Linear attribution is a measurement model that assigns equal credit for conversions across each marketing channel or touchpoint along a customer’s journey.

What is linear attribution?

Linear attribution is a multi-touch attribution model that measures how different touchpoints influence a customer’s journey before they complete a desired action. Unlike other models that give more weight to the first or last interaction, linear attribution assigns equal credit to every touchpoint.

Today’s multi-channel marketing world has customers interacting with brands across various platforms and touchpoints. Linear attribution tracks the entire user journey, ensuring that every marketing effort is recognized. This balanced method helps you make informed decisions and optimize your strategies effectively.

Key characteristics

• Multi-touch approach: Linear attribution acknowledges every point of interaction a customer has with your brand. This gives you a full picture of how different marketing channels work together to drive conversions.

• Equal credit for each touchpoint: Each interaction in the customer’s journey is given equal weight. This balanced view helps you see the true value of every marketing effort.

How does linear attribution work?

Linear attribution maps out all the touchpoints a customer interacts with before converting and gives equal credit to each. 

Let’s say you’re running a campaign to promote your app. If a user first sees your ad on Instagram, then visits your landing page from a Google search, reads a blog post, and finally downloads your app after clicking a remarketing ad, each of these interactions gets 25% of the credit for the conversion.

By valuing each interaction equally, you can see the full impact of your marketing ecosystem. If data shows that email campaigns and remarketing ads play significant roles in conversions, you can allocate more resources to these channels for better results.

Benefits and limitations of linear attribution

Like other marketing attribution methods, linear attribution has its pros and cons. While it can help you understand your customer journey, improve user experience, and allocate resource across channels, the insights it provides may lack nuance.  

Benefits

• Provides comprehensive multi-channel insights: You get a complete picture of your customer journey across all channels. By giving equal credit to each touchpoint, you can see how your entire marketing ecosystem works together. This helps you understand how different channels support each other and improve the overall customer experience.

• Facilitates omnichannel strategies: Linear attribution values every interaction, pushing you to create omnichannel strategies. This means your marketing efforts become more cohesive and integrated, ensuring a consistent customer experience whether through social media, email, content marketing, or paid ads.

• Supports data-driven decisions: With linear attribution, you can take a data-driven approach to your marketing. By analyzing the contribution of each touchpoint, you can make informed decisions on resource allocation and campaign optimization. This leads to better ROI and more effective marketing strategies.
• Takes every touchpoint into account: Equal credit distribution means no single touchpoint is overvalued. This is especially useful in complex customer journeys with multiple interactions influencing decisions. Instead of overemphasizing the first or last touchpoint, you can appreciate the full spectrum of your marketing efforts.
• Maintains consistent engagement: Knowing all touchpoints are valued equally motivates your marketing team to maintain consistent engagement across all channels. This consistency enhances brand awareness and reinforces your messaging, crucial for building trust and loyalty among customers.

Limitations 

• Oversimplifies the customer journey: Linear attribution oversimplifies how customers interact with your brand. Not all touchpoints are created equal; some influence conversions more than others. You might miss the real stars in your marketing lineup by giving equal credit to each touchpoint.
• Ignores touchpoint quality: Linear attribution doesn’t differentiate between the quality and impact of different touchpoints. For example, a high-quality blog post that deeply engages a customer may receive the same credit as a brief, low-impact social media interaction. This lack of differentiation can lead to suboptimal marketing strategies.

• Misleads credit for high-frequency channels: Marketing channels with high-frequency, low-impact interactions (such as social media) can receive too much credit under a linear attribution model. This skews the perceived effectiveness of these channels, potentially leading you to overinvest in them.

• Doesn’t factor in touchpoint timing: When interactions happen matters. Early touchpoints might build awareness, while later ones could close the deal. But linear attribution doesn’t consider the sequence or timing, making it harder to understand what truly drives conversions.
• Doesn’t fit different customer segments: Different customer segments may respond differently to various touchpoints. Linear attribution’s uniform approach doesn’t account for these variations, leaving you with generalized insights that don’t reflect the diverse behaviors and preferences of different customer groups.
• Inaccurate ROI measurement: By spreading credit evenly, linear attribution can distort the actual return on investment of specific channels. This can result in poor budget decisions and make it difficult to optimize marketing spend.

Is linear attribution the right model for you?

Linear attribution is a valuable attribution measurement tool when you want to grasp the combined impact of multiple touchpoints. It’s especially handy for multi-channel campaigns and early-stage marketing, where every interaction counts.

On the other hand, it’s tricky to appropriately weigh touchpoints. Does a user spending five minutes exploring your app have the same importance as following you on social media? And why, or why not? This remains a constant challenge. For instance, one user clicking on a push notification might have more influence on their decision to purchase than another user who also clicked.

For deeper insights into specific touchpoints or complex sales cycles, other attribution models may offer more precise and actionable data. But if you’re looking for a straightforward model that accounts for all relevant touchpoints, linear attribution is a solid starting point.

Let’s take a closer look into when using linear attribution makes sense and when it doesn’t.

When to use linear attribution

Multi-channel campaigns

Suppose your marketing strategy involves various channels like social media, email marketing, influencer partnerships, and paid ads. In this case, linear attribution helps you see how these channels collectively help you achieve your marketing goals.

Imagine a user who first sees an Instagram ad, then reads a blog review, and finally clicks on a remarketing ad to download your app. Under the linear model, each interaction gets equal credit, giving you a balanced view of your app marketing efforts.

Early-stage marketing

In the early stages of marketing your product, when you’re still figuring out what works, linear attribution offers a broad understanding of how different touchpoints contribute to user acquisition. This helps you fine-tune your strategy without prematurely prioritizing one channel over another.

Consistent engagement

If your business relies on maintaining consistent engagement across various touchpoints, linear attribution highlights the importance of each interaction.

