Lokalise Analytics delivers precise insights that help you make informed decisions, turning translation uncertainties into clear choices. This allows you to save time and better allocate your resources.

With Analytics, you can:

To get started with Lokalise Analytics, click the icon in the left-hand menu:

The Usage dashboard helps you analyze translation activity, estimate future resource needs, and optimize localization workflows. Here, you can review historical data, including processed word usage across your projects.

To access the Usage dashboard, open the corresponding tab on the Analytics page.

The Processed words tab in the Usage dashboard provides insights into translated word usage and translation methods across your projects. Using the Processed words tab, you can analyze historical usage data, estimate future resource requirements, and better plan localization costs.

You can filter data using the Date and Target language filters. The Product filter allows you to view usage data for specific Lokalise products, such as Expert or Vantage.

For example, you can filter by key tag to analyze translation usage for specific content types, such as software strings, customer support articles, or marketing content like announcement emails. You can also use project tags to monitor translation usage across different products in your organization’s portfolio.

The Processed words overview table displays the number of processed words and translation methods used for each language, based on the selected date range and filters.

Columns:

The Processed words by month chart shows how many words were processed in your projects each month. This includes words added or updated in the base language, as well as target-language words modified through translation activity, imports, automations, or AI/MT actions.

Chart details:

The Translated processed words by month chart shows how processed words are distributed across different translation methods over time. Each bar represents the total number of processed words for a specific month, grouped by translation method.

Translation methods include:

Hover over a chart segment to see the processed word count and percentage for each translation method for the selected month.

This chart shows how base processed words are distributed across different translation methods.

Each bar represents the total number of processed words for a specific translation method and is divided into:

This helps you understand how content is entering or changing in your localization workflow across imports, API updates, human translation activity, and other sources.

The Edit rate by translation method chart helps you evaluate the quality of translations produced by different translation methods over time.

Edit rate shows the percentage of translations that were meaningfully changed after they were initially created. A lower edit rate usually indicates that fewer changes were needed, which may suggest higher initial translation quality.

Each month shows edit rates grouped by translation method, such as Human translation, Translation memory, Standard AI/MT, API, Import, and Other.

The edit rate is calculated as the ratio of meaningfully changed translations to the total number of keys translated using a specific method.

The Processed words by project and action table shows created, updated, and total processed words for each project. Use this table to compare usage across projects and understand where most content is being created or updated.

Columns:

The Processed words by project and method table shows processed words for each project, grouped by translation method.

Use this table to compare how different projects rely on different translation methods, such as human translation, translation memory, AI/MT, API, imports, offline work, or other sources.

Columns:

The Translation method breakdown section shows detailed metrics for each translation method, such as Translation memory, Human translation, Pro AI, and Standard AI/MT.

Each method has its own section with summary metrics and a monthly chart, making it easier to compare how different translation methods contribute to your localization workflow.

Metrics include:

Use these metrics to evaluate the performance of each translation method and understand which methods produce translations that require fewer follow-up edits.

You can customize this section to view metrics for Processed words or Translations using dropdown in the right corner:

The OTA tab in the Usage dashboard provides insights into Over-the-Air (OTA) activity across your projects and bundles.

Using the OTA tab, you can monitor OTA adoption, analyze traffic and download activity, track platform distribution, and identify usage trends across your localization workflows. The available reports help you understand how OTA is being used across projects, bundles, and platforms such as iOS, Android, and Flutter.

You can filter OTA analytics data using the following filters:

These filters can be combined to analyze OTA usage trends across specific projects, platforms, and time periods.

The Current drivers section provides a high-level overview of OTA activity for the most recent period within the selected date range.

The period depends on the selected Time grouping filter. For example, if Week is selected, this section shows activity for the most recent week in the selected date range.

It highlights overall traffic and download trends, along with the most active projects and bundles in your OTA workflow.

Metrics include:

Each metric also shows how the current period compares to the previous one, helping you identify usage growth or declines over time.

The section also includes:

The Traffic by platform chart shows the distribution of OTA traffic across supported platforms during the selected time period. Traffic is measured in gigabytes (GB) and represents the amount of OTA content delivered to client applications.

Use this chart to understand which platforms generate the most OTA traffic and how OTA usage is distributed across your mobile ecosystem, such as iOS, Android, or Flutter.

Hover over a chart segment to view the exact traffic volume and percentage for each platform.

