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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What is cost per order (CPO)?

Cost per order (CPO) is a key performance indicator (KPI) measuring the average total cost to a business of selling and fulfilling a single order. 

This metric takes into consideration all associated costs, from advertising to warehousing to packaging. It’s often used in eCommerce, retail, and logistics to evaluate how profitable a company is and how efficient its marketing and operations are. Tracking CPO can also guide decisions on pricing. 

A low CPO indicates that your business is achieving sales at a relatively low cost, which often translates to healthy profit margins. A high CPO may suggest you need to optimize your marketing efforts or cut costs to stay profitable. 

How to calculate cost per order

When calculating CPO, consider both direct and indirect costs to get an accurate picture of the total price of doing business. Here are some costs you should factor in.

Direct costs

• Product costs: The cost of goods sold (COGS), including raw materials and labor.
• Shipping costs: The cost of transporting the product to the customer, including fuel, labor, and equipment.
• Packaging costs: The cost of packaging materials, such as boxes, bags, or wrapping paper.
• Handling costs: The cost of handling and processing the order, including labor and equipment costs.
• Fulfillment costs: The cost of fulfilling the order, including labor, equipment, and supplies.

Indirect costs

• Research and development costs: The cost of developing new products or services, including research, design, and testing expenses.
• Overhead costs: The cost of overhead expenses, such as rent, utilities, and insurance for your business. 
• Marketing and advertising costs: The cost of promoting the product or service, including advertising, marketing, and promotional expenses.
• Order management costs: The cost of managing orders, including software and equipment. 
• Inventory holding costs: The cost of holding inventory, including storage, handling, and maintenance costs.
• Customer service costs: The cost of providing customer service, including phone, email, and chat support.
• Warranty costs: The cost of warranty claims, including labor, parts, and shipping costs.
• Returns processing costs: The cost of processing returns, including labor, shipping, and handling costs.

To calculate your cost per order, you’ll need to add up all these direct and indirect costs and divide the total by the number of orders processed. This will give you a comprehensive picture of how much each order truly costs. 

Cost per order formula

To calculate cost per order, you can use the following formula.

For example, if your total costs for a month are $10,000 and you processed 1,000 orders, your cost per order would be:

Cost per order example

Let’s say you have an online store that sells kitchen gadgets and utensils, and your average order value is $85. You received 300 orders last month and your total monthly expenses were $19,200. When you break down all the associated costs, you can see the granular cost of each order.

Direct costs

• Product cost: $12
• Shipping cost: $5
• Packaging cost: $2
• Handling cost: $2
• Fulfillment cost: $3

Total direct cost: $24

Indirect costs

• Labor cost: $10
• Overhead cost: $5
• Inventory holding cost: $2
• Order management cost: $3
• Customer service cost: $2
• Returns processing cost: $1
• Warranty cost: $1
• Marketing and advertising cost: $5
• Research and development cost: $2
• Administrative cost: $5

Total indirect cost: $40

Total cost per order = ($19,200 / 300) = $64

In this scenario, you can see the business is making a profit: The store earned $6,300 more than it spent in a month. To lower your CPO, and further increase profits, you could look through each line item of cost to find room for savings and try to raise the number of orders. While direct costs multiply with each order, many indirect costs stay flat, bringing the cost per order down with increased sales.  

Advantages of using cost per order

CPO provides a bird’s eye view of your business profitability and the actual cost of selling and fulfilling an order. Overall, using CPO as a key metric helps businesses optimize their operations, improve profitability, and make data-driven decisions to drive growth. Let’s look at the advantages in more detail:

1. Identifies areas for cost reduction

By tracking CPO, you can identify areas to reduce or optimize cost, paving the way for higher profits.

2. Supports data-driven decision making

Using CPO as a KPI enables you to make data-driven decisions about pricing, marketing, and operational strategies.

3. Compare profitability between channels

When you segment out your CPO by marketing or sales channel, you can identify which channels are the most profitable. For instance, some channels may have higher acquisition costs than others and some may have a higher rate of returns.

4. Encourages efficiency and productivity

Understanding your CPO helps you focus on streamlining processes and improving productivity, resulting in lower costs and higher profitability.

5. Helps with budgeting and forecasting

Measuring CPO helps you predict your future costs and revenue, making it easier to create accurate budgets and forecasts.

6. Improve customer lifetime value

When you track CPO, you can identify opportunities to increase the average order value through loyalty programs, upselling, and cross-selling. By focusing on customer needs and satisfaction, you’ll generate repeat orders and referrals, increasing retention and lifetime value. 

7. Improves supply chain optimization

CPO highlights areas where supply chain inefficiencies are impacting profitability, so you can optimize your logistics and inventory management.

Cost per order vs. alternative metrics

CPO is useful in helping you understand costs in relation to sales, but it isn’t the only metric you should look at. Consider these alternative metrics which, alongside CPO, can enhance your understanding of your eCommerce business. 

CPA (cost per acquisition)

CPA measures the cost of acquiring a first-time customer, primarily through marketing and advertising. It’s closely related to CPO because it measures profitability correlated to sales. However, CPO gives a much fuller picture that includes COGS, fulfillment, and repeat purchases.

CPC (cost per click)

CPC is the cost of each individual click on an ad, regardless of whether or not it leads to a purchase. Use CPC to gauge how effective a campaign is at driving interest in a product, or as a bidding strategy for digital advertising.  