For example, a fitness app might engage users through blog posts, social media updates, email newsletters, and in-app notifications. Linear attribution ensures every engagement point is recognized, encouraging sustained efforts across all channels.

When NOT to use linear attribution

Unequal touchpoint impacts

If certain touchpoints have a significantly higher impact on conversions, linear attribution might not be ideal.

For example, if data shows a demo video on your app’s landing page is the primary driver of downloads, giving it the same credit as a less impactful touchpoint, like a single social media post, won’t accurately reflect its importance. In such cases, models like time decay or position-based attribution, which give more weight to key interactions, might be better.

Complex sales cycles

For apps with complex sales cycles, where the user journey involves multiple stages of engagement and decision-making, linear attribution may oversimplify the process. An enterprise app that requires demos, consultations, and multiple follow-ups before a download is completed would benefit more from a custom attribution model that reflects the true journey.

Resource optimization

If your goal is to optimize resources by investing more in high-impact channels, linear attribution might not provide the granular insights you need. For example, if you need to decide whether to invest more in Facebook ads or influencer partnerships for your gaming app, an attribution model that differentiates the impact of these channels, such as data-driven attribution, can offer better guidance.

Multi-touch alternatives to linear attribution

Choosing the right attribution model depends on your marketing goals and customer journey. While linear attribution offers an equitable view, models like first touch, last touch, time decay, U-shaped, and W-shaped provide nuanced insights for optimizing different aspects of your marketing strategy.

Read on for a brief introduction to each of these models. 

First-touch attribution

First-touch attribution gives all the credit for a conversion to the first interaction a user has with your brand. This model is great for understanding which marketing channels create initial awareness.

Last-touch attribution

Last-touch attribution credits the very last interaction before conversion. This model helps you see which touchpoints are best at closing the deal.

Time decay attribution

Time decay attribution assigns more credit to touchpoints closer to the conversion. You can use this model when timing significantly impacts your decisions.

U-shaped attribution

U-shaped attribution, or position-based attribution, gives most credit to the first and last interactions, with the remaining credit distributed among the middle touchpoints. It’s up to you to determine the percentage of credit assigned to each. This model highlights the importance of both initial engagement and conversion interactions.

W-shaped attribution

W-shaped attribution extends the U-shaped model by giving significant credit to three key touchpoints: the first interaction, a critical mid-funnel interaction, and the last interaction. This model shows the impact of pivotal touchpoints throughout the customer journey.

Comparing the models: A practical example

Let’s suppose you’ve launched a marketing campaign for your gaming app. The user sees an Instagram ad, reads a blog post, gets an email, and finally clicks on a remarketing ad to download the app.

Here’s how different attribution models would handle this:

• First-touch attribution gives all the credit to the Instagram ad, where the user first discovered your app. This helps you see which channels are best at creating initial awareness.
• Last-touch attribution gives all the credit to the remarketing ad, the final interaction before the user downloaded the app. This model highlights the importance of the last touchpoint that led to the conversion.
• Time decay attribution assigns more credit to touchpoints that occurred closer to the conversion. So, the remarketing ad and email newsletter get more credit, while the Instagram ad and blog post get less. This model emphasizes the growing influence of interactions as the user moves closer to downloading the app.
• U-shaped attribution splits, let’s say, 40% of the credit between the Instagram ad (first touch) and the remarketing ad (last touch). The remaining 20% is divided between the blog post and the email newsletter. This model highlights the importance of the first and last touchpoints, while still acknowledging the impact of middle interactions.
• W-shaped attribution gives significant credit to the Instagram ad (first touch), the blog post (key mid-funnel touchpoint), and the remarketing ad (last touch). The remaining credit is distributed among other touchpoints, like the email newsletter. This model underscores the importance of critical interactions throughout the user journey.

Key takeaways

• Linear attribution assigns equal credit to every touchpoint in a customer’s journey, recognizing all marketing efforts. This approach provides a comprehensive view of how various marketing channels work together, helping you better understand the overall customer journey.
• Linear attribution promotes consistent engagement across all channels. Because no single touchpoint gets overvalued, it’s particularly helpful in complex customer journeys, as well as early-stage marketing and multi-channel campaigns.
• However, linear attribution can oversimplify the customer journey. It doesn’t account for the quality and timing of touchpoints, offering generalized insights that might not fit all customer segments.
• Linear attribution helps you get a broad understanding of how different touchpoints contribute to conversions. But it’s less effective when touchpoints have unequal impacts or in complex sales cycles where the sequence and quality of interactions are crucial.
• Other multi-touch attribution models, such as time decay, U-shaped, and W-shaped, can offer more precise marketing measurement insights. By weighting interactions based on their actual influence, these models help you optimize resource allocation and improve marketing effectiveness.

The post Linear attribution appeared first on AppsFlyer.

Closed-loop attribution evaluates how each marketing channel and campaign affects overall business outcomes. Unlike other attribution models, it follows the entire customer journey to connect marketing efforts directly with sales.

What is closed-loop attribution?

Closed loop attribution, also known as closed-loop measurement, provides a detailed view of how each marketing activity contributes to sales and revenue by linking marketing efforts with sales data, effectively “closing the loop” between marketing actions and outcomes.

Let’s suppose you’re a retail company launching a new product line and execute the following multi-channel marketing campaign:

1. Social media ads: Targeted ads on platforms like Facebook and Instagram.
2. Email newsletters: Personalized emails sent to your subscriber list.
3. SEO and content marketing: Blog posts and optimized web pages to attract organic traffic.
4. In-store promotions: Special deals and promotions advertised both in-store and online.

This is how closed-loop attribution measures this campaign:

• A customer clicks on a Facebook ad and visits your website.
• On the website, they read a blog post about your new product line.
• They sign up for the email newsletter to get a discount code.
• They receive an email with the discount code, visit the store, and make a purchase.

