Criteo is an advertising company that provides online display advertisements, especially for retargeting campaigns.

Create a segment on DataCommander to build your audience. Then, you can push this audience to Criteo through our connector Criteo (audiences).

You need first to accept a consent link send by our consultants. This link is needed to authorize our DataCommander Criteo App to have access to your Criteo account to push audiences.

Then you can configure the connector, select the segment you created just before and simply enter your Criteo Advertiser ID (you can find it on your Criteo account).

Click on SAVE and it will automatically create a new audience (user list) on Criteo side.

You can send us your conversions data in 2 ways :

• Files importer

• API

The segment overlap feature allows you to compare your segments and visualize directly the audience shared between 2 segments.

This feature is very helpful in order to understand how your audience is divided and grouped in your segments.

It allows you to reduce the marketing pressure as it helps you to avoid activating similar audiences. For example, you can send an email to users on a segment. Then you can decide to send a SMS to users on another segment. However, it is recommended to check the overlap between the 2 segments in order to be sure you are not activating the same audience which can raise the number of opt-out.

It also allows you to refine your segments by divided a big segment into small pieces more relevant.

First step, select one of your segment and it will automatically calculate the overlap between the segment you choose and all your others segments.

Then you can visualize and analyse the overlap with the number of users in both segments and the percentage.

With the visual representation of each of your segments, you have an overview of the size of the segments compared, which is helpful to understand if your segment is part of a bigger one or is totally apart.

The Trade Desk is one of the largest independent demand-side platform (DSP) providing real-time ad pricing and placement for advertisers, ad agencies and brands.

Create audience segments on DataCommander and simply push these audiences (users) to The Trade Desk through our connector. As a result, you can then create personalized ads for these users on The Trade Desk platform. This connector uses the cookie-sync process to match users.

How to configure The Trade Desk Connector?

It is quite simple, you just have to enter your The Trade Desk Advertiser ID and The Trade Desk Secret Key:

You can find this information directly on your The Trade Desk account, in Preferences section.

For this connector, the concept is to send to Tableau each X minutes new events in append mode ( ex : pageviews, clicks, impressions, ...)

We are able to push Data to Tableau every 10 minutes (full export or delta).

Append mode will add new lines to existing table in Tableau, so the stream has to be configured to differential (export only new events), like this :

For users universe, it's different, you can't choose "append" because users are updated continously and you don't want to add each times the same users, so you have to choose overwrite mode for this univers, with an export each night (because it will take hours to send all users to Tableau) (edited)

To increase relevancy, many marketers are building a single view of their prospects and customers that goes beyond the devices they’re using to consume digital content or the channels they’ve previously engaged with. This is made possible with DataCommander as it allows enterprises to compile, standardize, and store all previous prospect and customer interactions in one place that’s accessible for future campaigns.

This solution seamlessly integrates with many CRM platforms enabling both sales and marketing teams to enhance their existing prospect and customer profiles with online and offline purchasing data, browsing histories, previous marketing campaign exposures, and even call center interactions.

The DataCommander platform automates the process of combining data across disparate solutions enabling marketers to do what they do best: understanding their audiences, designing relevant campaigns to target them, analysing campaign performance and leveraging the insights gained to optimise future campaigns.

Commanders Act’s DataCommander meets two main objectives: to help you better understand your data and to help you make the most of it. In a nutshell, it allows you to:

You can send us your users data in 2 ways :

• (Get in touch with your Account Manager)

File structure

• One line per user

• Header on the file is needed for the matching

• Encoding: utf8

• The variable can not be calculated. It must be one line, one variable

• PGP or GPG key encryption are not supported. Files must be unencrypted before import.

Variables :

• it must be the same name of data variable (Variables are in uppercase, they will transcribe automatically in lowercase)

• You can add new variables. Don't forget to define them in DataCommander variable interface

• The CRM variables should be prefixed with "person." and custom variables by "person.custom."

File example

Segment management

This is where you create, edit and/or delete segments.

1. The first column displays the segments’ names.

2. The second column displays the segments’ technical IDs (present in the “TC AUDIENCE” cookie we use to store information about a segment).

3. The third column shows the percentage of visitors that are included in a segment.

4. The fourth column specifies the associated streams, using this segment.

5. The fifth column shows when was the last time a segment was modified.

6. The sixth column is where you edit or delete a segment, by clicking the pencil or the trash can.

How to add multiple values in a segment?

Let’s say, for example, you want to match all users with ZIP code equals ‘75009’ or ‘92120’ or ‘94230’… and you realized you have more than 30 different values!

You can type one zip code per one, but it is long.

Or you can also copy and paste directly your values. However, you should use a separator between these values ; or |

How to segment on conversion items?

You can segment on conversion items, meaning products bought by users.

First, you have to select 'conversion' universe and define a condition for conversions, like 'conversion ID exists' = users have at least 1 conversion.

Then the button 'ADD CONDITION' will appear, and you can define more conditions, including conditions for conversion items, select 'product':

Then, you can easily segment on conversion items:

If you wish to share your data via flat file feed integration, please use a CSV feed format, following the structure below.