CPL (cost per lead)

CPL measures the average cost of generating one lead in your pipeline. For example, if you spend $50 on advertising and generate 10 leads, your CPL would be $5 per lead. This helps you assess how expensive your lead acquisition is and which channels are most cost-effective.

CPS (cost per sale)

CPO and CPS are closely related. CPS measures the total cost of acquiring each order, but does not include direct product costs or shipping and fulfillment costs. This metric tells you more about your marketing efficiency than it does about your operations. 

Tips to reduce your cost per order

Once you know your CPO, it’s time to start optimizing. There are two general strategies for optimizing CPO: lowering costs and increasing orders. Try these seven tactics to accomplish both. 

1. Optimize your marketing efforts

If you’re tracking marketing metrics like CPC and CPL, you can pinpoint which marketing channels are more cost-efficient than others. 

Let’s say you ran paid ads on Facebook, Instagram, and LinkedIn for the same campaign. Facebook cost $1 per click, Instagram cost $1.50 per click, and LinkedIn cost $3.50 per click. You may consider diverting funds from LinkedIn to Facebook and Instagram to optimize your spend, unless you can prove that the LinkedIn clicks resulted in more sales. 

To go a step further, use attribution to understand which marketing campaigns and channels are actually leading to purchases, then increase your investment in them. For instance, you might run a Google Ads campaign for multiple keywords. If your analytics show that the more expensive keywords are resulting in more sales, it’s a good idea to continue running them. 

2. Increase average order value

Consider using personalized product recommendations, upselling, and cross-selling to encourage customers to buy more with each purchase. You can also offer subscription services and free shipping or exclusive discounts to customers who make a purchase above a certain amount. The higher your average order value is, the more you’ll optimize costs like shipping and overhead. 

3. Remarketing campaigns

Remarketing campaigns encourage users to re-engage with a brand or app. You could focus on customers who looked at an app or website but never made a purchase, or customers who once made a purchase and are now inactive. 

These customers are more likely to engage than a person who’s hearing about your product for the first time, making it more cost-effective. Technology like programmatic advertising enables companies to nudge inactive users across multiple touchpoints, like web and mobile. 

4. Process orders faster

Slow order processing can add up to higher costs for a business, like longer warehousing and rush fees for expedited shipping. To avoid this, implement a streamlined order processing system to reduce manual errors and increase efficiency. Invest in staff training, and explore ways to automate tasks such as order processing and inventory management. 

5. Lower shipping costs

Shipping makes up 88% of eCommerce fulfillment costs in the US, so lowering it can make a big impact. Start by negotiating with shipping providers to get better rates for your business, or consider using third-party logistics providers to reduce costs. Use the most cost-effective shipping options for each order.

6. Reduce packaging costs

While packaging represents a smaller proportion of costs, every little bit still counts. Implement a packaging optimization strategy to reduce the amount of packaging used for each order. Consider using refillable or reusable packaging options to reduce waste and save money.

7. Automate tasks wherever possible

Look at automation options to streamline your operations, save time, and reduce cost. Examples of business automation include:

• A customer relationship management (CRM) system and marketing automation software to streamline sales and marketing
• A chatbot for common customer service queries
• An automated inventory management system to track inventory levels, alert you when items are running low, and automatically update your website

Key takeaways

Tracking CPO gives a comprehensive picture of how much each order truly costs your business. With this knowledge, you can identify areas to reduce or optimize costs, paving the way for higher profits.

• A low CPO means your business is achieving sales at a relatively low cost, while a high CPO can be a sign of inefficiencies or high costs.
• To calculate your CPO, add up all the direct and indirect costs of fulfilling one order. Then, divide that by the total number of orders you completed. 
• For a fuller picture of business health, track CPO in conjunction with related metrics, like CPA, CPC, CPL, and CPS. 
• Understanding CPO helps you make data-driven decisions for a more efficient and profitable business. This includes identifying ways to reduce costs, increase customer lifetime value, and optimize marketing channels and logistics. 
• To reduce CPO, look to increase average order value and reduce costs. Strategies include optimizing marketing efforts, increasing average order value, running remarketing campaigns, processing orders faster, lowering shipping costs, reducing packaging costs, and automating tasks wherever possible.

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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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What is viewability?

Viewability is crucial for you as an advertiser — it determines whether users actually see your ad placements. It’s a metric that measures the likelihood of your ads being noticed by those visiting websites or apps.

Note that viewability is not the same as impressions.

Traditionally, an ad impression is counted whenever an ad is served onto a webpage, regardless of whether the user actually sees it. Think of it like those flyers left on your doorstep — just because they’re there doesn’t mean you’ve read them.

With viewability metrics, you can ensure your ads aren’t just loaded but also seen by users. Viewability is typically measured as a percentage, indicating the portion of ad impressions meeting certain criteria for being considered “viewed.”

So, what are these criteria?

Ad visibility criteria 

According to the Interactive Advertising Bureau (IAB), an ad is considered viewable if it meets the following criteria regarding its visibility on the user’s screen:

• For display ads: At least 50% of the ad’s pixels are in view on the user’s screen for a minimum of one second. This means more than half of the ad must be visible within the specified time frame to count as viewable.
• For video ads: At least 50% of the ad’s pixels are in view on the user’s screen for a minimum of two consecutive seconds, as video ads may need a bit longer to capture the user’s attention.

These criteria ensure you’re paying for ad impressions more likely to be seen. 

By setting minimum viewability thresholds, you can gauge ad placement effectiveness and plan your advertising strategies wisely.

Keep in mind that viewability standards may vary slightly across industries or specific advertising platforms, with different SSPs using different measurements. 

