What is fractional attribution?

Fractional attribution is a marketing attribution method used to assign credit for a conversion to multiple marketing touchpoints — across the whole user journey, not just one key moment.

Instead of focusing solely on one point of contact (like a last-click attribution model), it distributes the credit among every interaction a user has along the way. For mobile app marketers, this is a game-changer. You get a fuller picture of how users go from curious to converted, whether they’re installing an app or making in-app purchases.

Suppose a user sees an Instagram story ad, later clicks a Google search ad, and finally converts after seeing your app ad on an Instagram story.

With fractional attribution, each touchpoint would receive partial credit for the conversion. Consequently, you get a clear view of what’s really working — whether it’s the ad format or the channel — and can use that insight to make smarter budgeting and campaign planning decisions.

Types of fractional attribution

Under fractional attribution, several popular models use different approaches to distribute credit. Common types include:

Linear attribution

With linear attribution, every touchpoint in the customer journey shares the credit equally. It’s simple and works well for campaigns where each interaction plays an equally important role.

For example, if a customer hits five different ads before buying, each one gets 20% (100% divided by five) of the credit. This method gives you a nice overview of the entire journey, but doesn’t show you which touchpoints were real decision-makers.

Time decay attribution

Time decay attribution gives more weight to touchpoints closer to the conversion. It’s perfect for fast-paced campaigns or quick-turn promotions since it assumes those last few interactions had a bigger influence on the user’s choice.

For example, if someone clicks on an ad a week before buying, and then sees another ad the day before their purchase, that last ad gets more credit. This model is great if you want to pinpoint the interactions that nudged users over the line while still acknowledging the earlier steps.

U-shaped attribution

Also called position-based, U-shaped attribution favors the first and last interactions, with a smaller slice for any touchpoints in between. It’s ideal when the focus is on creating initial awareness and then pushing for the final conversion, while still recognizing those mid-funnel engagements.

For instance, with five touchpoints, the first and last might each get 40%, while the middle three share the remaining 20%. This way, you’re highlighting both the initial draw and the final push to convert.

W-shaped attribution

In W-shaped attribution, the first interaction, a significant middle touchpoint (such as lead capture), and the final interaction get most of the credit, with all others receiving less weight. This model is handy for longer, multi-step journeys, especially when you want to emphasize distinct stages — like early awareness, a mid-funnel milestone, and the final conversion.

For a journey with six touchpoints, the first, third, and last might each get 30%, with the rest sharing the leftover 10%. It’s useful for tracking engagement at different funnel points.

Custom attribution model

With a custom attribution model, you set the rules based on what matters most to your business — whether that’s specific goals, customer behaviors, or unique campaign elements. For example, a mobile app marketer might give more credit to social media interactions if they’re proven to drive conversions, while dialing down on display ads. Custom models are super flexible and let you tailor credit exactly how you want, but they need solid data and insights to back them up.

One step further with Markov chains

Markov chains are a nifty way to model sequences of events, where each event’s likelihood depends only on the one that came right before it. Here’s the gist: rather than looking at the entire history, they just consider the latest step. This makes them ideal for predicting user behavior in dynamic systems, where each interaction influences the next, like a marketing journey where each step nudges users along the path.

Unlike fractional attribution — which splits credit across multiple touchpoints by set rules — Markov chains take a probability-based approach. They assess each touchpoint by its likelihood of pushing the user to the next step. As a result, you get deeper insights into each touchpoint’s role in driving conversions, making this a handy method for optimizing complex journeys with multiple possible paths.

Benefits of fractional attribution

1 — Provides a full picture of what drives conversions

Fractional attribution doesn’t just stop at the first or last click — it gives you a complete view of all the touchpoints that help lead to conversions. With this, you can see which channels and moments actually make a difference, giving you insights into patterns you might otherwise miss. These deeper insights help you make smarter, more informed choices about where to focus your advertising efforts and spend.

2 — Optimizes engagement across the entire user journey

When you understand each touchpoint’s role and value, it’s easier to fine-tune the full journey — from first spark to final conversion. Fractional attribution lets you see what’s nudging your target users down the funnel, so you can smooth out the bumps and create a seamless experience.

The result? Campaigns that connect with users at every stage and ultimately lead to better conversions.

3 — Considers and leverages indirect influences

Some touchpoints might not directly seal the deal but still play a key part in moving users closer to action. Fractional attribution captures these indirect influences or helpers, showing their role in the overall journey. Say an awareness ad might drive users to search for your brand later — that’s value you’d want to see. This approach helps you recognize such smaller but essential contributions, so you can support them properly.

4 — Maximizes ROI with smarter budget allocation

By spreading credit across all touchpoints, you can clearly see which channels and campaigns give you the best bang for your buck. Using fractional attribution, you can spot opportunities to reallocate budget in ways that get better results. No more over-investing in that last-click moment — instead, you can distribute your budget effectively across the whole journey to maximize the impact of your marketing dollars.

How to implement fractional attribution

Here’s a detailed breakdown of the key stages to ensure your model is both accurate and insightful:

1 — Use your existing data

Start by leveraging your existing data to understand your app users’ journeys. Check out historical data across all your campaign channels — think social ads, paid search, display ads, and emails. Look for patterns showing the typical paths to conversion — like how often touchpoints happen, the time from first interaction to install, and the role each channel plays.

Your historical data will help you decide which touchpoints to include and prioritize the ones that matter most. This sets you up for a model that’s accurate from the start.

2 — Onboard the right tools

Fractional attribution needs advanced tracking tools that can handle the complexity of mobile marketing. Pick one that’s got multi-touch attribution capabilities and works seamlessly with ad networks and in-app events. For mobile app marketing, tools like AppsFlyer’s offer SDKs that capture in-app behavior and integrate with third-party networks.

When picking a tool, look for:

• Multi-channel tracking: Make sure it catches touchpoints across all channels — social, search, in-app ads, SMS, you name it.
• Cross-device/platform tracking: Find one that can track users across devices (from mobile to desktop).
• Customizable attribution models: Options to adjust or switch models (like linear to U-shaped) as you gather more insights.

3 — Build your measurement matrix

The measurement matrix is a crucial planning tool that outlines each touchpoint, defines key metrics, and establishes rules for distributing credit. Start by identifying the primary metrics that will reflect conversion events, such as app installs, in-app purchases, engagement events, and lifetime value (LTV).

Once you’ve got those, it’s time to choose an attribution model. Maybe time decay or linear feels right. Either way, you’ll want to set some weights for each touchpoint. For example:

• Social media ads: If these are great for top-of-funnel awareness, give them a bit more credit here.
• Search ads: They’re often solid for driving re-engagement and consideration, so they might deserve mid-funnel credit.
• In-app ads and push notifications: These can be crucial for conversions, especially in the lower funnel, so they might get more weight.

Using this matrix, you can ensure consistency in how you assign credit across touchpoints. This allows for a structured approach to evaluate which interactions truly impact your app’s user journey.

4 — Choose your attribution window

Selecting the right attribution window is all about capturing relevant touchpoints without giving too much credit to older interactions. For mobile apps, the ideal window can shift based on the campaign, how long your user journey typically is, and the type of app. For instance:

• Short windows (like 7–14 days) work well for fast-moving, transactional apps where users convert quickly.
• Longer windows (30 days or more) are better for apps that need more consideration, like finance or subscription services.

We recommend experimenting with different attribution windows and seeing how they affect your data. Over time, this can reveal the sweet spot for your app — one where you’re capturing meaningful interactions without extra noise.

5 — Perform incrementality tests

Incrementality testing lets you see the real impact of each touchpoint by isolating how much it actually influences conversions. The first step is to conduct an A/B test, where one control group sees certain ads or interactions, while another control group doesn’t. This way, you can measure the “incremental lift” that each touchpoint provides.

Take a retargeting campaign, for example. Run it for some users but not others. If the group exposed to the campaign has a higher conversion rate, it’s a clear sign that touchpoint is driving real value. These tests help you pinpoint which interactions genuinely move the needle on conversions and which might just be adding clutter.

6 — Apply machine learning for continuous model improvements

Once your fractional attribution model is up and running, machine learning can take things up a notch. It spots subtle patterns, adjusting credit distribution as user behavior changes. Think of it as a fine-tuning process — catching interactions or touchpoints that might not stand out at first, but actually play a role in many user journeys.

Consequently, your model stays in sync with seasonal trends, new channels, or shifts in what users respond to. For example, if a fresh ad format takes off, machine learning assesses its impact and adjusts its importance in your model — no manual recalibration needed. This way, your model is always accurate and in tune with real-world behavior.

Future trends for fractional attribution

Privacy and security-first mindset

As privacy regulations tighten, from GDPR to CCPA, fractional attribution will need to keep up. Collecting and tracking user data is getting trickier, and you’ll likely face new challenges in creating detailed attribution models. With privacy concerns on the rise, we’re moving toward aggregated data and consent-based tracking — solutions that protect user privacy without skimping on insights. Future models will have to strike a balance: accurate data that keeps user confidentiality intact.