Files can be uploaded to the Commandersact FTP or downloaded from your own FTP by CommandersAct. Get in touch with your Account Manager to agree the integration method that is best for you.

Use our CSV conversion importer connector to configure the FTP import.

Structure of the file:

1 file with 1 line per product (conversion item)

Each line in the file should be a distinct product (conversion item) with their unit price and quantity. Transactions that include several products should be split into multiple lines, sharing the same conversion_id.

Example: conversion_ID_1, conversion_item_1, conversion_ID_1, conversion_item_2, conversion_ID_1, conversion_item_3,

Push your offline conversions, like purchases made in physical stores, directly to Criteo.

Store ID variable:

You must specify the Store ID variable, the variable corresponding to the store identifier on your conversions.

Offline conversions filter:

By default, we apply a filter on conversion_type = 'offline' to push only offline conversions. However, if you have a specific setup, you can change this value if needed.

Criteo Account ID:

Enter your Criteo Account ID, you can find it directly on Criteo interface.

PUT conversion

Google Customer Match allows you to push users to Google Ads. The matching key is email (SHA-256 or in clear only).

You can create an audience segment, push it to Google Ads and activate it. Users matched could be then activated on Google services (Search, Display network, Gmail, Youtube, Google Shopping...).

What are the requirement to sync. DataCommanders with Google Ads?

The connector Google Store Sales Direct allows you to send conversions to Google Ads.

Store sales measurement allows you to use your sales data in a privacy-safe way to understand how much value your ads are truly driving for in-store purchases. By uploading and matching transaction data from your business, you can see how your ads translate into offline purchases.

More information:

Send your offline conversions (like purchases made in stores) directly to Facebook.

Create a new stream, select Facebook Offline conversion connector and enter the information needed:

How to find the Offline Event Set Id?

On Facebook Business Manager,

This is where you create data streams and establish a connection between Commanders Act DataCommander and the partners you wish to share segments with.

1. The first column displays the name of the data stream.

2. The second column displays the type of transfer set up for the data stream: a one-time transfer or a scheduled transfer, with variable frequency.

Vip cart abandonment campaign

Goal:

Identify your best customers (VIP Flag) who where exposed to your Christmas Campaign and added a product to their cart without confirmation. Then launch a specific Email campaign to these customers with a 30% discount in order to convert them.

Step #1: adding the DataCommander tag

The first thing to do is to add Commanders Act’s Commanders Act tag into each and every container present on your site’s pages.

The tag must be added from the TagCommander interface.

1.Go to TagCommander

Go to the “EDITION” tab

2. Click “SELECT”

3. Click “ADD TAGS”

In the tag library look for the “DataCommander V.1.0” tag and add it to your container by clicking the “ADD TAG(S)”:

When the tag is added, you will need to generate (a) new version(s) of the container(s) you placed the tag into and proceed to deployment.

Step #2: creating a segment

Now that you have added the tag and deployed the containers, you can create segments. Follow the steps below to do so.

Log in to Commanders Act from our Website if you are not already logged.

Click the DataCommander menu, then the “SEGMENT” button and then “ADD SEGMENT” to the right.

In the window that pops up, name your segment (1), choose whether you want to use it also in the TagCommander product or not (2) and add it (3).

If you choose “no”, tag firing rules based on this segment will not be made available in the “RULES” step from TagCommander.

In the configuration window that appears next:

1.Your recently created segment will be selected by default. You can display the list of all your available segments (should you wish to edit a different one), by clicking the arrow next to the segment’s name.

2.The cog wheel allows you to change your segment’s name and select whether you want to use it in the TagCommander product or not.

3.This menu allows to include (HAVE) or exclude (DO NOT HAVE) visitors from your segment.

4.This menu allows you to select which data cluster you are wanting to consider information from. Note: if you want to enable “customer journey” and “conversion”, you must have subscribed to our “MixCommander” product and request the activation of the MixCommander data unit in the DataCommander module. Please contact your account manager or write to in this case.

5.Select your data universe (visitor “page views”, “properties”, “customer journey” or visitor “conversions”) and the corresponding variable from the dropdown menu (in the screenshot below, the data universe is “page views” and the variables suggested are those from your data layer).

6.The variable is selected

7.Select from the dropdown menu the type of matching you need (equals, contains, is bigger or smaller than, begins with, etc).

8.In this field, select the variable’s values you wish to define your segment with.

9.An auto completion feature will propose you all the values that have been collected so far and available in your global database.

Note: the field will be auto-completed taking the letters a variable starts with.

For example, if you are looking for “Insurance” and type “In”, “Insurance” will be proposed in the list (provided it is present in the database). But if you type “surance”, nothing will be proposed, even though “Insurance” is present in the database.

/!\ This field is also case sensitive: typing “Insurance” instead of “insurance” will have an impact in the listed values that will be displayed.

10.Chose the frequency range – exact, minimum or maximum occurrences – of the chosen values.

