Lift Study

Create and run an experiment to measure your Facebook campaign's efficiency. Determine what ads strategy drives the most business impact. See Ad Study, Reference.

When you create a lift study, you create a randomized test group of people that see your ads and control group who don't see your ads.

You can securely share conversion data from your ad campaign with Facebook using Facebook pixels, or App Events. Facebook determines the increased conversions generated from your campaign. We compare the number of conversions, people converting, and available sales revenue between test and control groups. Results appear in Ads Manager.

Set Up Studies

Set up a study with one or more groups, called cells. When you set up your study, Facebook randomizes the audience for your ads and assigns people to either the test or control group. After you run a study, Facebook calculates the difference between the test groups and control groups so that you evaluate the impact of your Facebook ads towards business goals.

To set up a study, make a POST call:

https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

You can set up a study with a single test group to see how Facebook ads lead to additional business. You can also set up a study with multiple test groups, which lets you determine what advertising approach works best for your audience.

Example - Set up a lift study with one test group

curl \
  -F 'name="new study"' \
  -F 'description="description of my study"' \
  -F 'start_time=1435622400' \
  -F 'end_time=1436918400' \
  -F 'cooldown_start_time=1433116800' \
  -F 'observation_end_time=1438300800' \
  -F 'viewers=[<USER_ID1>, <USER_ID2>]' \
  -F 'type=LIFT' \
  -F 'cells=[{name:"test group",description:"description of my test group",treatment_percentage:90,control_percentage:10,adaccounts:[<ACCOUNT_ID1>,<ACCOUNT_ID2>]}]' \
  -F 'objectives=[{name:"new objective",is_primary:true,type:"MAI",applications:[{id:<APP_ID>}]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

To create a new study, provide the following:

ParameterDescription

name

Name of study.

description

Brief description of the study's purpose.

cooldown_start_time

Deprecated. Facebook still delivers during the time between observation_end_time and end_time. If you use cooldown_start_time, you should now set this time using start_time.

start_time

Start time of campaign active period. Study start time must be in the future.

end_time

End time of campaign active period.

observation_end_time

End of your post-campaign quiet period which started when the study period ended. We attribute conversions during this period to your study. We still deliver your ads, but our systems stop creating new study audiences. If you don't need a quiet period for your study, set this to end_time.

cells

Cells in study that define test and control groups.

objectives

Objectives of the study. See Defining Study Objective.

viewers

Share this study to a list of Facebook user IDs.

type

For Conversion Lift, the type should be LIFT.

RESTRICTIONS - Once the study starts, you cannot update start_time and treatment_percentage of the cells. You also cannot remove the associated objects, such as adaccounts or campaigns, of the test groups. You can still update the end_time and observation_end_time to a future time if the study has not yet ended, and add new associated objects to test groups.

To run Reach and Frequency in conjunction with Lift measurement, you must set up a Lift study first and make sure the duration of the Reach and Frequency is within the duration of the Lift study.

Create a Test Group

To begin, determine how many people recieve your ads and how many people do not. You must create a test group when you set up the study; pass a list of JSON objects in cells under ad_studies. See Ad Study Cell, Reference. A test group contains the following information.

ParameterDescription

name

Name of test group.

description

Brief description of test group.

treatment_percentage

Defines the people who receive your ads.

control_percentage

Defines a holdout percentage of the people who will not see ads. Treatment plus control percentages must equal 100.

ad_studies

List of ad entities, such as adaccounts or campaigns, to study. Facebook runs and measures all ads under active ad entities during the study period.

