Hi! Sasha’s here, and I’m the Marketing Manager at Guru Apps. Over the past four years in marketing, I have worked with various businesses — from European e-commerce projects to Ukrainian FinTech startups. Each had its own acquisition strategy through Meta, Google, TikTok, and Twitter. Currently, I focus on developing new channels for Cleaner Guru, an app that attracts users worldwide via performance marketing and has over 20,000 active users.
If you are just starting out to work with multiple channels or trying to assess their effectiveness, this article is for you. By the end of it, you will learn:
The challenges and advantages of multichannel marketing;
How attribution can distort data and complicate analysis;
How to properly evaluate channel effectiveness.
What Is Multichannel Marketing?
Multichannel marketing is a user acquisition model that utilizes various channels, platforms, and interaction points with the goal of scaling business by reaching new audiences, increasing coverage, and strengthening brand presence.
Most businesses today operate through multiple channels. Even if you only invest in Meta Ads, users might find your product through organic Google searches, TikTok content, or by spotting the app on a friend’s phone. Each of these channels can be analyzed and attributed to business results.
Why Is This Important?
Understanding the effectiveness of each channel is key to scaling and profitability. When you know which channels perform well and which do not, it becomes much easier to optimize spending and reallocate the budget. But...
...what happens if a user interacts with multiple channels before converting? For example, they might see an ad for your product on Instagram but take no action. Three days later, they see you again on TikTok. Then, twenty days later, they watch a YouTube video about healthy eating and realize they need a calorie tracker. So, they search the App Store, recognize your app’s logo among many others, download it, and subscribe. Which channel should get credit for this conversion?
Most users follow a journey before taking a desired action. As shown in the example above, evaluating a channel’s impact on conversion is complex. If we consider a more realistic scenario where a business invests in multiple advertising platforms (Google, TikTok, Meta, etc.) while also growing organically on social media and search engines (ASO, SEO), the task becomes even more challenging for marketers.
Ad platforms provide their own attribution data, but it is difficult to treat them as a Single Source of Truth (SSOT). Without integration with other platforms, each ad service attributes the conversion to itself if the user interacts with it within the attribution window. The result? A single real action, but multiple recorded conversions.
*Attribution window: The period in which a user’s action (such as clicking an ad or installing an app) can be "credited" to a specific ad campaign.
So, performance analysis is one of the main challenges of multichannel marketing. To make better decisions, I suggest looking at the blockers platforms have in data interpretation and the best practices for working under such conditions.
Let's start with how attribution works, why it is needed in the first place, and what types exist. This will help us better understand why relying solely on platform data for marketing optimization is not enough.
Attribution: Pros and Cons
Attribution is the process of determining which marketing assets contributed to a user's actions. In other words, it “assigns” results to a specific channel, campaign, ad, etc.
Why Do We Need Attribution?
Conversion Optimization: Without attribution, ad platforms wouldn’t be able to target ads effectively or allocate budgets based on users who have already converted.
User Journey Tracking: Attribution tracks user interactions with a business and maps out their engagement sequence.
Effectiveness Analysis: Attribution allows comparison of marketing assets, but the results depend heavily on the chosen attribution model.
Types of Attribution Models
There are several key attribution models, each with its own advantages and disadvantages:
Last Interaction: The last channel gets 100% of the credit for the conversion.
First Interaction: The first touchpoint with the user receives all the credit.
Linear: Credit is distributed evenly among all interaction points.
Position-Based: The first and last touchpoints each get 40%, while the remaining 20% is shared among other interactions.
Time Decay: More weight is given to channels closer to the conversion event.
Algorithmic: Uses machine learning to analyze user behavior and proportionally distribute credit across touchpoints.
The same data period can be interpreted differently depending on the chosen model. For example, if a user:
Clicks an ad on TikTok but does not convert;
Clicks an ad on Google and converts.
Using First Interaction, TikTok gets all the credit. Using Last Interaction, Google is responsible for the conversion. In today’s marketing landscape, most businesses rely on Last Interaction, which works well for short user journeys. However, the fairness of this approach remains debatable.
Algorithmic models, available in some ad platforms and external tracking tools, offer a more data-driven approach. However, even these cannot fully resolve attribution questions. Attribution models should be treated as indicators, not definitive answers for budget allocation.
