Analytics

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Introduction

Analytics metrics help partners and advertisers understand the performance of the content they promote on X. This includes information such as impressions, clicks, video views, and spend. In addition, partners and advertisers are able to get detailed metrics for various segments of the audiences they reach.

The Ads API supports two ways of retrieving detailed campaign performance metrics: synchronously and asynchronously. With synchronous analytics calls, the requested metrics are returned in the response. With the asynchronous analytics endpoints, the requested metrics are available in a downloadable results file after the associated “job” has finished processing. The synchronous endpoint supports short time ranges and is ideal for real-time campaign optimizations. The asynchronous endpoints support much longer time ranges and are, thus, intended for fetching much more data, ideal for generating reporting or historical backfills.

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Details

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Synchronous vs. Asynchronous

The differences between the synchronous and asynchronous analytics endpoints are summarized in the following table. This information is intended to help developers choose which set of endpoints to use.

* This refers to the maximum number of jobs that may be in a processing state at any given time.

** Once the job has successfully finished processing, a URL is returned. This is where the compressed (gzip) results file can be downloaded from.

Outside of this, the endpoints offer the same functionality.

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Use cases

There are three major analytics use cases.

1. Real-time optimization: using performance metrics to update active campaigns
2. Synchronization: regularly-scheduled background syncs
3. New account on-boarding: backfilling historical data

The synchronous analytics endpoint may be used for real-time optimization to update campaigns based on changes to metrics within the last 5 to 15 minutes. Either endpoint can be used for analytics synchronization. Keep in mind that the desired time range and whether segmentation is required will determine which endpoint to use. New account on-boarding should only be done using the asynchronous analytics endpoints. (The synchronous analytics endpoint should never be used for retrieving large amounts of data.)

The asynchronous analytics endpoints can power dashboards and other UI elements if metrics are synced with a backend process. Your implementation should avoid calling the asynchronous analytics endpoints to fulfill user interface requests.

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Request Options

Analytics requests are scoped to ads accounts and, thus, require the account ID in the resource path. Request options, listed below, are specified as query parameters. The following types of values are required.

• Entities: the entity type as well as up to 20 entity IDs you’d like to request analytics for
• Time range: the start and end times, expressed in ISO 8601
  • Note: must be expressed in whole hours
• Metric groups: one or more sets of related metrics (see Metrics and Segmentation for a list of metrics within each metric group)
• Granularity: specifies the level of aggregation in which the metrics should be returned
• Placement: determines whether metrics are pulled for ads that served on or off of X
  • Note: only a single placement value can be specified per request

Use the start_time and end_time request parameters to specify a time range. These values must be aligned with the specified granularity in the following way.

1. TOTAL: specify any time range (within the endpoint’s limits)
2. DAY: both the start time and end time values must be aligned with midnight in the account’s time zone
3. HOUR: specify any time range (within the endpoint’s limits)

End time is exclusive. For example, a request with start_time=2019-01-01T00:00:00Z and end_time=2019-01-02T00:00:00Z will return a single day’s worth of analytics metrics (not two) as this time range covers only a 24 hour period.

Segmentation

Available only through our asynchronous analytics endpoints, segmentation allows partners and advertisers to retrieve metrics broken out particular targeting values. To request segmented metrics, use the segmentation_type request parameter. For more details on segmentation options, see Metrics and Segmentation.

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FAQs

Why don’t the Ads API numbers match what’s shown in the X Ads UI?

• Make sure you’ve requested data for both placements: ALL_ON_TWITTER and PUBLISHER_NETWORK
• Remember that end times in the Ads API are exclusive; they are inclusive in the Ads UI

Why do the numbers change depending on when I request data?

• As soon as reporting metrics are available, you are able to retrieve them. They are available in near real-time. These early results are estimates, though, and, as a result, are expected to change. Metrics are finalized after 24 hours, with the exception of spend data.
• Spend metrics are generally final within 3 days of the event. However, we process billing data for up to 14 days from the date of the event (for spam filtering, for example).

How can I determine which entity IDs to request for a specific time period?

• Use the Active Entities endpoint

Why are all of the values in the analytics response null?

• It’s likely that the campaign did not serve during during the requested time period
• Use the Active Entities endpoint to determine which entities to fetch analytics for and for what time period

Why does the API show null values while the UI shows 0s?

• The UI chooses to display these values as 0s, but the values are equivalent

How can I request metrics associated with a granular placement, such as the X timeline?

• We only support two placement values in analytics: ALL_ON_TWITTER and PUBLISHER_NETWORK (i.e., the X Audience Platform)

Is it possible to retrieve metrics for deleted or paused entities?