The Downloads by platform chart shows the distribution of OTA bundle downloads across supported platforms during the selected time period.

Use this chart to understand which platforms generate the highest number of OTA downloads and how bundle adoption is distributed across your applications.

Hover over a chart segment to view the exact number and percentage of downloads for each platform.

The Traffic over time by platform chart shows OTA traffic trends across supported platforms over time. Traffic is measured in gigabytes (GB) and grouped by the selected time interval, such as day, week, or month.

Use this chart to monitor how OTA traffic changes over time for platforms such as iOS, Android, or Flutter. Traffic spikes may reflect application releases, increased user activity, or large OTA bundle rollouts.

Hover over a chart segment to view the exact traffic volume for each platform during a specific time period.

The Downloads over time by platform chart shows OTA download trends across supported platforms over time.

Downloads are grouped by the selected time interval, such as day, week, or month, making it easier to identify changes in OTA adoption and bundle delivery activity.

Use this chart to monitor how OTA downloads evolve across platforms such as iOS, Android, or Flutter. Changes in download volume may reflect release frequency, adoption growth, seasonal usage patterns, or rollout activity.

Hover over a chart segment to view the exact number of downloads for each platform during a specific time period.

The Most active project — All time section highlights the project that generated the highest amount of OTA traffic across the selected time period.

The report includes the total OTA traffic for the project, traffic distribution by platform, and the date of the most recent OTA activity.

Use this section to identify which projects generate the most OTA usage and monitor platform-level traffic distribution across your applications.

The Most active bundle — All time section highlights the OTA bundle that generated the highest amount of traffic during the selected time period.

The report includes the bundle name, bundle ID, platform, total OTA traffic, and the date of the most recent activity.

Use this section to identify which OTA bundles are most actively distributed across your applications and platforms.

The Projects overview table provides a detailed view of OTA traffic and activity metrics for each project.

Use this table to compare OTA usage across projects, monitor recent activity, and identify traffic trends over time.

Columns include:

Use the table to identify the most active projects, compare platform distribution, and monitor changes in OTA usage over time.

The Bundles overview table provides detailed OTA traffic and activity metrics for individual bundles.

Use this table to analyze bundle-level OTA usage, compare traffic across releases, and monitor activity trends over time.

Columns include:

Use this table to identify the most actively distributed bundles, compare release adoption, and monitor OTA traffic patterns across platforms and projects.

The Tasks dashboard helps you analyze task activity, monitor workload distribution, and track productivity across projects, contributors, and target languages.

Task metrics are displayed using trend visualizations that compare current values with the previous comparable period.

You can filter task analytics data using the following filters:

The Tasks section includes several high-level metrics that help you monitor task activity and delivery performance over time.

Metrics include:

The Tasks chart provides a monthly breakdown of task activity, including:

The Tasks time section shows how long it takes to complete tasks, from creation to completion. Tasks that are still in progress are excluded.

Use this section to analyze task duration, identify bottlenecks, improve workflow efficiency, and plan future localization work more accurately based on historical data. This helps answer questions such as: “How long does it usually take to complete tasks, and where can we improve?”

The displayed data includes:

You can customize this section and sections below using the dropdown in the right corner:

We recommend using the 80th percentile instead of average time when analyzing how long it takes to complete translations. This metric shows the time needed to complete 80% of tasks, providing a more reliable measure that accounts for past data while ignoring outliers that might skew average calculations.

In the example above, 80% of the tasks were completed in less than six days.

These charts offer detailed insights into the time spent on tasks.

This chart provides a detailed view of how long it takes to complete individual task languages.

It helps you identify task languages that were completed within the expected range, as well as unusually long-running items that may indicate bottlenecks or delays.

Chart details:

Each bubble represents one target language within a task. The higher the bubble, the longer it took to complete that task language. Larger bubbles represent task languages with a higher word count.

Hover over a bubble to see additional details, including the task name, task ID, source language, target language, time to close, and word count.

This chart shows the average time it took to close task target languages, grouped by task size.

Use this chart to compare whether smaller or larger tasks are completed faster within the selected time period.

Chart details:

The chart helps you identify trends in task completion time based on task size and compare how quickly different categories of work are completed over time.

This heatmap shows the average number of days it took to close task target languages, grouped by task size and target language.

Use this chart to compare how quickly different languages are completed across small, medium, and large task sizes.