AI and machine learning-powered attribution

AI and machine learning are redefining fractional attribution by making models smarter and more responsive. These tools help process vast amounts of data in real time, revealing intricate user patterns and fine-tuning credit distribution as behavior shifts. Predictive analytics adds another layer, letting you anticipate which touchpoints will likely lead to conversions. And, as AI evolves, so will fractional attribution. Think: dynamic, real-time adjustments that help you stay ahead.

Rising use of ad blockers

With ad blockers on the rise, tracking the full user journey isn’t as straightforward as it used to be. Many touchpoints are now hidden, pushing you to explore other ways of measuring engagement, like first-party data or contextual targeting.

Attribution models will need to work with what’s available, possibly blending data from non-intrusive sources with consented interactions. Fractional attribution will need to be flexible enough to account for missing data, possibly using predictive modeling to infer the influence of hidden touchpoints.

The role of hyper-personalization

As hyper-personalization creates more tailored user experiences, fractional attribution models need to keep up. These personalized strategies use customized messaging across channels, leading to unique user journeys that aren’t easy to generalize.

To capture these individualized paths, you can start using AI to spot trends in specific user segments. The focus ahead will likely be on measuring how well personalization works, helping you see which tailored approaches drive the best results.

Key takeaways

• Fractional attribution lets you see which touchpoints in your user journey actually drive conversions by spreading credit across multiple interactions. Instead of giving all the credit to a single touchpoint, it provides the full picture — from a user’s first spark of interest to the final action.
• This attribution approach helps you pinpoint the channels and campaigns that offer the best return on investment. By seeing where each dollar actually makes an impact, you can put your budget toward touchpoints that matter, rather than relying on just that final click.
• Several fractional attribution models — like linear, time decay, U-shaped, and custom models — allow you to distribute credit based on specific campaign goals and the unique characteristics of the customer journey. Each model offers a different view of how touchpoints contribute to conversions.
• Machine learning enhances fractional attribution models by continuously adjusting credit distribution based on user behavior changes. This helps the model stay up to date with seasonal shifts and emerging channels and trends.
• As privacy rules tighten, fractional attribution will need to adapt, too. Using aggregated data and consent-based tracking will keep your performance insights intact while respecting user privacy.

The post Fractional attribution appeared first on AppsFlyer.

What is frequency capping?

Frequency capping is an online advertising technique that controls how often a single user sees an ad. It balances ad exposure with audience engagement.

With frequency limits, you avoid annoying your audience, protect engagement, preserve brand perception, and prevent wasted ad spend. Caps ensure ads remain fresh and relevant, reaching new potential customers rather than retargeting the same users excessively.

Why is frequency capping important?

It’s never a good idea to blow through your ad budgets without any control over who sees your campaigns and how often they see them. Here are a few reasons why frequency capping is essential for your online ad campaigns:

1.      Prevent irritation and fatigue

When audiences see the same ad over and over, they get bored and stop paying attention — a phenomenon known as ad fatigue. Constant repetition can also get pretty irritating. 

Frequency caps prevent over-serving the same audience. Consumers are becoming more intolerant of advertising, with 64% of US adults saying they spend extra time finding ways to avoid ads on free-with-ads video platforms like YouTube and Hulu.

2.    Extend your campaign longevity

Limiting ad frequency can help spread your impressions over a longer period of time without losing effectiveness. This also encourages advertisers to use more variety in ad creatives.

3.    Optimize ad budgets

Frequency capping adds control to help stretch your dollar. It ensures your budget is spent targeting new audiences instead of showing ads repeatedly to the same users. By managing ad frequency by placement, time of day, and day of the week, you gain greater control over your ads’ effectiveness.

How does frequency capping work?

Frequency capping is a common feature offered by ad networks like Google Ads, Meta Ads, and programmatic ad platforms. Some demand-side platforms (DSPs) also provide frequency capping.

There are three elements to consider when setting caps:

• Frequency — how many times to show an ad to a user
• Creative — which creative or campaign should be limited
• Timeframe — how long the cap should be in place

Ad platforms track how often an ad or campaign is shown to the same users using unique identifiers like cookies, device IDs, or tracking pixels. Before serving the ad, the platform checks the frequency cap to see if the limit the advertiser sets is reached. If not, the ad is served, and the impression is logged within milliseconds to maintain a seamless user experience.

The impact of cookie deprecation

Frequency capping hinges on identifying the user and checking if they’ve seen the ad previously. But times are changing in the industry. With increasing privacy regulations like Apple’s ATT framework and cookie deprecation, ad networks are finding new ways to fill the data gap.

Mobile advertisers are shifting from relying on device-level tracking that identifies specific users to more aggregated data and probabilistic modeling. Mobile measurement partners (like AppsFlyer) effectively use probabilistic modeling as a fallback method when unique IDs are unavailable, employing machine learning to estimate user behavior based on large data sets and aggregated insights.

How to set the right frequency cap

While setting frequency caps from the start of your campaign is a good idea, always check your data to get them right. Here are the key metrics to guide you.

Ad schedule performance: On platforms like Google Ads and Meta Ads, you can check what time or day of the week your ads are most effective. Consider day or time capping based on this report.

Conversion rate: Compare conversion rates across users who saw the ad multiple times. If conversions drop significantly after a certain number of views, it’s a sign that frequency capping may help improve efficiency.

Frequency: High ad frequency with low conversion rates could indicate that your ads are no longer connecting with users. It may be time to limit that creative to prevent fatigue. 

Impressions: Review the total impressions to understand how often your ads are shown. High impressions with low engagement indicate ad fatigue.

Click-through rate (CTR): Declining CTR over time may signal ad fatigue and a need to refresh your creatives. What initially grabbed attention has become wallpaper.

Best practices for frequency capping

Your ad campaigns should objectively be measured by total impact, as frequency directly influences brand lift. You can maximize campaign potential by tailoring frequency to specific market, message, and media factors — there’s no one-size-fits-all approach.

As a good starting point, Meta’s research condenses the “best practices” of frequency capping into one handy chart.

This chart outlines the various market, message, and media factors that influence frequency capping:

• Lower frequency: Ideal for established brands with high market share, long purchase cycles, unique messages, and multiple media channels. Suitable for continuous or long-duration campaigns in low-season periods.
• Higher frequency: Recommended for new brands with low market share, short purchase cycles, and frequent usage. Applies to complex or less unique messages, new campaigns, short-duration, high-season campaigns, or single-channel (such as Facebook-only) strategies.

Here are some further tips to help you make frequency capping work for you:

Rotate creatives

Crafting a lot of good creatives can be resource-intensive, but it’s a worthwhile investment. When there’s more variation between creatives within one campaign, your audience is less likely to experience ad fatigue across the board. It’s not always about how many different creatives you have, but the distinct variety between them.

Not all impressions are created equal

Not every impression is worth the same. Some ad placements may have lower visibility and be more crowded than others, hence making your impressions less effective. Platforms with multiple placements, high ad density, or overlapping audiences can increase exposure, leading to higher frequency. Engaging placements may need less frequency to be effective, while less engaging spots must be shown more often.

Key takeaways

• Frequency capping is an online advertising technique that limits how often a single user sees an ad, helping prevent ad fatigue.
• Frequency capping prevents user irritation, extends campaign longevity by spreading impressions over time, and optimizes budgets by preventing overspending on the same audience.
• Ad platforms use unique identifiers like cookies or device IDs to track impressions per user and enforce frequency limits in real time, maintaining a seamless experience.
• With cookie deprecation and privacy rules, platforms are shifting to probabilistic modeling and aggregated data, enabling marketers to approximate frequency control without identifying individual users.
• Adjust frequency caps based on market, message, and media factors. Other best practices include regularly analyzing performance metrics like CTR and conversion rate, and rotating ad creatives to maintain audience interest and prevent fatigue.

The post Frequency capping appeared first on AppsFlyer.

What is the Apple Privacy Manifest?

Apple requires every app in its App Store to have a Privacy Manifest. This is a file that must be added to the app’s code, setting out the type of data it collects and the reasons for the collection. Xcode summarizes these files into a report, then the information is scanned and uploaded into the app’s privacy information section on the App Store. 

You can read Apple’s official documentation here.

Introduced at Apple’s Worldwide Developers’ Conference 2023 (WWDC23), the Privacy Manifest is part of Apple’s ongoing privacy initiatives aimed at enhancing user transparency and protecting personal data. It ensures developers clearly disclose their data collection practices, specifically in relation to third-party SDKs embedded within an app. 

Before this requirement, app developers often didn’t know what data their third-party SDKs were collecting or where it was used. 

Why did Apple introduce the Privacy Manifest?

In 2022, Apple announced Privacy Nutrition Labels, which required apps to provide information about an app’s privacy practices for an average user.

Privacy Manifests are the next iteration of Nutrition Labels, providing users with even more information about how their data is being collected and used. Since all apps, including third-party apps and plugins connected to your app, have their own Privacy Manifests, Apple simplifies the process by allowing you to roll them up into a single Manifest for your app.