Attribution Limitations
For accurate analysis, it is important to recognize key attribution blockers:
Attribution Windows: Each platform sets its own conversion tracking period, which can be too short, too long, or overlap with others, leading to data loss or duplicated conversions.
iOS 14.5 Privacy Updates: Apple’s App Tracking Transparency (ATT) limits access to user data, making it harder to track actions without user consent.
Cookie Restrictions: New browser privacy policies reduce reliance on cookies, forcing businesses to find alternative tracking solutions.
Ad Blockers: These tools prevent ads from being displayed and tracked, reducing attribution accuracy.
Non-Digital Interactions: Most attribution models focus on online interactions, but offline touchpoints (word-of-mouth, physical store visits) remain difficult to track.
Understanding these challenges is crucial for developing a robust attribution strategy and making informed marketing decisions.
In summary, attribution is a useful tool that allows businesses to track user interactions with marketing assets and compare their performance. Additionally, this technology helps identify the target audience and optimize advertising budgets more effectively.
However, it is also important to consider the specific characteristics of attribution. When choosing an attribution model, it is crucial to understand that each approach has its strengths and weaknesses, and data interpretation can vary depending on the selected model. Moreover, this technology has several blockers that should be recognized and taken into account during data analysis.
How to Analyze Marketing in a Multichannel Environment?
User Journey Research
Understanding the paths your users take provides significantly more qualitative "levers of influence" than relying solely on individual touchpoints that attribution models prioritize. Furthermore, studying user journeys helps reveal how different channels reinforce each other to achieve the final goal.Understanding Channel Functions
To properly utilize the "levers of influence" mentioned above, it is essential to comprehend how each channel in the user journey operates. For example, this understanding can explain why an increased budget on Meta has resulted in a rise in organic downloads or why launching YouTube ads has improved email open rates. The answer lies in the "superpower" of these platforms in generating awareness. Users from Meta may not download an app immediately after seeing an ad but remember it and search for it later in the App Store. Similarly, YouTube helps create brand awareness, leading to higher engagement with emails from a familiar sender.Focusing on Overall Metrics
The incompleteness of attribution data, multiple interpretation options, and bias from advertising platforms highlight that businesses should primarily focus on internal metrics such as CPA, LTV, ROI, etc. No matter how high the data quality or how advanced the AI providing it is, you need your own SSOT (Single Source of Truth). This SSOT, combined with well-constructed User Journeys, should guide decision-making for scaling and optimization. In this context, attribution serves as an additional indicator that can confirm or refute hypotheses.Testing and Dynamic Evaluation
To analyze the effectiveness of a channel or assess the impact of changes (e.g., budget increase), a dynamic evaluation approach can be applied. Analysis should include both attributed data and general performance indicators. Attributed data will reveal internal platform changes, while internal metrics will ensure these changes align with overall results. For example, if you hypothesize that Meta Ads yield a higher return, you may increase the budget. Over time, platform metrics may remain unchanged, but overall profitability increases. Additional analysis of attributed data confirms the higher return, validating the hypothesis.External Analysis Tools and MMM
One of the approaches to evaluating channel effectiveness is using external services. Google Analytics, AppsFlyer, Adjust, Amplitude, and Mixpanel are independent of advertising platforms and help consolidate data from multiple sources for a more objective assessment. Combined with the previously mentioned points, these tools can significantly improve decision-making within your team. The next level of analytical system development involves implementing Marketing Mix Modeling (MMM).
MMM is an analytical method that builds statistical models by considering all possible factors, such as profit metrics, channel spending over time, product seasonality, and marketing/product metrics. These models illustrate how different channels influence each other and predict the long-term impact of marketing changes. This approach can significantly enhance understanding of channel effectiveness and help optimize budgets more efficiently.
What Does This All Mean?
As you can see, multichannel marketing presents both new opportunities and challenges, particularly in terms of analysis and performance evaluation. Each conversion path is unique and involves multiple channels. In this context, attribution, while a useful tool, cannot be the sole source of truth due to data limitations, platform biases, and complex user journeys. However, a proper understanding of user journeys and channel functions, focusing on overall business metrics, and a systematic approach to testing can greatly simplify decision-making for businesses.
For deeper analysis, using external analytical tools that integrate data from multiple sources is advisable, as well as considering the implementation of MMM to evaluate long-term and short-term marketing effects. Nevertheless, it is important to remember that no tool or model can fully replace a strategic approach and having your own SSOT that aligns with your business specifics and overall objectives.
Ultimately, multichannel marketing is not just a challenge, but also a significant advantage. When understood and utilized correctly, it unlocks enormous potential for business growth and expansion.