• Yes. The entity’s status does not impact the availability of analytics metrics.

Why don’t the segmented values match the non-segmented ones?

• Segmented data is not expected to roll-up 100% to the non-segmented data, due to how this information is derived.

Is it possible to request data segmented by multiple dimensions?

• We do not support multi-segmentation.

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Best Practices

Some best practices when collecting analytics data from the Ads API.

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Rate Limiting and Retries

• On queries that are rate limited (those that return an HTTP 429 status code), you must inspect the x-rate-limit-reset header and retry only at or after the time indicated.
• On queries that result in an HTTP 503 Service Unavailable status code, you must inspect the retry-after header and retry only after the time indicated.
• Applications that do not respect the times indicated for retries could have their access to the Ads API revoked or throttled without notice.

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Analytics Metrics In a Nutshell

• All analytics metrics are locked and will not change after 24 hours, with the exception of billed_charge_local_micro.
• The billed_charge_local_micro metric is an estimate for up to 3 days after the data is returned.
• After 24 hours, this metric can decrease due to credits for overspend (ads served after the given end_time) and for billable events that are determined to be junk. This metric changes minimally after 24 hours.
• Please see Analytics for more information.

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Fetching Real-time, Non-segmented Data

• Always provide both a start_time and an end_time.
• Do not pull data for any entities older than 7 days.
• Do request data (ideally) with HOUR granularity, as you can always aggregate and roll metrics up to get DAY and TOTAL granularity.
• Do request data (ideally) at the line_items and promoted_tweets level, as you can always aggregate and roll these metrics up to get totals across the entire ads entity hierarchy (i.e. for the campaign, funding instrument or account levels).
• Save and store the values of analytics metrics on your side (locally).
• Do not repeatedly query for data that is older than 30 days. This data will not change and should be stored locally.
• All non-segmented data is real-time and data should be available within seconds of an event occurring.
• Group conversion metrics and non-conversion metrics into separate requests.

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Fetching Segmented Data

• Refer to guidelines provided for “Fetching Real-time, Non-segmented Data” above. Additional advice provided below.
• For most segmented data types, it is possible for data to not be complete for up to 1 hour at times. Data segmented by INTERESTS can be delayed for up to 12 hours.
• Segmented data is not expected to roll-up 100% to the non-segmented data, due to how this information is derived.

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Fetching Historical Data

• When backfilling data (i.e. adding a new advertiser account), you may need make several requests in smaller start_time and end_time chunks.
• Limit your fetches to 30-day date windows.
• Throttle these requests and distribute over time so as not to exhaust your rate limits for these fetches.

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Sample

You can find a sample script demonstrating some of these best practices (fetch_stats) on our ads-platform-tools github repository.

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Metrics by Objective

Which metrics are applicable for an entity depends on the campaign objective. Use this guide to determine the relevant metric groups to fetch for each objective type, as well as how additional derived metrics can be calculated.

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ENGAGEMENTS

Relevant metric groups:ENGAGEMENT and BILLING. MEDIA is also applicable if media is used in creatives.

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WEBSITE_CLICKS and WEBSITE_CONVERSIONS

Relevant metric groups:ENGAGEMENT, BILLING, and WEB_CONVERSION. MEDIA is also applicable if media is used in creatives.

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APP_INSTALLS and APP_ENGAGEMENTS

Relevant metric groups:ENGAGEMENT, BILLING, MOBILE_CONVERSION, and LIFE_TIME_VALUE_MOBILE_CONVERSION. MEDIA and VIDEO are also applicable if media or video app card is used in creatives.

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FOLLOWERS

Relevant metric groups:ENGAGEMENT and BILLING. MEDIA is also applicable if media is used in creatives.

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LEAD_GENERATION

Relevant metric groups:ENGAGEMENT and BILLING. MEDIA is also applicable if media is used in creatives.

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VIDEO_VIEWS

Relevant metric groups:ENGAGEMENT, BILLING, and VIDEO.

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VIDEO_VIEWS_PREROLL

Relevant metric groups:ENGAGEMENT, BILLING, and VIDEO.

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Metrics and Segmentation

This document is an overview of the metrics available from our Analytics for each entity type, as well as the available segmentation for each metrics.

*Some metrics in the ENGAGEMENT metrics family are not available at the account and funding instrument level. See the ENGAGEMENT section for details.