This ultimately brings the industry closer to ending probabilistic attribution and fingerprinting, or data connected to a specific device like IP addresses, phone model, installed apps, and even GPS location. Requiring developers to declare their data usage prevents API misuse and ultimately pushes SKAdNetwork as the main attribution tool for advertisers.

What are the main features of an Apple Privacy Manifest? 

Starting with iOS 17, every new or updated app must include the following information:

Data usage: NSPrivacyTracking

This is a key that indicates if the app asks for permission to track users across other companies’ apps and websites. It’s based on Apple’s App Tracking Transparency (ATT) framework, which mandates that apps must get user consent before engaging in any tracking.

External domains: NSPrivacyTrackingDomains

Any external domains used by the app or a third-party SDK must be listed in the Privacy Manifest to ensure transparency about potential tracking. Domains that don’t follow Apple’s latest privacy rules and ATT requirements may be blocked by Apple unless the user opts in.

Nutrition Labels: NSPrivacyCollectedDataTypes

These labels list the types of data your app or third-party SDK collects about users and why it’s collected. Developers can easily use Xcode to generate a privacy report, which provides a summary of the data collected by the app and any linked third-party SDKs.

Each NSPrivacyCollectedDataTypes will include:

• The type of data collected
• Indication if data is linked to the user
• Indication if data tracks the user
• Reasons for collecting data

Here’s an example of contact information being collected via Apple’s official documentation:

Required reasons API: NSPrivacyAccessedAPITypes

Fingerprinting is prohibited, even if a user consents to tracking. This eliminates fingerprinting whether in your own code or through a third-party SDK. In this section, you need to clearly explain why your app or third-party SDK on iOS, iPadOS, tvOS, visionOS, or watchOS uses these APIs, and ensure they’re only used for their intended purposes.

How to create and implement an Apple Privacy Manifest

Now that you understand all the pieces that go into the Apple Privacy Manifest, here’s a step-by-step guide to creating one of your own.

Step 1: Open Xcode

Step 2: Choose File > New File.

Step 3: Scroll down to “Resource” and select App Privacy File type.

Step 4: Click Next.

Step 5: Check your app or third-party SDK’s target in the Targets list.

Step 6: Click Create.

Note that the file is automatically named PrivacyInfo.xcprivacy, and this is the required file name for bundled Privacy Manifests. You will also need to add the Privacy Manifest file to your target’s resources for Xcode when you generate a privacy report.

Once the file is created, add the following keys to the dictionary at the top level of this property list file:

• NSPrivacyTracking
• NSPrivacyTrackingDomains
• NSPrivacyCollectedDataTypes
• NSPrivacyAccessedAPITypes

Key takeaways 

• The Apple Privacy Manifest is a required document for all App Store apps, outlining the data collected and its intended use.
• Introduced to enhance transparency and build on Privacy Nutrition Labels, it helps prevent fingerprinting and pushes SKAdNetwork for attribution.
• It includes key components like tracking permissions (NSPrivacyTracking) and external domain declarations (NSPrivacyTrackingDomains).
• Developers must list the types of data collected (NSPrivacyCollectedDataTypes) and the APIs used (NSPrivacyAccessedAPITypes), ensuring compliance with Apple’s rules.
• Developers can easily create a Privacy Manifest in Xcode, following a straightforward process to meet Apple’s privacy standards.

The post Apple Privacy Manifest appeared first on AppsFlyer.

What is an ad click?

An ad click happens when someone interacts with an online ad — whether it’s a banner, button, or video — by clicking on it (or tapping, on a mobile device). This click typically redirects the user to an app store page, a landing page, or a website with more info about the app. And that, in turn, could lead to them downloading an app, signing up for a service, or checking out a special offer.

In mobile app marketing, the goal of an ad click is to move users from just browsing to actually engaging. When users click, they’re showing interest, signaling they’re ready to explore what’s next. For marketers, ad clicks are a key app metric to track how well campaigns are performing. More clicks usually mean more engagement, which often leads to higher app downloads or in-app actions.

Ad clicks vs. click-through rate

Ad clicks are closely related to another digital marketing metric: click-through rate, or CTR. But don’t confuse the two terms, because they tell us different things. 

• An ad click is simple — it’s the actual physical action a user takes when they see an ad and click on it. It shows that the ad was compelling enough to get their attention.
• The click-through rate measures an ad’s effectiveness in driving engagement. It’s expressed as the percentage of people who clicked on the ad out of everyone who saw it (impressions — how many times the ad was shown). For example, if an ad is shown 1,000 times and receives 50 clicks, the CTR would be 5%. 

To put things into perspective, while ad clicks measure individual user actions, CTR gives a more comprehensive view of ad performance by calculating the ratio of clicks to impressions.

Why are ad clicks an important metric?

Ad clicks are an essential metric for marketers. Here’s why:

Understanding click-through rates

You need to count clicks in order to calculate your click-through rate (CTR). This number indicates how engaging your ad is — a higher CTR means something’s working, whether it’s the message, design, or placement. On the flip side, lots of impressions but few clicks could be a cue for you to tweak your ad creative, copy, or positioning.

Take banner ads, for instance. Their CTRs are usually lower than native ads due to “banner blindness” (meaning they’re easy to ignore). If your native ads are getting more love, it’s a good sign that integrating ads into organic content might be a more effective strategy.

Effectiveness of targeting

Ad clicks also reveal how well you’re reaching the right people. 

If an ad gets clicks from a specific demographic or location but conversions are low, you might be hitting the wrong crowd. In other words, it’s not reaching users with purchasing intent. Maybe the targeting is off, or the message isn’t clicking (pun intended) with the audience.

Suppose you have an app targeting tech enthusiasts aged 25 to 35. If you’re getting more clicks from an older crowd, it’s time to recalibrate your targeting. The idea is that analyzing clicks can help fine-tune your approach to focus on the right audience.

Understanding user behaviors

Analyzing where and when users click on ads can tell you a lot about their behavior. Do they click more in the mornings, or on specific types of ads? These behavioral patterns help you refine your organization’s broader strategies, like ad scheduling or even what types of offers to run. 

Tracking post-click behaviors — whether users browse, install, or abandon — offers another layer of insight into what resonates with them​ best.

For instance, tracking mobile vs. desktop clicks might show certain products do better on mobile, so you can optimize mobile ads and landing pages accordingly.

Targeting the right keywords

In search ads, clicks reveal whether your ASO keyword strategy is working. A spike in clicks on a particular keyword? You’ve probably hit the right intent, where the search terms align with what users are actively looking for. But if clicks are low, the keyword might be too broad or off-target.

For instance, a high click rate on long-tail keywords (for example, “best free fitness apps for beginners”) helps you precisely target specific user intent. Meanwhile, broad keywords (such as “fitness apps”) might attract clicks, but often fail to convert due to a mismatch between user expectations and the app’s specific offering.

Optimizing your offering

Ad clicks give you a clear picture of what grabs your audience’s attention. When certain ads pull in more clicks, it means the product or promotion resonates with them. But if those clicks aren’t turning into sales, it’s a sign that something’s off — maybe the landing page, pricing, or overall follow-up isn’t matching expectations.

Comparing those clicks with sales data helps you adjust the details. You might need to tweak the pricing, rethink app features, or switch up your promo approach to better align with what your audience actually wants.

How do ad clicks work?

When someone clicks on an ad, it triggers a fee for the advertiser. This model, called pay-per-click (PPC), charges advertisers only for clicks, not just for showing the ad. Each click is a potential customer, making it a key way to gauge ad performance.

The fee, known as cost-per-click (CPC), varies depending on how competitive the ad space is. Industries like insurance or legal services tend to pay more per click since leads are so valuable. On the flip side, niche markets might see lower costs. You can set a maximum bid for clicks, but you’ll often pay less, just enough to outbid competitors.

Once they click, the user is directed to a landing page — like an app page or a sign-up form — aiming to turn that click into a sale or lead. Not only does this drive traffic, but it also gives you insights into user behavior and campaign performance.

The price you pay per click isn’t just about bids, though. The quality score of the ad — based on relevance, expected CTR, and landing page experience — plays a role too. Ads that score higher are seen as more useful and tend to cost less per click. So, improving your ad’s relevance and optimizing landing pages not only boosts CTR but also keeps costs down.

How to avoid click fraud

Click fraud can be a major headache in digital advertising. It messes with your ad metrics and drains your budget by piling on fake or meaningless clicks. To protect your campaigns, it’s crucial to know what suspicious activity looks like and take steps to stop it. Here’s how you can spot and prevent click fraud:

1. High conversion rates paired with high bounce rates

Getting lots of conversions sounds great — until those same users bounce right after. 

While high conversions are generally a good thing, if users convert but quickly leave without further interaction, you might be dealing with bots mimicking real clicks. Similarly, a sudden spike in bounce rates can point to bots or fraudulent actors who click on ads but have no real interest in the content​.

Monitoring for these patterns over time can help flag unusual behavior, especially if your historical data suggests more balanced user engagement.