As mentioned, closed-loop attribution monitors and connects each touchpoint. So, when a sale happens, your CRM system records it, attributing the sale to the combined efforts of the Facebook ad, blog post, email newsletter, and in-store promotion. This data reveals which channels were most effective.

For instance, if social media ads had the highest engagement but the email newsletter converted the most leads, you might increase email marketing efforts while maintaining a strong social presence.

Think of it like this: Closed-loop attribution shows how each marketing activity contributes to sales, enabling data-driven decisions to improve your marketing efforts and boost revenue.

How closed-loop attribution works

1. Data collection: Closed-loop attribution begins with collecting data from various marketing channels. Think: online ads, social media, email campaigns, SEO, and content marketing. Every interaction a potential customer has with these channels gets meticulously measured.
2. Lead monitoring: As potential customers engage with your content, their interactions are captured using specific tools and software. This means recording visits to your website, clicks on ads, form submissions, and resource downloads via cookies, website pixels, and unique identifiers.
3. Sales integration: Next, these leads are integrated with your sales data, usually through a CRM system. This step is crucial to link your marketing efforts directly to sales activities and outcomes.
4. Attribution analysis: With both marketing and sales data combined, you can analyze the impact of each marketing activity on conversions and revenue. This involves examining the customer’s journey from the first touchpoint to the final sale. You can use different attribution models (like first-touch, last-touch, and multi-touch attribution) to determine how much credit each touchpoint deserves.
5. Optimization: Using the insights gained from attribution analysis, you’ll refine and optimize future marketing strategies. Focus on the most impactful channels and campaigns to improve your marketing ROI.

How is closed-loop attribution different from other attribution models?

Here’s a detailed look at how closed loop attribution differs from other common attribution models.

The different attribution models

First, let’s quickly define the “other” attribution models:

First-touch attribution

First-touch attribution gives 100% of the credit for a conversion to a customer’s first interaction with a business. It emphasizes initial touchpoints that create awareness and attract customers. While this model highlights channels that generate initial interest, it ignores subsequent interactions that contribute to the final decision.

Last-touch attribution

Last touch attribution assigns all the credit for a conversion to the final interaction before the sale. This model focuses on the touchpoint directly preceding the conversion, assuming it had the greatest influence. Although straightforward, it overlooks earlier interactions that helped nurture the customer toward the final decision.

Multi-touch attribution

Multi-touch attribution distributes credit for a conversion across multiple touchpoints in the customer journey. It recognizes that various interactions collectively influence the customer’s decision to convert. Different multi-touch models allocate credit differently among touchpoints, providing a more balanced view of how marketing efforts contribute to conversions.

Key differences

Data integration

• Closed-loop attribution: Integrates both marketing and sales data, closing the loop between marketing efforts and actual sales outcomes. This integration often involves using CRM systems to ensure that every touchpoint is measured and linked to revenue.
• Other attribution models: Typically focus on marketing data alone, without necessarily connecting it to sales data. They monitor interactions across various marketing channels but may not link these interactions to sales transactions.

Full customer journey measurement

• Closed-loop attribution: Monitors the entire customer journey from the first touchpoint to the final sale, providing a comprehensive view of how each interaction influences the purchase decision.
• Other attribution models: May only consider parts of the customer journey. For example, first touch credits the first interaction, while last touch credits the final interaction before the sale. Multi-touch models consider multiple interactions but might not link them directly to sales data.

Revenue attribution

• Closed-loop attribution: Directly attributes revenue to specific marketing efforts, allowing businesses to see the exact financial impact of each campaign or channel.
• Other attribution models: Focus on attributing conversions or leads rather than direct revenue. They might show which channels drive the most leads or conversions but not necessarily how those leads translate into sales revenue.

Feedback loop

• Closed-loop attribution: Provides a feedback loop between marketing and sales teams. Sales data informs marketing strategies and marketing efforts are continuously optimized based on sales outcomes.
• Other attribution models: Often lack this direct feedback loop, as they don’t integrate sales data comprehensively. Marketing teams might optimize campaigns based on leads or conversions — but without clear insights into how they impact overall revenue.

Optimization and resource allocation

• Closed-loop attribution: Enables precise optimization and resource allocation by showing which marketing activities generate the most revenue. This facilitates informed decision-making about budget distribution and strategy adjustments.
• Other attribution models: Allow for optimization based on lead or conversion data but might not provide the same level of insight into revenue impact. Decisions might be made based on the number of interactions rather than their financial effectiveness.

What are the benefits of closed-loop attribution?

• Accurate ROI measurement: Closed-loop attribution lets you measure the revenue generated by each channel. For instance, you might find paid ads drive the highest sales, while content marketing brings in the most repeat customers. This insight helps you allocate your budget accordingly, boosting ROI and justifying your marketing spend with concrete data.

• Enhanced marketing and sales alignment: Closed-loop bridges the gap between your marketing and sales teams. Sharing data and insights aligns both teams toward common goals, fostering better collaboration. This ensures marketing strategies support sales objectives, leading to more cohesive marketing campaigns.

• Improved decision-making: Let’s say your company has a diverse customer base engaging through various touchpoints like blog posts, social media, and online ads. Closed-loop attribution reveals that customers who interact with your blog are more likely to make higher-value purchases. Based on this insight, you can invest more in content marketing, and ultimately drive conversions and revenue.

• Comprehensive customer insights: Measuring a customer’s journey from the first ad click to the final purchase (and beyond) reveals critical behavior patterns. You’ll know which product features are most appealing or what type of content keeps customers engaged. This data helps you create personalized marketing campaigns that enhance customer loyalty, such as targeted email offers based on past purchases.

