Adjust Dimensions & Metrics: Complete Reference Guide 2026

Adjust is one of the leading mobile measurement platforms (MMPs), providing app marketers with attribution data, audience analytics, and fraud prevention. Whether you're pulling data through the Adjust API, building dashboards in Datascape, or exporting raw callback data, understanding every available dimension and metric is essential for accurate mobile marketing analysis.

This guide covers every dimension and metric available in Adjust as of 2026. We've organized them by category, included API field names, and added practical context on when and how each one matters. From basic install attribution through advanced fraud detection and SKAdNetwork decoding, this is your complete reference.

What Are Dimensions vs Metrics in Adjust?

In Adjust's reporting model, dimensions are the attributes that describe and segment your data — like app name, network, campaign, country, or OS version. They define how your reports are sliced and filtered.

Metrics are the quantitative measurements — installs, clicks, sessions, revenue, retention rates. They tell you what happened and how well your campaigns performed.

Adjust's reporting interface (Datascape) and API let you combine any dimensions with any metrics to build custom views. The raw data export (callbacks and CSV exports) provides event-level records with all dimension fields attached to each activity.

App Dimensions

App dimensions identify the application being tracked and its configuration within Adjust. These are the top-level identifiers that separate data across different apps in your Adjust account.

Tracker and Campaign Dimensions

Adjust uses a four-level tracker hierarchy to organize attribution data. Each level provides increasingly granular detail about where users came from. These dimensions are the backbone of all acquisition analysis in Adjust.

Network and Partner Dimensions

Network dimensions provide metadata about the advertising partners and their integration type with Adjust. These are especially useful when managing multiple ad networks and comparing partner performance across your portfolio.

Core Metrics: Installs, Clicks, and Impressions

These fundamental metrics measure the volume and efficiency of your user acquisition campaigns. They form the basis of all performance analysis in Adjust — from initial ad exposure through successful app installation.

Conversion Metrics: Events, Sessions, and Revenue

Conversion metrics track what happens after the install — in-app events, session activity, and revenue generation. These metrics connect acquisition spend to actual business outcomes and are essential for calculating ROI and optimizing toward high-value users.

Retention Metrics

Retention metrics measure how well you keep users engaged over time. Adjust provides cohort-based retention data that groups users by their install date and tracks how many return on each subsequent day. These metrics are critical for evaluating user quality across acquisition channels.

How to read retention data: If your Day 1 retention is 35%, it means 35% of users who installed on a given date opened the app exactly one day later. Industry benchmarks vary widely: gaming apps typically see 25-35% Day 1 retention while utility apps may see 15-25%. Compare retention by network and campaign to identify which acquisition sources bring the highest-quality users.

Cohort Metrics

Cohort metrics group users by their install date and track cumulative behavior over time. Unlike standard retention (which measures if users returned on a specific day), cohort metrics aggregate totals within time windows, giving you a longer-term view of user value development.

Cohort analysis is particularly powerful for comparing acquisition channels on equal footing. A campaign with a higher CPI but better Day 30 cohort revenue may ultimately deliver superior ROI compared to a cheaper campaign that acquires low-value users. Always evaluate campaigns using cohort ROAS at 7, 14, and 30 days rather than relying solely on install volume.

SKAdNetwork Dimensions

Apple's SKAdNetwork (SKAN) provides privacy-preserving install attribution for iOS campaigns. Adjust decodes SKAN postbacks into meaningful dimensions and metrics. These fields are essential for iOS campaign measurement in a post-ATT world where device-level attribution is limited.

Fraud Prevention Metrics

Adjust's Fraud Prevention Suite detects and rejects invalid traffic in real time. These metrics quantify the volume and type of fraudulent activity across your campaigns, helping you protect ad spend and maintain clean attribution data.

Interpreting fraud metrics: A healthy fraud rate is below 10% across your portfolio. Rates above 20% for specific networks or campaigns indicate serious issues that require investigation. Click injection is the most common mobile ad fraud type — it doesn't generate fake installs but steals attribution from organic or other paid sources. SDK spoofing is more damaging because it creates entirely fake installs that never represent real users.

Cost Metrics

Cost metrics connect your acquisition spending to performance outcomes, enabling ROI analysis and budget allocation decisions. Adjust ingests cost data from integrated ad networks and combines it with attribution data to calculate efficiency metrics.

How to Use Adjust Metrics for Campaign Optimization

Having access to all these metrics is powerful, but knowing which ones to prioritize at each stage of optimization determines your effectiveness as a mobile marketer. Here's a practical framework for selecting the right Adjust metrics for different objectives.