2. Suspicious form submissions and low-quality user accounts

Fake form submissions are a dead giveaway of click fraud. 

If your forms are flooded with incomplete or nonsensical information, like gibberish names and fake emails, something fishy is going on. Also, be wary of low-quality accounts. Think: missing profile details, random usernames, or identical sign-up times. Regularly auditing these submissions and account activity can help you catch fraudulent activity before it snowballs.

3. Use click fraud prevention tools

There’s a range of click fraud prevention tools out there that can save your campaign. These tools monitor and filter out invalid clicks — like repeat clicks from the same source — and block suspicious behavior before it harms your metrics. 

Popular solutions include ClickCease, PPC Protect, and Google Ads Click Fraud Protection. These platforms often provide real-time analysis and detailed reports on click behavior to help you stay ahead of fraudsters.

4. Consistent traffic from a single IP but no conversions

If you notice consistent traffic from a single IP address with no conversions or sales, consider it as a major red flag for click fraud. 

Legitimate users from a specific region or network may visit repeatedly, but a consistent lack of conversion could indicate fraudulent behavior, such as bots programmed to click but not engage further. Monitor IP addresses continuously and blacklist those that show strange traffic patterns.

How to improve your ad click rate

Use the following tactics to improve your ad click rate:

Optimize your CTA 

In mobile app marketing, it’s essential to use action-oriented and app-specific calls to action (CTAs) like “Download Now” or “Get the App Today.” Mobile users respond well to short, clear commands that fit within their screen size. Additionally, positioning your CTA in a high-visibility area of the ad — such as the headline or near the app icon — can significantly improve CTR​.

For app install campaigns, adding a sense of urgency, such as “Limited Time Offer” or “Free for Today,” can nudge users to take immediate action. We also recommend continuously testing different CTA wording (for example, “Explore Features” vs. “Start Your Journey”) to find what resonates best with your target audience​.

Test out different ad formats 

Here, the idea is to experiment with various ad types to see what attracts the most attention. 

For example, interstitial ads (full-screen ads that appear at natural breaks in app usage) can lead to higher engagement compared to smaller banner ads. Similarly, video ads or playable ads, which allow users to experience your app before downloading, have been shown to significantly increase CTR in mobile campaigns. 

These formats show users a preview of the app’s functionality — something that’s more likely to make them click through and install​.

Additionally, mobile users are often attracted to visually rich formats. Ensure your ads are mobile-optimized, with responsive designs that adjust to different screen sizes without losing clarity​.

Know your target audience

As a mobile app marketer, you should use audience segmentation to tailor ads specifically to different user groups based on demographics, behavior, or interests. 

Let’s say you’re marketing a fitness app. Instead of a one-size-fits-all approach, you’d focus on young adults passionate about health and wellness, crafting offers that speak directly to them. Platforms like Facebook Ads and Google Ads help you zero in on the right people using data-driven insights, so your message lands where it matters most.

Remarketing is another effective strategy. Reaching out to users who’ve already shown interest in your app keeps it fresh in their minds and boosts the chances they’ll engage.

Do keyword research

Ensure that you’re targeting long-tail keywords — they have lower competition and align more closely with user intent, resulting in higher-quality clicks. For example, instead of just using “fitness app,” try “free fitness app for beginners,” which targets a more specific audience that’s more likely to download your app.

Don’t forget about negative keywords. Excluding irrelevant search terms ensures your app isn’t shown to users unlikely to engage, improving click-through rates and reducing wasted ad spend. This keeps your marketing efforts focused and efficient.

Top Tip: For mobile-specific campaigns, take action by specifically targeting user intent with keywords like “Download” or “Install.” These terms directly align with what mobile users are likely searching for, making your ads more relevant and improving CTR. 

Diversify marketing channels

The trick to effective mobile app marketing is diversifying your marketing efforts across different channels to connect with users where they spend their time. 

For instance, in-app advertising — placing ads inside other mobile apps — is a smart way to catch users’ attention right where they are. Simultaneously, you can use social media platforms like Instagram, TikTok, and Facebook to reach active mobile users. Each has its own ad style built for mobile, like Instagram Stories or TikTok videos, which often drive more engagement than your typical display ads.

And if you’re ready to level up, try programmatic advertising. It uses machine learning to serve personalized ads across multiple mobile platforms. These ads hit the sweet spot, delivering content that feels relevant, boosting both engagement and click-through rates.

Key takeaways

• An ad click occurs when a user interacts with an online ad. Clicking redirects the user to an app store page or landing page, often leading to app downloads, sign-ups, or exploring offers.
• Ad clicks indicate how well your ad is catching users’ attention. Tracking them can help you optimize targeting, keywords, and other campaign elements. 
• When it comes to cost-per-click advertising, each click costs money, and how much depends on the competition in your industry. Advertisers have to make sure those clicks are worth it, balancing the cost with their overall return. Smart ad targeting and relevance are key to keeping those costs down.
• Click fraud is a big problem in mobile advertising. Bots or other bad actors can rack up fake clicks, skewing your metrics and wasting your budget. Keeping an eye out for unusual patterns, like repeated clicks from the same IP or high bounce rates with no conversions, is key to stopping fraud in its tracks.
• Properly targeting your audience and selecting the right keywords are crucial for improving ad performance. Poor targeting or irrelevant keywords can lead to low conversion rates despite high clicks.

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What is data-driven attribution?

Data-driven attribution is a smart way to figure out the impact of different marketing touchpoints along the user journey.

Unlike traditional attribution models that stick to rigid rules, like giving credit only to the first or last interaction, data-driven attribution looks at the big picture. It considers every interaction a user has with your brand, both online and offline, to understand how these touchpoints work together to lead to a conversion (such as a purchase or sign-up).

This approach is much more holistic, as it dives deep into the data to uncover the true value of each touchpoint, rather than just following a set formula. As a result, you enjoy a clearer, more accurate view of complex user behavior and what’s really driving your marketing success, helping you make better, data-backed decisions.

How does data-driven attribution work?

Data-driven attribution uses machine learning and advanced algorithms to analyze every touchpoint in a user’s journey, determining how each contributes to a conversion.

A key part of data-driven attribution is using probabilistic models like the Markov chain. This model helps predict the likelihood of a user converting after interacting with various touchpoints. It analyzes the order of these interactions to identify which touchpoints are most likely to lead to a conversion and assigns credit accordingly.

Machine learning further enhances this process by continuously learning from data, improving the accuracy of credit assignment as user behavior and marketing strategies evolve.

Let’s take Google Analytics 4 as an example. GA4 uses machine learning to create a tailored data-driven attribution model that reflects the true influence of each touchpoint, with insights constantly updated to stay relevant.

Benefits of data-driven attribution

Let’s dive into they key benefits of data-driven attribution and their impact on your marketing strategy:

1 — Visualizes marketing efforts across multiple channels

Data-driven attribution offers a clear view of how various marketing channels work together along the user journey. It might show you that a user often starts with a social media ad, follows up with an email, and finishes with a Google search before converting. This interconnected view lets you see which channels pull the most weight and how they complement each other.

2 — Optimizes based on historical data

With data-driven attribution, you can use historical data to pinpoint which touchpoints and channels have delivered the best results over time. For example, you might find that combining paid search with organic content regularly leads to high-value conversions. Armed with this knowledge, you can tweak future campaigns to mirror these successful patterns, ensuring a more efficient use of your marketing budget.

3 — Helps prove marketing ROI

One of the biggest challenges in marketing is proving the return on investment (ROI) of your efforts. Data-driven attribution gives you a more accurate way to measure the impact of each marketing touchpoint, allowing you to show how your marketing activities drive revenue.

Suppose you’ve been investing heavily in display ads but haven’t seen a clear connection to conversions. Data-driven attribution might reveal that these ads play a critical role in the early stages of the user journey, justifying continued investment. This level of insight can help you defend and optimize your marketing spend.

4 — Provides valuable insights into user behavior

Data-driven attribution does more than just show you which channels are performing — it gets to the heart of how and why customers are engaging with them. This kind of insight is gold when it comes to crafting effective marketing strategies. For example, if you discover that users who watch your video content early on are more likely to convert later, you can adjust your marketing messages and tactics to better connect with their needs.

5 — Directs your ad spend and budgets more effectively

By showing you which touchpoints are most effective in driving conversions, data-driven attribution helps you allocate your ad spend and budgets more wisely.

Let’s say your model identifies that certain touchpoints, like retargeting ads, are crucial in closing sales. With this information, you can confidently increase spending on these ads while reducing budgets for less impactful channels. This ensures every dollar you spend is working hard to drive the best possible outcomes.

How does data-driven attribution compare to other attribution models?

When comparing data-driven attribution to traditional rules-based models, it’s crucial to see how each one works and where data-driven attribution stands out. Here’s how data-driven attribution stacks up against these marketing attribution models:

First-touch attribution

How it works: Gives all the credit for a conversion to the very first touchpoint the customer interacted with.