Example - Read test groups in a study

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>/cells

Example - Update or modify cell information and treatment and control percentages by providing the cell ID in cells

curl \
  -F 'cells=[{id:<CELL_ID>,treatment_percentage:80,control_percentage:20}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>

Example - Read all the studies that you created at ad_studies for your business

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

Set Up Multiple Test Groups

Set up a study with multiple test groups of Facebook users. This helps measure incremental impact of different Facebook strategies on business goals, such as using different ads targeting options. To set up a study with multiple test groups, provide a list of test groups in cells.

curl \
  -F 'name="new study"' \
  -F 'description="description of my study"' \
  -F 'start_time=1435622400' \
  -F 'end_time=1436918400' \
  -F 'cooldown_start_time=1433116800' \
  -F 'observation_end_time=1438300800' \
  -F 'viewers=[<USER_ID1>, <USER_ID2>]' \
  -F 'type=LIFT' \
  -F 'cells=[{name:"group A",description:"description of group A",treatment_percentage:50,control_percentage:20,campaigns:[<CAMPAIGN_ID1>]},{name:"group B",description:"description of group B",treatment_percentage:20,control_percentage:10,campaigns:[<CAMPAIGN_ID2>]}]' \
  -F 'objectives=[{name:"new objective",is_primary:true,type:"MAI",applications:[{id:<APP_ID>}]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

control_percentage determines the holdout for each test group respective to the total population. For example, you have a study with two test groups: group A is 50% treatment with 20% control and group B is 20% treament with 10% control. This results in ~28.6%, or 20%/70% of the population in group A, to be control users and ~33.3%, or 10%/30% of the population in group B, to be control users.

The sum of treatment and control percentages across test groups normally should equal 100. However, it can be less than 100 for some specific use cases. For example, when you have three test groups that are split evenly at 33%.

You can update, add, and remove test groups in a study.

  • To update an existing test group, refer to its ID in test group.
  • To add a new test group, provide a new test group object.
  • To remove a test group, simply omit it from cells when you update the study:
curl \
  -F 'cells=[{id:<CELL_ID1>,treatment_percentage:60,control_percentage:10},{name:"group C",description:"replacing group B",treatment_percentage:25,control_percentage:5,campaigns:[<CAMPAIGN_ID3>]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>

Define Advertising Objectives

Define advertising objectives you want to measure and how you pass conversion data to Facebook. A lift study requires at least one objective. You cannot modify objectives after the study starts running. See Ad Study Objective, Reference.

Example - Create and add the MAI objective to a study

curl \
  -F 'name="new study"' \
  -F 'description="description of my study"' \
  -F 'start_time=1435622400' \
  -F 'end_time=1436918400' \
  -F 'cooldown_start_time=1433116800' \
  -F 'observation_end_time=1438300800' \
  -F 'viewers=[<USER_ID1>, <USER_ID2>]' \
  -F 'type=LIFT' \
  -F 'cells=[{name:"test group",description:"description of my test group",treatment_percentage:90,control_percentage:10,adaccounts:[<ACCOUNT_ID1>,<ACCOUNT_ID2>]}]' \
  -F 'objectives=[{name:"new objective",is_primary:true,type:"MAI",applications:[{id:<APP_ID>}]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

Name Description Measurement Sources

SALES

Measure the lift in purchases. The reporting will contain dollar amount.

Conversion Pixels, Facebook Pixels, Mobile App, Offline Event Sets, and Custom Conversions

NONSALES

Measures the lift in non-purchase conversions.

Conversion Pixels, Facebook Pixels, and Mobile App

MAI

Measure the lift in mobile application installs.

Mobile App

If you use SALES and NONSALES and use Facebook pixel or Mobile App as event sources, you must provide a list of the event names that you want to capture for the objective. Facebook can then report results based on these specific conversion events.

Measurement Source Event Names

Facebook Pixel

fb_pixel_view_content, fb_pixel_search, fb_pixel_add_to_cart, fb_pixel_add_to_wishlist, fb_pixel_initiate_checkout, fb_pixel_add_payment_info, fb_pixel_purchase, fb_pixel_lead, fb_pixel_complete_registration, custom

Mobile App

fb_mobile_activate_app, fb_mobile_complete_registration, fb_mobile_content_view, fb_mobile_search, fb_mobile_rate, fb_mobile_tutorial_completion, fb_mobile_add_to_cart, fb_mobile_add_to_wishlist, fb_mobile_initiated_checkout, fb_mobile_add_payment_info, fb_mobile_purchase, fb_mobile_level_achieved, fb_mobile_achievement_unlocked, fb_mobile_spent_credits

Create an Objective

Create an objective by passing a list of JSON objects objectives when you create a new study. Objectives contain the following information:

ParameterDescription

name

Name of the objective.

is_primary

A boolean specifying that this is your primary advertising objective. A study can only have one primary objective.

type

Objective value of SALES, NONSALES, or MAI.

adspixels

List of Facebook pixel IDs along with the relevant list of event_names per ID, if applicable.

applications

List of your mobile apps including relevant event_names per ID.

offsitepixels

List of Conversion pixel IDs, if applicable.

offline_conversion_data_sets

List of Offline Event set IDs if applicable. Currently, we don't support event breakdowns for Offline Conversion.

customconversions

List of Custom Conversion IDs, if applicable.

You can also have multiple objectives per study. The result will be aggregated based on objectives. Below is an example of a study with multiple objectives—the MAI objective as seen previously and another SALES objective with Facebook pixel and application as measurement sources.

curl \
  -F 'name="another study"' \
  -F 'description="description of another study"' \
  -F 'start_time=1435622400' \
  -F 'end_time=1436918400' \
  -F 'cooldown_start_time=1433116800' \
  -F 'observation_end_time=1438300800' \
  -F 'viewers=[<USER_ID1>, <USER_ID2>]' \
  -F 'type=LIFT' \
  -F 'cells=[{name:"test group",description:"description of my test group",treatment_percentage:90,control_percentage:10,adaccounts:[<ACCOUNT_ID1>,<ACCOUNT_ID2>]}]' \
  -F 'objectives=[{name:"MAI objective",is_primary:true,type:"MAI",applications:[{id:<APP_ID1>},{id:<APP_ID2>}]},{name:"SALES objective",type:"SALES",applications:[{id:<APP_ID3>,event_names:["fb_mobile_purchase"]}],adspixels:[{id:<FB_PIXEL_ID>,event_names:["fb_pixel_purchase","fb_pixel_lead"]}]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<BUSINESS_ID>/ad_studies

You can update, add, and remove objectives in a study by doing so at the study level similar to modifying test groups. To update an existing objective, refer to its ID in the objectives object. To add a new objective, provide a new objective object. To remove an objective, simply omit it from the objectives parameter when you update it.

Example - Update an objective's applications measurement sources and remove its adspixels measurement sources

curl \
  -F 'objectives=[{id:<OBJECTIVE_ID>,name:"new objective name",applications:[{id:<APP_ID>}],adspixels:[]}]' \
  -F 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>

Example - Read objectives for a study

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>/objectives

Reporting

Retrieve Objectives

A study's objectives are defined during the study setup. See the setup guide on how to define your study's objectives

You can read the objectives that were created for a study by making a GET call to the study's objectives edge.

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_ID>/objectives

For more details on objectives, refer to the Ad Study Objective reference documentation

Retrieve Results

To retrieve results for an objective, you can make a GET call to the objective node by specifying results in the fields parameter. last_updated_results field also tells you when the results data was last updated.

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_OBJECTIVE_ID>?  fields=results,last_updated_results

The resulting data is a JSON object, containing strings with the following default fields:

Name Description

advancedConversions.baseline

advancedConversions.reached subtracted by advancedConversions.incremental.

advancedConversions.bayesianCILower

The lower bound of the 90% symmetrical credible interval for advancedConversions.incremental after applying advanced adjustments.

advancedConversions.bayesianCIUpper

The upper bound of the 90% symmetrical credible interval for advancedConversions.incremental after applying advanced adjustments.

advancedConversions.control

Number of conversion events, such as purchases, from the experiment's control group after applying advanced adjustments.

advancedConversions.incremental

Net increase in number of conversions as a result of running ads after applying advanced adjustments. Calculated as advancedConversions.test minus advancedConversions.scaled.advancedConversions.baseline.

advancedConversions. informativeMultiCellBayesian Confidence

Probability that the ads in the test group of the specified experiment cell had the lowest cost per incremental conversion across all cells in the experiment after applying advanced adjustments.

advancedConversions. informativeSingleCellBayesian Confidence

Probability that the ads in the test group of the experiment caused positive advancedConversions.incremental after applying advanced adjustments.