11.Enter the amount of occurrences.

12.Select the period of time.

13.“SAVE” your segment configuration.

14.A counter indicates the size of your segment in real time: the number of individuals belonging to that segment and the percentage it represents compared to the global population of your visitors.

You can add more complex segments by adding more conditions: everything in a row is called a “condition”. When it is met, a visitor will be associated to your segment. Segments can be wider and more complex by adding more conditions, based on coding logical operators AND/OR.

To do so, click “ADD CONDITION” (1): It will add a row that will need to be configured just like you just did for the first one.

Once configured it will look like this:

The example above can be read as:

Segment name: Hot leads

Description: Hot leads are visitors whose page views’ information contain a page type that is equal to “Produit/assurance-auto” or “Actu-mobile” AND who are between 26 and 49 years old. In addition, to be included in said segment, the variables Page name and Age must have been populated with the aforementioned values at least four times in the past seven days.

In this example, no visitors within a pool comprising 10.9 million people matched that criteria. This is how segments are created within DataCommander, you can try any combination you like and see how many individuals your segments have and modify their configuration according to your needs.

Nevertheless, when the segment was made of only one type of visitors (those who had looked for the mentioned products at least once in the past 67 days), the population contained in that segment totaled 46,439 visitors. These are examples meant to illustrate the possibilities offered by EDataCommander in terms of segment configuration.

When you are satisfied with you segment, click save and proceed to creating more segments or adding connectors in the “SHARE” tab.

Step #3: sharing data streams

When you have finished creating your segments and wish to add connectors, go to the “SHARE” tab.

Note: Prior to doing this, please make sure your Commanders Act consultant has configured a data transfer protocol to connect with your partners. The most commonly used is FTP. You can contact your Commanders Act consultant or write to to do so.

1. Go to “SHARE”

2.Click “ADD STREAM”

3.In the window that pops up, enter the name of your stream.

4.Click “ADD”.

5.The configuration window will appear and will allow you to select the data category you wish to share with partners (“Page Views” or “Visitors” in this case [A]), and the segment among those you have created (in this case, “Hot Leads” [B]).

6.Select a partner.

7.STREAM LIST displays all your streams.

8. Once you select your partner, a new configuration window will appear for you to enter the required details.

Note: you can have the stream transferred through an FTP or by Email, all you need to do is select the corresponding icon.

In this “new” (enlarged) window (CONFIGURATION) you will have to:

A. Click “EDIT” to select the information available and related to the segment that you wish to share with your partners.

B. Select the information to be transferred (variables): one by one, all at the time or all but one, two or three, etc.… Selected variables will turn purple; unselected variables remain gray.

C. Select the export frequency (once or at a later date).

D. Name the file/export.

E. Enter the time range (period during which the data transfer will happen).

F. Define the transfer frequency.

G. Define the period of time for which you wish to send the information (last 20 days for example).

H. Enter the email addresses of the people who should receive a notification when the stream is shared.

In the Advanced settings, you will need to select:

I. The CSV separator you wish to use.

J.The Encoding type.

9. When you are done configuring the segment and sharing options, click “SAVE” and wait a little while to allow the first file to be transferred.

Step #4: Using segments into TagCommander

Once your segments are created, you can use them immediately in the TagCommander interface.

To do so,

1.Go to the TagCommander interface.

2.Once there, select the container where the tags you want to fire based on a segment are.

3. When it opens, go to the RULES section.

4. Click “Constraints”.

Note: segment-based rules can only be of the “constraint” type.

5.Click “ADD TAG CONSTRAINT”

6.Select the “DataCommander” trigger type

7.Select the type of rule you wish to activate:

-“DataCommander OR Condition (Up to Six Possible Audiences)”: It allows to fire one or more tags depending on the segment a visitor belongs to (can be one or another).

-“DataCommander AND Condition (Up to Six Possible Audiences)”: It allows you to trigger one or more tags if a visitor belongs to several segments.

-“Engage Event Activation”: It allows you to define which events you wish to trigger based on the segment a visitor belongs to. After defining the events, all you need to do is create one or more event constraints based on the segment(s) a visitor belongs to. You can do this with the DataCommander OR/AND conditions.

8.Name the rule.

9.Check the boxes corresponding to the tags you wish to trigger based on that rule.

10.Select your audience(s) from the dropdown menu.

11.Add your rule.

When the rule(s) are added, you will need to generate and deploy a new container*.

How to read it

The color red represents a high probability of purchase (the more intense the red is the higher the probability) The blue color represents a high probability of no purchase (the darker the blue is, the less likely users are to buy)

The first point on the left represents 100% of the population, a first split is made on the variable total_order_amount.

On the one hand, those who have bought in total (over their entire purchase history) for less than 25€, have very little chance of buying (very dark blue); on the other hand, those who have bought for more than 25€, they have a little more chance of buying (light blue) Among those who have recently been in the funnel have a good chance of buying (light red circle), and among them, those whose first visit date is less than 28 days, have a higher probability of buying even more (very dark red circle).

