Mobile Marketing Analytics

Acquisition metrics: Measuring reach and cost-efficiency.

Acquisition metrics focus on how users are brought into the mobile ecosystem, whether it’s downloading an app or visiting a mobile website, and the costs associated with these activities.

• Installs/downloads: For mobile applications, the number of installs or downloads serves as the initial entry point metric. While fundamental, it requires context. A high volume of installs does not necessarily equate to success if these users are low quality, meaning they churn quickly or never engage meaningfully with the app.

• Cost per install (CPI) and cost per acquisition (CPA): These metrics quantify the cost-efficiency of user acquisition campaigns. CPI represents the average cost incurred to generate one new app install, calculated by dividing total ad spend by the number of installs attributed to that spend. CPA broadens this concept to measure the cost associated with a specific desired action beyond just an install, such as user registration, completion of an onboarding sequence, or making an initial purchase. Both CPI and CPA are vital for evaluating the financial performance of different advertising channels and campaigns.

• Customer acquisition cost (CAC): CAC provides a more holistic view of acquisition costs by focusing specifically on acquiring a paying customer. It is calculated by dividing the total sales and marketing costs (including salaries, overheads, ad spend, and so on) over a given period by the number of new paying customers acquired during that period. CAC is typically higher than CPI or CPA because it accounts for the fact that not all acquired users (installs or initial actions) will eventually monetize. It directly relates acquisition spending to revenue generation potential.

 
Engagement metrics that measure user interaction.

Once users are acquired, engagement metrics measure how actively and frequently they interact with the mobile app or website. High engagement is often a precursor to monetization and long-term retention.

• Daily active users (DAU) and monthly active users (MAU): DAU represents the number of unique users who engage with the app on a given day, while MAU measures the unique users over a 30-day period. These metrics indicate the overall size of the active user base and the app’s general “stickiness.” The ratio of DAU to MAU (often expressed as a percentage) provides insight into the frequency of engagement — a higher ratio suggests users return more regularly within a month.

• Session length and frequency: Session length measures the average duration users spend within the app during a single session. Session frequency tracks how often users initiate new sessions over a specific timeframe (for example, per day, per week). Together, these metrics gauge the depth and regularity of user interaction. Longer sessions and higher frequency typically correlate with greater user investment in the app.

• Retention rate: This critical metric measures the percentage of users who return to the app after a specific period following their first use (for example, day 1 retention, day 7 retention, or day 30 retention). High retention rates are essential for sustainable growth, indicating that the app provides ongoing value and keeps users coming back. It is a strong indicator of product-market fit and long-term viability.

• Churn rate: Churn rate is the inverse of retention, representing the percentage of users who stop using the app over a defined period. A high churn rate signals problems, potentially related to the user experience, lack of perceived value, technical issues, or ineffective onboarding. Minimizing churn is crucial for maintaining a healthy user base and maximizing lifetime value.

Strong engagement metrics serve as essential prerequisites for achieving substantial customer lifetime value (CLV). They function as leading indicators of future monetization potential and overall business health. Poor engagement metrics inevitably forecast low CLV, irrespective of how many users were initially acquired or how cheaply. Consequently, strategic efforts focused on improving user engagement and retention, even by seemingly small percentages, can yield substantial positive impacts on long-term revenue and profitability.

Monetization metrics to measure revenue.

Monetization metrics directly track the financial performance of the mobile marketing efforts and the app itself, assessing how effectively the user base is converted into revenue.

• Average revenue per user (ARPU): ARPU is calculated by dividing the total revenue generated over a specific period by the total number of active users during that same period. It provides a broad measure of the average revenue contribution across the entire user base, including both paying and non-paying users.

• Average revenue per paying user (ARPPU): ARPPU focuses specifically on the revenue generated by users who make purchases or contribute financially. It is calculated by dividing the total revenue by the number of paying users. ARPPU offers a clearer view of the monetization effectiveness among the converting user segment and helps understand the spending patterns of valuable customers. Comparing ARPU and ARPPU can highlight the proportion of users driving revenue.

• Customer lifetime value (CLV): CLV (which is sometimes called LTV) is a predictive metric representing the total net revenue a business can expect to generate from a single average customer throughout their entire relationship with the app or brand. Calculating CLV typically involves factoring in average purchase value, purchase frequency, and customer lifespan (often derived from retention/churn rates). It is arguably one of the most crucial metrics for sustainable growth, as it shifts focus from short-term gains to long-term customer relationships and profitability. A successful business model generally requires LTV to be significantly greater than CAC.