• Continuous optimization: Closed-loop attribution provides real-time feedback on campaign performance. For example, if a new Instagram ad format significantly boosts app engagement, you can quickly shift resources to that format. This continuous campaign optimization keeps your marketing efforts agile and responsive to market trends and customer preferences.

• Enhanced reporting and accountability: You also get clear, data-driven reports that demonstrate the impact of your marketing efforts on sales. This creates transparency and accountability within your marketing team, plus you can communicate the value of your campaigns to your stakeholders more effectively.

• Competitive advantage: Closed-loop gives you a deeper understanding of what drives your sales. For instance, you might discover that a particular type of content or ad format significantly outperforms others. Using this knowledge, you can tweak your marketing strategies to stay ahead of competitors who might still be using less comprehensive attribution models.

How to implement closed-loop attribution

Follow these steps to implement closed-loop attribution and facilitate better decision-making:

1 — Define your objectives and KPIs

Start by setting clear marketing objectives and KPIs. What do you want to achieve with closed-loop attribution? Are you aiming to improve ROI, optimize marketing spend, or gain better customer insights?

Clear goals will guide your process and help measure success. For instance, if improving ROI is your goal, pay attention to how marketing investments convert into sales revenue.

2 — Prepare your toolset and data

Invest in marketing automation and CRM systems that can seamlessly integrate with each other. Tools like HubSpot and Salesforce are excellent choices.

Make sure they support your attribution models and enable seamless data transfer between marketing and sales. Use APIs or built-in integrations to connect your tools, setting up automated workflows for smooth data sharing and precise attribution. For example, linking HubSpot with Salesforce synchronizes marketing and sales data, giving you a unified view of customer interactions.

3 — Implement measuring mechanisms

Next, set up comprehensive measurement to capture data from all your marketing channels.

Use UTM parameters for online campaigns and cookies (while it’s still allowed) and pixels for website interactions. Be sure to monitor every touchpoint, from the first ad click to the final purchase, to get a complete view of the customer journey.

4 — Map the customer journey

Create a detailed map of your typical customer journey, identifying key touchpoints and interactions. This will help you understand how customers move through your sales funnel and where they engage with your business.

Then, use this information to set up your attribution models and allocate credit across touchpoints. For instance, a customer journey map might show that customers often engage with your brand through social media before making a purchase on your website.

5 — Configure attribution models

Closed-loop attribution provides a complete view, but to get specific insights — like identifying the initial source of customer interest or the final action leading to a conversion — you need first-touch, last-touch, or multi-touch models.

Choose and configure the right attribution models based on your business needs. Adjust your analytics tools accordingly by tweaking the settings to apply these models.

For example, in Google Analytics or similar platforms, you can set up custom attribution models to reflect your chosen approaches. Check their effectiveness to ensure they accurately assign conversions to the correct touchpoints.

6 — Analyze and interpret data

Once your system is set up, monitor and analyze the collected data continuously.

Look for patterns and insights that indicate which marketing activities are driving sales. Additionally, use dashboards and reports to visualize the data and make it easier to interpret. This approach will help you identify trends and optimize your marketing strategies.

7 — Optimize campaigns based on insights

Use the insights from your closed-loop attribution analysis to fine-tune and optimize your marketing campaigns. Focus on the channels and tactics that perform well, adjust or cut those that don’t, and experiment with new strategies to improve results.

8 — Ensure data quality and accuracy

Regularly updating and verifying your data is essential for building solid marketing campaigns. Reliable information leads to better decisions, while inaccurate or incomplete data can result in costly mistakes.

To keep your data in check, regularly review your tools and integrations. Implement strong data validation processes to catch errors early, and use cleaning tools to maintain accurate datasets.

Key takeaways

• Closed-loop attribution connects marketing efforts directly to sales data, giving you a clear picture of how each marketing activity leads to sales and revenue. This means every touchpoint is measured and correctly attributed.
• It follows the entire customer journey, from the first interaction to the final sale. You can see how each of your touchpoints — like social media ads, email newsletters, SEO, content marketing, and in-store promotions — influences the purchase decision.
• By combining marketing and sales data, closed-loop attribution accurately assigns revenue to specific marketing efforts. You can see the financial impact of each campaign or channel, making it easier to measure ROI.
• The closed-loop method allows for ongoing analysis and optimization of your marketing strategies. You can refine your campaigns based on real-time feedback, keeping your marketing efforts agile and responsive to customer behavior and market trends.

The post Closed-loop attribution appeared first on AppsFlyer.

First-touch attribution is a way of measuring marketing performance that gives full credit for a conversion (like a sign-up or purchase) to the user’s very first interaction with your business.

What is first-touch attribution?

First-touch attribution is a marketing measurement model that assigns 100% of the credit for a conversion to a potential customer’s first digital interaction with your brand. (A conversion can be any action you want the user to complete, like a download, a purchase, or a sign-up.)

This model overlooks other touchpoints that might have contributed to closing the deal, even the one where the actual conversion took place.

Let’s break it down with an example scenario.

• Initial interaction: Jane sees a Facebook ad for your mobile app and clicks on it. This is her first encounter with your brand.
• Subsequent interactions: Jane then explores your app store page, reads reviews, downloads a free trial of the app, and eventually makes an in-app purchase after receiving a push notification.

In first touch, the Facebook ad is given all the credit for Jane’s purchase because it’s the first interaction that introduced her to your brand.

Marketers use this marketing attribution model to understand the early stages of the customer journey. First touch helps identify which channels are most effective at attracting new leads and driving early-stage engagement. However, this approach has its limitations, as it doesn’t consider the impact of subsequent interactions in the conversion process.

How does first-touch attribution work?

First-touch attribution works on a simple idea: the first interaction is crucial in sparking a customer’s journey.