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Available Metrics by Metrics Group

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ENGAGEMENT

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BILLING

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VIDEO

Notice about video metrics definition changes:

The video_total_views metric within the VIDEO metrics group will report on any views which are at least 50% in-view for 2 seconds, as per the MRC standard.

Our original video view definition of 100% in view for at least 3 seconds will continue to be available as a new video_3s100pct_views metric in the VIDEO metrics group. To continue to bid and be charged based on the original view definition, use the newly available VIEW_3S_100PCT bid_unit.

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MEDIA

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WEB_CONVERSION

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MOBILE_CONVERSION

Mobile conversion stats are available only to advertiser accounts enabled for MACT.

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LIFE_TIME_VALUE_MOBILE_CONVERSION

Lifetime mobile conversion stats are available only to advertiser accounts enabled for MACT.

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Segmentation

Segmentation reporting allows the retrieval of metrics broken out by the values of a given targeting type. Segmentation is only available through asynchronous analytics queries due to their significant added complexity.

Segmentation not supported for MEDIA_CREATIVE or ORGANIC_TWEET entities.

Some segmentation types require additional parameters to be passed in. These are documented below.

When segmenting by CITIES or POSTAL_CODES, the API will only returned targeted locations. Region and metro segmentation will return both targeted and non-targeted locations.

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Derived Metrics

Campaign metrics depend on their campaign objective. Use this guide to determine how to calculcate derived metrics for use based on the objectives in place.

Any metric without curly brackets is one that is returned by the Ads API analytics endpoints. Any name surrounded by {curley brackets} indicates a derived metric for that category.

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ENGAGEMENTS

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WEBSITE_CLICKS

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APP_INSTALLS and APP_ENGAGEMENTS

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FOLLOWERS

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LEAD_GENERATION

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VIDEO_VIEWS

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QUALIFIED_IMPRESSIONS

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CUSTOM

For placement_type of PROMOTED_ACCOUNT see the FOLLOWERS objective above. For all other placements with this objective, see ENGAGEMENTS for the corresponding derived metrics.

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Guides

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Active Entities

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Introduction

The Active Entities endpoint is designed to be used in conjunction with our synchronous and asynchronous analytics endpoints as it provides information about which campaigns to request analytics for. It does this by returning details about ads entities and when their metrics changed. Using this endpoint will greatly simplify your code and analytics fetching logic.

This guide includes information and context about the endpoint and its data source. It also provides usage guidelines and a series of example requests, demonstrating how to use Active Entities in conjunction with our analytics endpoints. The Summary section provides a high-level description of the recommended approach.

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Data

Whenever an ads entity metric changes, we record information about that change. These change events are stored in hourly buckets and include details about the entity as well as the time that the change applies to. The latter is necessary because change events do not always correspond to when they were recorded. Billing adjustments are a common reason for this, but there are others, too.

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Endpoint

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Request

Active Entities requests are scoped under ads accounts and have three required query parameters: entity, start_time, and end_time.

twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t/active_entities?entity=PROMOTED_TWEET&start_time=2019-03-05T00:00:00Z&end_time=2019-03-06T00:00:00Z"

The following entity values are supported: CAMPAIGN, FUNDING_INSTRUMENT, LINE_ITEM, MEDIA_CREATIVE, PROMOTED_ACCOUNT, and PROMOTED_TWEET. This reflects the entity types that our analytics endpoints support.

The start_time and end_time values must be expressed in ISO 8601 and specify which hourly buckets to query. These must be expressed in whole hours.

This endpoint also supports three optional parameters that can be used to filter results: funding_instrument_ids, campaign_ids, and line_item_ids. These work at all levels of the ads hierarchy and with any specified entity type.

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Response

The Active Entities response for the request above is shown below.

    {
      "request": {
        "params": {
          "account_id": "18ce54d4x5t",
          "entity": "PROMOTED_TWEET",
          "start_time": "2019-03-05T00:00:00Z",
          "end_time": "2019-03-06T00:00:00Z"
        }
      },
      "data": [
        {
          "entity_id": "2r0wxw",
          "activity_start_time": "2019-03-04T20:55:20Z",
          "activity_end_time": "2019-03-05T03:43:56Z",
          "placements": [
            "ALL_ON_TWITTER"
          ]
        },
        {
          "entity_id": "2r30fn",
          "activity_start_time": "2019-03-05T08:11:08Z",
          "activity_end_time": "2019-03-05T14:40:59Z",
          "placements": [
            "ALL_ON_TWITTER",
            "PUBLISHER_NETWORK"
          ]
        }
      ]
    }

The data array includes an object for every entity that should be included in a subsequent analytics request. You should not request analytics for IDs outside of this set.

