Linear Attribution Model: How It Works and When to Use It
Library > Linear Attribution Model
Written by Maimouna Corr Fonsbøl
Published on 26 October 2026
What is linear attribution?
Linear attribution is a multi-touch attribution model that divides credit equally among the eligible interactions before a selected outcome. It gives B2B marketers an easy-to-explain view of shared credit across a recorded buying journey.
If a $90,000 opportunity has three eligible interactions, each receives $30,000 of pipeline credit. The equal split is the model's rule; it does not mean each interaction caused the same amount of business.
Linear is useful as a comparison because its calculation is straightforward. You can inspect an account journey, count the included interactions, and understand how each share was assigned.
Use it to examine shared credit alongside first-touch or last-touch results.
Use it to investigate which channels appear across the measured journey, including activity between the first and final interaction.
Use it when an equal-weighting assumption is useful for the question, while checking the underlying data and event definitions.
A longer journey does not automatically make linear the best model. The important questions are what each counted touch represents and whether equal weighting helps you interpret the result.
The formula and the scope behind it
For a journey with at least one eligible touchpoint:
Credit per touchpoint = outcome value ÷ number of eligible touchpoints
The denominator matters as much as the value. Adding an eligible interaction reduces each touch's share, even if nothing about the buyer's decision has changed.
Eligibility determines the count: recorded activity must be included under the selected outcome, time coverage, and exclusion rules.
Grouping determines the unit: a page view, a website session, and a meeting are different events. State what the report counts before interpreting the shares.
A three-touch example
Suppose a $90,000 opportunity follows a paid social visit, webinar attendance, and a search visit, with all three eligible. Each receives one-third of the value, or $30,000. Rounded percentage labels may show 33.3%, but the underlying shares total 100%.
Equal touch credit does not mean equal channel credit
Consider a separate $120,000 won deal with six eligible interactions: two paid social visits, three email interactions, and one event attendance. Each interaction receives $20,000.
Grouped by channel, paid social receives $40,000, email receives $60,000, and the event receives $20,000. Email gets more total credit because it has more eligible interactions, not because the model established that email was more persuasive.
The interactions may come from several people in the same buying group. Connect them to the right account and opportunity, and check repeated events before treating the channel totals as an investment recommendation.
How linear attribution works in Dreamdata
Dreamdata's linear attribution model assigns equal credit to eligible touches in the journey leading to a selected stage. The selected stage and activity scope determine what the calculation includes.
Outcome: opportunity creation and a won deal are different endpoints. Selecting a later outcome can include additional eligible activity.
Included activity: review which interactions are counted and how exclusions affect the allocation. A channel cannot receive credit for activity missing from the measured journey.
Account relationships: Dreamdata's account-based data model connects contacts, activity, companies, and business outcomes so relevant buying-group activity can be analyzed together.
Start by inspecting known opportunities and the activity leading to your chosen milestone. Confirm the relevant dates, value field, and relationships before interpreting an aggregate channel report.
Use the result to investigate a campaign
Linear attribution can identify campaigns worth a closer look. Pair the allocated value with campaign cost, account fit, and later deal progress to decide what to investigate.
Read the report in its defined scope
Confirm the outcome: pipeline credit represents potential business, while won-deal credit uses the selected revenue measure.
Review the accounts: check whether credited opportunities fit your target market and whether one large deal dominates the campaign total.
Inspect the interactions: understand why the campaign receives its share and whether repeated activity explains much of the allocation.
Check the cost base: include the relevant costs for the comparison rather than comparing a full event budget with only one part of another campaign's expense.
Allow time to progress: compare similarly mature deal groups before treating a recent campaign's lack of won revenue as its final result.
Choose the next action from the evidence
If credited accounts are a poor fit, review campaign targeting and messaging.
If relevant accounts repeatedly ask the same evaluation question, improve the supporting content.
If a channel's credit comes from repeated routine events, examine the event definitions before changing spend.
If a promising campaign is supported by several progressing opportunities, define a test of additional investment and the outcome you expect.
Keep measurement rules stable during the test, or explain any changes so a different allocation is not mistaken for improved performance.
Dreamdata Activation supports audience and conversion syncing to supported ad platforms. Relevant account activity or business milestones can inform advertising, with campaign outcomes evaluated after the change.
The main limitations of equal
weighting
Linear shows one allocation of observed value. Understanding its assumptions helps you use the report without asking it to answer a different question.
What can distort the interpretation?
Treating credit as causal effect: an allocated $20,000 does not mean removing the interaction would reduce revenue by $20,000.
Counting frequency as importance: repeated interactions can collect a large share even when each provides little new information to the buyer.
Incomplete relationships: missing contacts or incorrectly linked opportunities change the eligible journey and allocation.
Changing definitions: adding an event type or changing a lookback can shift channel totals without changing the underlying business outcome.
Checks that keep comparisons useful
Use the same outcome, value field, currency, and date definitions across reports.
Document event grouping and exclusions so the denominator is understandable.
Identify material gaps in the available activity and use buyer or sales feedback to add context.
Compare another model when its different assumptions could affect the decision you are considering.
When to compare another model
Position-based models emphasize specified parts of the journey, while data-driven models calculate weights from patterns across journeys. Compare them with linear using the same data scope to see which conclusions depend on equal weighting.
A different allocation is a reason to inspect the underlying accounts and rules. Keep the primary reporting method consistent and explain any change to stakeholders before comparing performance over time.
FAQs
Can linear attribution include several people at the same account?
Yes. When relevant activity is connected to the account and opportunity, eligible interactions from different people can share credit in one journey. Equal weighting applies to the counted interactions, not automatically to each person or channel.
How does linear differ from data-driven attribution in Dreamdata?
Linear uses an equal-weighting rule. Dreamdata's data-driven attribution model uses patterns across journeys leading to a selected stage to calculate weights. With limited data, its allocations can approach linear attribution. Both still depend on the activity and business records included in the analysis.