For example, for large-size creatives (display ads sized at 242,500 pixels or more), Xandr and OpenX consider an ad viewed when 30% of the creative is visible for one second. Nonetheless, the IAB’s guidelines are widely accepted as a benchmark for measuring viewability in digital advertising.

How to measure viewability

Measuring viewability involves comparing the number of ad impressions that meet the viewability criteria to the total number of ad impressions measured. 

To calculate viewability, use the following formula:

Let’s break that down:

• Total viewable ad impressions: The number of ad impressions that meet the criteria for being considered “viewed.” These impressions are typically counted based on whether a certain percentage of the ad’s pixels are in view for a specified duration (for example, at least 50% of pixels visible for one second for display ads).
• Total measured ad impressions: The total number of ad impressions that were measured, regardless of whether they met the criteria for viewability.

You multiply by 100 to obtain a percentage. 

Here’s an example to bring it to life:

Suppose you ran an ad campaign and measured 10,000 ad impressions in total. Out of these, let’s say 6,000 ad impressions met the criteria for being viewable.

Here, your ad campaign’s viewability is 60%. This means that 60% of the measured ad impressions were viewable according to the specified criteria.

Measuring visibility benefits both advertisers and publishers

Viewable impressions give you, the advertiser, insights into how well your ads are being seen by users. 

At the same time, they help publishers ensure your ads are displayed correctly to their audience. Here’s how they generally measure it:

1 — Measurement based on ad container

Some measurements focus on the ad container rather than the ad itself. So, if the container meets certain criteria (like having at least 50% of its pixels in view for a set time), the ad is counted as viewable. 

However, this can be tricky because it doesn’t guarantee that users saw your ad. For instance, if your ad is at the bottom of a webpage and a user never scrolls down, they may miss your ad altogether, even if the container is visible.

2 — Measurement based on the ad itself

Other methods focus directly on the ad. They measure if a certain percentage of the ad’s pixels are visible for a set time. This gives a clearer picture of whether users actually see your ad.

Both methods have their pros and cons, but most advertisers like focusing on the ad itself. After all, you want to ensure your ad content is actually seen by users. It’s also useful for publishers, helping them offer high-quality ad placements that benefit both advertisers and their audiences.

Which viewability KPIs should you measure?

1 — Ad calls/Ad requests

Ad calls, also known as ad requests, indicate how many times an ad is displayed on a page during a user’s session. While ad calls don’t guarantee views of your ad, they show the opportunities for impressions.

2 — Average viewable CPM

Average viewable CPM is the average cost for 1,000 viewable impressions (the M stands for mille, which is Latin for thousand). It shows how much you’re paying for impressions actually seen by users, excluding those that weren’t viewed.

3 — Active View

Provided by Google, Active View is a tool that helps publishers measure and report on viewability. It’s native to Google Ad Manager, AdSense, and AdMob.

4 — Burned budget

Burned budget shows how much of your ad spend has been used to buy ads that weren’t seen by your audience — essentially wasted money.

5 — Measurable cost

Measurable cost is the total cost of all ad impressions that were measured. It helps you see how much of your budget went towards impressions you could keep tabs on.

6 — Measurable impression

Measurable impression is the number of ads served that your reporting tool was able to measure. 

Note: Not all impressions can be measured — factors like technical issues, ad blockers, or certain types of ad placements can all prevent measurement. Knowing how many impressions you can measure compared to those served helps you understand how reliable your ad performance data is.

7 — Measurable rate

Measurable rate refers to the percentage of impressions your reporting tool was able to measure, out of the total impressions served.

8 — Viewable CTR

Viewable CTR measures the number of clicks on viewable impressions. It shows the engagement of visitors with viewable ads (ads that were seen).

9 — Viewability rate

Viewability rate is the percentage of impressions that qualify as viewable. It indicates how many impressions were seen by users.

10 — Viewable impression distribution

The viewable impression distribution metric shows the percentage of both measurable and non-measurable impressions that were seen by users. It helps you understand how viewable impressions are spread across your ad campaign, so you can better optimize placements and strategies for better visibility

Why is viewability important?

Tracking viewability is crucial for both advertisers/marketers and publishers.

• From the advertiser’s/marketer’s perspective, viewability ensures ad placements are actually seen by real human users.

Without viewability metrics, you might be paying for ad impressions that are not being viewed, essentially wasting your ad budget. In contrast, when you bid on viewable inventory, you can improve return on ad spend (ROAS) and ensure you’re getting the best possible value for your investment. 

What’s more, viewability allows you to measure ad performance more accurately and optimize campaigns accordingly, leading to higher conversions.

• From the publisher’s perspective, viewability is useful for maximizing ad revenue. 

While some publishers initially feared the shift to viewability-based payments would lose them revenue, others saw the opportunity to position their inventory as premium and command higher bids. With high-viewability ads becoming more important, viewable CPM (vCPM) might replace the old standard, CPM.

Additionally, understanding and tracking ad viewability metrics helps publishers boost their ad revenue. They can offer advertisers better placements, making sure ads are seen by users. This keeps advertisers happy and helps build long-term partnerships.

How to improve your viewability score

1 — Create and offer quality, relevant content

Quality content that resonates with your target audience has a twofold effect: it attracts and engages the viewer.

So, make sure to conduct thorough audience research to better understand your target user’s preferences, pain points, and interests. Additionally, tailor your content to provide value and relevance, whether it’s through informative articles, entertaining videos, or compelling graphics. 