Each object includes four fields: entity_id, activity_start_time, activity_end_time, and placements. The activity start and end times represent the time range that the associated entity’s change events apply to and, thus, determine the dates that should be specified in subsequent analytics requests. The placements array can include either or both of the following values: ALL_ON_TWITTER, PUBLISHER_NETWORK. It indicates which placements should be requested for the given entity ID.

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Usage

The Active Entities endpoint should dictate how analytics requests are made. The following usage guidelines are written to support analytics synchronization, enabling partners to keep their data stores in sync with Twitter. In other words, it describes how to perform regularly-scheduled background syncs.

There are two decisions a developer must make.

1. How often to request active entities information and, thus, how often to pull analytics.
2. How to use the activity start and end times to determine the analytics request’s start_time and end_time values.

These are discussed in greater detail in each of the two subsections, below, after the summary.

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Summary

Use the Active Entities endpoint in the following way to dictate how analytics requests are made. Follow this after you’ve decided how often to request active entities information and, thus, how often to pull analytics.

1. Make the Active Entities request.
2. Split the response by placement. One group for ALL_ON_TWITTER and one for PUBLISHER_NETWORK.
3. For each placement group, do the following.
   1. Extract the entity IDs.
   2. Determine the analytics start_time and end_time values.
      • Find the minimum activity_start_time. Round this value down.
      • Find the maximum activity_end_time. Round this value up.
   3. Make the analytics request(s).
      • Group entity IDs into batches of 20.
      • Use the start_time and end_time values from #3b.
      • Specify the appropriate placement value.
   4. Write to your data store.

Please see active_entities.py as an example that uses the Python SDK.

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Frequency

The answer to the first question determines the time range that should be used in Active Entities requests. For example, if requesting active entities information every hour, the time range should be an hour. If requesting active entities information once a day, the time range should be a day. In other words, time ranges should be selected such that the current request’s start_time is equal to the previous request’s end_time.

Note: A time window should only be requested once. Requesting a time window more than once will lead to unnecessary analytics requests. (Exception below.)

For partners wishing to request analytics multiple times an hour for the current hour, the same pattern applies—the frequency determines the time range. The table below shows example Active Entities start and end timestamps for this scenario.

Given the way that change events are stored, all four Active Entities requests above query the same hourly bucket, which is necessary for this use case. However, after the current hour, this hourly bucket should no longer be queried.

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Activity Times

We recommend the following approach to working with activity start and end times. Across all objects in the Active Entities response, find the minimum activity_start_time and the maximum activity_end_time. Modify these values by rounding the minimum activity start time down and rounding the maximum activity end time up. Specifically, set the timestamps to zero for both and add one day to the end time, as illustrated in the following table. These are the start and end times that should be specified in subsequent analytics requests.

Note: It’s important to include the timestamps with hours, minutes, and seconds set to zero. Otherwise, if only the date is passed in, we will assume you’re requesting analytics starting and ending at midnight in the ads account’s timezone, which may not be desirable. For example, if the minimum activity start time is 2019-02-28T01:30:07Z and the timestamp is omitted for an ads account with an offset of -08:00:00, the analytics request will miss changes that happened between 01:30 and 08:00.

Alternatively, if you would prefer to request analytics for just the returned activity time window without expanding to full days, you can. Using this approach, the derived start and end times would be 2019-03-04T20:00:00Z and 2019-03-05T15:00:00Z, respectively. (Note that ranges like these are not accepted if you specify DAY granularity in the analytics request.)

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Example

This section demonstrates how to use Active Entities in conjunction with the synchronous analytics endpoint. (The responses have been slightly modified for readability.) In this example, the Active Entities endpoint is called at the top of each hour, with each request looking at the previous hour. The response determines how the synchronous analytics endpoint is used.

The first Active Entities request is made at 03:00:00. The response indicates that line item dvcz7’s metrics changed and that those change events apply to the window between 02:02:55 and 02:28:12.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t/active_entities?entity=LINE_ITEM&start_time=2019-02-11T02:00:00Z&end_time=2019-02-11T03:00:00Z"`

    {
      "request": {},
      "data": [
        {
          "entity_id": "dvcz7",
          "activity_start_time": "2019-02-11T02:02:55Z",
          "activity_end_time": "2019-02-11T02:58:12Z",
          "placements": [
            "ALL_ON_TWITTER"
          ]
        }
      ]
    }