And for those whose first visit is more than 28 days, we see that those who have seen less than 22 pages recently are unlikely to buy, except for those who have a total page view history of less than 55 and who have recently seen more than 4 pages.

Unlike those who have a total page view history of more than 55 and who have recently seen less than 8 pages, who will not buy.

Etc., etc. by following all nodes.

Lessons you learn from it

1. The most predictive variable is the total amount of purchases (total_order_amount), I have to take it into account when I create my segments

2. The recent presence in the purchasing tunnel significantly changes the deal (not surprisingly, hence the interest in making the relaunch abandon baskets)

3. The volume of pages viewed is an indicator of the probability of purchase and depends on the date of first visit (it is deduced that there is a kind of ratio that allows to deduce an intentionist, according to his date of first visit, his total number of pages viewed ever and his number of pages viewed recently) So I have to create as many segments as there are red nodes on the far right of the screen to be able to find all the visitors who are intentionalists (or dig this ratio story to create a new score variable to facilitate the rest)

The default dashboards provide real time figures about your site’s visitors (returning versus new, traffic, source of traffic) and conversions, allowing you to create more efficient segments and optimize results very quickly.

The period of analysis reflected in the dashboards consists of the last 24 hours. All indicators are updated every five seconds.

What is a control group?

A control group is very helpful to measure the performance of a campaign. A control group is a small group of users (a percentage of users from associated segments) which is willingly not exposed to an ad or an email in order to measure the difference in terms of engagement or conversion between the population not exposed (control group) and the population exposed (users in the associated segments).

In our case, we take into consideration only conversions for now (online and offline purchases).

How it works?

A control group is setup on a stream (share menu). Check the box ‘Set control group’ to activate the possibility to configure a control group.

Specify the % of users you want to set for your control group: these users will be not pushed on the stream (and so not exposed to the campaign).

10% is generally enough to have statistically significant metrics, however for very small segments it is recommended to double this %.

As soon as you start the stream, the control group will be launched also, and results will be recorded.

⚠️ Limitations

• be careful with the modification of streams with an active control group as it impacts the results on the campaign performance dashboard. Same with segments used in a stream with an active control group, if you modify a condition it impacts all the results.

• When a stream is over you cannot relaunch it, you should duplicate it

How can I read the results?

When a stream with a control group is activated, on the stream list page an icon will appear on the right of the stream’s name.

Click on this icon to access to the analytics part.

First, select the period of analysis: you can choose for example to read the results on the last 3 days or last week.

All the figures will be related to this analysis period.

Definition of each metrics:

Total population: number of users in segments which were selected on the stream. It is equals to Audience activated + Control Group.

Audience activated: number of users that were pushed on the stream (and exposed to the campaign). It is equals to Associated segments – control group.

Control Group: number of users in the control group, it represents the % of users selected to be in the control group and not exposed to the campaign. It is equals to Associated segments – audience activated.

Cards

You have here some metrics related to conversions.

We compare the performance between the users exposed and not exposed in terms of conversions (online and offline purchases).

In details:

Incremental revenue

Incremental revenue represents the campaign impact on your revenue.

All starts with a supposition: if we consider the full population in the control group what are the performances, and what is the incremental revenue generated by the campaign?

This incremental revenue could be positive or negative, and it represents the campaign performance and impact.

In details, we consider the full population of users (= Audience activated + users in control group) and we apply control group results for Conversion Rate and Average Basket Amount to all these users:

Then, we can easily compare the revenue generated by the Audience activated and the simulated revenue generated by the total population as control group and see if the incremental revenue is negative or positive.

Formula:

Incremental revenue = (Revenue generated by the Audience activated + Revenue generated by the Control group) - (simulated Revenue generated by the total population as control group)

Conversion rate

For each population, we calculate here the conversion rate over the period selected.

Formula:

Conversion rate = number of conversions ÷ number of users

(we compare the conversion rate calculated for the population ‘audience activated’ and for the population ‘control group’)

Uplift (% difference for the conversion rate)

Represents the impact of the campaign. As we compare users in the activated audience and users in the control group, we have to determine the impact of the campaign in terms of conversions.

If the uplift is low or negative, that means users in control group have good results without the exposure to the campaign and so the campaign is not as efficient as it should be.

On contrary, if the uplift is high, that means the campaign is over performing because users exposed to the campaign convert more than users in the control group, the impact is very positive on purchases.

Formula:

Uplift = ((conversion rate ‘Audience activated’ - conversion rate ‘Control group’) ÷ conversion rate ‘Audience activated’) x 100

Average Revenue Per User (ARPU)

We compare here the revenue per user for each population (audience activated and control group).

Formula:

Average Revenue Per User (ARPU) = Total revenue ÷ number of users

(we compare the ARPU calculated for the population ‘audience activated’ and for the population ‘control group’)

Average basket amount

We compare here the average basket amount for each population.