Here’s how it plays out:

1. Identify the first interaction: The process starts with identifying and tracking the first touchpoint a customer engages with. This could be through various channels — a paid ad, a social media post, an organic search result, or a direct landing page visit.
2. Assign credit: Once the first interaction is identified, any subsequent conversion (such as a purchase, sign-up, or inquiry) is fully credited to this initial touchpoint. This means that regardless of the number of interactions or the influence of later touchpoints, the first touchpoint is deemed the most important in initiating the customer journey.
3. Analyze performance: Marketers then analyze the data to determine which channels or campaigns are most effective at generating initial interest. They use this insight to allocate budgets, optimize campaigns, and refine marketing strategies to attract new customers.

Benefits and limitations of first-touch attribution

Like any other attribution model, first-touch measurement has both strengths and weaknesses. In short, the simplicity that makes it appealing also limits the insights it can provide.

Benefits

• Makes it easy to identify effective marketing channels: When you attribute the entire conversion credit to the first touchpoint, it’s easier to spot the marketing channels or campaigns that spark initial interest. This helps you fine-tune your marketing campaigns and allocate resources to channels attracting quality leads.

• Increases awareness and brand exposure: First touch highlights the impact of early touchpoints, such as ads or content, that create brand awareness and initiate user engagement. For example, if a user finds your app’s blog post through a search engine and later downloads the app, the search engine touchpoint gets full credit for introducing the user.

• Simple and accessible: Unlike more complex models, such as multi-touch or position-based attribution, first touch requires minimal data collection and analysis. This simplicity makes it appealing if you have limited resources or are new to attribution models. It’s also quick to adopt and integrate into existing marketing strategies and tech stacks.

• Validates TOFU ROI: First touch gives you clear evidence of how your top-of-funnel (TOFU) content and brand initiatives are performing. With this information, you can confidently invest in TOFU strategies that boost brand recognition and engage potential users. Think of it as a data-driven approach that highlights each channel’s contribution to overall marketing success and ROI.

Limitations

• Oversimplification: First-touch attribution assumes every user follows a linear path from initial contact to conversion, which isn’t often the case. This approach misses the mark, especially for repeat users or those interacting with multiple touchpoints before purchasing.

• Ignores the involvement of offline touchpoints: First touch zeroes in on digital touchpoints, overlooking offline interactions like in-store visits, phone calls, or printed ads. This funneled focus can paint an incomplete picture of user behavior, leading to decisions based on partial insights.

• Limited insight into the full user journey: Relying solely on the first touchpoint for attribution gives you a limited perspective. In reality, user interactions often involve multiple touchpoints across various channels.
  
  For example, a user might discover your app through a search engine, engage with content on social media, and then download the app through a paid ad. First-touch attribution would credit the search engine alone, ignoring the impact of subsequent touchpoints that nurtured the user’s interest and led to the final conversion.

• Challenges in accurate identification: Sometimes, it’s difficult to determine which specific touchpoint initiated the user’s journey. This is especially true for user journeys involving user referrals, community recommendations, podcasts, and dark social. For this reason, many revenue teams prefer using the last-touch attribution model, which credits the last, most easily tracked touchpoint.

• Unsuitable for complex journeys: For businesses with long and complex sales cycles, relying on first-touch attribution is like trying to judge a movie by its opening scene. It misses the rich story that unfolds over multiple interactions and touchpoints.
  
  Successful conversions often come from the collective impact of many channels working together. By focusing only on the first touch, you risk undervaluing the full scope and effectiveness of your marketing strategy.

First touch vs other attribution models

Knowing how first-touch attribution stacks up against other models helps you choose the best fit for your needs. Let’s break down how it compares to last-touch and multi-touch attribution.

Last-touch attribution

Last-touch attribution is the flip side of first touch. Here, all the credit for a conversion goes to the final interaction a user has with your brand before completing a desired action.

Here are the key similarities and differences:

• Focus: First-touch attribution emphasizes the initial engagement, whereas last-touch attribution focuses on the final touchpoint that led to the conversion.
• Insight: Last-touch attribution helps you understand which touchpoints are most effective at closing sales and converting leads.
• Limitation: Similar to first-touch attribution, last touch tends to oversimplify the customer journey by ignoring earlier touchpoints.

Multi-touch attribution

Multi-touch attribution spreads credit across multiple touchpoints, recognizing the contribution of each interaction.

Here’s an overview:

• Holistic view: Unlike single-touch models, multi-touch attribution offers a comprehensive view of the customer journey by considering all interactions.
• Types: This attribution model has three types. Linear attribution distributes credit equally across all touchpoints, while time-decay attribution gives more credit to interactions closer to the conversion. Finally, position-based attribution assigns the most credit to the first and last touchpoints, with the remaining credit spread among the middle interactions.
• Complexity: Compared to first touch, multi-touch models are more complex to implement and analyze. However, they provide deeper insights into how different touchpoints drive conversions.

Example

Imagine a potential user interacts with your mobile app in the following way:

• Facebook ad: User first sees a Facebook ad for your app and clicks on it (initial touchpoint).
• App store page: User visits your app store page but doesn’t download the app immediately.
• Email campaign: A few days later, user receives an email campaign reminder about the app, clicks the link but still doesn’t download.
• Google search ad: Finally, user sees a Google search ad, clicks on it, and decides to download the app.

Under first-touch attribution, all credit goes to the Facebook ad because it was the first interaction. This highlights Facebook’s role in generating initial interest.

Under last-touch attribution, all credit goes to the Google search ad because it was the final interaction before the conversion. This shows Google ads’ effectiveness in closing the deal.

Under multi-touch attribution, credit is distributed across all interactions depending on your chosen model. For example, you might assign 40% to the Facebook ad, 20% to the email campaign, and 40% to the Google ad. This balanced view shows how each touchpoint contributed to the user’s decision to download and purchase the app.

First-touch vs Last-touch vs Multi-touch attribution: At a glance

Does first-touch attribution still matter?