Based on these activity start and end times and using the approach described above, the analytics start_time and end_time values are set to 2019-02-11T00:00:00Z and 2019-02-12T00:00:00Z, respectively. We see that the third element in each of the metrics arrays below are non-zero, as we expected based on the active entities information.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t?entity=LINE_ITEM&entity_ids=dvcz7&start_time=2019-02-11T00:00:00Z&end_time=2019-02-12T00:00:00Z&granularity=HOUR&metric_groups=ENGAGEMENT,VIDEO&placement=ALL_ON_TWITTER"`

    {
      "data_type": "stats",
      "time_series_length": 24,
      "data": [
        {
          "id": "dvcz7",
          "id_data": [
            {
              "segment": null,
              "metrics": {
                "impressions": [
                  0,0,2792,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "engagements": [
                  0,0,60,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "video_total_views": [
                  0,0,1326,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ]
              }
            }
          ]
        }
      ],
      "request": {}
    }

The next Active Entities request happens at 04:00:00 and only looks at the previous hour. As mentioned above, a time window should only be requested once. Based on the response, we see that change events for this line item apply to both 02:00:00 and 03:00:00. In the subsequent analytics request, we expect to see changes for both hours.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t/active_entities?entity= LINE_ITEM&start_time=2019-02-11T03:00:00Z&end_time=2019-02-11T04:00:00Z"`

    {
      "request": {},
      "data": [
        {
          "entity_id": "dvcz7",
          "activity_start_time": "2019-02-11T02:07:17Z",
          "activity_end_time": "2019-02-11T03:49:22Z",
          "placements": [
            "ALL_ON_TWITTER"
          ]
        }
      ]
    }

In addition to seeing non-zero metrics for 03:00:00, we see that the impressions, spend, and MRC video views have been updated from their previous values. Impressions, for example, are now 2,995 for the 02:00:00 hour, up from 2,792. This demonstrates how change events that were recorded during the 03:00:00 hour apply to the 02:00:00 hour.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t?entity=LINE_ITEM&entity_ids=dvcz7&start_time=2019-02-11T00:00:00Z&end_time=2019-02-12T00:00:00Z&granularity=HOUR&metric_groups=ENGAGEMENT,VIDEO&placement=ALL_ON_TWITTER"`

    {
      "data_type": "stats",
      "time_series_length": 24,
      "data": [
        {
          "id": "dvcz7",
          "id_data": [
            {
              "segment": null,
              "metrics": {
                "impressions": [
                  0,0,2995,734,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "engagements": [
                  0,0,65,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "video_total_views": [
                  0,0,1449,342,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ]
              }
            }
          ]
        }
      ],
      "request": {}
    }

The Active Entities request at 05:00:00, again looking at just the previous hour, shows that change events apply to the 03:00:00 hour only. The changes to analytics metrics in the subsequent request reflect this.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t/active_entities?entity=LINE_ITEM&start_time=2019-02-11T04:00:00Z&end_time=2019-02-11T05:00:00Z"`

    {
      "request": {},
      "data": [
        {
          "entity_id": "dvcz7",
          "activity_start_time": "2019-02-11T03:42:39Z",
          "activity_end_time": "2019-02-11T03:48:48Z",
          "placements": [
            "ALL_ON_TWITTER"
          ]
        }
      ]
    }

The analytics response shows that only metrics for the 03:00:00 hour have changed; the values for the 02:00:00 hour are the same as they were during the previous analytics request.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t?entity=LINE_ITEM&entity_ids= dvcz7&start_time=2019-02-11T00:00:00Z&end_time=2019-02-12T00:00:00Z&granularity=HOUR&metric_groups=ENGAGEMENT,VIDEO&placement=ALL_ON_TWITTER"`

    {
      "data_type": "stats",
      "time_series_length": 24,
      "data": [
        {
          "id": "dvcz7",
          "id_data": [
            {
              "segment": null,
              "metrics": {
                "impressions": [
                  0,0,2995,753,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "engagements": [
                  0,0,65,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ],
                "video_total_views": [
                  0,0,1449,351,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
                ]
              }
            }
          ]
        }
      ],
      "request": {}
    }

Finally, at 06:00:00 we see that there are no additional change events. Note: This does not imply that metrics for this line item cannot change in the future, though.

`twurl -H ads-api.x.com "/11/stats/accounts/18ce54d4x5t/active_entities?entity=LINE_ITEM&start_time=2019-02-11T05:00:00Z&end_time=2019-02-11T06:00:00Z"`

    {
      "request": {},
      "data": []
    }

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Asynchronous Guide

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API Reference

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Asynchronous Analytics

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Introduction

The asynchronous analytics endpoints allow partners and advertisers to request metrics by submitting create requests that the server processes asynchronously. (We refer to these as asynchronous analytics “jobs.“) With this approach, the client’s connection does not need to remain open until the request has been fulfilled.