All this encourages users to spend more time on your webpage, leading to boosted ad visibility.

2 — Improve ad position

Aim for prime ad placements that maximize visibility. For instance, Google found that above-the-fold (ATF) video ads have a 73% viewability score on average, compared to 45% for below-the-fold (BTF) ads.

How do you identify the best positions? Conduct A/B tests to identify the most effective ad placements based on user engagement metrics.

You can also consider implementing dynamic ad insertion techniques that optimize ad placement in real-time based on user behavior and content relevance.

Then monitor and adjust your ad positions regularly to capitalize on high-performing placements and improve overall viewability.

3 — Use appropriate ad sizes

Ad size is another factor that directly impacts visibility and engagement.

Larger formats command more attention and have a higher viewability score. Case in point: A 2560 x 1440 video player has a 95% viewability rate, whereas an 854 x 480 player size has an 88% viewability rate. 

However, you must strike a balance between size and user experience.

Avoid oversized ads that detract from content readability or disrupt the user journey. Instead, use ad sizes that complement the web page or app layout and seamlessly integrate with the surrounding content.

A good tip is to conduct usability testing to evaluate the impact of different ad sizes on user experience and adjust accordingly.

4 — Ensure faster loading times

Any website with ads that take longer to load negatively impacts ad viewability. This holds true even if the website itself is fast.

So you should optimize ad creatives and landing pages to minimize loading times across various devices and network conditions.

You can do this by:

• Compressing images, streamlining code, and using caching techniques to reduce latency and improve page load speed.
• Prioritizing lightweight ad formats that load quickly without compromising visual quality.
• Reducing passbacks to decrease the number of ad calls between servers. Fewer ad calls lower page latency and boost ad viewability.

Another effective option is implementing lazy loading for BTF ads. This defers loading non-essential ad content until the user is most likely to see it. No point in implementing lazy loading for ATF banners, as you want them to upload right away for users.

5 — Optimize for different channels and devices

Users see your ads across multiple channels and devices, so it’s crucial to optimize the experience for each one. 

To start, adopt a responsive design approach that renders ads seamlessly across devices, including desktops, smartphones, and tablets. Then customize creatives and formats to suit each platform’s unique characteristics and the user behavior associated with it.

In addition, leverage advanced targeting capabilities. This way, you can deliver personalized ads based on device type, location, demographics, and browsing history.

Finally, continuously monitor cross-channel performance metrics and iterate your performance optimization strategies to enhance viewability across all touchpoints.

6 — Place above-the-fold content strategically

While top-of-page placements may seem prime, they often suffer from “banner blindness” as users habitually scroll past them. 

That’s why we recommend focusing on the “scroll zone,” which refers to the area just below the top. Place your ads strategically within content or at mid-page positions, where users engage more actively, leading to higher viewability rates.

7 — Optimize your header bidding stack

Optimizing your header bidding stack involves strategically managing the order and setup of your header bidding partners. 

The first step? Dig into the details of each seller’s performance data. Look and prioritize for partners who consistently bring in high-quality ad placements that are actually seen by users. 

Next, keep refining your bidding strategy based on what’s happening in real-time. Adjust the prices you’re willing to pay and the order in which you let partners bid to make sure you’re getting the most out of each ad placement.

8 — Use stick ads (when appropriate)

Stick ads, also known as sticky ads or persistent ads, remain fixed in a specific position on the screen as users scroll through content. Like this:

When used correctly, stick ads significantly improve viewability by ensuring your ad remains visible to users for an extended period. 

But take care not to compromise user experience in your quest for maximum viewability. 

Overusing these ads can lead to ad fatigue and user annoyance, ultimately undermining the effectiveness of your advertising strategy. So, test stick ads thoroughly to find out the best timing, position, and length. Make sure they blend well into the user’s journey.

9 — A/B test to improve viewability

A/B testing involves comparing two or more versions of an ad or webpage to determine which one performs better in terms of viewability. 

But the whole process isn’t just about tweaking colors or headlines. It’s also about understanding the best ways to make an impression on your audience. Look at the results to refine your advertising approach and optimize for maximum viewability over time.

When doing A/B tests, analyze the data thoroughly to grasp how users interact with various ad styles, positions, and creative options. Don’t be afraid to try out new ideas to grab and retain users’ attention.

10 — Only refresh ads that pass viewability criteria

Ad refreshing, or ad refresh, is the practice of dynamically reloading ads within a web page or app after a set time to increase impressions. However, this can be a double-edged sword.

While ad refreshing can boost ad numbers, it also makes it tricky to know if people are actually viewing them. To navigate this issue, your best bet is to take a strategic approach.

Only refresh ads that have already met your viewability standards during their first display. This way, you prioritize quality over quantity, ensuring that ad impressions aren’t wasted on placements that aren’t viewed. Instead, you focus on serving ads that have a higher chance of being seen by users.

Challenges for measuring viewability

When you start optimizing viewability, be prepared for the following challenges:

• Ad fraud: Sophisticated fraudulent techniques, like making fake traffic look real (impression laundering) or using bots to fake views (bot traffic), fool the tools that measure ad views. This makes it seem like more people are seeing the ads than they really are, tricking advertisers and publishers.
• Poor ad placement: Ads may end up in less visible areas of a webpage due to factors like page layout or user behavior. This makes it hard to know for sure how many people are actually seeing the ads.

Balancing visibility with user experience: Ensuring ads are visible without compromising user experience demands finesse. Ads that pop up a lot might be seen more, but they could also annoy people.