advancedConversions.lift

advancedConversions.incremental divided by advancedConversions.baseline.

advancedConversions.scaled

Number of conversion events in the control group after applying advanced adjustments, factored by the ratio of population.test divided by population.control. This produces a fairer comparison for advancedConversions.test.

advancedConversions.test

Number of conversion events, such as purchases, from the experiment's test croup, after applying advanced adjustments.

advancedIncrementalROAS

Incremental return on ad spend (ROAS). Calculated as advancedSales.incremental divided by spend.

advancedSales.baseline

advancedSales.reached minus by advancedSales.incremental.

advancedSales.bayesianCILower

The lower bound of the 90% symmetrical credible interval for advancedSales.incremental after applying advanced adjustments.

advancedSales.bayesianCIUpper

The upper bound of the 90% symmetrical credible interval for advancedSales.incremental after applying advanced adjustments.

advancedSales.control

Amount of sales from the control group of the experiment after applying advanced adjustments.

advancedSales.incremental

Net increase in amount of sales as a result of running ads after applying advanced adjustments. Calculated as advancedSales.test minus advancedSales.scaled.

advancedSales. informativeMultiCellBayesian Confidence

Probability that the ads in the test group of the specified experiment cell had the highest incremental ROAS across all cells in the experiment after applying advanced adjustments.

advancedSales. informativeSingleCellBayesian Confidence

Probability that the ads in the test group of the experiment caused positive advancedSales.incremental after applying advanced adjustments.

advancedSales.lift

advancedSales.incremental divided by advancedSales.baseline.

advancedSales.scaled

Amount of sales from the control group after applying advanced adjustments, factored by the ratio of population.test divided by population.control. This produces a fairer comparison for advancedSales.test.

advancedSales.test

Amount of sales from the test group of the experiment after applying advanced adjustments.

buyers.baseline

buyers.reached minus by buyers.incremental

buyers.bayesianCILower

The lower bound of the 90% highest posterior density (HPD) credible interval for buyers.incremental using Bayesian statistics with an uninformative prior distribution.

buyers.control

Number of people from the experiment's control group who purchased.

buyers.bayesianCIUpper

The upper bound of the 90% highest posterior density (HPD) credible interval for buyers.incremental using Bayesian statistics with an uninformative prior distribution.

buyers.delta

The 90% statistical confidence interval associated with the estimated buyers.incremental.

buyers.incremental

Net increase in number of people who made purchases as a result of the ad campaigns. Calculated as buyers.test minus buyers.scaled.

buyers.isStatSig

Boolean indicating statistical significance. 1 means buyers.incremental is statistically significant at 90% confidence level. 0 means buyers.incremental is not statistically significant at 90% confidence level.

buyers.lift

buyers.incremental divided by buyers.baseline

buyers.multiCellBayesianConfidence

Probability that the ads in the test group of the specified experiment cell had the lowest cost per incremental buyer across all cells in the experiment using Bayesian statistics with an uninformative prior distribution.

buyers.pValue

Based on regression analysis, the p-value associated with buyers.incremental.

buyers.reached

Number of people reached by your ads who also converted.

buyers.reachedPercent

buyers.reached divided by buyers.test

buyers.scaled

Number of people in the control group, factored by population.test divided by population.control. This produces a fairer comparison with buyers.test.

buyers.singleCellBayesianConfidence

Probability that the ads in the test group of the experiment caused positive buyers.incremental using Bayesian statistics with an uninformative prior distribution.

buyers.test

Number people from the experiment's test group who purchased.

conversions.baseline

conversions.reached minus by conversions.incremental

conversions.bayesianCILower

The lower bound of the 90% highest posterior density (HPD) credible interval for conversions.incremental using Bayesian statistics with an uninformative prior distribution.

conversions.bayesianCIUpper

The upper bound of the 90% highest posterior density (HPD) credible interval for conversions.incremental using Bayesian statistics with an uninformative prior distribution.

conversions.control

Number of conversion events, such as purchases, from the experiment's control group.

conversions.delta

The 90% confidence interval associated with the estimate for conversions.incremental.

conversions.incremental

Net increase in number of conversions as a result of running ads. Calculated as: conversions.test minus conversions.scaled.

