Most marketing teams can tell you how many leads or sales came in last month. Far fewer can tell you what actually led up to those conversions.
Somewhere between the first ad impression and the final “yes”, a lot happens, and most of it never shows up in a standard analytics dashboard. That’s the path to purchase, and it’s a lot messier than the tidy funnel diagrams suggest.
In our own work with clients, we consistently see the same pattern. People research across several channels, come back more than once, and often convert in ways that leave little or no digital trail.
In this article, we’ll explain what the path to purchase actually means, walk through the stages people typically move through, and look at why mapping this journey has become one of the harder jobs in marketing.
We’ll also share how first-party data can bring some clarity back to the picture.
Pro Tip
If you’re trying to piece together your customers’ path to purchase from platform data alone, you’re only ever going to see part of it. Ruler uses first-party tracking to collect every touchpoint, call, form fill and chat enquiry a visitor makes, then matches that activity back to the revenue it eventually produces. The result is a single, joined-up view of what’s actually driving sales, not just what each ad platform claims. Book a demo to see how it works with your own data.
How we explain the path to purchase
When we talk to customers about the path to purchase, we’re really talking about the full sequence of interactions someone has with a brand before they buy.
That covers the ad they scrolled past, the review they read, the comparison site they visited, the email they opened but didn’t click, and the eventual conversion itself, whether that’s a form fill, a phone call or an in-person sale.
The stages themselves aren’t fixed. They shift depending on the industry, the price point, and how considered the purchase is.
A pair of trainers might involve two or three touchpoints over a few days. A B2B software purchase might involve a dozen touchpoints over several months and multiple people. That said, most versions of the path to purchase tend to map onto a similar structure, even if the labels change.
| Stage | What’s happening | Typical touchpoints |
| Awareness | The buyer becomes aware they have a need or a problem | Paid social, display ads, PR, organic search, word of mouth |
| Consideration | The buyer starts researching options and comparing solutions | Blog content, comparison sites, reviews, webinars, retargeting ads |
| Evaluation | The buyer narrows down their shortlist and weighs up specifics | Case studies, product demos, pricing pages, sales calls |
| Decision | The buyer converts, whether online or offline | Form submissions, phone calls, in-store visits, checkout |
| Post-purchase | The buyer becomes a customer and, potentially, an advocate | Onboarding emails, support interactions, referrals, reviews |
We often find marketers use slightly different names for these stages, and some industries collapse two stages into one, or add extra steps for approval processes. The structure above is a useful starting point rather than a strict rulebook.
Understanding this shape matters because it shapes how marketing gets measured.
In our own research, 47% of respondents said marketing attribution enhances their understanding of the customer journey and sales cycle. Without visibility into these stages, it’s genuinely difficult to know which activity is doing the work.
Why the path to purchase isn’t a straight line anymore
The stages above look neat on paper, but in reality people rarely move through them in order.
Someone might land in the evaluation stage from a referral, skip straight past awareness altogether, then drop back into consideration a few weeks later after a competitor’s ad catches their eye.
Journeys loop back on themselves, pause for weeks at a time, and jump between devices and channels without much regard for the funnel we’ve drawn for them. This isn’t a new phenomenon exactly, but it has become far more pronounced.
Buyers now have access to more information, more channels and more opinions than ever before, and they use all of it.
A single purchase decision might touch paid search, organic content, a podcast ad, a recommendation from a colleague, and a direct visit to the website weeks later once the decision has more or less already been made.
That fragmentation is one of the biggest headaches in modern marketing measurement.
In fact, 44% of respondents cite cross-channel journeys, both online and offline, as a challenge for effective marketing attribution, and it’s easy to see why.
Platforms are generally good at reporting what happened within their own walls. They’re far less good at recognising the influence of everything that happened outside them.
Add offline activity into the mix, phone enquiries, in-person visits, referrals, and the picture gets murkier still. Interestingly, 56% of respondents say the majority of their leads come from inbound marketing, which tends to involve exactly this kind of long, winding, multi-touch research process rather than a single quick click-through.
How we map and track path to purchase
This is genuinely where we spend most of our time with clients, so it’s worth walking through how we actually approach it, rather than just the theory.
We tend to start by telling clients the same thing. Platform-level reporting, GA4 included, is built around sessions and last-click conversions, and that structure quietly throws away most of the nuance in a longer journey.
If you want to see the whole path rather than the last step of it, you need to be collecting the data yourself, not borrowing whatever each platform decides to hand back to you.
That’s the thinking behind first-party tracking, and it’s the foundation everything else is built on. We drop a first-party JavaScript tag onto the site, which sits on your own domain rather than relying on third-party cookies that browsers are increasingly blocking anyway.
From the moment someone lands on the site, that tag starts quietly recording what happens, every visit, every UTM parameter, every page viewed, and it keeps doing that across sessions, not just the one where a conversion happens to occur. Calls, form submissions and live chat enquiries all get logged the same way, so you’re not just looking at web conversions in isolation.
The part that makes this useful, rather than just another log file, is how we tie it all together. Each visitor gets a first-party identifier that follows them as they come back to the site, whether that’s tomorrow or in six weeks’ time.
Most visits start out anonymous, which is normal, but the moment someone converts and gives us something like an email address, we can retrospectively link that identity back through every earlier touchpoint.
From there, we connect into whatever system holds the outcome that actually matters to the business.
For some clients that’s a CRM, but it doesn’t have to be. It might be an ecommerce platform, a booking system, a payment processor, or an order management tool, whatever holds the record of a sale actually happening.
This is where a lot of tracking setups fall down, because a form fill, an add to basket or a call isn’t really the outcome anyone cares about, revenue is.