While first-touch (or first-click) attribution is handy for spotting which channels spark initial interest, it ignores the influence of later touchpoints that may have sealed the deal. Data-driven attribution, however, spreads the credit across the entire user journey, recognizing the real impact of each touchpoint.

Last-touch attribution

How it works: Attributes 100% of the conversion credit to the last touchpoint the user interacted with before converting.

Last-touch (or last-click) attribution assumes the final interaction is solely responsible for the conversion, ignoring the contributions of earlier touchpoints. That’s a very limited approach. In comparison, data-driven attribution offers a more nuanced view, showing how each touchpoint influenced the final decision and ensuring credit is given where it’s due.

Time decay attribution

How it works: Gives more credit to touchpoints that occurred closer in time to the conversion. The further back a touchpoint is, the less credit it receives.

Time decay attribution recognizes that touchpoints closer to the conversion might have a stronger influence, but it still follows a set rule. Data-driven attribution, however, skips the assumptions, using real data to assess each touchpoint’s true contribution, no matter when it happened.

Linear attribution

How it works: Distributes conversion credit evenly across all touchpoints.

Linear attribution treats all touchpoints equally — which might sound fair, but can oversimplify the user journey. Data-driven attribution improves on this by analyzing the actual impact of each interaction, allocating credit based on how much each touchpoint genuinely contributed to the conversion.

Position-based attribution

How it works: Gives most credit to the first and last touchpoints (and sometimes a middle one), with the remainder spread equally across the other interactions.

Position-based attribution tries to balance the importance of the first and last interactions while giving some credit to the middle ones. The U-shaped model gives the majority of credit to the first and last touchpoints, splitting the rest equally among those in between. The W-shaped model has an additional “spike” in the middle, recognizing the importance of an intermediate touchpoint.

While adding some nuance, these models still follow an arbitrary distribution of credit. Data-driven attribution doesn’t rely on fixed rules — it looks at how users engage with each touchpoint throughout their journey, reflecting the actual contribution of each touchpoint based on real data.

Limitations of data-driven attribution

While data-driven attribution offers a lot of benefits, it does come with a few challenges that are worth keeping in mind:

• Requires high volumes of data: For data-driven attribution to work its magic, it needs a lot of data to analyze. Without sufficient data, the model’s insights may be less reliable, particularly for smaller campaigns or brands with limited user interactions.
• Complex to implement and optimize: Setting up and optimizing a data-driven attribution model isn’t exactly a plug-and-play situation. It can get pretty technical, so you might need some expert help or specialized tools to get it right and keep it running smoothly.
• Data privacy regulations: With all the data privacy rules out there, such as GDPR, collecting and using user data for attribution is tricky. You’ll need to make sure you’re staying compliant, but this can sometimes limit the data you have available, which might affect the accuracy of your attribution model.
• Credit is not assigned to non-ad touchpoints: Data-driven attribution usually focuses on advertising touchpoints, which means it might miss out on other important interactions. Think: word-of-mouth referrals or in-store experiences.
• Hard to see how attribution is modeled: The algorithms behind data-driven attribution can be pretty complex, making it tough to fully understand how credit is being distributed. This “black box” nature can make it challenging to explain or justify the results to stakeholders.

How to implement data-driven attribution

Finally to the fun bit!

Let’s decode how you can implement data-driven attribution.

1 — Set your attribution goals

First up, figure out what you want to achieve with data-driven attribution. Is it to discover which marketing channels are the heavy lifters for conversions? Or maybe you want to fine-tune your budget across different touchpoints? Knowing your goals will shape how you set up and measure your attribution model.

For example, if boosting app installs is your aim, your model should spotlight the touchpoints that are the real MVPs in driving those downloads.

2 — Identify conversion paths and marketing touchpoints

Now, map out the user journey — from the first hello to the app download and beyond. Think about every interaction, from social media ads to in-app referrals. The more detailed you get, the better your model will mirror the real-world customer journey.

Keep a close eye on app store optimization (ASO) and how users bounce between your ads and the app store. Knowing these key touchpoints will help you see where potential users are engaging with your app marketing and what’s pushing them to convert.

3 — Start collecting data

The next step is to set up tracking across all relevant channels. For mobile apps, this usually means integrating an SDK (software development kit) to measure installs, in-app behavior, and more. Gather data from ad networks, social media, app stores, and don’t forget those crucial in-app events like first purchases or level completions.

And of course, keep it legal! Make sure you’re in line with privacy regulations like GDPR and CCPA when handling user data.

4 — Analyze the data for insights

With data in hand, it’s time to dig in. Look at which touchpoints are driving the best results — like app installs or repeat purchases. You might spot trends, like users who see both a Facebook ad and an in-app notification being more likely to buy.

Advanced analytics tools can help you identify these patterns and understand how different touchpoints work together to boost user acquisition and retention.

5 — Optimize your campaigns

Based on the insights from your data, refine your app marketing strategy. This could mean reallocating budget towards channels that bring in high-quality installs, or personalizing push notifications based on ad interactions.

For instance, if users who install your app after watching a video ad are more engaged, then you might want to double down on video content in your campaigns. The goal is to make sure every marketing dollar is working hard to drive user acquisition and retention.

6 — Ongoing training of the model

The only constant is change, and that goes for your attribution model too. As user behavior evolves, keep gathering new data — especially after big app updates or shifts in your marketing strategy — and retrain your model to stay on point.

Adjust the weight of certain touchpoints or explore new channels that are trending. Regularly fine-tuning your model ensures it stays in sync with the ever-changing mobile app marketing trends, keeping you ahead of the game.

Key takeaways

• Unlike traditional attribution methods, data-driven attribution looks at the entire user journey, considering every touchpoint — online and offline. This gives you a clearer, more complete picture of what really drives conversions.
• Data-driven attribution uses machine learning and probabilistic models to continuously learn from data, improving how it assigns credit to each touchpoint based on its actual impact on conversions.
• Data-driven attribution offers a more accurate way to measure and prove the ROI of your marketing efforts. By showing exactly how each touchpoint contributes to revenue, it helps you justify and optimize your marketing spend.
• Getting started with data-driven attribution isn’t without its challenges. It’s a complex model that requires a lot of data, technical know-how for setup and fine-tuning, and careful attention to data privacy regulations.
• The key to success with data-driven attribution is keeping the model up to date. As new data comes in and user behaviors shift, continuous training is essential to ensure the insights stay relevant and accurate.

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What is time decay attribution?

Time decay attribution is a marketing attribution model where touchpoints closer to the conversion are given more credit than earlier ones. It’s a form of multi-touch attribution, but rather than crediting all touchpoints equally, it assumes that those closest to the time of purchase (or other desired activity) have more impact on the customer’s decision.

Think of time decay attribution like a road trip. The whole journey is exciting and part of the experience. But there’s something extra special, interesting, and unique about the last part of the road trip. The idea that your destination is right around the corner makes those last few miles the most memorable.

[image suggestion: visual representation of the time decay model through a timeline with multiple touchpoints?]

When to use time decay attribution

Time decay attribution works best when the customer journey is relatively long and has multiple touchpoints.

For example, if you’re selling enterprise software or high-end cars, your customers likely interact with various marketing channels over weeks or months before finally making a decision. Time decay attribution helps you understand which recent interactions pushed them to act, while still acknowledging the role of earlier touchpoints in nurturing the lead.

This model is also valuable for businesses looking to optimize their conversion funnel. By highlighting the impact of touchpoints close to the point of purchase, it can help marketers tweak their strategies for closing sales and guide budget allocation toward the most effective late-stage marketing efforts.

[image suggestion: customer journey]

Benefits of time decay attribution

The time decay model can be an effective strategy for companies when used properly. Let’s take a look at some of the benefits of this approach.

• Highlights influential interactions: The time decay model gives credit to crucial late-stage interactions that tip the scales in favor of a purchase. This can help marketers identify which closing strategies work best for their brand.
• Considers all touchpoints: By looking at the entire funnel, marketers can take a more holistic approach to understanding their campaigns. This provides a long-term perspective that you don’t get with single-touch models.

• Aligns with customer behaviors: This model reflects people’s natural tendency to be influenced by recent experiences, making it a good fit for many real-world scenarios.
• Best for optimizing conversions: By emphasizing recent touchpoints, time decay attribution is excellent for fine-tuning your conversion funnel and improving late-stage marketing efforts.

• Balances simplicity and sophistication: While more nuanced than basic models, time decay isn’t overly complicated to understand, making it accessible to many marketing teams.
• Integrates easily with other models: You can use the time decay model together with other data sources, such as a position-based model, to understand your campaigns even better.

Drawbacks of time decay attribution

Like most models and marketing techniques, there are also disadvantages to the time decay model. Consider the following before you decide if it’s right for you.

• Disregards the value of early interactions: By placing such a heavy emphasis on the end stages of campaigns, the time decay model may undervalue or underestimate important, early-stage interactions. These interactions are often critical in brand awareness and sparking initial interest.
• Oversimplifies complex customer behaviors: Not all customer journeys are the same or follow similar paths. In certain cases, this model may miss complex learning curves or customer experiences.
• Requires resources to define the parameters of the model: Setting up a time decay model involves decisions about the half-life and other parameters, which can be resource-intensive and may require ongoing adjustments.
• Doesn’t fully capture long-term brand building: Channels and touchpoints that contribute to brand awareness and loyalty over time may not get the credit they deserve in this model.

