The attribution model refers to how LinkedIn assigns credit for a conversion or sale to the different ads and channels involved in influencing that action. Understanding attribution is crucial for optimizing LinkedIn ad campaigns and accurately measuring performance.
First Touch Attribution
First touch attribution gives 100% credit to the first ad or touchpoint that started the user’s journey. For example, if a user clicks a LinkedIn Sponsored Content ad, but doesn’t convert until clicking a different LinkedIn ad later, the first Sponsored Content ad would still get full credit for the conversion under first touch attribution.
First touch can be useful for understanding which channels and campaigns are effective at initiating interest and driving new leads. However, it does not account for influence from other touchpoints that happened later in the buyer’s journey. First touch tends to favor upper-funnel and brand awareness style campaigns.
Last Touch Attribution
Last touch attribution gives 100% of the credit to the final touchpoint before conversion. Using the example above, the second LinkedIn ad that the user clicked before converting would receive full credit under last touch attribution.
Last touch can be helpful for optimizing campaigns that target users lower in the funnel who are already familiar with the brand. However, it ignores the impact of media or ads that started the journey, so last touch tends to favor lower-funnel and direct response style ads.
Linear Attribution
Linear attribution splits credit evenly between all ads and touchpoints in the user’s journey. If a user clicked 3 different LinkedIn ads before converting, each ad would get 1/3 of the credit with linear attribution.
Linear attribution values all touchpoints equally, but fails to account for the disproportionate influence that certain ads and channels may have had on the user. It works best when all touchpoints are generally equal in their impact at each stage of the buyer’s journey.
Time Decay Attribution
Time decay attribution allocates more credit to recent ads and touchpoints closer to the time of conversion. It gradually decays or decreases the value of older touchpoints in the buyer’s journey. For example, an ad clicked 1 week before conversion will receive more credit than one clicked 6 weeks before.
Time decay models the impact of recency and tries to value media interactions appropriately based on how close in time they occurred relative to the conversion event. More recent ads likely had a stronger direct effect on conversion.
Custom Attribution in LinkedIn
Beyond the standard models above, LinkedIn also offers the ability to create custom attribution models for your account based on conversion data. Custom attribution allows you to analyze historical conversion paths and assign credit to different touchpoints based on their observed correlation with driving conversions for your business.
With a Custom Model in LinkedIn Campaign Manager, you can see how much credit each ad, campaign, channel, etc. truly deserves based on their effect on your goals. This helps optimize spending and prevent overvaluing or undervaluing the impact of certain touchpoints.
Using Attribution to Improve LinkedIn Ads
Understanding attribution is crucial for analyzing the true return on ad spend and performance of LinkedIn campaigns. You can use attribution data to:
- Identify the most influential ad formats, placement types, audiences, and campaigns for driving conversions
- Shift budget to the highest converting ads and away from those with lower attribution
- Optimize creative, messaging, offers for the channels with highest attribution
- Build attribution-informed audiences to target your best customers
- Feed insights back into account strategy and media planning
Attribution Delays in LinkedIn
One challenge with attribution is delays in data reporting. LinkedIn notes it can take up to 5 days for conversion data to be fully processed and attributed to the correct campaigns. This is because they wait to confirm that conversions are valid and no returns or cancellations occurred before assigning attribution credit. Keep this delay in mind when analyzing performance trends or changes in attribution over the past few days, as data is still updating.
Impression vs Conversion Attribution
When analyzing attribution, impression attribution and conversion attribution give different perspectives:
- Impression attribution shows which ads and channels are driving the most impressions, clicks, and website traffic
- Conversion attribution looks specifically at which ads and channels are driving the most conversions and revenue
Make sure to look at both, as they can reveal insights about the user journey. High impression volume but low conversion attribution could signal an issue with converting visitors once they reach your site or poor audience targeting. High conversion attribution but low impressions could mean your ads aren’t reaching enough of your potential target audience.
Single vs Multi-Touch Attribution
Single touch attribution models like first and last touch assign 100% of the credit to one ad or touchpoint. Multi-touch attribution models like linear and time decay spread credit across multiple different touchpoints in the conversion path.