Formula:

Average basket amount = Total revenue ÷ number of conversions

(we compare the average basket amount calculated for the population ‘audience activated’ and for the population ‘control group’)

Charts

Conversion rate evolution

You can see here the conversion rate evolution day after day for the two populations: the audience activated and the control group.

This chart is cumulative in order to reflect more the impact in terms of users, exposure and conversions, that means for each day conversions are added.

Radar

You can visualize directly here the comparison between the two populations (audience activated and control group) for the following metrics: ARPU (Average Revenue Per User); Average Basket Amount; Conversion Rate. It allows you to analyze quickly which metrics are very impactful and relevant to compare the populations.

Table

You have here a summary of all metrics needed to compare the populations, such as the number of users and number of conversions (needed to calculate the conversion rate for example). The turnover is based on total_final_conversion_amount, meaning it could evolve depending on products returns, for example.

Can I export the control group?

Yes, you can select an export (email, S3 or FTP for example), and select the universe 'campaign':

You can select 1 segment or choose 'all users' and you can export the following variables:

• Control Group: does the user belong to the control group? True / False

• Exposure count: number of exposure to campaign for a user = number of times a user entered a segment and was pushed through the connector

• First exposure: date of first exposure

Default variables

User universe

• 'User ID'

• 'Hashed email address'

• 'First visit date'

• 'Last visit date'

• 'Total page views'

• 'Total conversion amount'

• 'First conversion date'

• 'Last conversion date'

• 'Continent' 'Country'

• 'Region' 'City'

• 'Postal code'

• 'User gender'

• 'User age in years'

• 'User postal code'

• 'User address'

• 'User firstname'

• 'User lastname'

• 'Timestamp'

• 'Stream segment public IDs'

• 'Stream segment labels'

• 'Audience public IDs'

• 'Audience labels'

• 'User alias'

• 'User category'

• 'User agency'

• 'User loyalty card id'

• 'Last Device Name'

• 'Last Device Type'

• 'Total sessions'

• 'User Id List'

• 'User Email Hash List'

• 'Is master user'

• 'Original Products Count'

• 'Cancelled Products Count'

• 'Returned Products Count'

• 'Exchanged Products Count'

• 'Final Products Count'

• 'Total Original Conversion Amount'

• 'Total Cancelled Conversion Amount'

• 'Total Returned Conversion Amount'

• 'Total Exchanged Conversion Amount'

• 'Total Final Conversion Amount'

• 'Number of conversions'

• 'Date of First Conversion'

• 'Date of Last Conversion'

• 'Flags'

Pageviews universe

• 'New session'

• 'Env template'

• 'Env work'

If you need to map your segments:

• Stream segment labels => Labels of segments selected for the stream

• Stream segment public IDs => IDs of segments selected for the stream

• Audience labels => Labels of all segments to which the user belongs

Q&A

"Default variables" means ...

1. They are declared on each account per default? --> Yes, by our servers

2. They are populated automatically by DataCommander tag? No

'Custom variables' are variables that could be created especially for you, to suit to your business. Limits:

Variable name : varchar(128) Variable shortname : varchar(55)

TRUST variables

Consents from our product Trust (our Consent Management Platform) are stored on users and you can segment or export these values with these variables:

Variables types

String

• Allows to treat all kinds of data, incl. "" (empty)

• Can come from collect, API, import

• Can be used with text operator such as 'contains', 'begins with'...

Integer

• Is either negative {-1, -2,-3, -4, -5, ... }, positive {1, 2, 3, 4, 5, ... }, or zero {0}

• Can come from collect, API, import

• Can be used with numeric operators for segmentation

Date

• Please be careful when you try to use this very specific type

• Format is very specific: YYYY-MM-DDTHH:mm:ss

• Cannot be empty

Boolean

• Is used to retrieve true / false for a specific information

• Can come from collect, API, import

• Can be used in the segmentation either with 1/0 - true/false - T/F: these 3 options retrieve the same results, any other usage will retrieve the value of true

Float

• Is whatever figure you want

• Cannot be empty

• Can come from collect, API, import

Object

• Used to store several information in one variable

Session variables

This is the list of proper variables you can use

How to use them?

Variables available in the user universe

• First session date / Last session date = the first / last session date that is associated to a TCID

  • date format

  • can be used for segmentation

Variable available in the page view universe

• New session = the first session variable populated on pageviews

  • boolean

  • can be used for segmentation (but honestly quite complicated to really use it...)

Variable encryption

Some information about how data is encrypted in DataCommander (in AES 256 format)

If you need encrypted data in DataCommander don't forget to pick 'Yes' in encrypted option in Create variable step.

This is the encryption scheme:

It means that we store encrypted data, and we are able to export clear data for our partners.

An API based Facebook Connector can be set up with the following procedure. The connector will send users data of all the users belonging to a given segment. The audience sent can contain both FB subscribers and non subscribers.

User variables mapping

More information you send to Facebook, better is the matching with Facebook. You can choose to send email, phone number or any other information to increase the matching with Facebook.

First step:

Ensure that you already have a Google account (Google Ads, DV360,...)