Chart details:

Languages are sorted from the highest number of tasks to the lowest. Darker cells indicate longer average completion times. Values are displayed in days, where:

Use this chart to identify languages or task sizes that consistently require more time to complete.

This heatmap shows the average number of days it took contributors to close task target languages, grouped by task size.

Use this chart to compare how quickly contributors complete small, medium, and large task languages within the selected time period.

Chart details:

Contributors are sorted from the highest number of tasks to the lowest.

Darker cells indicate longer average completion times. Values are displayed in days, where:

Use this chart to identify contributors or task sizes that consistently require more or less time to complete.

The Task progress detail by contributor table provides an in-depth view of task performance, broken down by target language and contributor.

This report helps you analyze translation, review, and AI task performance across your localization workflows, including completion time, word counts, TM leverage, review activity, post-editing effort, and AI quality metrics.

Each row represents a unique combination of task + target language + contributor. For example, if a task includes two target languages and each language has two contributors, the report displays four rows.

Columns include:

This chart shows how task words were distributed across translation memory (TM) match levels at the moment tasks were created.

The values are displayed as percentages of the total task word count and include both completed and pending keys.

Use this chart to understand translation leverage and content complexity before work begins. Higher TM match percentages generally indicate that more content could potentially be reused from translation memory, while higher TM 0% values suggest a larger amount of new content requiring translation.

Chart details:

Use this chart to monitor how TM leverage changes over time and evaluate the expected translation effort for newly created tasks.

This chart shows the percentage of task words that had a translation memory (TM) match of 95% or higher at the moment tasks were created.

The calculation includes both completed and pending keys.

Use this chart to understand how much content could potentially be translated using high translation memory reuse. Higher percentages generally indicate lower expected translation effort and greater reuse of existing translations.

Chart details:

Use this chart to monitor how high-quality TM reuse changes over time and evaluate how much newly created work can benefit from existing translation memory content.

The Translation quality dashboard provides insights into translation review effort, editing activity, and workflow quality across your localization projects.

Use this section to analyze post-editing patterns, compare translation quality across languages and workflows, and identify areas that may require additional review effort or process improvements.

The available reports help you monitor:

These insights can help you identify workflows that require additional review, compare translation quality across methods and languages, detect inconsistent review patterns, spot tasks with New text or Retranslation activity, and monitor quality trends over time.

You can filter translation quality analytics data using the following filters:

These filters can be combined to analyze translation quality trends across projects, workflows, languages, and contributors.

The Quality overview section provides a high-level summary of review activity and translation quality for the selected period.

These metrics help you understand how much content was reviewed, how much reviewer effort was required, and whether translation quality is improving or declining over time.

Metrics include:

Each metric also shows how the current period compares to the previous comparable period, helping you monitor quality and review workload trends over time.

The Translation effort summary chart shows reviewed translations grouped by edit effort.

Use this chart to distinguish between standard review effort and cases where review turned into translation work. Accepted means no edits were needed, while Light edit, Medium edit, and Heavy edit indicate increasing levels of reviewer changes. New text and Retranslation mean reviewers created new translations instead of editing existing ones.

Categories include:

Higher shares of Heavy edit, New text, or Retranslation may indicate quality, context, or workflow issues that require further investigation.

The Translation effort by workflow chart shows reviewed translations grouped by workflow type.

Use this chart to understand which translation and review workflows generate the most review activity and where quality or process improvements may be needed. This includes review tasks where reviewers created new translations due to missing source content or retranslated existing content.

Workflow examples include:

Higher shares for a specific workflow may indicate where most review effort is concentrated.

The Quality trends chart shows how post-edit rate and average edit distance change over time.

Use this chart to monitor whether translation quality and review effort are improving, declining, or staying stable across the selected period.

Metrics include:

Rising trends may indicate declining translation quality or increasing review effort. Stable or decreasing trends usually suggest healthier translation workflows.

The Quality by translation method chart compares translation quality metrics across different translation methods.

Use this chart to identify which methods require more review effort and where additional context, workflow improvements, or quality checks may be needed.

Metrics include:

Higher post-edit rate or average edit distance values may indicate lower initial translation quality or workflows that require more reviewer effort.

The Edit effort by translation method chart shows how review and translation effort is distributed across translation methods.