We match records using a consistent identifier, usually email address, order ID or phone number, so that once someone moves through to a confirmed booking, purchase or closed deal, that revenue gets pulled back and reattached to the original marketing journey.
It doesn’t matter if the sale happens in the same session, two days later, or four months later, or if it closes entirely offline, over the phone, in a showroom or at the till. The join holds, whatever the setup looks like.
The beauty of having that closed loop, from first touch through to confirmed revenue, is what we can then do with it.
Once we know where revenue came from which campaign, keyword or channel, we push that signal straight back into the ad platforms themselves.
Google Ads, Meta and others receive offline conversion data with real revenue values attached, not just a lead count.
That means their bidding algorithms stop optimising for the easiest thing to measure, form fills, and start optimising for the thing that actually matters to the business.
In our experience, that shift alone tends to be where the real performance gains show up, because you’re finally feeding the platforms the same picture of success that the business itself is working towards.
Pro Tip
Everything above, the tag, the identity matching, the join through to revenue, is what Ruler does for you out of the box. We handle the collection and the matching, then push confirmed revenue straight back into Google Ads, Meta and your other platforms as offline conversions, so their bidding algorithms are working from real outcomes rather than form fills. Book a demo and we’ll show you how it maps onto your own setup.
How our customer increased revenue 6x tracking path to purchase
One of our clients, an online tax return service, ran into exactly the kind of fragmented path to purchase we’ve been describing.
Customers might discover them through paid search, come back organically a few days later, sign up during a direct visit, then complete payment through a completely separate offline process.
Each of those steps happened on a different platform, sometimes a different domain, which made it extremely difficult to connect any of it back to marketing spend.
Reporting was split across Google Ads, GA4 and Looker, each giving a slightly different version of the truth, and the team had no reliable way of tagging users with the campaign or channel that originally brought them in.
Earlier tracking attempts had only captured around 20% of users, and even more recent server-side tracking topped out at roughly 50%.
We implemented first-party tracking across the full journey, using a JavaScript tag to capture visits, UTM parameters and click data, then stitched sessions together using a consistent identifier once a user converted.
That closed the loop between anonymous browsing and known customers. Once a sale was confirmed, the revenue was matched back to the original lead and pushed into Google Ads as an offline conversion, giving the platform’s bidding algorithm real revenue to optimise towards instead of form fills alone.
Over the three months this was in place, spend tripled while revenue grew considerably faster:
- Form fills increased 4.3 time
- Sales closed increased 4.4 times
- Unified ROAS climbed to 1.98x
- Overall revenue grew 6x
The bigger shift, though, was qualitative. The client could finally see which channels were genuinely driving paying customers, rather than just leads.
How to track path to purchase when there’s no clicks
Even with solid first-party tracking in place, there’s a persistent problem that trips up a lot of marketers, upper-funnel activity that never produces a click.
An ad impression, a piece of PR, a podcast mention or an offline campaign can all shape a decision without leaving a single trackable interaction behind. By the time someone finally converts, the tools recording that conversion have no way of knowing what nudged them there in the first place.
We see this play out clearly in our own analysis. Across the industries we looked at, direct traffic averaged 24.3% and conversions reported as direct averaged 24.1%.
Some of that is genuinely untagged links or conversions analytics simply can’t connect back to a source. But a large chunk of it is upper-funnel influence that never gets properly credited. A
Anyone trying to map a path to purchase eventually runs into this same wall, a big pile of conversions labelled “direct” with no real indication of what actually drove them.
Traditional analytics isn’t built to solve this, because it’s fundamentally reliant on clicks.
The answer tends to lie in statistical modelling rather than click tracking alone. Marketing mix modelling looks at the relationship between spend and outcomes across every channel, including offline activity such as TV, radio and print, accounting for factors like seasonality and competitor activity along the way.
Data-driven attribution models, similarly, can incorporate impression-based weighting rather than relying purely on the last thing someone clicked before converting.
Used together, these approaches shift credit away from over-attributed channels like direct and brand search, and towards the upper-funnel activity that was quietly doing a lot of the persuading all along.
Get visibility over the path to purchase
The path to purchase has never been a straight line, and it’s only getting more tangled as customers spread their research across more channels, more devices and more offline moments.
Trying to reconstruct it from platform reporting alone means working with an incomplete picture, and incomplete pictures lead to budget decisions that miss the mark.
Ruler was built to close that gap. By tracking every touchpoint with a first-party tag, matching conversions back to CRM and revenue data, and layering in marketing mix modelling for the activity that never earns a click, Ruler gives you a much fuller view of what’s actually driving your pipeline.
If you’d like to see how it maps onto your own customer journeys, book a demo and we’ll walk you through it using your own data.
Path to purchase FAQs
The path to purchase is the full sequence of interactions a customer has with a brand before they buy, from the moment they first become aware of a need through to the point of conversion. It includes both online touchpoints, such as ads, content and search, and offline ones, such as calls, referrals and in-person visits.
Most versions of the path to purchase move through awareness, consideration, evaluation, decision and post-purchase, though the exact labels and number of stages can vary by industry and by how considered the purchase is.
A common example is someone seeing a paid social ad, researching the brand over several weeks, comparing it against competitors, returning through organic search, and eventually converting through a phone call or offline sale rather than a simple online checkout.
Understanding the path to purchase helps marketers see which channels and touchpoints genuinely influence a decision, rather than giving all the credit to the last click before conversion. That leads to better budget decisions and a clearer picture of overall marketing performance.
racking the path to purchase reliably usually means moving beyond platform-reported data and using first-party tracking to capture every touchpoint, including calls, forms and offline conversions, then matching that activity back to revenue. For upper-funnel activity that doesn’t produce a click, statistical modelling such as marketing mix modelling can help fill in the gaps.