Contact your Google Account Manager to whitelist our DMP + get your Client ID (Client Customer ID: Your DoubleClick Client Customer ID)

GET segments list

Returns the list of segments created in Engage UIX

Resource URL

https://api.commander1.com/api/dms/segmentation/segments/list

Resource Information

Parameters

Example Request

GET

https://api.commander1.com/api/dms/segmentation/segments/list?site=1234&token=WvNIX8955cnZ7WF0f632s0Wb99Ql3rtA

Example Result

GET segments IDs

Returns the list of segments for the user

Resource URL

https://api.commander1.com/api/dms/segmentation/segments

Resource Information

Parameters

Example Request

GET

https://api.commander1.com/api/dms/segmentation/segments?site=1234&callback=tC_funcEngage

Example Result

Visitor

GET https://api.commander1.com/v1.0/engage/visitors/

This endpoint allows you to get properties for one specific visitor. When you create the token, you can define which properties to return. This API is more designed to be called from a tag in each user's browser.

Query Parameters

User

GET https://api.commander1.com/engage/user/

This endpoint allows you to get properties for one specific user based on a user_id. When you create the token, you can define which properties to return.

Query Parameters

User

PUT https://api.commander1.com/engage/user/

Insert or update a user

Query Parameters

Example Request

PUT

https://api.commander1.com/engage/user/?site=1234&user_id=1234&tc_id=1234&token=WvNIX8955cnZ7WF0f632s0Wb99Ql3rtA

DELETE user

Delete a user

Resource URL

https://api.commander1.com/engage/user/

Resource Information

Parameters

Example Request

DELETE

https://api.commander1.com/engage/user/?site=1234&user_id=1234&tc_id=1234&token=WvNIX8955cnZ7WF0f632s0Wb99Ql3rtA

Response

What is an augmented user attribute?

This feature allows you to create enriched properties on your user: flags, ratio, rolling count/average, conditional value, boolean...

Enrich your data with your business rules in a user-friendly UI.

An enrichment transforms a property from a static value to a dynamic/enriched one.

By introducing business rules to your attributes, you give them context and more meaning. These enriched attributes then become the building blocks for dynamic segmentation, analytics, or can be retrieve in the datalayer via TagCommander to enrich your tags.

Flag

Description

Flag allows you to tag your customers who met all the conditions you defined that are relevant to your business. It is helpful to create advanced segmentation based on criteria. You can tag your best customers or identify visitors who have an interest on your products but did not buy. Then you can create new segments based on these specific flags and mix it with more granular conditions (ex : have not seen a specific page within last 2h).

→ Segments can help you to identify and activate your customers based on their live behaviour: page views, clicks, sign-up, orders… Your customers are interacting with you, your websites, your applications, your customer service… you have to identify these live actions in order to prepare the next best action that will enhance your customer experience: for example onsite personalization after a click on a remarketing ad and a specific product view, personalized email after a contact with the customer service…

Segments are built for instant reaction : customer actions 〉identification 〉marketing activation Because of this, segments are dynamic. Your users, according to their actions, will dynamically enter and most importantly exit automatically your segments the second they no longer meet the conditions of the segment . You are sure to work on fresh data.

→ In a different way, Flags can help you to categorize your customers based on what really matter for your business: revenue.

You have to define which dimensions are key for your business and define a matrix that can help you to categorize your customers. For example, you want to flag your customers based on their order frequency and average order amount:

However, you can go further with flags because you can integrate other dimensions such as period, online/offline purchase or whatever you need. This is allowed because there is no data retention for a flag, data is stored for a long time: the flag defined will not be removed on the user, except if you defined some exit conditions.

Your new flag will take into consideration all the future actions/events/hits from the date you have created it.

Popular use cases

• Flag VIP customers: flag all your best customers

• Flag window shoppers: customers interested but not converted

• Review our

Example

You would like to launch a fidelity program with different status for each customer: Platinum, Gold, Silver, Bronze… You have to define all characteristics for each status and then create flags in order to categorize your customers according to the criteria you decided.

For example:

• Platinum

  • Total amount orders over the last 6 months >800€

  • Number of visits over the last 6 months >6

Now it is your turn ;)

How it works

1. Name your new flag attribute and describe it

2. Define your conditions to ADD the flag

Add conditions among these universes:

• For visitor

  • Filter among all the variables available through your web / mobile tracking

• For page-view

You can add multiple variables with the AND/OR function.

3. Define your conditions to REMOVE the flag

• Never remove the flag

• Remove the flag IF all conditions defined to add the flag are no longer met

• Remove the flag IF specific conditions are met:

You can use the same conditions and filters as defined for adding the flag.

Rolling Sum / Average / Min / Max

Description

Calculate cumulative sum, average, minimum or maximum for one variable based on a specific date range. It allows you to create new business rules based on a variable aggregation.

You can use these new indicators to create new segments or to flag your customers.