conversions.isStatSig

Boolean indicating statistical significance. 1 means conversions.incremental is statistically significant at 90% confidence level. 0 means conversions.incremental is not statistically significant at 90% confidence level.

conversions.scaled

Number of conversion events in the control group, factored by the ratio of population.test divided by population.control. This produces a fairer comparison for conversions.test.

conversions.lift

conversions.incremental divided by conversions.baseline

conversions. multiCellBayesianConfidence

Probability that the ads in the test group of the specified experiment cell had the lowest cost-per-incremental conversion across all cells in the experiment using Bayesian statistics with an uninformative prior distribution.

conversions.pValue

The p-value for conversions.incremental.

conversions.reached

Number of conversion events from people who purchased and were reached by your ads.

conversions. singleCellBayesianConfidence

Probability that the ads in the test group of the experiment caused positive conversions.incremental using Bayesian statistics with an uninformative prior distribution.

conversions.reachedPercent

conversions.reached divided by conversions.test

conversions.test

Number of conversion events, such as purchases, from the experiment's test group.

frequency

The average number of times your ad displayed to each person in population.reached.

impressions

Number of billable ad impressions for ads associated with the lift study.

population.control

Number of people in the control group of the experiment.

population.reached

Number of people in the test group reached by one or more ads associated with the study.

population.test

Number of people in the test group for the experiment.

sales.baseline

sales.reached minus sales.incremental

sales.bayesianCILower

The lower bound of the 90% highest posterior density (HPD) credible interval for sales.incremental using Bayesian statistics with an uninformative prior distribution.

sales.bayesianCIUpper

The upper bound of the 90% highest posterior density (HPD) credible interval for sales.incremental using Bayesian statistics with an uninformative prior distribution.

sales.control

Amount of sales from the control group of the experiment.

sales.delta

90% confidence interval associated with the estimate for sales.incremental.

sales.incremental

Net increase in sales as a result of running campaigns. Calculated as: sales.test - sales.scaled.

sales.isStatSig

Boolean indicating statistical significance. 1 means sales.incremental is statistically significant at 90% confidence level. 0 means sales.incremental is not statistically significant at 90% confidence level.

sales.lift

sales.incremental divided by sales.baseline

sales.multiCellBayesianConfidence

Probability that the ads in the test group of the specified experiment cell had the highest incremental ROAS across all cells in the experiment using Bayesian statistics with an uninformative prior distribution.

sales.pValue

The p-value associated with sales.incremental.

sales.reached

The amount of sales from purchasers reached by your ads.

sales.scaled

Amount of sales in the control group, factored by the ratio of population.test divided by population.control. This produces a fairer comparison for sales.test.

sales.singleCellBayesianConfidence

Probability that the ads in the test group of the experiment caused positive sales.incremental using Bayesian statistics with an uninformative prior distribution.

sales.test

Amount of sales from the test group of the experiment.

spend

Total amount spent up to the current time for your study.

Sample response shown as parsed JSON for ease of reading:

{
    "last_updated_results": "2019-04-30",
    "id": "<STUDY_OBJECTIVE_ID>",
      "results": [{
        "population.test": 8008,
        "population.control": 8069,
        "population.reached": 7329,
        "impressions": 151928,
        "spend": 2367.74,
        "frequency": 20.72970391595,
        "buyers.test": 795,
        "buyers.control": 685,
        "buyers.scaled": 679.82153922419,
        "buyers.incremental": 115.17846077581,
        "buyers.reached": 720,
        "buyers.reachedPercent": 0.90566037735849,
        "buyers.baseline": 604.82153922419,
        "buyers.lift": 0.19043379460915,
        "buyers.delta": 60.074001807654,
        "buyers.pValue": 0.0016110673739798,
        "buyers.isStatSig": 1,
        "buyers.singleCellBayesianConfidence": 0.98727038188854,
        "buyers.multiCellBayesianConfidence": 0.78486645400638,
        "buyers.bayesianCILower": 30.473292849176,
        "buyers.bayesianCIUpper": 199.95922666997,
        "conversions.test": 871,
        "conversions.control": 736,
        "conversions.scaled": 730.43598958979,
        "conversions.incremental": 140.56401041021,
        "conversions.reached": 789,
        "conversions.reachedPercent": 0.90585533869116,
        "conversions.baseline": 648.43598958979,
        "conversions.lift": 0.21677391857774,
        "conversions.delta": 67.978575551168,
        "conversions.pValue": 0.00067032207485984,
        "conversions.isStatSig": 1,
        "conversions.singleCellBayesianConfidence": 0.99176024719258,
        "conversions.multiCellBayesianConfidence": 0.80160595182145,
        "conversions.bayesianCILower": 44.564010410212,
        "conversions.bayesianCIUpper": 236.54889081671,
        "advancedConversions.test": 870,
        "advancedConversions.control": 771.72138217184,
        "advancedConversions.scaled": 765.88732537268,
        "advancedConversions.incremental": 104.11267462732,
        "advancedConversions.baseline": 684.88732537268,
        "advancedConversions.lift": 0.1520143106323,
        "advancedConversions.informativeSingleCellBayesianConfidence": 1,
        "advancedConversions.informativeMultiCellBayesianConfidence": 0.819,
        "advancedConversions.bayesianCILower": 56.7105227,
        "advancedConversions.bayesianCIUpper": 156.976729,
        "incrementalROAS": 1.3104231791133,
        "sales.test": 54663.94,
        "sales.control": 51953.96,
        "sales.scaled": 51561.198621886,
        "sales.incremental": 3102.7413781138,
        "sales.reached": 49594.66,
        "sales.baseline": 46491.918621886,
        "sales.lift": 0.06673721950148,
        "sales.delta": 5708.9583320282,
        "sales.pValue": 0.37130299866185,
        "sales.isStatSig": 0,
        "sales.singleCellBayesianConfidence": 0.74013779586612,
        "sales.multiCellBayesianConfidence": 0.70927872163835,
        "sales.bayesianCILower": -4827.3888400561,
        "sales.bayesianCIUpper": 11330.643600358,
        "advancedIncrementalROAS": 1.4777176303831,
        "advancedSales.test": 54619.86,
        "advancedSales.control": 51510.417142093,
        "advancedSales.scaled": 51121.008857837,
        "advancedSales.incremental": 3498.8511421632,
        "advancedSales.baseline": 46095.808857837,
        "advancedSales.lift": 0.075903888636687,
        "advancedSales.informativeSingleCellBayesianConfidence": 0.97125,
        "advancedSales.informativeMultiCellBayesianConfidence": 0.7505,
        "advancedSales.bayesianCILower": 596.181983164,
        "advancedSales.bayesianCIUpper": 6470.00248918
    }]
}],
}

Breakdown Results

In addition to retrieving the results per objective, you may choose to breakdown the results by providing the breakdowns parameter.

curl -G \
  -d 'access_token=<ACCESS_TOKEN>' \
  https://graph.facebook.com/<API_VERSION>/<STUDY_OBJECTIVE_ID>?fields=results&breakdowns=['cell_id']

The following are the available breakdown dimensions:

Breakdown Values

age

13-17, 18-24, 25-34, 35-44, 45-54, 55-54, 65+

cell_id

IDs of the available cells in the study.

gender

M or F

country

Two letter country codes (ISO 3166-1 alpha-2). Example: US, GB, IN, AU.

Currently supported only when queried in combination with cell_id.

Example: breakdowns=['cell_id','country']

The results return multiple JSON objects in the array based on the available breakdowns. For example, if cell_id is provided, the results are broken down by the number of cells in the study. You may provide one or more breakdowns; however, the combination of breakdowns must at least 100 conversions from test and control groups combined for results to display.

{
  "id": "<STUDY_OBJECTIVE_ID>",
  "results": [
  {
    "cell_id": "<CELL_ID1>",
    ...
    Default fields where the values are specific to the <CELL_ID1> breakdown
    ...
  },
  {
    "cell_id": "<CELL_ID2>",
    ...
    Default fields where the values are specific to the <CELL_ID2> breakdown
    ...
  }],
}