The future of ad viewability 

Below are some potential developments we believe will shape the future of ad viewability. Let’s check them out,

Cross-platform viewability

Going forward, the focus will be on making ads look good and work well on all screens. This means using designs that adapt to different devices and picking platforms that deliver ads smoothly across screens.

Impact of privacy

With stricter rules and greater awareness around online privacy, companies will have to get people’s consent before tracking them. Consequently, more advertisers will prioritize finding ways to measure ad success that also respect user privacy. 

AI optimization for viewability

AI and advanced machine learning technologies will transform how ads are optimized. Companies will use them to determine who to show ads to and create ads that resonate with audiences. AI will also help place ads where they’re most likely to be seen.

Advanced measurement techniques

As the industry progresses, better and more accurate measurement techniques for ad viewability will emerge. Innovations may include using sophisticated algorithms that can predict if ads will be seen before showing them. These advancements will provide deeper insights into ad performance and facilitate campaign optimization.

Key takeaways

• Viewability measures the likelihood of your ads being seen by users rather than just loaded on a website or app. To count as viewable, an ad needs at least 50% of its pixels to be visible on a user’s screen for a certain time. This ensures advertisers pay for ads more likely to be seen.
• To measure viewability, compare the number of viewable ad impressions to the total measured ad impressions, usually shown as a percentage. 
• Key metrics for viewability focus on how often your ad is displayed and viewed, and how effective your budget spend is. Examples include ad calls, average viewable CPM, measurable rate, and burned budget.
• Advertisers can boost viewability by improving ad position, using the right ad sizes, ensuring faster loading, and optimizing for different devices. Optimizing header bidding, A/B testing, and refreshing ads also help.
• Challenges in optimizing viewability include ad fraud, poor ad placement, and balancing it with user experience. 
• In the future, expect ads to be viewable on all devices, with clearer rules and privacy-friendly measurement methods. AI will make ads more targeted, while better tools will measure how well ads are seen and engaged with.

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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.

The post Last-touch attribution appeared first on AppsFlyer.

In digital marketing, ad revenue is income generated through online or in-app advertising. 

What is ad revenue?

Ad revenue is income generated through advertising. In the digital marketing world, this advertising may be on the web, through CTV (connected television), or within an app (more on this below). 

For app and website owners looking to make the most of their digital real estate, ad revenue can be a significant source of income. Globally, internet advertising revenue has been climbing steadily in recent years, and the trend is set to continue:  

App ad revenue

In-app ad revenue is an important source of income for apps today, especially when many are free to download. Think of it like the ads you see on traditional TV: You don’t have to pay to watch your favorite show, but the channel makes a profit from the commercials that you view. 

The same goes for apps — with the added benefit of reaching a highly targeted and engaged audience. 

With global in-app advertising expected to reach $352.70 billion in 2024, this is a market that app marketers don’t want to miss out on.

How to calculate ad revenue

The formula for calculating ad revenue is:

To break that down:

• Ad impressions refer to the total number of times an ad is displayed within the app.
• eCPM (effective cost per mille) represents the average revenue earned per 1,000 ad impressions. 

When you multiply the total ad impressions by the eCPM, you can determine the overall ad revenue generated by your app. 

Good ad revenue is considered one that generates a high return on investment (ROI). To achieve that, you need high eCPM, optimized ad placements, diverse ad formulas, and targeted audiences. 

How does ad revenue work? 

Publishers (app or website owners) generate ad revenue by selling digital ad space, known as inventory, to brands that want to get their message in front of a particular audience. 

Here, we’re focusing on the app world — and different apps offer different types of inventory that serve different purposes and goals. 

• Native ads: These ads make an effort to blend seamlessly into the content of an app. The benefit of this method is that it’s less intrusive and matches the look and feel of the app. Think of ads on a Facebook feed that look like regular posts. 
• Display ads: This is a more traditional form of advertising that offers a range of sizes and formats, including static images or interactive media. Banner ads (see below) are a popular example. 
• Video ads: An increasingly popular form of advertising, these ads are engaging and immersive. They can appear at various points in the app user journey.
• Takeover ads: These ads capture a user’s attention by taking over all, or a large portion, of the screen. While they can be effective, takeover ads must be used strategically to avoid disrupting the user experience. 
• Banner ads: A staple of digital advertising, banner ads are typically rectangular ads placed at the bottom, top, or side of a mobile app. 

The rise of programmatic advertising

We mentioned above that publishers sell inventory to brands looking to reach their ideal audience. Today, the majority of those deals are done using programmatic technology. 

Programmatic advertising is the automated buying and selling of ad inventory. It uses real-time bidding and algorithms to match advertisers with the most relevant ad placements. 
This approach allows for in-app bidding, where advertisers can compete in real-time to display their ads. Publishers can use waterfall optimization techniques to prioritize and maximize the revenue they generate from ad inventory.

The benefits of ad revenue 

Ad revenue offers a number of benefits for publishers looking for an easy way to monetize their apps: 

• Simple setup process: Setting up ad placements within your app is generally a straightforward process, so publishers can start earning ad revenue quickly. 
• Generates revenue from otherwise dead space: All apps have unused or under-used areas. Ad placements can turn these “dead spaces” into revenue-generating opportunities.
• Easy to optimize: Optimize ad revenue over time by testing different ad formats, placements, and targeting strategies. 
• Scalable: Ad revenue potential increases over time as an app’s user base grows. 
• Attracts more viewers: Ads can help drive more traffic and engagement to your content, as users are incentivized to interact with the ad-supported experience. 
• Diversifies income streams: It’s always good to have multiple income streams. Adding ad revenue together with other strategies, including in-app purchases and subscriptions, means you don’t have to rely on a single source of revenue. 
• Offers insights into user behavior: By monitoring how users interact with your app, you can better understand your audience and optimize content and monetization strategies.  