• Return on ad spend (ROAS): ROAS measures the gross revenue generated for every dollar spent on advertising. It is calculated by dividing the revenue directly attributed to an advertising campaign by the cost of that campaign. ROAS provides a direct measure of the profitability of specific advertising efforts and is commonly used for tactical campaign optimization.

Common attribution models.

Several models exist for assigning credit, and the choice of model significantly impacts how channel performance is perceived.

• First-touch attribution: Assigns 100% credit to the very first marketing touchpoint a user interacted with before converting.

• Last-touch attribution: Assigns 100% credit to the final touchpoint before conversion. This is historically common due to simplicity but often overlooks earlier influential interactions.

• Multi-touch attribution: Attempts to distribute credit across multiple touchpoints in the user journey. Common variations include:

• Linear: Distributes credit equally across all touchpoints.

• Time decay: Gives more credit to touchpoints closer in time to the conversion.

• U-shaped (position-based): Assigns higher credit to the first and last touchpoints, distributing the remainder among the middle interactions.

The selection of an attribution model should ideally reflect the typical customer journey and business objectives, but practical limitations and data availability often influence the choice.

Mobile attribution challenges.

Attribution in the mobile environment faces several unique and significant challenges.

• View-through attribution (VTA): This refers to attributing conversions that happen after a user is exposed to an ad (an impression) but does not click on it. While ad views can influence behavior, measuring this link definitively and avoiding false correlations is technically challenging and often relies on specific tracking methodologies employed by ad networks and MMPs. Determining the appropriate lookback window (how long after viewing an ad a conversion should be credited) is also complex.

• Cross-device tracking: Users frequently interact with brands across multiple devices — discovering on a mobile app, researching on a desktop website, and perhaps converting afterwards on a tablet. Linking these interactions back to a single user journey is notoriously difficult due to fragmented user identifiers and different tracking mechanisms across platforms (web cookies vs. mobile ad IDs). This fragmentation hinders a truly unified view of the customer path.

• Walled gardens: Major advertising platforms like Meta and Google operate as “walled gardens.” They possess extensive data on user activity within their own ecosystems and often utilize their own internal attribution systems. Data reported by these platforms may sometimes differ from data reported by third-party MMPs due to variations in attribution logic, lookback windows, or data access. Marketers must often reconcile these differing reports.

• Data privacy regulation: This represents the most profound challenge currently facing mobile attribution. Privacy regulations like GDPR and CCPA, coupled with platform-enforced policies, most notably Apple's App Tracking Transparency (ATT) framework, have fundamentally impacted data privacy and mobile analytics tracking. ATT requires apps to obtain explicit user consent before accessing the device’s unique Identifier for Advertisers (IDFA) for tracking purposes across different companies’ apps and websites. Since many users opt out, the availability of the IDFA for deterministic, user-level cross-app tracking has drastically decreased. In response, Apple introduced SKAdNetwork, a privacy-preserving attribution framework. SKAdNetwork provides attribution data directly from the operating system to ad networks, but it is aggregated (not user-level), delayed, and limited in the amount of campaign granularity and post-install event data it conveys. This shift significantly impacts the ability to perform precise, real-time, user-level attribution for iOS campaigns. Similar privacy considerations affect Android advertising identifiers (Google Advertising ID or GAID) and browser-based tracking.

Event tracking.

Event tracking involves configuring an analytics platform (such as Adobe Analytics) to record specific, predefined user actions or occurrences within the app. These events can be anything deemed important to track, ranging from simple interactions like button clicks, screen views, or scrolling activity, to more significant milestones like completing a tutorial, adding an item to a shopping cart, finishing a level in a game, sharing content, or successfully completing a purchase.

Implementing comprehensive event tracking provides granular data on how users interact with different features and content within the app. It allows marketers and product teams to measure feature adoption rates, understand user engagement patterns at a detailed level, track progress through key workflows, and pinpoint specific actions that correlate with retention or monetization. This granular data is the raw material for more sophisticated analyses like funnel and user flow analysis.

Funnel analysis.

Funnel analysis visualizes the sequential steps users take to complete a specific, desired goal or conversion path within the app. Examples include the user registration process, the ecommerce checkout flow, the onboarding tutorial sequence, or the steps required to complete a core action in a productivity app. The analysis measures the number of users who successfully complete each step and, crucially, calculates the conversion rate — or drop-off rate — between consecutive steps.

The primary value of funnel analysis is identifying bottlenecks and points of friction within critical user flows. By pinpointing exactly where users are abandoning a process (high drop-off rates between specific steps), teams can focus their optimization efforts on improving those stages. For example, if a checkout funnel shows a large drop-off between viewing the cart and initiating payment, it signals a potential issue with the cart screen’s design, clarity, or the available payment options.