• May not suit all industries: For businesses with very short sales cycles or impulse-driven purchases, the time decay model might not provide significant insights over simpler attribution models.

Time decay attribution vs. other attribution models

Time decay is just one of a whole range of marketing attribution models. We’ve rounded up some of the most popular models and how they differ from time decay.

• First-touch attribution: Unlike time decay, first touch gives 100% of the credit to the first interaction, ignoring subsequent touchpoints. Time decay provides a more balanced view by considering all interactions, even if they aren’t all equal.
• Last-touch attribution: While last touch focuses solely on the final interaction before conversion, time decay considers the entire journey. It gives more weight to recent touchpoints but doesn’t completely disregard earlier ones.
• Linear attribution: Linear models distribute credit equally across all touchpoints. Time decay, in contrast, uses a weighted approach, reflecting the idea that not all interactions are equally influential.
• Data-driven attribution: Data-driven models use advanced algorithms to determine the impact of each touchpoint based on vast amounts of data. Time decay is simpler and based on a predefined logic, making it more accessible but potentially less accurate than data-driven approaches.
• Position-based (U-shaped or W-shaped) attribution: These models give more credit to specific touchpoints (the first and last ones for U-shaped, with an additional middle point for W). Time decay differs by continuously increasing the weight as touchpoints get closer to conversion.

Key takeaways

•  Time decay attribution is a marketing attribution model that gives more credit to touchpoints closer to the conversion point. It assumes these late-stage interactions have the strongest impact on the customer’s decision.
• This model is best suited to businesses with longer sales cycles and multiple customer interactions.
• Time decay recognizes all touchpoints with an emphasis on recent, influential interactions, making it one of the more balanced models. It’s particularly useful for conversion optimization and understanding late-stage marketing effectiveness.
• While comprehensive, time decay may undervalue early-stage marketing efforts and brand-building activities which build awareness, consideration, and trust.
• Time decay offers a middle ground between simpler models and more complex data-driven approaches. It can also be combined with other models for a well-rounded view of marketing performance.

FAQs

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What is video completion rate (VCR)?

In video marketing campaigns, video completion rate (VCR) shows the percentage of viewers who watch your video through to the end. Some companies set a different completion point, for instance 95% or 75% of the video.

VCR is important as it measures how engaging a video is. You’ve probably started to play a video before, then closed it after just a few seconds or minutes. Perhaps the content didn’t address what you were looking for, you didn’t like the style, or you realized it was much longer than you wanted to watch.

VCR doesn’t tell you everything about a video (for example, when viewers are dropping off or how they’re engaging), but it does give a helpful snapshot of whether the content holds people’s interest.

VCR formula: How to calculate video completion rate

Calculating VCR is relatively simple. While some platforms calculate it for you in their metrics, with others you’ll have to pull your view-throughs and impressions to compute it yourself. View-throughs are the completed views, while impressions are the times your video was displayed to a user.

To calculate your VCR, use this simple formula:

VCR = (View-throughs x 100) ÷ Impressions

VCR example

So, let’s say that your recent video had 1,000 impressions, and 600 of those resulted in completed views. You would calculate this as:

(600 x 100) ÷ 1,000 = 60% VCR

Calculating VCR on YouTube, TikTok, and more

Each video platform uses slightly different terminology for the metrics you need to calculate VCR. Here’s how to find it in three major platforms, and where to find it in their analytics.

YouTube

VCR = (Video completes x 100) ÷ Video starts

Where to find it: From a computer, sign into YouTube Studio. Click on Analytics on the left-hand menu to find complete views and completion rates. From the YouTube Android app, click on your profile picture, then View channel, then Analytics.

TikTok

VCR = (Completed video views X 100) ÷ Total video views

Where to find it: Under the TikTok Creator Marketplace, click Campaigns, then Reporting. Point to the campaign you want to analyze for and click the > arrow on the right of your screen.

Meta (Facebook and Instagram)

VCR = Video plays at 100%

Meta also has the ThruPlays, which measure the percentage of videos completed or played for at least 15 minutes.

Where to find it: Log into Ads Manager, click Columns, then Video engagement.

Why VCR is important

VCR is helpful in understanding viewer behavior, optimizing content, and measuring the success of video marketing campaigns. Let’s take a look at what you can learn and do with VCR.

1. Measure viewer engagement

While video views tell you about potential interest in a video or topic, VCR gives insights into how engaging the content is. A high VCR indicates that the video kept your audience’s interest, while a low VCR may suggest that the content isn’t resonating.

2. Gain insights to optimize videos

By analyzing VCR, marketers and content creators can identify which videos performed well and which didn’t. With this data, you can make data-driven decisions to optimize your video content, such as adjusting the video’s length, structure, or quality, to improve viewer retention and engagement.

3. Reach more people

Social media platforms take engagement metrics like completion rates into account when they serve up organic videos to people’s feeds. The better your engagement metrics are, the more feeds you’re likely to reach.

4. Boost conversions

Advertisers often place important information at the end of a video — a CTA, for instance, or link for conversions. Creators who host mid-roll or post-roll in-stream ads need a high completion rate to gain as many impressions and click-throughs as possible.

5. Measure campaign effectiveness

If you’re an advertiser, VCR is a vital metric to gauge the performance of your video ad campaigns. It helps you understand whether your messaging and creative content are compelling enough to keep viewers watching to the end. With this information, you can refine your targeting, messaging, and placement strategies to enhance your campaign performance.

What are the drawbacks of measuring VCR?

VCR is a powerful metric, but it doesn’t tell the full story of how a video ad’s performing. Keep the following limitations in mind as you use the metric:

• Video length matters. A short video will have a higher completion rate, but that doesn’t necessarily mean it’s more effective.
• No engagement metrics. VCR doesn’t tell you about how long most viewers watched or whether they paused or replayed any sections. Use average view time to see at which point users dropped off.
• A completed viewing still doesn’t mean a conversion. Look at follow-up metrics like click-throughs or brand recall to understand the full impact.
• Negative viewer experience. A high VCR might be achieved at the cost of viewer experience if videos are non-skippable or autoplay in disruptive ways. This can lead to negative perceptions of your brand, which VCR doesn’t measure.
• Oversimplification. CTV streaming consumption has skyrocketed in recent years, and with it, customer expectations. Advertisers need more nuanced metrics than VCR to measure this growing segment.
• External factors. Platform performance issues can impact the VCR, distorting performance. Keep the distribution network and platform in mind as you evaluate the metric.

What are the factors that affect your VCR?

Comparing one campaign’s VCR with another is a bit like comparing apples and oranges. You need to keep the context in mind when analyzing a video’s completion rate.

Video length 

Data from Wistia’s State of Video shows that the longer a video is, the lower the engagement rate and the lower the average time watched. That means that fewer people complete longer videos. For videos over 60 minutes, the average time watched was just 16:40 – meaning it’s likely only a small percentage of people watched to the end.

Video quality and loading speed

Video tools have evolved — it’s easier than ever to make good videos without expensive production equipment and professional videographers. There’s no excuse to use low-quality videos in your content campaigns. Even on a small budget, you can leverage good storytelling and authenticity to shoot high-quality videos from a phone with an external mic. 

Slow load times can frustrate viewers, leading them to abandon the video before it starts. Optimizing video delivery through techniques like adaptive streaming protocols and using content delivery networks (CDNs) can help reduce load times and improve completion rates.

Platform

Different formats work better for different platforms. A 30-minute podcast interview video may do well on YouTube or LinkedIn, but it won’t do as well on Facebook or TikTok where users look for fast, entertaining clips. Create different cuts of your content for each platform, or consider different content altogether.

Format of the video ad

When analyzing VCR, it’s important to keep the video ad format in mind when evaluating the metrics.

Non-skippable

Viewers must watch non-skippable ads in full before they can access the main content. Because they can’t be skipped, you’ll typically see a higher completion rate and higher message retention and brand recall. However, they may lead to user frustration due to the constraints.

In-banner

In-banner ads appear in a pre-defined ad space on a website, such as a banner. They can be effective for capturing attention quickly as users browse a webpage, but their impact may be limited by their size and lack of connection to the main content.

Out-stream

Out-stream ads play outside of traditional video players, often embedded within text content or social media feeds. Many start by autoplay, drawing viewers in and reaching a broad audience. While they’re less intrusive than in-stream ads, they may be skipped by users who scroll past quickly or use ad blockers.

In-feed

In-feed ads appear in social media feeds and are designed to blend seamlessly with organic content, making them less disruptive. They can achieve high engagement rates due to their native placement, but their performance heavily depends on the relevance and quality of the content.

