The Tasks dashboard in Lokalise Analytics helps you analyze task activity, workload, and productivity across your localization projects. You can monitor task creation and completion, compare performance across languages and contributors, and evaluate translation memory coverage at the time tasks are created.
This article covers the reports, metrics, and filters available in the Tasks dashboard. For an overview of Lokalise Analytics and the other available dashboards, see Lokalise Analytics.
Accessing the Tasks dashboard
To access the Tasks dashboard, open the corresponding tab on the Analytics page.
Task metrics are displayed using trend visualizations that compare current values with the previous comparable period.
Task insights
Filtering data
You can filter task analytics data using the following filters:
Date — filters data by the task creation date range.
Project ID — filters tasks by specific Lokalise project IDs.
Project — filters tasks by project name.
Target language — filters tasks by target language and calculates word-based metrics only for the selected languages.
Task type — filters tasks by task type. AI task types are excluded by default.
Task — displays only selected tasks.
Contributor — filters tasks assigned to a specific contributor and calculates word-based metrics only for languages assigned to that contributor.
Show deleted — includes deleted tasks in analytics results when enabled.
Basic task metrics
The Tasks section includes several high-level metrics that help you monitor task activity and delivery performance over time.
Metrics include:
Tasks created — the number of tasks created during the selected time period.
Tasks closed — the number of tasks closed during the selected time period.
Overdue ongoing tasks — tasks that are still ongoing and have passed their due date.
Overdue closed tasks — tasks that were completed after their due date.
The Tasks chart provides a monthly breakdown of task activity, including:
Closed — tasks completed within the selected period.
Overdue closed — tasks completed after their due date.
Ongoing — tasks that are currently active.
Overdue ongoing — active tasks that are overdue.
Words — the total number of words associated with tasks during the selected time period.
Task time overview
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 potential bottlenecks, and use historical completion times when planning future localization work. This helps answer questions such as: “How long does it usually take to complete tasks, and where can we improve?”
The displayed data includes:
Average — the arithmetic average time it takes to complete a task.
Median — the median time it takes to complete tasks.
80th percentile — the time within which 80% of tasks are completed.
Longest time — the maximum time taken to complete a task.
Average words per day — the number of target-language words divided by the time it took to complete a language, averaged across completed task languages.
You can customize this section and sections below using the dropdown in the right corner:
Note on task time
For human tasks, time is measured in days and hours.
For AI-powered tasks, time is measured in hours and seconds, as these tasks are typically completed much faster.
Time is always counted from the moment the task is created.
Lokalise calculates time per language, not only per overall task. For example, if a task includes two languages—one completed in 1 day and the other in 7 days—the average time is shown as 4 days, based on the individual language durations.
The Task created or completed filter controls how the selected date range is applied. You can filter task time data by task creation date or completion date.
Note on the 80th percentile
We recommend using the 80th percentile alongside the average when analyzing task completion time. It shows the time within which 80% of tasks were completed and is less affected by unusually long-running tasks than the average.
In the example above, 80% of the tasks were completed in less than six days.
Task time charts
These charts offer detailed insights into the time spent on tasks.
Time to close a task by target language
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:
Y-axis — days to close the task language.
X-axis — month when the task was created or completed, depending on the selected Task created or completed filter.
Bubble size — total number of words in the task language.
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.
Average task time by task size
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:
Y-axis — average number of days to close a task language.
X-axis — month when the task was created or completed, depending on the selected Task created or completed filter.
Series — task languages grouped by word count:
< 50 words
50–250 words
+250 words
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.
Average time (days) on task by task size and language
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:
Y-axis — target languages, grouped by language name regardless of language ID differences between projects.
Columns — task size groups based on word count:
< 50 words
50–250 words
+250 words
Values — the average number of days required to close task languages.
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:
0 means less than one day
1.5 means one and a half days
Use this chart to identify languages or task sizes that consistently require more time to complete.
Average time (days) on task by task size and contributor
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:
Y-axis — contributors assigned to the task language. If multiple contributors were involved, the contributor who closed the task language is used. Contributors are grouped by name, even if contributor IDs differ between projects.
Columns — task size groups based on word count:
< 50 words
50–250 words
+250 words
Values — the average number of days required to close task languages.
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:
0 means less than one day
1.5 means one and a half days
Use this chart to identify contributors or task sizes that consistently require more or less time to complete.
Task progress detail by contributor
The Task progress detail by contributor table is available to Enterprise customers only. Speak with us.
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:
Task ID — unique identifier of the task.
Title — task title.
Project — project where the task resides.
Type — task type, such as translation, review, or AI task.
Source Language — the language from which translation or review is performed.
Target Language — the target language for the specific row.
Created Date — date when the task was created.
Due Date — deadline set for the task.
Completion Date — date when the task language was completed.
Status — current task status, such as completed or in progress.
Keys — total number of keys included in the task.
Task Base Words — number of source words included in the task.
Processed Words — number of words processed during the task, including words handled through manual edits, imports, AI/MT, automations, or other workflows. Learn more in the Processed words article.
Time To Complete — human-readable time elapsed between task creation and completion.
Created By — user who created the task.
Completed By — contributor who completed the work for the specific language entry.
Reviewed by — user who reviewed the translations.
Closed By — user who officially closed the task.
TM 0% — number of base words with a 0–49% translation memory match.
TM 50% — number of base words with a 50–74% translation memory match.
TM 75% — number of base words with a 75–84% translation memory match.
TM 85% — number of base words with an 85–94% translation memory match.