While single touch models are simpler, multi-touch better accounts for the influence across multiple branded touchpoints typical of today’s buying journey. Sophisticated multi-touch attribution provides a more realistic and nuanced understanding of performance.
Algorithmic Attribution Models
More advanced attribution approaches use machine learning algorithms to analyze huge volumes of data and dynamically determine the ideal attribution weightings across touchpoints. For example, LinkedIn and Microsoft Advertising leverage probabilistic machine learning models that incorporate billions of data points for enhanced attribution insights.
Cross-Device Attribution
Cross-device attribution is crucial for media accessed on different devices like smartphones, tablets, desktops. LinkedIn provides cross-device tracking and attribution by matching users across devices using advanced probabilistic modeling and by offering view-through conversions across devices.
This means LinkedIn can attribute a conversion on a smartphone back to an initial Sponsored Content impression or click that happened on that user’s desktop. Cross-device attribution provides a more complete and accurate picture of omni-channel media performance.
View-Through Attribution
LinkedIn measures not just last-click conversions, but also incremental view-through conversions driven by ads that did not receive the final click leading to conversion. If a user sees but doesn’t click a LinkedIn ad, then later converts, the view-through impression can receive attribution credit in LinkedIn’s multi-touch models.
View-through attribution can increase conversion volume attributed to brand awareness and upper-funnel campaigns that influence decisions later in the buyer’s journey. It provides a more complete picture than only last-click conversion attribution.
View-Through Attribution Window
LinkedIn’s view-through attribution window for counting view-through conversions is 28 days. Any user who converts within 28 days after viewing but not clicking a LinkedIn ad impression can drive a view-through conversion.
Incremental View-Through Conversions
To ensure view-through conversions are incremental and would likely not have happened otherwise, LinkedIn does not count view-through conversions from users who have already clicked a LinkedIn ad promoting the same product or service. Only new conversion volume influenced by the ad impressions receives attribution.
Cadence Attribution
Cadence reporting in LinkedIn Campaign Manager provides another perspective on attribution, grouping conversions by users’ paths over custom time periods. You define the cadence period, like 7, 14, or 28 days, and the report shows which channels drove new visitors, repeat visitors, and conversions over each period.
Cadence attribution reveals how different channels work together over time to influence conversions. It provides visibility into the synergies across touchpoints at different buyer’s journey stages, especially for purchases with longer sales cycles.
FAQs About LinkedIn Ad Attribution
What attribution models does LinkedIn support?
LinkedIn supports First Touch, Last Touch, Linear, and Time Decay attribution models. It also provides the ability to create custom attribution models based on your historical conversion data and algorithmic predictive models.
What is LinkedIn’s default attribution model?
LinkedIn Campaign Manager defaults to using algorithmic multi-touch attribution models for the most accurate analysis of how each touchpoint contributes to conversions.
Does LinkedIn offer cross-device attribution?
Yes, LinkedIn uses advanced identity graph technology to provide cross-device tracking and attribution across desktop, smartphones, tablets, and other devices.
How far back is LinkedIn’s attribution window?
LinkedIn allows attribution over either a 28-day or 60-day lookback window, depending on account configuration. So touchpoints up to 28 or 60 days prior to a conversion can receive attribution credit.
When does LinkedIn report on attribution data?
LinkedIn notes attribution reporting can take 3-5 days to populate as conversion data is confirmed and credited to the appropriate touchpoints. Attribution insights are generally delayed compared to last-touch conversion data.
Conclusion
Understanding attribution is essential for measuring true campaign performance and continuously optimizing LinkedIn ad accounts. While last-touch models provide a limited perspective, leveraging multi-touch attribution provides a more realistic view of how different media and messages combine to influence conversions across today’s fragmented buyer journeys.
By digging into impression, click, view-through, and conversion attribution data, LinkedIn advertisers can gain actionable insights to improve campaign strategies, creative, audiences, and budget allocations for driving measurable business impact.