Use this chart to compare how often translations from each method are accepted as-is, require light, medium, or heavy edits, or require reviewers to create new translations. Larger shares of Heavy edit above 30%, New text, or Retranslation may indicate lower translation quality, insufficient context, or workflows that require new translations.

Categories include:

Larger shares of Heavy edit, New text, or Retranslation may indicate lower initial translation quality, insufficient context, or workflows that require optimization.

The Review volume by effort chart shows review volume over time, grouped by edit effort.

Use this chart to understand whether review workload is shifting toward lighter or heavier edits. Healthy growth is typically reflected by Accepted and Light edit volumes growing proportionally with overall review activity.

Rising shares of Heavy edit, New text, or Retranslation may indicate declining translation quality or review tasks that require reviewers to create new translations.

Categories include:

Healthy growth is usually reflected by Accepted and Light edit volumes growing proportionally with overall review activity. Rising shares of Heavy edit, New text, or Retranslation may indicate declining translation quality, insufficient context, or scaling issues.

The Language evaluation tab helps you compare translation quality and review effort across language pairs, translators, reviewers, and translator-reviewer pairings.

Use this section to identify language pairs that require more corrections, spot inconsistent review patterns, compare contributor performance, and understand where additional context or workflow improvements may be needed.

The Language pair difficulty ranking chart compares language pairs by review effort and translation quality indicators.

Use this chart to identify language pairs that may require more reviewer corrections, additional context, or longer turnaround times.

Metrics include:

Higher values for both post-edit rate and avg. edit distance may indicate language pairs that require more review effort or quality improvements. The number of translations helps you understand whether the results are based on a meaningful review volume.

The Translation edit rate by language chart shows how review effort is distributed across edit types for each language pair.

Use this chart to identify language pairs that are frequently accepted without changes, require reviewer edits, or include cases where reviewers created new translations. Larger shares of Heavy edit may indicate lower translation quality, recurring reviewer corrections, or language-specific challenges. New text and Retranslation indicate that reviewers created new translations instead of editing existing ones.

Categories include:

Larger shares of Heavy edit, New text, or Retranslation may indicate lower translation quality, recurring reviewer corrections, language-specific challenges, or review steps that include translation work.

The Translator quality leaderboard compares translators by post-edit rate, average edit distance, and review outcomes.

Use this table to understand how translations created by each translator performed during review. Higher post-edit rate or avg. edit distance values may indicate translations that required more reviewer corrections.

The metrics are based only on translations that were reviewed, not on all translations created by the translator. New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Columns include:

Higher post-edit rate, avg. edit distance, or heavy edit values may indicate translations that required more reviewer corrections. Higher New text or Retranslation values may indicate cases where reviewers had to create or retranslate content instead of only reviewing it.

Results based on low reviewed translation volumes should be interpreted cautiously.

The Reviewer performance table compares reviewer activity, review activity, translation effort during review, and turnaround time across reviewers.

Use this table to understand how reviewers handle review work, identify differences in review patterns, and spot potential workflow inconsistencies. Large differences between reviewers working on the same language or workflow may indicate inconsistent review standards, content complexity differences, or workflow variations.

The metrics are based on translations that were reviewed, not on all translations in the project. New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Columns include:

Higher post-edit rate, avg. edit distance, or heavy edit values may indicate that the reviewer is handling content that requires more corrections, or that review standards vary across reviewers or workflows. Higher New text or Retranslation values may indicate cases where reviewers had to create or retranslate content instead of only reviewing it.

The Translator x reviewer matrix shows quality metrics across translator and reviewer pairings by language.

Use this table to identify collaboration patterns that may require closer review. Higher post-edit rate or avg. edit distance values may indicate collaboration mismatches, language-specific challenges, or quality issues.

The metrics are based only on translations that were reviewed, not on all translations created by the translator. New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Columns include:

Higher post-edit rate, avg. edit distance, or heavy edit values for specific pairings may indicate review style differences, collaboration mismatches, language-specific challenges, or quality issues. Higher New text or Retranslation values may indicate cases where reviewers had to create or retranslate content instead of only reviewing it.

The Task quality by project tab helps you analyze review quality at the project and task level.

Use this section to compare post-edit rates across tasks, understand how review effort is distributed by project, and identify projects or review tasks that may require additional context, quality improvements, or workflow adjustments.

The Task post-edit rates chart shows tasks grouped by post-edit rate bucket.