The disadvantages of ad revenue

Despite all the advantages, ad revenue does come with a few pitfalls that publishers need to watch out for: 

• Reduced control over what appears on your content: You have less control over the specific advertisements displayed on your app when relying on ad networks to fill ad inventory. 
• Volatility of revenue based on traffic: Ad revenue is directly related to the amount of traffic and engagement an app receives. Fluctuations can lead to a drop in ad revenue.
• Risk of turning off your audience with non-relevant ads: Displaying ads that aren’t relevant or appealing to your audience can negatively impact the user experience and potentially drive users away. 
• Difficulty of striking the right balance: It can be hard to maximize ad revenue while maintaining a positive user experience. With too many in-app ads, customers may become frustrated and therefore less engaged.

How to increase ad revenue

There are a variety of tactics that can help increase ad revenue in apps: 

• Focus on the user experience: Users come to the app for its purpose, not to consume ads. It’s important to make ads relevant and non-intrusive to keep audiences engaged and loyal, which in time can boost ad revenue. 
• Learn about — and experiment with — ad placement: Strategically placing ads in high-visibility areas of an app, and testing the ad placements, can up ad performance significantly. 
• Create original content and update it regularly: By keeping an app up to date and interesting, publishers can attract and retain a loyal audience. This will lead to more ad impressions and higher revenue potential. 
• Optimize the number of ads displayed: Try to find the sweet spot between number of ads and great user experience. 
• Measure performance: Monitor metrics such as eCPM, click-through rates, and overall ad revenue so you can make data-driven optimizations. 
• Diversify ad types and formats: Using a variety of ad types and formats can attract a wider range of advertisers, as well as keeping things interesting for users. 
• Choose the right partners: It’s important to partner with reputable ad networks and platforms that offer competitive rates, advanced targeting capabilities, and reliable fill rates to boost ad revenue. 
• Offer exclusive sponsorships: Gain reliable and potentially higher-value revenue streams by giving sponsorship opportunities to select advertisers. 

Increasing ad revenue with AppsFlyer

AppsFlyer helps you boost your ad revenue through more accurate marketing performance measurement. This improved visibility lets you focus on data to make better decisions together with ad partners, optimizing profits and results for both sides. 

Key takeaways

• Ad revenue is income generated from advertising. In the digital marketing world, this includes ads on web, CTV, and in apps. 
• In-app advertising is an important revenue stream for mobile app developers, offering access to highly engaged, targeted audiences.
• The formula to calculate ad revenue is: Ad revenue = Ad impressions x eCPM. A good ad revenue is one that brings a strong return on your investment.  
• Publishers generate ad revenue by selling ad inventory, which is available in various forms such as native ads, display ads, video ads, takeover ads, and banner ads.
• Programmatic advertising, including in-app bidding and waterfall optimization, is a significant part of the ad revenue landscape.
• Benefits of ad revenue include a simple setup process, monetizing otherwise unused space, scalability, and diversification of income streams. Ads can also increase traffic to your content, while user data and insight helps you optimize your app. 
• Disadvantages include reduced control over ad content, revenue volatility based on traffic, ad blocking, and the challenge of balancing ad revenue with user experience.
• Tactics to increase ad revenue include focusing on the user experience, experimenting with ad placements, creating original content, optimizing ad numbers, measuring performance, and choosing the right partners. AppsFlyer can support you by providing accurate and visible performance data, leading to better decision-making. 

The post Ad revenue  appeared first on AppsFlyer.

What is cost per lead (CPL)?

Cost per lead measures how much it costs a business on average to acquire one lead. A lead is a potential customer, either an individual or a company, who’s taken an action that indicates they’re likely to make a purchase. Typical actions include things like creating an account, requesting information, or adding items to a shopping cart. 

Generally speaking, a lower CPL is considered better than a higher one. 

Why is CPL important? 

Monitoring your CPL helps your business identify which lead generation channels are most cost-effective, and which may need adjustments or a reallocation of resources. If your lead acquisition is too expensive, it might mean that your marketing strategy isn’t effective or that certain channels aren’t worth the investment. 

When you track marketing attribution, you can analyze your channel-specific CPL. This lets you increase your investment in cost-effective marketing channels, while tweaking or discontinuing investment in channels that don’t deliver value.

CPL also helps you budget appropriately for your marketing campaigns. For instance, if your goal is to increase your leads by 50% in a quarter, you can use CPL to estimate how much to increase your marketing budget to reach your goal.

Additionally, you can use CPL to gain insights into other metrics, like cost per sale and customer lifetime value, to assess the health of your business. 

How to calculate CPL

To calculate CPL, first choose the timeframe you want to measure. This is typically by month, quarter, or year. Next, calculate your total marketing spend for the period: this includes ad spend and administrative costs (in-house payroll, consultants, fixed costs, and so on) for marketing and outbound lead development.

Divide this by the number of leads you acquired in this period. You can clean your data to differentiate qualified leads from unqualified leads if you choose. (Qualified leads are those that meet the criteria most likely to result in a sale.) However you define it, this calculation will tell you the average acquisition cost for a lead.

