A last-click attribution model alone can’t explain the value of content marketing. That’s because blog posts usually do their work not right before purchase, but at the moment customers first discover you. Knowing how the main attribution models differ helps you choose the one that fits your team.
Commonly used attribution models
Each model makes a different assumption about who gets the credit.
Before you talk about models, check your data quality first. If campaign names are inconsistent or links are missing tracking parameters, the results will be muddy no matter which model you use. If the team standardizes how traffic sources are labeled first, half the work of choosing a model is done.
- Last click: 100% to the touchpoint just before conversion
- First click: 100% to the touchpoint where they first discovered you
- Linear: equal credit to every touchpoint
- Position-based: more to the first and last, less to the middle
- Data-driven: learned from actual conversion path data
Choosing the model that fits your team
If the time to purchase is short and there are few touchpoints, last click may be enough. Conversely, for a B2B product with a long evaluation period and several departments involved, a model that reflects the middle touchpoints, such as position-based or linear, is closer to reality.
When you look at first-click attribution, look not only at channel names but at which content served as the entry point. Knowing which articles bring in people who later become customers at a higher rate gives you a grounded basis for deciding which topics to invest in next.
An attribution model isn’t a tool that tells you the truth; it’s a tool that gets you debating where to spend the budget.
The Story Light editorial team
Two lenses instead of one right answer
The most practical approach is to look at two models side by side instead of choosing one. For example, putting last-click and first-click results together shows which channels open the door and which close it.
When you share analysis, talk about the model’s limits as well. No model perfectly shows the true influence of each touchpoint. Teams that acknowledge the limits and present two or more perspectives keep leadership’s trust longer.
Common reasons attribution analysis goes off track
As important as choosing a model carefully is the analysis window. If a product usually takes a month to buy but you only look at attribution in one-week windows, early touchpoints are consistently undervalued. Check the average time to conversion first and build reports on a window longer than that.
There is also the problem of the same person being counted as different people when they switch devices or browsers. A customer who read a post on mobile and signed up later on desktop easily hides the content’s credit. It can’t be fully solved, so know the limitation and interpret the numbers somewhat conservatively.
Keep in mind, too, that easily tracked channels like ads and email tend to look relatively better. Among visits recorded as direct or search, many are actually people who saw your content, remembered it, and came back to find you. You can’t uncover all of this hidden contribution, but adding a one-line question at signup such as “How did you hear about us?” gives you a clue to supplement the numbers. Even short answers add up to surprisingly useful evidence.
How to report on content performance
When explaining content’s value to executives, showing the path customers walked is more persuasive than listing model names. Showing the journey of a few real customers from first touch to signup, as examples, makes visible the role of content that numbers alone don’t convey.
The items in the report can be as simple as the following.
- The content that first brought them in and the conversion path afterward
- The difference in time to convert between customers who engaged with content and those who didn’t
- A ranking comparison under last-click and first-click
- Topics to invest in next quarter, and why
Numbers should be delivered with interpretation. Don’t stop at reporting which content produced how many conversions; go on to propose what to change as a result. That’s how attribution analysis becomes a basis for decisions rather than a report. A proposal is what starts the next meeting.
Set a realistic reporting cadence, too. Reporting changing numbers every week leads to overreacting to fluctuations, so showing trends monthly and reviewing the model and criteria once a quarter is lighter and more convincing. Show first whether the direction reads consistently rather than how big the number is.
Summary
Whichever model you use, it’s important to agree on the same standard within the team and hold to it for a while. Change models often and you can’t read the trend in performance. A review once a quarter is enough.
Add your first comment to this post