These endpoints, like their synchronous counterpart, allow partners and advertisers to request detailed statistics on campaign performance. They support requesting data for accounts, funding instruments, campaigns, line items, promoted Tweets, and media creatives. The difference between these and the synchronous endpoint is that the asynchronous analytics endpoints support longer date ranges, up to 90 days, as well as segmentation. Additional details on the differences between the two can be found on our Analytics Overview page.

Unlike our synchronous endpoints, rate limiting is based on the number of concurrent jobs for a given account. In other words, it’s based on the number of jobs that can be in a processing state at a given time. We count this at the ads account level.

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Usage

Retrieving campaign metrics using the asynchronous analytics endpoints is a multi-step process. It involves creating a job, checking whether the job has finished processing, and, finally, downloading the data. The data file must be decompressed. The four specific steps are outlined below.

1. Create the job using the POST stats/jobs/accounts/:account_id endpoint.
2. Make requests at regular intervals to the GET stats/jobs/accounts/:account_id endpoint to determine whether the job has finished processing.
3. Once the job has finished processing, download the data file.
4. Unzip the data file.

The response object returned in the data file has the same JSON schema as the synchronous analytics endpoint’s response.

Segmented campaign metrics are only available via the asynchronous analytics endpoints. Campaign metrics can be broken out by location, gender, interest, keyword, and more. For a full list of options, see the Metrics and Segmentation page. In order to request segmented metrics, use the segmentation_type request parameter when creating the job.

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Example

This section demonstrates how to use the asynchronous analytics endpoints.

Start by creating a job using the POST stats/jobs/accounts/:account_id endpoint. The example below requests engagement metrics—such as impressions, likes, clicks, etc.—for a specific line item over a week’s time. (Note that the requested time range goes up to, but does not include March 20th since the timestamp is set to midnight.)

$ twurl -X POST -H ads-api.x.com "/9/stats/jobs/accounts/18ce54d4x5t?entity=LINE_ITEM&entity_ids=el32n&start_time=2019-03-12T00:00:00Z&end_time=2019-03-20T00:00:00Z&granularity=TOTAL&placement=ALL_ON_TWITTER&metric_groups=ENGAGEMENT"

    {
      "request": {
        "params": {
          "start_time": "2019-03-12T00:00:00Z",
          "entity_ids": [
            "el32n"
          ],
          "end_time": "2019-03-20T00:00:00Z",
          "placement": "ALL_ON_TWITTER",
          "granularity": "TOTAL",
          "entity": "LINE_ITEM",
          "metric_groups": [
            "ENGAGEMENT"
          ]
        }
      },
      "data": {
        "start_time": "2019-03-12T00:00:00Z",
        "segmentation_type": null,
        "url": null,
        "id_str": "1120829647711653888",
        "entity_ids": [
          "el32n"
        ],
        "end_time": "2019-03-20T00:00:00Z",
        "country": null,
        "placement": "ALL_ON_TWITTER",
        "id": 1120829647711653888,
        "expires_at": null,
        "account_id": "18ce54d4x5t",
        "status": "PROCESSING",
        "granularity": "TOTAL",
        "entity": "LINE_ITEM",
        "created_at": "2019-04-23T23:19:46Z",
        "platform": null,
        "updated_at": "2019-04-23T23:19:46Z",
        "metric_groups": [
          "ENGAGEMENT"
        ]
      }
    }

This response does not return the line item metrics. It simply provides information about the job you just created. The job ID is needed to check on the status of the job. This is shown in both the id and id_str response attributes.

Next, you’ll want to check whether the job you’ve created using the id_str from the previous response, has finished processing as indicated by "status": "SUCCESS" in the response. This means the data is ready to download. The url field contains the download link.