For user acquisition scaling

Focus on CPI, install volume, and CVR to understand acquisition efficiency. Compare CPI across networks and campaigns, but never optimize for CPI alone — a low CPI with poor retention wastes budget. Always pair CPI analysis with Day 7 retention to ensure you're acquiring quality users, not just cheap installs.

For retention optimization

Track Day 1, Day 7, and Day 30 retention rates broken down by network and campaign. Identify which acquisition sources produce the stickiest users. If Day 1 retention is strong but Day 7 drops sharply, the app's onboarding may succeed but the core experience fails to retain. If Day 1 is already low, the targeting or creative messaging may be attracting the wrong users.

For revenue optimization

Use cohort ROAS at Day 7, Day 14, and Day 30 to compare the true revenue return from different campaigns. Combine with ARPPU and payback period to understand how quickly campaigns become profitable. Campaigns with a payback period under 14 days are typically safe to scale, while those exceeding 30 days require careful evaluation of long-term LTV projections.

For fraud monitoring

Review rejected installs and fraud rate by network weekly. Set up alerts for sudden spikes in fraud metrics. Pay special attention to click injection rates on Android campaigns and SDK spoofing across all platforms. If a network consistently shows fraud rates above 15%, escalate to the partner or consider pausing the source entirely.

For iOS campaign measurement

With limited device-level data on iOS, rely on SKAN conversion values, decoded revenue, and coarse conversion values to understand campaign performance. Configure your conversion value schema carefully to encode the metrics that matter most — typically a combination of revenue ranges and key event completions. Monitor null conversion value rates to understand how much data you are losing to Apple's privacy thresholds.

Key Differences Between Adjust and Other MMPs

While Adjust shares many concepts with other mobile measurement platforms like AppsFlyer, Branch, and Singular, there are important differences in terminology and data modeling that affect how you work with the data.

Tracker hierarchy vs. campaign hierarchy

Adjust uses a unique tracker-based hierarchy (Network > Campaign > Adgroup > Creative) tied to tracker tokens, while other MMPs typically use a campaign-based hierarchy pulled directly from ad network APIs. This means Adjust's attribution structure is defined at the tracker link level rather than inherited from the ad network's campaign structure.

Session definition

Adjust defines a new session when the user opens the app after the subsession interval (default 30 minutes of inactivity). This differs from some analytics platforms that use 30-minute inactivity windows calculated differently. When comparing session metrics across platforms, ensure you understand each platform's session definition.

Reattribution window

Adjust's default inactivity window for reattributions is 7 days — meaning a user must be inactive for 7 days before a new paid touchpoint can trigger a reattribution. This is configurable per app. Other MMPs may use different default windows, making direct reattribution comparisons unreliable unless windows are aligned.

Common Mistakes When Analyzing Adjust Data

Even experienced mobile marketers make these mistakes when working with Adjust metrics. Avoiding them will improve the accuracy of your analysis and optimization decisions.

1. Comparing CPI without retention context

A network delivering installs at $0.50 CPI looks better than one at $2.00 CPI — until you check retention. If the cheap installs have 10% Day 7 retention versus 40% for the expensive ones, the $2.00 source delivers far more value. Always pair cost metrics with retention and cohort revenue data before making budget allocation decisions.

2. Ignoring fraud metrics

If you don't regularly review rejected installs and fraud rates by source, you're likely paying for fraudulent traffic without knowing it. Even with Adjust's automatic rejection, some fraud can evade detection. Cross-reference abnormally high install volumes or unusually low post-install event rates as additional fraud indicators.

3. Mixing attributed and organic data

Adjust tracks both attributed (paid) and organic installs. When analyzing campaign performance, always filter to attributed installs only. Including organic installs in network-level reports inflates apparent performance and distorts CPI and ROAS calculations.

4. Misinterpreting SKAdNetwork conversion values

SKAN conversion values are encoded — a value of 42 does not mean 42 purchases or $42 in revenue. The meaning depends entirely on your configured conversion value schema. Always decode values using your schema mapping before drawing conclusions. Also account for null conversion values, which represent users who didn't meet Apple's privacy threshold and provide no post-install signal.

5. Using daily retention instead of rolling retention for LTV models

Daily retention (exact day return) understates ongoing engagement because many active users skip specific days. Rolling retention (active on Day N or later) gives a more accurate picture of how many users remain active over time. For LTV projections, rolling retention curves produce more reliable forecasts than exact-day retention rates.

6. Not accounting for attribution windows

Adjust's default click attribution window is 7 days and impression window is 24 hours. If you change these windows, historical comparisons become invalid. A campaign measured with a 14-day click window will naturally show more installs than the same campaign measured with a 7-day window. Document your attribution settings and keep them consistent when comparing time periods.