Popular use cases

• Total order: cumulative total order amount for last 6 months

• Page views: total of pages views for last week

• Orders: Average orders amount for last 6 months

Example

You would like to identify customers who have ordered more than 300€ in the product category ‘clothes’ over the last 2 months. You set up the new rolling sum attribute:

You can now create a new segment based on the new attribute ‘[total orders amount clothes <2 months] >300€’ which concerns, on this example, only the customer B.

With our example we can also calculate the average orders amount or the minimum / maximum amount.

How it works

1. Name your new attribute

2. Specify the universe (page view, view, click, conversion) and the variable you want

3. Define the date range you want

• Forever: no period defined, you consider all the dates

• Relative: you can set a duration on rolling hours or days (last 3 days for example)

• Absolute: data is aggregated from a specific date (from 01/09/2019 for example)

4. [Optional] If needed, define conditions in order to calculate the aggregation only if the conditions are met

Rolling Count

Description

Create a countdown attribute for one variable, based on a specific date range. This feature is helpful to define your best customers based on KPI like order frequency or number of visits on your website.

Popular use cases

• Frequency: conversions number in last 6 months

• Discount efficiency: number of conversions with discount codes

• Cross-selling: number of conversions with more than 2 product categories.

Example

You would like to segment users who have 2 or more conversions with a discount code. You set up the new rolling count attribute: Count [Conversions] where [Conversion discount code] = 'True'

Then you can create a segment with the rolling count attribute 'Users with total conversions with a discount code = 2 or more' which returns, on our example, only the customer B. However you can go further by adding a period or others conditions.

How it works

1. Name your new attribute

2. Specify the universe (page view, view, click, conversion) you want

3. Define the date range you want

• Forever: no period defined, you consider all the dates

• Relative: you can set a duration on rolling hours or days

• Absolute: data is aggregated from a specific date

4. Define conditions in order to count only if the conditions are met

Filter on a specific page, product, ad…

Calculus

Description

Create a new calculated attribute. You can add, subtract, divide or multiply 2 or more variables. These new calculated attributes can be part of a segmentation. This feature allows you to define new KPIs based on mathematical formulas.

Popular use cases

• Ratio: repartition online/offline conversions

• CLV: Customer Lifetime Value

Example

Ratio: You would like to identify cross-channel customers meaning the partition of customers who are buying both online and offline. You need a ratio between online and offline conversions:

Channel ratio: number of online conversions / number of offline conversions

Higher is the rate, more the preferred channel is online and lower is the rate more the preferred channel is offline.

Customer Lifetime Value (CLTV): This metric helps you to estimate the income per customer during his entire relationship with your company (present and future). It allows you to establish forecasts on profits based on future cash flows.

CLTV = Customer Value ✕ Customer Average Lifespan

Customer Value = Average purchase value ✕ Average purchase frequency (on 1 year)

Customer Average Lifespan depends on your business model (subscriptions service, freemium, retailer...). In general we consider the customer average lifespan is comprised between 1 and 3 years.

To sum-up, in order to calculate the customer lifetime value with the augmented user attributes feature, you have to:

1. Use a Rolling Average attribute to determine the average purchase value and the average purchase frequency

2. Use a Calculus attribute to define the Customer Value by multiplying the average purchase value and the average purchase frequency (determined previously on step 1)

3. Ultimately, use again a Calculus attribute to estimate the Customer Lifetime Value by multiplying the Customer Value (as calculated on step 2) and the Customer Average Lifespan (determined by your business model).

You can calculate the CLTV step by step or you can also create a formula that combines all the dimensions.

Ex: Average purchase value (on 1 year) = 50€ Average purchase frequency (on 1 year) = 5 orders/year Customer Value = 250€ (50x5) Customer Average Lifespan = 2 years Customer Lifetime Value = 500€ (250x2)

Or: CLTV = (50x5)x2 = 500€

How it works

1. Name your new calculated attribute

2. Specify the mathematic formula (rule) to calculate with the variables

You have to type the variable name and add the operator. When a variable name is found, the autocompletion feature will suggest you the exact name.

Ex: type ‘Lab’ and the platform will suggest you ‘Label’.

Copy

Description

This feature allows you to copy values stored at the event level (pages, views, clicks, conversions) and paste to the user level. You can aggregate data at the user level in order to consolidate all the data around one unique user.

New functionality: copy the data you need stored on all universes (pages, views, clicks and conversion) to the user level. As a result, the data will be stored for life (in respect with GDPR) and not deleted after 30 days (usually 30 days but depends on your contract).

It is useful to have a global view of dimensions such as product categories viewed or last order date.

Popular use cases

• Last checkout date

• Product categories viewed

Example

You are looking for a travel for your next holidays. You visit many websites, blogs, travel agencies websites... It takes time to choose the best offer that will suit you perfectly.

As a travel agency, you have many visitors on your website who left many informations such as trip dates, destinations... This online data will be stored 30 days, however your sales cycle could be long, more than 30 days. In order to don't loose this precious information, it could be useful to keep this data and this is what Copy allows you to do.