$ twurl -H ads-api.x.com "/9/stats/jobs/accounts/18ce54d4x5t?job_ids=1120829647711653888"

    {
      "request": {
        "params": {
          "job_ids": [
            1120829647711653888
          ]
        }
      },
      "next_cursor": "1120828505715920896",
      "data": [
        {
          "start_time": "2019-03-12T00:00:00Z",
          "segmentation_type": null,
          "url": "https://ton.twimg.com/advertiser-api-async-analytics/zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz",
          "id_str": "1120829647711653888",
          "entity_ids": [
            "el32n"
          ],
          "end_time": "2019-03-20T00:00:00Z",
          "country": null,
          "placement": "ALL_ON_TWITTER",
          "id": 1120829647711653900,
          "expires_at": "2019-04-25T23:19:48Z",
          "account_id": "18ce54d4x5t",
          "status": "SUCCESS",
          "granularity": "TOTAL",
          "entity": "LINE_ITEM",
          "created_at": "2019-04-23T23:19:46Z",
          "platform": null,
          "updated_at": "2019-04-23T23:19:48Z",
          "metric_groups": [
            "ENGAGEMENT"
          ]
        }
      ]
    }

While we’re passing in a single job ID in the above example, in practice, you’ll want to use the job_ids parameter to check on the status of multiple jobs at a time by specifying up to 200 job IDs.

Next, download the data file using the listed url value.

    $ wget https://ton.twimg.com/advertiser-api-async-analytics/zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz
    --2019-04-23 17:52:12--  https://ton.twimg.com/advertiser-api-async-analytics/zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz
    Resolving ton.twimg.com (ton.twimg.com)... 72.21.91.70
    Connecting to ton.twimg.com (ton.twimg.com)|72.21.91.70|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 381 [application/gzip]
    Saving to: 'zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz'

    zBkuuPeEVx-5OygDVcZpqNtwt51Z5 100%[=================================================>]     381  --.-KB/s    in 0s

    2019-04-23 17:52:12 (5.27 MB/s) - 'zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz' saved [381/381]

Finally, unzip the file.

`$ gunzip zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json.gz`

The contents of the file are shown below.

`$ cat zBkuuPeEVx-5OygDVcZpqNtwt51Z5X9d-_AXNRcyhBlhBOgOfi6UDmGBvUFJAKnHY9ABN8z9f9V3Wn4l3OmF4KzJDmTUjNCikq9JwBUYm2AP8pRRoV-kPUgR0PaIqAb4.json`

    {
      "data_type": "stats",
      "time_series_length": 1,
      "data": [
        {
          "id": "el32n",
          "id_data": [
            {
              "segment": null,
              "metrics": {
                "impressions": [
                  3482
                ],
                "tweets_send": null,
                "qualified_impressions": null,
                "follows": null,
                "app_clicks": null,
                "retweets": [
                  102
                ],
                "unfollows": null,
                "likes": [
                  15
                ],
                "engagements": [
                  171
                ],
                "clicks": [
                  30
                ],
                "card_engagements": null,
                "poll_card_vote": null,
                "replies": null,
                "carousel_swipes": null
              }
            }
          ]
        }
      ],
      "request": {
        "params": {
          "start_time": "2019-03-12T00:00:00Z",
          "segmentation_type": null,
          "entity_ids": [
            "el32n"
          ],
          "end_time": "2019-03-20T00:00:00Z",
          "country": null,
          "placement": "ALL_ON_TWITTER",
          "granularity": "TOTAL",
          "entity": "LINE_ITEM",
          "platform": null,
          "metric_groups": [
            "ENGAGEMENT"
          ]
        }
      }
    }

​

Reach and Average Frequency

​

GET stats/accounts/:account_id/reach/campaigns

Retrieve reach and average frequency analytics for specified campaigns.

​

Resource URL

https://ads-api.x.com/stats/accounts/:account_id/reach/campaigns

​

Parameters

​

Example Request

GET https://ads-api.x.com/12/stats/accounts/18ce54d4x5t/reach/campaigns?campaign_ids=8fgzf&start_time=2017-05-19&end_time=2017-05-26

​

Example Response

    {
      "request": {
        "params": {
          "campaign_ids": [
            "8fgzf"
          ],
          "start_time": "2017-05-19T00:00:00Z",
          "end_time": "2017-05-26T00:00:00Z",
          "account_id": "18ce54d4x5t"
        }
      },
      "data_type": "reach",
      "data": [
        {
          "id": "8fgzf",
          "total_audience_reach": 1217,
          "average_frequency": 1.01
        }
      ]
    }

​

GET stats/accounts/:account_id/reach/funding_instruments

Retrieve reach and average frequency analytics for specified funding instruments.