The short answer is yes, but there’s a catch: first touch is useful, but not on its own.

Why is that? First-touch attribution is great for pinpointing which initial marketing efforts are pulling in new leads and boosting brand awareness. Knowing the first point of contact helps you fine-tune your top-of-funnel activities and use your resources strategically.

However, using first touch alone means you risk missing out on the big picture, as it ignores the influence of later interactions in a user’s journey. Instead, consider combining first touch with multi-touch models to better understand how all your touchpoints drive conversions.

This will give you a more accurate analysis of your marketing efforts, leading to more effective optimization throughout your entire funnel.

Who should use first-touch attribution?

First-touch attribution is particularly relevant for the following types of companies and marketing strategies:

• Brand awareness builders: If you’re a new company aiming to build brand awareness, identifying which initial touchpoints attract potential customers can guide your marketing investments effectively.
• High-conversion, low-sales companies: If your company enjoys high conversion rates but struggles with low total sales, understanding which channels generate the most initial interest can drive more traffic into your sales funnel.
• Short sales cycle organizations: For businesses with short sales cycles, first-touch attribution helps quickly identify the most effective marketing efforts — those that spark immediate interest and drive swift conversions.
• Demand generation-focused companies: If your organization is focused solely on creating demand, pinpointing which initial interactions capture potential customers’ attention is crucial.
• Budget-conscious marketers: For companies with tight marketing budgets, first touch helps optimize spending by identifying the most cost-effective channels for generating initial engagement.
• TOFU-focused businesses: If you’re focused on top-of-funnel activities, such as content marketing or social media engagement, first-touch attribution can help attract new leads.
• Simple strategy executors: Smaller companies or those with straightforward marketing strategies, where the customer journey is less complex, may find first-touch attribution sufficient for their needs.
• Data-limited businesses: If your business lacks sophisticated data analysis capabilities, first-touch attribution offers simple but valuable insights without the need for complex analytics.

Key takeaways

• First-touch attribution gives full credit for a conversion to the very first digital interaction a user had with a business.
• This model is great for highlighting how initial interest and engagement are generated. It’s simple and easy to implement, making it a good fit for businesses with limited resources or those new to attribution models.
• While useful, first-touch attribution oversimplifies the customer journey by ignoring subsequent interactions that contribute to the final conversion. This limitation means it provides only a partial view of the user journey, which can be especially problematic for complex or long sales cycles.
• It’s best not to use first-touch attribution in isolation. Combining it with multi-touch attribution models offers a clearer, more accurate analysis of how all touchpoints contribute to conversions, so you can optimize your marketing funnel and allocate resources efficiently.

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AdAttributionKit, introduced at WWDC 2024, is Apple’s new attribution framework designed to enhance user privacy and comply with regulatory standards across various marketplaces. It builds on SKAdNetwork with expanded capabilities, and starting from iOS 17.4 the two systems will coexist.

What is AdAttributionKit?

AdAttributionKit is Apple’s innovative attribution framework designed to enhance user privacy and comply with regulatory standards across various marketplaces, including the Apple App Store. 

Introduced at WWDC 2024 and built on the robust foundation of SKAdNetwork (SKAN), AdAttributionKit offers expanded capabilities and better integration for app attribution.

What can AdAttributionKit do?

One of the key features of AdAttributionKit is its re-engagement capabilities, which allow advertisers to measure conversions from ads clicked by users who have already installed the app. 

This is a significant enhancement for marketers focused on user retention and re-engagement strategies. By consolidating all attributions, AdAttributionKit aims to streamline attribution reporting and improve the accuracy and efficiency of campaign performance analysis.

AdAttributionKit also introduces a new developer mode, simplifying the development and testing processes by providing real-time data and debug information. This makes it quicker and easier to identify and resolve attribution issues, ensuring reliable and accurate attribution mechanisms.

What does this mean for SKAN?

AdAttributionKit will be supported from iOS 17.4 onwards, with some features still in beta and slated for release in iOS 18. This means advertisers and ad networks can start planning to use the new AdAttributionKit capabilities, while still relying on SKAN during the transition phase. The interoperability between the two systems ensures they can co-exist without causing data duplications, with the most recent ad impression taking precedence.

AdAttributionKit retains several key capabilities similar to SKAN, including cryptographically signed postbacks and support for 64 conversion values. These features ensure secure and privacy-centric attribution while allowing advertisers to measure the effectiveness of their campaigns.

Advertisers are being encouraged to adopt AdAttributionKit to benefit from its new capabilities, including expanded marketplace support and enhanced re-engagement tracking. However, there is no immediate pressure to switch, as both frameworks can coexist, and you can keep existing SKAN conversion schemes without creating new measurement strategies. This makes it easier for marketers to adapt and optimize their campaigns effectively.

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One of the highlights of WWDC23, at least for mobile app marketers, was Apple’s announcement of SKAN 5 and its new re-engagement capabilities. Fast forward one year to WWDC24, and SKAN 5 is not even mentioned. 

What did come out during the event? Apple introduced new APIs and developer tools—including MarketplaceKit, which allows alternative app marketplaces to operate securely on iOS. 

But more importantly, Apple also introduced AdAttributionKit, signaling a significant shift in app attribution. But what exactly does it mean and how will developers and marketers be affected? Let’s dive into yet another change in the ever-evolving app ecosystem.

What is AdAttributionKit?

To put it simply, AdAttributionKit is Apple’s new attribution framework, which enables wider ad attribution capabilities. It was built to further tighten privacy measures in the Apple App Store and other marketplaces, while seeking to meet regulatory demands, particularly when it comes to Apple’s interpretation of the EU’s Digital Markets Act (DMA).

AdAttributionKit vs. SKAN: What’s the difference?