To sum up, the formula for CPL looks like this:

CPL example 1

Let’s say that Company A has one marketer with an annual salary of $80,000. The company spends $40,000 on ad spend and sponsorships, and another $20,000 on other costs like video production. 

If they bring in 2,800 leads in a year, then their CPL averages $50. 

CPL example 2

Here’s a channel-specific CPL example. Company B spent $15,000 on a Google Ads campaign in Q2 and paid a digital marketing agency $3,600 to manage the pay-per-click campaign. As a result, the company gained 620 leads. 

What is a good CPL?

There’s no absolute benchmark for a good CPL, but there are some guides. B2B leads typically cost more to acquire than B2C. A study by Sopro named $100 as an average CPL across industries, but don’t compare yourself too quickly without context. 

To assess your number, you need to consider:

• The type of business you run
• Your industry
• Your lead-to-sales ratio 
• Your customer lifetime value

A CPL should be lower than the value a sale brings into your company, or you’re losing money. For example, a $200 CPL would be too high for a consumer product worth $20, but would be justified for a B2B sale with a customer lifetime value in the thousands of dollars. 

CPL benchmarks by industry

Each industry has different lead acquisition costs. Consider this sampling of industry CPLs from Sopro:

• Education: $40 CPL
• Hospitality: $73 CPL
• Retail: $87 CPL
• Business services: $144 CPL
• SaaS company: $180 CPL
• Healthcare: $386 CPL

CPL benchmarks by marketing channel

Like industries, marketing channels vary wildly by cost and effectiveness. Not every marketing channel is appropriate for every business or industry, but it’s worth exploring cost-effective marketing channels to lower your CPL. 

Least expensive marketing channels:

• Referrals: $25 CPL
• SEO: $35 CPL
• Email marketing: $50 CPL
• Social media marketing: $65 CPL

Most expensive marketing channels:

• PPC: $175 CPL
• Direct mail: $250 CPL
• Cold calling: $300 CPL
• Events and trade shows: $1,000 CPL

CPL vs other metrics

The benefit of measuring your CPL is the insights it gives you into other areas of your marketing performance. These three metrics, while different from CPL, can be paired with it to give a fuller picture. 

CPL vs CPA (Cost per action)

While CPL measures the cost of acquiring potential customers who show interest, cost per action (CPA) calculates the cost of specific actions like purchases or sign-ups. Analyzing both metrics together helps assess your lead quality, optimize campaigns, and determine return on investment (ROI) more effectively.

For example, if CPL is low but CPA is high, it may indicate issues with the conversion process or targeting. You can then make adjustments to improve overall campaign performance. 

CPL vs CPC (Cost per click)

CPC measures the cost your business incurs each time a user clicks on your online ad. This metric is common in PPC and display ad campaigns. Like CPL, CPC helps you analyze your ROI and optimize campaigns.

One difference is that CPC evaluates the effectiveness of ad campaigns in driving traffic, while CPL gives insight into the quality of leads generated from those campaigns. Higher CPC may indicate competitive ad spaces or inefficient targeting, while higher CPL may suggest you need to optimize your lead generation strategies. 

CPL vs CPS (Cost per sale)

Cost per sale measures the total cost of converting a lead into an actual sale or customer. By comparing CPL with the number of leads that convert into sales, you can calculate the conversion rate and assess the effectiveness of your sales process. 

When you identify areas where leads are dropping off or conversion rates are low, you can improve your performance with data-backed decisions. 

How to lower your CPL: Best practices

Lowering your cost per lead takes a sustained effort over time. A smaller CPL can be achieved one of two ways: lowering your marketing spend or increasing your leads. Use the following strategies to improve performance and bring your CPL down. 

1. Targeted audience segmentation

Tailoring your marketing efforts to specific audience segments can significantly reduce CPL. When you advertise to too broad an audience or use the wrong channel, you waste marketing spend on someone outside your ideal buyer persona. By understanding the demographics, behavior, and preferences of your target audience, you can lower your CPL. 

2. Personalized messaging

As you hone in on your ideal buyer with audience segments, deliver personalized messaging for a greater impact. HubSpot found that three-quarters of marketers believe a personalized experience increases sales and the likelihood of a contact becoming a repeat customer.

3. A/B testing

Test different elements of your campaigns such as ad copy, visuals, call-to-action buttons, or landing page layouts to identify what resonates best with your audience. A/B testing lets you make data-driven decisions that optimize performance and drive down CPL over time.

4. Alternative marketing channels

Pay-per-click advertising costs have gone up significantly in recent years – Wordstream found that Google Ads CPL rose 20% in 2023. To avoid the sticker shock of paid ads, explore non-traditional marketing tactics like email marketing, public relations, organic social media, and content marketing to attract inbound leads. 

5. Marketing automation

Marketing automation tools streamline lead generation processes by automating repetitive tasks such as email marketing, lead nurturing, and scoring leads based on their interactions with your content. This not only saves time but also helps lower CPL by targeting high-quality leads more efficiently.

Key takeaways

• CPL, or cost per lead, indicates the average cost to your business of acquiring a new lead (potential customer). 
• Calculate CPL with this simple formula: Total marketing costs / Number of leads = CPL.
• A good CPL varies by industry and channel, but should always be lower than the value a sale brings in. 
• Monitoring CPL helps businesses identify cost-effective lead generation channels, optimize marketing budgets, and gain insights into overall marketing performance and ROI.
• Comparing CPL with cost per action, cost per click, and cost per sale gives a fuller picture of marketing effectiveness, enabling you to optimize campaigns. 
• Strategies such as targeted audience segmentation, personalized messaging, A/B testing, exploring alternative marketing channels, and leveraging marketing automation can help reduce CPL over time.