Story

Story ads are full-screen video ads appearing within story features on platforms like Instagram and Snapchat. They’re typically short and designed to be immersive, making use of the vertical format. These can be highly engaging due to their interactive elements and the personal storytelling format. However, the impact can vary based on the platform’s user base and the ad’s creative execution.

How to improve your VCR

VCR is a valuable learning metric. It can help you flag underperforming videos and optimize them. Follow these six tips to create engaging videos your viewers will want to watch till the last second.

1. Optimize video length

Shorter videos generally keep viewers’ attention better. That’s especially true of younger generations, whose attention span averages just eight seconds. However, you need to balance length with the message you want to convey. Experiment with different video lengths to find the optimal duration for your audience.

2. Optimize for device

It’s smart to optimize your content for each type of device users may watch on, from mobile to desktop to CTV. Mobile users may look for a different format, like portrait over landscape, and may prefer shorter video lengths. When designing a video for CTV, consider the platform and context.

3. Create engaging content

Of course, the biggest factor impacting your VCR is the content itself. Research topics your audience is most interested in, leverage emotion and storytelling, and integrate high-quality visuals to boost your videos’ appeal.

4. Create compelling thumbnails

Design visually appealing thumbnails and compelling titles that accurately represent the video content. This can increase click-through rates and set clear expectations for viewers.

5. Add engaging visual elements

Most viewers don’t just want to watch a talking head or text on a slide. Use visuals like B roll, data visualization, and text on screen to engage viewers and boost recall. Captions are a powerful way to draw viewers in, especially when videos are played sound-off.

6. Target the right audience

Fine-tune your targeting to match your content with the right audience. Understand your audience’s preferences and interests so you can tailor content that resonates with them, increasing completion rates.

Key takeaways

• VCR shows the percentage of viewers who watch your video ads or content to the end (or up to a set point), providing a useful indicator of engagement.
• VCR can help you measure and optimize campaign performance. But bear in mind that this metric can lack some nuance (like where or why viewers drop off), and views don’t mean conversions.
• Video length, quality, and loading speed, as well as platform and ad format impact a video’s VCR.
• To improve your VCR, create engaging, high-quality content and compelling thumbnails. Optimize for length and device, and be sure to target the right audience.

FAQs

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What is vCPM?

vCPM stands for viewable cost per mille, and it shows the cost of 1,000 views of a digital ad. 

The key word here is viewable. vCPM is not just about users landing on the page where the ad appears — they need to actually see it. This gives both advertisers and publishers a truer picture of ad performance, helping them make strategic decisions that maximize revenue. 

To count as viewable, an ad must meet these guidelines from the Media Rating Council (MRC):

• At least 50% of its area is visible on the screen for at least one second
• At least 30% of its area is visible for at least one second for large display ads (over 242,500 pixels)
• At least 50% of the video ad area is visible on the screen while video plays for at least two seconds

The digital advertising industry has improved viewability standards as a whole over the past few years. The global digital video viewability rate in 2017 was just 59%, but this increased to 73% in 2022.

What are the benefits of using vCPM?

Focusing on actual views has a number of benefits for both advertisers and publishers.

Promotes transparency between advertisers and publishers

Clear guidelines on what ads are being seen by consumers are critical to accurately reporting the effectiveness of your ad spend. These guardrails hold publishers to a higher standard, preventing them from over-reporting ad performance, and ultimately boost ROI for advertisers.

Shows advertisers the true value of an impression

With vCPM, advertisers can measure with confidence, knowing that they’re paying for ads that are actually viewed. For example, if an ad performs poorly, you can attribute it to targeting or the creatives — and improve these accordingly — rather than worrying about whether the ad is being seen at all.

Helps publishers improve the user experience

Measuring vCPM gives publishers a better understanding of how their audience is engaging with their content. With this insight, they can improve the user experience — resulting in improved fill rates, increased competition, and ultimately higher revenue.

How to calculate vCPM

Now that you understand when and why to use vCPM, let’s examine the numbers in a real-world scenario.

The vCPM formula is as follows:

vCPM = Budget / [(Total ad impressions x % of Ad viewability) / 1,000]

Let’s say you’re advertising your tennis scorekeeping app, Tenniscore, with the following variables:

• Ad campaign budget: $100,000
• Percentage of ad viewability: 75%
• CPM: $1.50
• Total impressions: 10,000,000
• Viewable impressions: 7,500,000
• Maximum vCPM bid: $5.00

Now let’s plug the variables back into the vCPM formula.

So, the cost per 1,000 viewable impressions for Tenniscore is 13 cents.

vCPM vs. CPM — what’s the difference?

CPM is the cost per thousand impressions of an ad, and vCPM is the cost per thousand viewable impressions. CPM measures the number of times an ad loads on a website, without any guarantees that the ad will be viewed. Because this represents a larger gamble, it’s often much cheaper than vCPM.

The benefit of CPM for publishers is that they receive a guaranteed payment for an ad to load without having to measure the viewability percentage of every ad inventory slot. However, this comes at the cost of the ad inventory being less desirable, meaning lower revenue potential and fill rates.

What are the main causes of low ad viewability?

Before we discuss the different strategies for improving viewability, let’s consider the biggest pitfalls that cause low viewability rates.

• Page length: Shorter pages with less content load faster, and have higher viewability. Single-fold pages (that don’t require scrolling) are especially fast.
• Ad placement: Most ads placed below the fold (so users have to scroll to see them) have lower viewability rates.
• User experience: Websites with poor overall user experience cause users to click away and not engage with the content.
• Internet speed: Slow internet connections won’t load bloated websites and ads.

How do you improve ad vCPM?

Now that we understand the primary causes of low visibility, let’s discuss ways to improve ad vCPMs.

1. Experiment with different ad placements and formats

Remember to conduct A/B tests on various ad sizes, positions, and formats, including video and static ads. Even if you have space for a huge banner across the top of your website, it might not be the highest-performing ad unit. In fact, according to Google, the most viewable position is at the bottom of the page, right above the fold.

Some publishers claim that the most viewable ad sizes are vertical ads, which stay in view longer than horizontal ones. Banners that stick to the top or bottom of the page can also be effective. 

2. Improve ad load speed

If you load all your ads at once, regardless of whether they’re placed above or below the fold, there’s a chance that a user might not see any ads at all. Instead, implement lazy loading, which prioritizes loading the ads above the fold, and then the ads below it. Another tip is to reduce the number of ad calls from one server to another. This reduces page latency and improves the overall user experience.

3. Use heatmap tools

The best way to understand your audience’s behavior on your website is to implement heatmap tools that can measure where user eyeballs are going.

This data provides an objective view of where to place ads and how new ad placements may be affecting your user experience.

4. Find balance

While it may be tempting for publishers to squeeze as many ads as possible above the fold of the website, consider the user-experience trade-off. A website with a great user experience and engaging content will yield high ad viewability and keep users coming back.

Key takeaways

• Viewable CPM, or vCPM, is the cost per thousand actual views of a digital ad, rather than just page impressions.
• vCPM enhances transparency for advertisers and sets higher standards for publishers. Stricter measurement guidelines give advertisers a better understanding of ad performance, and enable publishers to charge more for desirable inventory.
• Low viewability can be due to wrong ad sizes or placements, slow load times, long page length, and poor user experiences.
• To improve viewability, publishers should optimize their ad sizes and formats, improve load times, and focus on improving the overall user experience.

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What is cost per sale?

Cost per sale (CPS) measures the amount spent to generate a single sale from an advertising campaign. It’s calculated by dividing the total cost of the campaign by the number of sales it produces.

This metric helps you understand the direct financial impact of your marketing campaigns in relation to revenue generation. In other words, you get a straightforward look at how efficiently your ad spend translates into revenue.

How to calculate CPS

Use this formula to calculate CPS:

Cost per sale = Total marketing and sales cost / Number of sales

“Total marketing and sales cost” covers everything you spend on those areas, including things like ad spend, salaries for your sales team, and costs for marketing materials. Meanwhile, “number of sales” is simply the total sales you make from these efforts within the same timeframe.

Suppose you run a mobile marketing campaign with the following details:

• Total marketing and sales cost: $5,000
• Number of sales: 200

Using the CPS formula:

This shows that your business spent $25 to acquire each sale. You can use this info to understand the cost-effectiveness of your campaign and set a baseline for optimizing future marketing strategies.

Advantages of using CPS as a metric

CPS is a straightforward metric that neatly connects spend with revenue. That brings a number of advantages for your business:

• Performance-based cost efficiency: Whereas other metrics like cost per impression or cost per click are useful when you’re building awareness, CPS focuses specifically on closing the deal. By tying your budget to actual sales in this way, you can not only justify ad spend to stakeholders but also take practical steps to optimize performance — by lowering your costs, increasing sales, or a combination.

• Enhanced targeting and personalization: CPS encourages marketers to get laser-focused with their advertising strategies. When you focus on specific audience segments most likely to convert, you make your campaigns more effective. Advanced data analytics and user behavior insights play a crucial role here — use them to identify the most promising customer profiles.