On the surface, AdAttributionKit might look like a rebrand of SKAN 5, mirroring the framework with three cryptographically signed postbacks, 64 conversion values, and similar privacy features. However, there are still some differences:

Alternative app store support

AdAttributionKit is built on SKAdNetwork‘s foundation to offer better integration and expanded capabilities across more marketplaces, making it easier for marketers familiar with Apple’s tools to adapt seamlessly.

AdAttributionKit will be available from iOS 17.4 onwards, with some features still in beta slated for release in iOS 18. Unlike SKAN, support now extends beyond the Apple App Store, embracing alternative marketplaces. This expansion enables cross app store attribution – a highly sought after feature aiming to maximize reach across diverse platforms. However, since there are practically zero marketplaces live at the moment, this feature’s immediate impact is minimal.

Re-engagement capabilities

Re-engagement capabilities previewed during WWDC23 have been incorporated into AdAttributionKit, enabling tracking conversions from ads clicked by users who have already installed the app—a significant enhancement for advertisers focusing on user retention. However, since re-engagement isn’t supported by SKAN, marketers have successfully used deep linking and other methods to measure user interactions. There is no immediate pressure to adopt AdAttributionKit for this purpose.

Apple’s focus on incorporating re-engagement into AdAttributionKit aims to consolidate all attributions in one place. By integrating re-engagement, Apple seeks to streamline reporting and encourage wider adoption. However, the lack of view-through attribution for re-engagement might limit the depth of insights marketers can gather, missing opportunities to capture more nuanced ad efficacy.

New developer mode and fraud prevention 

AdAttributionKit introduces a new developer mode that facilitates the development and testing of apps by simplifying the measurement process. The new developer mode simplifies the measurement process, making it easier to test attribution setups without complex configurations or waiting for live data. This mode enhances debugging by providing real-time data and debug information, allowing developers to quickly identify and fix attribution issues, ensuring that the attribution mechanisms are accurate and reliable.

Additionally, Apple has implemented stricter measures to combat ad fraud, such as requiring ads to be displayed in the foreground and limiting the use of timers to end impressions prematurely. 

These changes highlight Apple’s commitment to enhancing the integrity and accuracy of ad measurement. For developers, the new mode could significantly speed up the testing phase, while the anti-fraud measures can ensure that engagement metrics are not only accurate but also meaningful.

Easing into AdAttributionKit: What ad networks and advertisers need to know

For networks already integrated with SKAN, the shift to AdAttributionKit should be relatively straightforward. Both systems are designed to co-exist without causing data duplications. AppsFlyer’s customers in particular can rest assured that they can continue using the same dashboards to monitor both SKAN and AdAttributionKit activities without missing critical information.

While AdAttributionKit offers promising new features, the current landscape suggests that there’s no immediate urgency for advertisers to adopt it. With few alternative marketplaces live and re-engagement already well-supported, advertisers can continue leveraging SKAN’s robust capabilities while keeping an eye on AdAttributionKit’s future developments.

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What is last-touch attribution?

Marketing attribution helps you understand how specific touchpoints contribute to a conversion (the user taking a desired action). There are various different models to determine this, but last-touch (or last-click) attribution is one where all the credit is given to the final marketing channel the user engaged with before converting. 

It’s like a game of soccer. After a string of several passes and dribbles, eventually one player shoots the ball and scores. Even when one player might’ve dribbled half the field leading up to the goal, the last player to touch the ball gets the credit for the goal.

How does last-touch attribution differ from other attribution models?

The user journey to downloading an app is rarely straightforward – it can be a winding road of watching a TV ad, seeing a billboard, then engaging with multiple social media posts before installing the app.

Different attribution models give credit to different points along that journey. While last-touch keeps things simple, focusing on only the final interaction, other models have more complex weighting systems to provide a fuller picture.

Whichever model you use, the aim is to understand how your marketing touchpoints contribute to driving conversions. That enables you to allocate resources to the best-performing channels, improving campaign performance and ROI.

Single-touch vs multi-touch attribution 

Single-touch attribution models assign full credit to one touchpoint along the user journey – typically the first or the last touch. 

Multi-touch attribution models are a lot more complicated, assigning different weights to different touchpoints along the journey. Here are the different attribution models and how they stack up against last touch attribution.

First touch 

Also known as first interaction or first click, first-touch attribution gives full credit to the very first touchpoint the customer engages with. This single-touch attribution model helps measure the effectiveness of top-of-funnel campaigns to see how many new leads are entering the marketing pipeline.

Linear

Linear attribution is a multi-touch attribution model that assigns equal weight to every touchpoint along the customer journey. A social media post, TV ad, and remarketing ad would all be given the same credit.

Time decay

Time decay is a multi-touch attribution model that gives more weight to touchpoints closer to the time of conversion. If a purchase cycle takes 30 days, 10% credit will be given to the first few days, 30% to the following two weeks, and 60% for the final week.

U-shaped

U-shaped attribution is a multi-touch attribution model that assigns more weight to first and last touchpoints. Every touchpoint in between gets equal credit. The most common distribution is 40% to the first and last touchpoints respectively, and 20% shared across the middle interactions.

W-shaped

W-shaped attribution is a multi-touch attribution model that assigns the most credit to three touchpoints in the customer journey: first touchpoint, intermediate touchpoint (the one that is most impactful in the consideration and decision stages), and the final touchpoint. 

Advantages of last-touch attribution 

Last-touch attribution is the easiest model to understand and implement. Giving 100% credit to the final touchpoint makes it easy for marketers to laser in on the channels that work best. 

This model looks at the bottom of the funnel to see what’s directly contributing to the conversion, which is effective when evaluating the performance of marketing campaigns with shorter sales cycles. 

Using last-touch attribution also reduces the risk of errors, as it’s so easy to measure. Sales teams tend to be more aligned with last touch as they’re more likely to be contributing to this part of the funnel.