TM 95% — number of base words with a 95–99% translation memory match.
TM 100% — number of base words with a 100% translation memory match.
Light Edit — number of translations with light edits.
Medium Edit — number of translations with medium edits.
Heavy Edit — number of translations with heavy edits.
Translations reviewed — number of translations reviewed.
Translations edited — number of reviewed translations that were edited.
Post Edit rate — percentage of reviewed translations that were edited after review.
New Text — number of translations treated as new text.
Avg. Edit Score — average edit score for the reviewed translations.
Avg AI score — average AI quality score for the relevant translations.
Avg. Turnaround Hours — average turnaround time in hours.
How to use it
Use filters such as project, contributor, language, task type, or date range to narrow the report down to specific workflows or teams.
You can export the data to spreadsheets or BI tools for further analysis, such as calculating average completion time by contributor, language, task type, TM match range, or review effort.
For larger localization programs, this table can be used as a foundation for custom reporting, such as contributor performance, language-level turnaround time, TM efficiency, post-editing effort, or AI quality trends.
Important notes
Because rows are split by language and contributor, tasks with multiple languages or multiple assignees can appear as multiple rows.
The Completed By column shows the contributor responsible for that specific language entry.
Some metric columns may appear empty at first, depending on the currently visible rows. To view available values for a specific metric, sort the table by that column. For example, sorting by Avg. Edit Score can bring rows with edit score data to the top.
Words distribution by TM leverage at task creation
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:
Y-axis — percentage of total task words.
X-axis — month when the task was created.
Series — TM match ranges:
TM 0%
TM 50%
TM 75%
TM 85%
TM 95%
TM 100%
Use this chart to monitor how TM leverage changes over time and evaluate the expected translation effort for newly created tasks.
Percentage of words with TM leverage ≥ 95% by created task
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:
Y-axis — percentage of words with TM leverage ≥ 95%.
X-axis — month when the task was created.
Series — aggregated values across all selected languages.
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.
TM Coverage at task creation
The TM Coverage at task creation tab in the Tasks dashboard shows how much of a task could potentially be translated using the existing translation memory at the moment the task was created.
Unlike the TM leverage tab in the Usage dashboard, which measures actual translation memory usage, this tab estimates the translation memory coverage available before work begins. The available reports help you estimate translation effort, identify gaps in translation memory, monitor how translation memory maintenance improves future coverage, and compare coverage across projects, language pairs, and task types.
Filtering data
You can filter TM coverage data using the following filters:
Date — select the time range for the displayed analytics data.
Time grouping — group data by day, week, month, quarter, or year, depending on the level of detail you need.
Project ID — filter results by specific Lokalise project IDs.
Project — filter data by project name.
Task ID — filter results by specific task IDs.
Task — filter data by task name.
Task type — filter data by task type.
Target language — filter data by target language.
Base language — filter data by base language.
These filters can be combined to analyze translation memory coverage across specific projects, tasks, language pairs, and time periods.
TM coverage overview
The summary cards at the top of the dashboard show the following metrics for the selected period:
Total words at task creation — the total number of source words included in tasks at the time they were created.
Weighted word count at task creation — the source word count adjusted by translation memory match levels to estimate potential translation effort savings.
TM coverage score — the potential reduction in translation effort based on the available translation memory matches. It is calculated as
1 − (weighted word count ÷ total words). A higher score indicates that translation memory could cover more of the content and reduce the translation workload.
TM coverage score over time
The TM coverage score over time chart shows how the estimated translation memory coverage changes over the selected period.
Higher scores indicate greater potential for translation memory to reduce translation effort.
TM coverage score by top language pairs
The TM coverage score by top language pairs chart compares translation memory coverage scores for the five language pairs with the highest word volume during the selected period.
Use it to identify which high-volume language pairs have the greatest potential for translation memory to reduce translation effort.
TM match distribution
The TM match distribution chart shows the breakdown of source words by translation memory match level at the time tasks were created.
Use it to understand how much of your content falls into each translation memory match bucket, from the lowest match range to exact 100% matches.
TM match distribution over time
The TM match distribution over time chart shows how the distribution of source words across translation memory match levels changes over the selected period.
Use it to monitor trends in translation memory coverage and see whether more content is falling into higher translation memory match buckets over time.
TM match by task type
The TM match by task type chart compares the distribution of source words across translation memory match levels for each task type.
Use it to understand how translation memory coverage differs between translation, review, and Lokalise AI tasks.
TM match by language pair
The TM match by language pair chart compares the distribution of source words across translation memory match levels for each language pair.
Use it to identify which language pairs have stronger translation memory coverage and where additional translation memory content may be needed.
TM coverage by project
The TM coverage by project table provides a project-level breakdown of translation memory coverage. Use it to identify projects with lower translation memory coverage and estimate where the greatest translation effort is likely to be required.
The table includes the following columns:
Project — the project name.
Words — the total number of source words in the project.
0–74% — the number of source words with translation memory matches between 0% and 74%.
0–74% share — the percentage of source words with translation memory matches between 0% and 74%.
75–94% — the number of source words with translation memory matches between 75% and 94%.
75–94% share — the percentage of source words with translation memory matches between 75% and 94%.
95–99% — the number of source words with translation memory matches between 95% and 99%.
95–99% share — the percentage of source words with translation memory matches between 95% and 99%.
100% — the number of source words with exact (100%) translation memory matches.
100% share — the percentage of source words with exact (100%) translation memory matches.