and app via GA4, Google Tag Manager, AppMetrica and Yandex Metrica. Tagging is unified to a single schema so sources and campaigns map correctly to spend.
Linking marketing to sales
I join marketing data with leads, orders and payments from your CRM, matching them by customer. This way a click and a lead can be traced through to the actual deal and revenue.
Attribution model
I set up an attribution model — from last-click to multi-touch — and distribute channel contribution to sales. The model is chosen to fit your funnel structure and available data.
Calculating end-to-end metrics
I build data marts with end-to-end metrics: ROI, ROMI, CAC and LTV by channel, campaign and segment. The calculations are transparent and reproducible, and the numbers can be verified.
Dashboards
I assemble dashboards with the funnel and end-to-end analytics in one place — from spend to profit per source. The reports support budget decisions without exporting to Excel.
How it works
- 01
Audit of sources and tagging
I review ad accounts, website and app analytics, and the CRM. I check tagging quality and data completeness and note what needs fixing before collection.
- 02
Setting up data collection
I connect the sources and set up regular loading of spend, events and leads into the warehouse. I normalize the data to a single format so channels and campaigns map correctly.
- 03
Linking the data
I build a model that connects spend, leads and sales, matching them by customer. On this link a click is traced through to payment and repeat purchases.
- 04
Attribution model and metric calculation
I set up attribution and distribute channel contribution, then build data marts with ROI, ROMI, CAC and LTV. I align the calculation logic so the numbers reconcile with actual sales.
- 05
Dashboards and acceptance
I assemble end-to-end reports with the funnel and payback metrics, and verify the accuracy of the attribution and the numbers. I hand the dashboards over and show how to use them.
- 06
Support and evolution
I connect new channels and sources, and refine the model and reports as you grow. I ensure regular data refresh and stable operation of the analytics.
Tech stack
dbt Clients
Testimonials
End-to-end analytics answers marketing’s central question: what makes money and what doesn’t. I connect spend, leads and sales into a single model and measure each channel’s payback in money, not clicks. As a result, the budget is allocated on facts rather than feelings, and arguments over channel contribution are settled with numbers.
Pricing
I price each project individually after a short discussion of the task, with the scope agreed in advance and no hidden charges. The total depends on a few factors:
Shall we discuss your task?
FAQ
What's needed to launch end-to-end analytics? +
Access to your ad accounts, your website or app analytics system, and your CRM. At the start I run an audit of sources and tagging and advise what to fix. If there isn't enough data yet, the audit makes clear where to begin.
Which attribution model do you use? +
I choose it to fit the business — from last-click to multi-touch attribution, depending on data and goals. For a long sales cycle with several touchpoints, a multi-touch model reflects channel contribution more accurately. The model can be changed as data accumulates and the metrics recalculated.
How do you link ads to sales if the CRM has no deal data or the tagging is weak? +
First, in the audit I look at what already exists and pinpoint where the chain from click to payment breaks. I usually start by getting the tagging in order and setting up source passthrough into the CRM so leads are linked to a channel. If there's little deal data, I build the model on the available funnel stages and expand it as sales start appearing in the CRM.
How are offline sales and calls accounted for? +
I connect calls through call tracking, which ties an inquiry to a source and campaign. Offline sales and payments I pull from the CRM and link to the customer, so they enter the end-to-end model on equal footing with online leads. This way payback also accounts for sales that don't close on the website.
How often is the data refreshed, and is near real-time possible? +
By default I set up a regular scheduled refresh — usually enough for budget decisions. If the task calls for fresher data, the load frequency can be increased up to near real-time. The exact mode depends on the sources and the load on their APIs.
Can end-to-end analytics be built on top of an existing warehouse and dashboards? +
Yes. If you have a working warehouse on BigQuery, ClickHouse or PostgreSQL, I build the end-to-end model and metric marts on top of it without breaking your current processes. I extend your existing dashboards or connect them to the new marts so as not to duplicate what already works.