Disadvantages of last-touch attribution 

Simplicity also comes with drawbacks, the main one being that last-touch attribution doesn’t look at the full user journey. As mentioned above, the path from a lead to becoming a customer can include touchpoints across multiple marketing channels – from word of mouth, to social media, and CTV advertising – that all contribute in their own way.

Giving 100% credit to only the last touch can be misleading. Early touchpoints may have more impact than expected, and the lack of depth with last-touch attribution overlooks important variables when looking at the full picture. This can lead to short-term thinking, and overemphasize marketing efforts that may be working now while neglecting the long-term benefits of others.

Who should use last-touch attribution?

Last-touch attribution is particularly effective for high-volume transactions with short sales cycles. This means you’re targeting a large audience that makes purchase decisions quickly. 

Last-touch is also effective if you don’t have the resources to properly set up more complicated attribution models. Complex models like time decay or W-shaped attribution take in-house data scientists and developers to ensure the full marketing funnel is measured accurately. While last-touch may not be the most effective, a good plan today is better than a perfect plan tomorrow.

The future of last-touch attribution

With the growing complexities of data privacy regulations, marketing attribution needs to continually adapt and innovate. Last-touch attribution may not be perfect, but its simplicity likely means it’s here to stay — even as alternative solutions emerge. 

One of these solutions is likely to be artificial intelligence (AI). The ability to process large volumes of data and connect the dots of user behavior mean AI can accurately predict who is more likely to convert. Predictive analytics and modeling can be dynamically adjusted in real-time, attributing different weights depending on the individual user journey. 

Today, last-touch attribution is popular with connected TV (CTV). Whereas traditional TV advertisers had to rely on probabilistic attribution based on Nielsen data, CTV advertisers can send viewers directly to a website or app. This gives them more detailed data and makes last-touch attribution a simple but effective model.

Key takeaways 

• Last-touch attribution is the marketing attribution model that credits 100% of a conversion to the final touchpoint the user engaged with. 
• Unlike more complex multi-touch attribution models, last-touch is simple to set up and easy to understand, making it ideal for marketers focused on channels contributing directly to conversions. This is best for businesses with shorter sales cycles, and those that don’t have the resources for complex measurement.
• The downside of last-touch is that it overlooks the full customer journey, potentially misleading marketers to ignore the positive influence of other marketing touchpoints. Oversimplification can result in missing long-term opportunities.
• Despite its limitations, last-touch will continue to be popular for its simplicity, especially for CTV. However, as data privacy evolves, AI may offer more privacy-compliant ways to measure and attribute conversion accurately, shifting reliance away from last-touch models.

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In the fast-paced world of digital advertising, evolution is not a luxury but a necessity.

A mere two months ago, we advocated for standardized ad engagement metrics, urging the industry to embrace a transparent and unified approach. We were thrilled  by the excitement and interest to join this initiative from all sides, which makes us even more confident it’s the right move for everyone: advertisers, networks, publishers, and end users.

Today, we bring news that the landscape has not only shifted but taken a giant leap forward. Meta’s release of their engaged views and Tiktok’s recently announced a comparable solution Enhanced View-Through Attribution (EVTA) mark significant milestones in the evolution of ad engagement. As pioneers in this frontier, they are setting new standards for what advertisers can achieve in understanding user interactions.

At AppsFlyer, we’ve already begun measuring and reporting enriched engagement data from Meta, TikTok, and additional participating networks, with more partners joining as we write this blog.

Recognizing the magnitude of this shift, we’ve decided to roll out the Enriched Engagement Types in beta to selected advertisers and networks. Through this initiative, we extend an invitation for them to experience, adapt, and harness the transformative potential of these changes in their attribution logic, reporting practices, and signaling strategies – changes that are poised to intelligently scale their marketing efforts and fuel growth.

Bottom line: Enriched Engagement Types introduce higher accuracy and transparency, leading to more options to scale through the right channels, resulting in better ROAS, and thus faster and more efficient growth.

But wait, what’s actually changing with Enriched Engagement Types?

1. Updated attribution priorities

The refined attribution logic underpinning these changes is anchored in updated priorities, which dictate how various types of engagements are weighed in the attribution logic. These priorities represent a significant advancement in accurately attributing user interactions, leading to a more enriched and accurate attribution waterfall.

Outlined below are the updated priorities for different engagement types:

2. Reporting transparency and standardized metrics

Such reporting is paramount in the evolving landscape of digital advertising. Ad networks, previously grappling with the challenge of accurately representing user intent, are now experiencing a significant shift. With the introduction of enriched engagement types, the age-old dilemma of under-representing engagement views as mere ‘views’ or over-representing them as ‘clicks’ is being resolved.

Before:

After:
This change ensures that each interaction, from fleeting exposures to more robust engagements, is standardized and fairly credited.

3. Deeper insights – beyond views and clicks

Enriched engagement types provide in-depth insights into how users engage with ads beyond views and clicks. This isn’t just about numbers; it’s about understanding ad engagement behavior and user intent at a deeper level.

Engagement types

Enriched Engagement Types provide various touchpoints to measure upon. While previously engagements were associated only as a view or a click (and a very thin line for one or the other), now you gain additional visibility in-between, providing better understanding and insight into user’s intent thanks to their engagement type.

What now?

The industry is undergoing a significant shift, fueled by standardized ad engagement metrics and an unwavering commitment to transparency.

With leading ad networks and advertisers joining this revolution, the momentum is building for a more insightful, responsive, and engaging advertising ecosystem.

As we forge ahead, our commitment to innovation remains strong. We’re proud to join industry leaders and forward-thinking advertisers toward a future where every engagement type tells a meaningful story, and every campaign receives the credit it truly deserves.

The future of ad engagement measurement is unfolding before us, and together, we’re shaping an advertising landscape that thrives on interaction and transparency where advertising isn’t just seen, it’s experienced.

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