• Tailored customer experiences: CPS also pushes you to tailor ads to individual interests and behaviors, creating a more engaging, relevant experience that drives sales, satisfaction, and loyalty. For instance, a subscription-based app might advertise to users who have shown interest in similar services, highlighting features that these users value most.

• Higher ROI: CPS inherently ties your marketing spend to sales revenue, giving a clear measure of marketing ROI. This performance-based model ensures every marketing dollar is spent efficiently, with a direct impact on revenue generation. Plus, you can easily calculate the profitability of campaigns by comparing CPS with the average revenue per sale.

• Comprehensive campaign performance insights: CPS serves as a transparent and quantifiable metric for assessing campaign performance. You can use it to pinpoint which campaigns are cost-effective and which aren’t, helping you make data-driven decisions and optimize marketing strategies.
  
  Imagine you run multiple campaigns across different channels (like social media, search engines, and in-app ads). Comparing the CPS of each campaign, you can identify which channel delivers the best results and allocate more budget accordingly.

• Iterative campaign optimization and improvement: By regularly monitoring and analyzing CPS, you can experiment with different ad creatives, targeting approaches, and messaging to find the best combinations. This iterative process helps you steadily lower CPS and improve campaign efficiency.

• Enhanced performance accountability and collaboration: With costs directly tied to sales, it’s easier to evaluate the performance of individual campaigns and initiatives. This creates a sense of accountability and builds a connection between sales and marketing teams, ensuring all app marketing efforts focus on achieving measurable sales targets.

What are the factors that can influence CPS?

CPS in mobile app marketing hinges on various factors that shape the efficiency and success of your campaigns. Knowing these factors helps you fine-tune your strategy for best results:

1 — Target audience

Identifying and reaching the right audience is crucial for lowering CPS, because it boosts conversion rates. For mobile app marketers, this means zeroing in on users most likely to buy in-app items or subscribe to premium services. For instance, you can target users based on their app usage patterns or past purchasing behavior to execute more effective campaigns.

2 — Marketing channels

Channels like social media ads, in-app ads, and search ads — each with unique advantages and cost structures — yield different results. For mobile apps, user acquisition through social media platforms like Facebook or Instagram might be more cost-effective than other channels. Picking the most efficient channels for your audience can lower CPS.

3 — Lead conversion time and funnel efficiency

The efficiency of your conversion funnel is crucial. You need to ensure the journey from app discovery to purchase is seamless. Reducing friction points, such as complex registration processes or slow load times, can speed up conversions and lower CPS. Optimizing your app UX and streamlining the purchasing process can also make a positive difference.

4 — Implementing conversion rate optimization (CRO)

CRO involves tweaking various elements of your app to boost the percentage of users who make a purchase. This can include A/B testing different versions of app screens, optimizing onboarding flows, and enhancing call-to-action buttons. Effective CRO leads to higher conversion rates, meaning more sales from the same ad spend, which helps trim down your CPS.

5 — Customer service and support costs

While not always a direct factor in mobile app marketing, customer service quality can still impact CPS. Efficient customer service that resolves issues quickly and enhances user satisfaction can reduce churn rates and improve overall user retention. And it typically costs less to sell to existing customers than to acquire new ones. 

6 — Dissatisfied customers (returns and refunds)

High rates of refunds and returns can negatively impact CPS. For mobile apps, this might mean users uninstalling the app or canceling subscriptions shortly after purchase. Ensuring high app quality, delivering on promises made in marketing materials, and providing excellent customer support can minimize dissatisfaction and reduce the number of refunds, thus lowering CPS.

CPS vs other metrics

CPS is great for judging how well your marketing campaigns are doing, especially in mobile app marketing. However, it’s best not to rely on it alone.

Comparing CPS with other key metrics helps you get a fuller, more accurate picture of your campaign’s performance and overall business health. Let’s understand how CPS stacks up against other important app metrics:

Cost per click (CPC)

Definition: CPC measures the cost incurred for each click on your ad.

CPS focuses on the cost of an actual sale, while CPC only tracks the cost of attracting a click, regardless of whether that click leads to a sale. Additionally, CPS gives you a direct link between ad spend and revenue, whereas CPC is more about driving traffic to your app or site.

Cost per mille (CPM)

Definition: CPM measures the cost per thousand impressions of your ad.

CPM focuses on the exposure of an ad, regardless of clicks or sales, which makes it useful for brand awareness campaigns. On the other hand, CPS is concerned with the actual sales generated from the ad impressions, making it more suitable for performance-driven campaigns.

Cost per lead (CPL)

Definition: CPL measures the cost incurred to acquire a lead.

CPL is about the cost of acquiring potential customers (leads), while CPS measures the cost to acquire paying customers. Consequently, CPL comes in handy for campaigns aimed at generating interest or capturing contact info, and CPS serves as the final step in the conversion funnel.

Lifetime value (LTV)

Definition: LTV estimates the total revenue a customer generates over their relationship with your business.

CPS measures the cost of acquiring a sale, but LTV provides insight into the long-term value of that customer. A low CPS is great, but it’s more meaningful when paired with a high LTV, indicating that those customers will bring substantial long-term value.

Customer acquisition cost (CAC)

Definition: CAC measures the total cost of acquiring a new customer, including marketing and sales expenses.

CAC is broader than CPS, covering all costs associated with customer acquisition — not just marketing expenses. CPS is part of CAC, focusing specifically on the direct cost of generating sales through advertising.

Top Tip: Use our metrics comparison tool to compare app marketing metrics and take informed marketing decisions.

How to reduce your CPS

Lowering your CPS is all about using your budget as efficiently as possible —  ensuring all your marketing efforts are geared towards maximizing conversions (sales), and nothing is wasted. Follow these best practices to keep your campaigns on track:

1 — Refine target audience

Zero in on your target audience to make sure your ads reach those most likely to convert. Use demographic data, user behavior, and psychographic profiles to tailor your ads to specific segments.

For instance, a fitness app could focus on users who’ve downloaded health-related apps or shown interest in fitness content on social media. By not not wasting ad spend on uninterested users, you’ll lower your CPS.

2 — Improve the performance of ads

Create compelling ad creatives and copy that resonate with your audience. Think: high-quality visuals, clear messaging, and strong CTAs. These high-impact elements can significantly boost engagement and conversions. A tool like AppsFlyer’s AI-powered Creative Optimization can help you find the winning formula.

3 — Focus on lead generation

Lead generation builds a pipeline of potential customers you can nurture over time. Capture leads through free trials, email sign-ups, or pre-registration to engage with users and convert them into paying customers eventually.

4 — Leverage search traffic

Search traffic is a goldmine because users actively looking for solutions are more likely to convert. With search engine advertising (like Google Ads), you can capture this intent-driven audience, leading to higher conversion rates and lower CPS.

For example, if you have a language-learning app, target keywords like “best app to learn Spanish.” Your ads will be shown to users already interested in finding a language-learning solution, boosting conversions.

5 — Use negative keywords

Negative keywords keep your ads from appearing in irrelevant searches, saving you money. By excluding terms that don’t align with your app, your budget only goes toward high-intent searches.

For example, using negative keywords like “free” or “download” helps avoid showing ads to users looking for free options, focusing instead on those ready to purchase.

6 — Use data analytics to test and optimize ads

Use data analytics to pinpoint which aspects of your campaigns are working and which need improvement. Creative testing and optimizing ad elements can further reduce CPS by enhancing ad effectiveness. Test various ad headlines and images to find the highest conversion rate, using tools like Google Analytics and Facebook Ads Manager to track performance.

7 — Optimize landing pages

A seamless user experience on your landing page can increase conversion rates and reduce CPS.

Make sure your mobile app landing pages are optimized for conversions, with fast load times, clear and compelling content, easy navigation, and a straightforward call to action. For example, a finance app should highlight key benefits, include user testimonials, and have a prominent download button to encourage immediate action.

8 — Use email marketing to nurture leads

Use email marketing to keep potential customers engaged, by sending personalized email campaigns that guide leads down the sales funnel.

For instance, if you have a music streaming app, target users who signed up for a free trial but haven’t yet subscribed with updates on new features, exclusive content, or limited-time offers to boost conversion rates.

Key takeaways

• Cost per sale (CPS) shows how much you spend to make a single sale from an ad campaign. You find it by dividing total marketing and sales costs by the number of sales.
• CPS helps you understand how effectively your ad spend translates into sales, which is key for measuring the profitability and success of your campaigns.
• In mobile app marketing, several factors can affect your CPS. These include honing your target audience, picking the right marketing channels, optimizing your conversion funnel, using conversion rate optimization (CRO), and cutting down on customer service costs and refunds. Getting these elements right will improve conversion rates and reduce CPS.
• While CPS is important, it works best when used with other metrics like CPC, CPM, CPL, LTV, and Customer Acquisition Cost (CAC). Together, they give a full picture of your campaign’s performance.
• Lowering your CPS is about using your budget efficiently to maximize conversions. Strategies include focusing on users most likely to convert, using keywords effectively, optimizing landing pages, and continually testing and optimizing your ads.

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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.

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