Use this chart to understand how much reviewer editing was required across tasks in the selected period. Lower post-edit rates usually indicate that translations required minimal reviewer changes, while higher post-edit rates may point to quality, context, or workflow issues.

Post-edit rate calculations exclude cases where the reviewer created new text or retranslated existing content, since those represent translation work rather than review edits.

Buckets include:

Use this chart to quickly assess the overall quality distribution of tasks and identify whether a significant share of tasks required heavy reviewer intervention.

The Project review summary chart shows review effort by project, grouped by edit intensity.

Use this chart to compare review patterns across projects and identify where additional quality checks, context improvements, or workflow changes may be needed. Higher shares of Heavy edit, New text, or Retranslation may indicate quality or workflow issues.

New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Categories include:

Higher shares of Heavy edit, New text, or Retranslation may indicate quality, context, or workflow issues that require additional reviewer effort.

The Detailed project review table provides project-level review quality metrics, including post-edit rate, average edit distance, edit distribution, approvals, and recent review activity.

Use this table to identify projects with higher review effort, quality issues, missing context, or workflow patterns that may require improvement. New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Post-edit rate is color-coded to make project quality easier to scan: green indicates healthier values below 30%, while red highlights values above 50% that may need closer attention.

Columns include:

Post-edit rate is color-coded to make project quality easier to scan: green indicates healthier values below 30%, while red highlights values above 50% that may need closer attention.

The Review task detail table provides detailed review quality metrics for individual review tasks, including post-edit rate, average edit distance, edit distribution, turnaround time, and review activity.

Use this table to identify review tasks with potential quality, context, or workflow issues that may require closer analysis. New text and Retranslation indicate cases where reviewers created new translations instead of editing existing ones.

Post-edit rate is color-coded to make tasks easier to scan: green indicates healthier values below 30%, while red highlights values above 50% that may need closer attention.

Columns include:

Post-edit rate is color-coded to make tasks easier to scan: green indicates healthier values below 30%, while red highlights values above 50% that may need closer attention.

A translation method indicates how a key was first translated. It is determined by the source of the first meaningful translation added to the key.

For example:

Lokalise tracks the following translation methods:

Translation quality reports include two types of insights:

Use Post-edit rate and Avg. edit distance to evaluate pure post-editing effort. Use charts and tables that include New text and Retranslation to understand the full scope of reviewer activity and identify workflows where review may be turning into translation work.

An edit is considered meaningful if it meets all of the following conditions:

The following actions are not considered meaningful edits:

These actions do not modify the translation content in a way that is considered meaningful for reporting purposes.

Word-count metrics across Tasks, Analytics, and Statistics may differ because each view represents a different part of the workflow. Some metrics reflect task scope, some reflect processed content, and others reflect translation or review activity.

In addition, these views may use different counting methods. For example, Statistics may attribute translation activity using the source-language word count, while Analytics may reflect words that were actually created or modified during processing. Counts may also be based on source-language words or target-language words, which do not always produce the same totals.

Because of this, these metrics should be interpreted in context rather than compared as exact equivalents.

A high number of reviewed words does not imply a high edit rate. Reviewed words show how much content went through review, while edit rate shows how much of that content was actually changed.

This means a user may review a large amount of content, confirm most of it as-is, and edit only a small portion. In that case, reviewed-word totals will be high, while edit rate will remain low.

When testing translations in Lokalise — whether using AI, machine translation (MT), or human translation — it’s important to prepare the project in a way that allows Analytics to track translation activity correctly. This includes metrics such as translation methods, post-editing effort, and overall workflow insights.

Copying both source and target content from one project to another is not recommended for testing or proof-of-concept scenarios. If both values are copied without changes, Analytics may retain the original translation method associated with those translations.

As a result, any new translations applied later may not be tracked as expected. This can lead to incomplete or misleading data when analyzing translation methods, post-editing effort, or workflow performance.

To ensure clean and reliable analytics data, the recommended approach is to create a new project, upload only the source texts, and generate translations within that project. This allows Analytics to recognize all translation activity as new and track it accurately.

If both source and target content have already been copied, modifying the source text may help trigger proper tracking. However, for the most reliable results (especially when testing AI workflows or comparing translation approaches) it's best to start with source-only content in a new project.

Lokalise Analytics allows filtering by date ranges shorter than a full month. However, the feature is optimized for monthly filtering. When selecting shorter periods (for example, a week), the displayed data may be less accurate.