The post Cost per lead (CPL) appeared first on AppsFlyer.

What are Universal Links?

Universal Links are Apple’s iOS version of deep links, which are unique URLs that direct a user to a specific webpage or a piece of content within an app. If the user doesn’t have the app installed, the Universal Link will send them to the app store to download it. Once it’s installed, they’re directed to the intended section on the app.

What are the differences between Universal Links, app links, and deep links?

Universal Links, app links, and deep links are essentially the same thing, but there are some small differences you should know about. Deep link is the umbrella term for links that direct a user to a website or a destination within an app. Android refers to these links as app links, while on iOS they’re known as Universal Links. 

On a more technical level, there is a difference in URL formats: 

• A deep link uses a custom scheme with a path that defines the action: appname://open-app?name=appname. 

• Universal Links, on the other hand, are still web URLs that open a web page if the app isn’t installed: www.appname.com/app-page. 

This means deep links only work if the app exists, while Universal Links have a fallback to the web page. 

Benefits of Universal Links

The main benefit of Universal Links is improving user experience, which results in higher conversions, retention, and engagement. Let’s take a deeper look at how this works.

Uninterrupted user experience

Making users jump from one app to another is a distracting and disruptive experience. When a user clicks your Universal Link, it will direct them to exactly where they need to be — whether it’s the app store, or a specific piece of content within your app.

Increased retention rates

Universal Links play a key role in bringing users into your app. And once you get them through the door, it’s easier to keep them engaged with personalized recommendations, ongoing deals, and other retention-boosting tactics. 

Let’s say a user clicks on your ad for the latest sneakers. Instead of directing them to the website — where they might get distracted — a Universal Link can direct them to the exact product within your app (stopping off at the app store if they don’t have it installed). 

That’s a positive experience for the user, and a chance for you to showcase the benefits of your app so they keep coming back.   

Boost app conversions

The sooner a user gets to the content they’re looking for, the more likely they are to convert. And Universal Links conveniently remove the friction of users searching for your app in the app store, installing it, and looking for the page they initially planned on visiting. 

And let’s not forget Universal Links are universal – they work across any device or platform, meeting users wherever and however they choose. 

Build a secure user path

Universal Links leave no room for hackers to hijack links or send users to a fraudulent app. Developers are given full control of directing users across different channels in a safe environment.

How to implement Universal Links

As well as all the benefits mentioned above, Universal Links are easy to implement. Here are the steps to start using them in your app:

1. Obtain the app bundle ID and prefix ID.
2. Associate your app and your website. Specify the URLs that your app handles. This is a crucial step to ensure your Universal Links can’t be attacked. Limit available actions to minimize the risk to user data. For example, don’t let the Universal Links delete content or access sensitive information about the user. When testing, use improperly formatted URLs.
3. Update your app delegate to respond when it receives an NSUserActivity object with the activityType set to NSUserActivityTypeBrowsingWeb. Here’s Apple’s documentation for reference.
4. Configure mobile apps to register approved domains.
5. Configure the URI scheme.
6. Test the URI scheme.

Troubleshooting Universal Links

Although they’re simple to implement, there are a few issues you may encounter when setting up Universal Links. 

Apple has set out an 8-step diagnostic procedure to help you figure out what’s going wrong with your Universal Links – this is a good place to start if you run into difficulties. 

We’ve also addressed a couple of common challenges below. 

Blacklisted apps

Universal Links won’t work on any apps blacklisted by Apple. Apps that will always work include Messages, Mail, WhatsApp, Gmail, and Inbox. 

Link wrapping, click measurement, link redirects

One common issue with marketing automation tools is sending Universal Links through an automatic redirect to measure performance. The result is that users are redirected to the web fallback URL instead of the app. This essentially kills the main functionality of Universal Links, affecting marketers who manage paid ads or have click measurement enabled with their email service providers (ESP).

The solution is to work with a deep linking provider that has direct integrations with ESPs and provides attribution measurement.

Measuring for success with Universal Links

While we’ve covered the general advantages of Universal Links, it’s important to see how they’re benefiting your app specifically. Here are a few KPIs you should measure:

Click-through rate (CTR): Measures the percentage of users who click on a Universal link, compared to the total number of users who view it. A high CTR shows the link is relevant and engaging.

Conversion rate: Measures the percentage of users who complete the desired action after clicking the Universal Link. This can be anything that’s important to the success of your app, including making a purchase, signing up for a subscription, or installing the app. 

Retention rate: Measures how many users engage with the app after clicking on the Universal Link. This metric is even more compelling when you measure it against users who did not come from a Universal Link. You may also consider measuring session durations and bounce rates in the process.

To learn more about these and other app marketing metrics, watch our video:

Key takeaways 

• Universal Links are deep links for iOS devices that direct users to specific content within an app or webpage. Users who don’t have the app installed are first directed to the app store, and then onwards to the relevant in-app location. 
• Deep link is the umbrella term for links that redirect users to a specific location. Universal Links are the iOS version, while Android calls them app links. 
• Universal Links can help improve user experience, and in turn boost engagement, retention, and conversion rates while building a secure user path.
• Apple provides guidance to help you implement Universal Links and troubleshoot any problems you may encounter. 
• Tracking metrics like click-through rate, conversion rate, and retention rate provides an insight into the success of your Universal Links.

The post Universal Links appeared first on AppsFlyer.