A linear attribution model splits the credit for a sale equally across every recorded touch. If a client saw an ad, read two emails and then called, each of those four touches earns a quarter of the deal. This article answers a practical question: how do you apply linear credit to your own pipeline using the touch history the levelpartner.ai platform already stores, and when is that worth the effort? Done properly, it shows you which channels quietly carry deals through the middle of the journey.
Why equal credit is useful
First-touch and last-touch views each favour one end of the journey. The middle disappears. That middle is where nurture emails, webinars, case studies and follow-up texts do their work, and it is usually the part a stalled business has underinvested in or cannot prove.
Linear credit is not more accurate than other models. It is more even-handed. It says: every channel that showed up deserves a share. That makes it a good check on decisions made from a single-touch view, and a fair way to settle arguments between the people who run each channel.
What the platform gives you
Each contact record keeps an activity timeline: page visits from tracked links, form submissions, emails opened and clicked, SMS replies, calls and bookings. Dashboard widgets filter on first or latest attribution directly. A linear split is something you calculate from that history, either by export or with a custom field updated by a workflow.
For most firms the export route is enough. You do not need a linear figure every day. You need it once a quarter when budgets are set.
Before you start
- At least one quarter of contacts with complete activity timelines.
- A short list of channels you care about, no more than eight.
- Tracking parameters on every outbound link, so touches are attributable to a channel.
- Won opportunities marked with real values.
- A spreadsheet, or a person comfortable building one.
How to set it up
- Decide which touch types count, such as tracked clicks, form fills, calls and bookings.
- Leave out passive events like email opens, which inflate whichever channel sends most.
- On your dashboard, click a Won Opportunities widget to open its records.
- Use Export to take last quarter's won deals into a spreadsheet.
- Read each won contact's Activity tab and list its qualifying touches.
- Divide each deal's value by its touch count.
- Add that share to the channel behind each touch.
- Total the shares by channel and set them beside first-attribution totals.
Take a client whose history shows five qualifying touches: a LinkedIn post, a webinar sign-up, a case study download, a reply to a follow-up email and a discovery call booked from a tracked link in that email. On an illustrative £30,000 programme, each touch earns £6,000. Now suppose that over a quarter the webinar series collects £96,000 of linear credit but only £18,000 under first source. A gap that size would mean the webinars do most of their work in the middle, where single-touch views cannot see it.
Where it helps a business with flat revenue
When turnover stalls, the usual reflex is to cut whatever looks unproductive. Nurture activity almost always looks unproductive under single-touch models because it rarely starts or finishes a journey. A linear view protects the middle of the funnel from a cut that would hurt three months later.
It also gives your team a common currency. The person running events and the person running paid search can both see their share of the same won revenue. Compare it with the view in cross channel attribution, which looks at how channels hand off to each other.
The mistake most people make
Counting every logged event as a touch. A contact who opens twelve newsletters and clicks one ad will hand most of the credit to email simply because email sends more. Decide which actions show intent, count only those, and write the rule down so next quarter uses the same one.
Time it takes
The first run takes half a day, mainly deciding the rules. After that, each quarterly refresh takes one to two hours. There is no weekly work. If you find yourself wanting the figure weekly, that is a sign you need a proper multi touch attribution set-up rather than a spreadsheet.
When not to use it
If your clients typically convert after one or two touches, linear credit gives the same answer as first or last touch with more effort. Skip it. It also misleads when one touch clearly did the heavy lifting, such as a referral from an existing client followed by routine admin emails. Equal credit there would understate the referral.
Troubleshooting
One channel takes most of the credit. Check your touch rules. A channel that sends often, such as email or SMS reminders, can swamp the totals. Exclude service messages like appointment reminders.
Deals show only one touch. The activity history is thin, usually because enquiries arrive by phone on an untracked number. Add call tracking before trusting a linear split.
Totals do not match won revenue. Some won deals have zero qualifying touches and drop out of the calculation. Give them an "unknown" row so the totals reconcile.
Frequently asked questions
Is linear better than time decay or position-based models?
None is better in general. Linear treats every touch alike, time decay favours recent touches, and position-based favours the first and last. Pick the one whose bias you can live with, and keep using it so quarters are comparable.
Can the platform calculate linear credit automatically?
Dashboard widgets show first and latest attribution natively. A linear split is calculated from the contact activity history, usually by export. A workflow can also update custom fields as touches occur if you want a running tally.
How many touches is too many to count?
If won deals routinely carry more than fifteen qualifying touches, your rules are too loose. Tighten them to actions a person chose to take, such as clicking, replying, booking or calling.
Does linear credit work for sales activity too?
It can, but it answers a different question. Splitting credit across sales calls and meetings belongs in sales attribution, where the goal is to understand rep activity rather than marketing spend.