​

Resource URL

https://ads-api.x.com/stats/accounts/:account_id/reach/funding_instruments

​

Parameters

​

Example Request

GET https://ads-api.x.com/12/stats/accounts/18ce54d4x5t/reach/funding_instruments?funding_instrument_ids=lygyi&start_time=2017-05-19&end_time=2017-05-26

​

Example Response

    {
      "request": {
        "params": {
          "funding_instrument_ids": [
            "lygyi"
          ],
          "start_time": "2017-05-19T00:00:00Z",
          "end_time": "2017-05-26T00:00:00Z",
          "account_id": "18ce54d4x5t"
        }
      },
      "data_type": "reach",
      "data": [
        {
          "id": "lygyi",
          "total_audience_reach": 1217,
          "average_frequency": 1.01
        }
      ]
    }

​

Synchronous Analytics

​

GET stats/accounts/:account_id

Retrieve synchronous analytics for the current account. A maximum time range (end_time - start_time) of 7 days is allowed.

​

Resource URL

https://ads-api.x.com/12/stats/accounts/:account_id

​

Parameters

​

Example Request

GET https://ads-api.x.com/12/stats/accounts/18ce54d4x5t?entity=LINE_ITEM&entity_ids=8u94t&start_time=2017-05-19&end_time=2017-05-26&granularity=TOTAL&placement=ALL_ON_TWITTER&metric_groups=ENGAGEMENT

​

Example Response

    {
      "data_type": "stats",
      "time_series_length": 1,
      "data": [
        {
          "id": "8u94t",
          "id_data": [
            {
              "segment": null,
              "metrics": {
                "impressions": [
                  1233
                ],
                "tweets_send": null,
                "qualified_impressions": null,
                "follows": null,
                "app_clicks": null,
                "retweets": null,
                "likes": [
                  1
                ],
                "engagements": [
                  58
                ],
                "clicks": [
                  58
                ],
                "card_engagements": null,
                "poll_card_vote": null,
                "replies": null,
                "carousel_swipes": null
              }
            }
          ]
        }
      ],
      "request": {
        "params": {
          "start_time": "2017-05-19T07:00:00Z",
          "segmentation_type": null,
          "entity_ids": [
            "8u94t"
          ],
          "end_time": "2017-05-26T07:00:00Z",
          "country": null,
          "placement": "ALL_ON_TWITTER",
          "granularity": "TOTAL",
          "entity": "LINE_ITEM",
          "platform": null,
          "metric_groups": [
            "ENGAGEMENT"
          ]
        }
      }
    }

​

Active Entities

​

GET stats/accounts/:account_id/active_entities

Retrieve details about which entities’ analytics metrics have changed in a given time period.

This endpoint should be used in conjunction with our analytics endpoints. The results of this endpoint indicate which ads entities to request analytics for. See our Active Entities Guide for usage guidelines.

Change events are available in hourly buckets.

• The start_time and end_time values specify which hourly buckets to query.
• The returned data array will include an object for every entity that should be included in subsequent analytics requests.
• IMPORTANT: The dates that should be specified in subsequent analytics requests should be determined based on the activity_start_time and activity_end_time values.
  • These values represent the time ranges that the stored change events apply to. This is returned per entity.

Note: A maximum time range (end_time - start_time) of 90 days is allowed.

​

Resource URL

https://ads-api.x.com/12/stats/accounts/:account_id/active_entities

​

Parameters

​

Example Request

GET https://ads-api.x.com/12/stats/accounts/18ce54d4x5t/active_entities?entity=PROMOTED_TWEET&start_time=2019-02-28&end_time=2019-03-01

​

Example Response

    {
      "request": {
        "params": {
          "account_id": "18ce54d4x5t",
          "entity": "PROMOTED_TWEET",
          "start_time": "2019-02-28T08:00:00Z",
          "end_time": "2019-03-01T08:00:00Z"
        }
      },
      "data": [
        {
          "entity_id": "2mvb28",
          "activity_start_time": "2019-02-28T01:30:07Z",
          "activity_end_time": "2019-03-01T07:42:55Z",
          "placements": [
            "ALL_ON_TWITTER"
          ]
        },
        {
          "entity_id": "2mvb29",
          "activity_start_time": "2019-02-27T11:30:07Z",
          "activity_end_time": "2019-03-01T07:42:50Z",
          "placements": [
            "ALL_ON_TWITTER",
            "PUBLISHER_NETWORK"
          ]
        },
        {
          "entity_id": "2mvfan",
          "activity_start_time": "2019-02-27T09:00:05Z",
          "activity_end_time": "2019-03-01T06:06:36Z",
          "placements": [
            "PUBLISHER_NETWORK"
          ]
        },
        {
          "entity_id": "2n17dx",
          "activity_start_time": "2019-02-28T02:02:26Z",
          "activity_end_time": "2019-03-01T07:52:44Z",
          "placements": [
            "ALL_ON_TWITTER",
            "PUBLISHER_NETWORK"
          ]
        }
      ]
    }