You can create a new attribute called 'Trip dates' and store the dates considered. You can do the same for 'destination' or whatever that could be useful for you. Then you can launch a dedicated campaign 3 months later with a segment based on this data (if there is no conversion for these customers).

How it works

1. Name your new attribute

2. Specify the universe (page view, view, click, conversion) and the variable you want

3. Define the date range you want

• Forever: no period defined, you consider all the dates

• Relative: you can set a duration on rolling hours or days

• Absolute: data is aggregated from a specific date

4. If needed, define conditions in order to copy the aggregation only if the conditions are met

Boolean

Description

Boolean allows you to create True / False conditions. Ask you a question, set conditions and the value will be set to True on the user if the conditions are met, otherwise it will be set to False.

Popular use cases

• Has the user seen the campaign '10% discount'? True / False

• Has the user added an item in the cart over the last 2 days? True / False

• Has the user opened the last email sent? True / False

How it works

1. Name your new attribute

2. Define the conditions you want to use.

This connector allows you to push every kind of events directly to Facebook through API. Online conversions, offline conversions... send it to Facebook and it will help you to increase the reach and accuracy of your campaigns.

You can, for example, not send campaigns related to a specific product to users who already bought it, or you can also send campaigns to users who bought a specific product in cross-sell logic.

How to send events to Facebook?

Facebook developed an API called 'Facebook Conversions API'

You need a Facebook Business Manager account

Then on the menu, click on 'Events Manager':

Here you have to create a new Web Pixel:

Select Conversions API and give a name to your connection:

Now your pixel is created and you will have access to the IDs needed on our connector.

Where can I find the Pixel ID?

You need to fill the pixel ID on our connector, it is the ID of the pixel you just created on steps above.

You can find this ID when you click on the pixel's name and on the right of the graph activities. You can find it also on the settings tab.

You can now copy and paste this ID on our connector.

Then you need the Access Token

Where can I find the access token?

Click on the settings tab.

Scroll until the section 'Conversions API'

Click on the button 'Create Access Token':

Then you can copy and paste the Access Token on our connector.

How to manage consents?

• Only events with a consent will be sent to Facebook

• Only purchases with personal information (email and/or phone number...) will be sent to Facebook

For customers with our product TRUST Commander:

TRUST Commander is our Consent Management Platform. (More information: )

On the connector, the consent is managed with the field 'User Consent Category'. You should enter a category ID, the one corresponding to Facebook (advertising) on Trust consent categories.

For customers without our product TRUST Commander:

We should distinguish 3 cases:

• Your online events are collected through our Commanders Act event's tag: You have to provide, in the event tags, the list of category ids consented by the user, through the consent_categories property.

• You are pushing your events to us through API or CSV file: a field consent_categories must be added on the JSON or CSV to precise the consent category IDs of the user. Then inside the connector setting, use the field 'User Consent Category' to enter a category ID, the one corresponding to Facebook (advertising)

How the deduplication between the pixel and server is managed?

Using both the pixel and server is recommended per Facebook as it could avoid losing data.

To make it works, you should have the same configuration for both the pixel and server, using same Facebook parameters.

On the pixel, event_id is automatically generated by our Commanders Act Tag and we retrieve the same value for the server onintegrations.facebook.event_id. As a result, these 2 values should be the same. Event_name should be the same also.

Fbp parameter is automatically retrieved to keep the same value between pixel and server.

Deduplication works when the same event is sent first from the browser and then from the server, otherwise it creates a duplicate. Events are pushed in real-time.

Examples

On pixel:

eventID: tc.uniqueEventId is automatically generated.

On server:

integrations.facebook.event_id automatically retrieves the eventID value coming from the pixel (eventID: tc.uniqueEventId) for standard events.

Default Mappings to Facebook Standard Events

The following mappings are fully automated and do not require any additional configuration. Any of the Commanders Act Standard Events in the table below will be sent as the corresponding Facebook Standard Event. The Facebook pixel has more information on these.

Facebook Custom Events

To send any of your Commanders Act events (that are not listed in the table above) to Facebook custom events, you don't have anything to do. By default, your unmapped events are automatically sent as a Facebook custom events with the name of your Commanders Act events. If you want to change the name of the custom event that will receive Facebook, you can overwrite the event_name property using integrations.facebook.event_name:'yourCustomEventName'

Default Mappings to Facebook Parameters

Official documentation

Every property can be overridden using integrations.facebook.user_data.<property> (for standard data) or integrations.facebook.custom_data.<property> (for custom data).

Minimal required information

Events can only be used if there is enough information to match a user. Facebook expects at least one user_data property, but strongly advises sending as many properties as possible.

Here are our conditions to send the events :

• at least 1 of those fields: em, ph, external_id, fbp, fbc

• at least 3 of the other fields

Note : external_id, fbp, fbc will allow matching event with other events. But to match a user, one of those events shall contain additional information (em and ph are best suited for matching)

Additional custom Facebook parameters

Custom Facebook parameters could be added.

Facebook allows you to send any data you want in custom data parameters. By default, we fill generic fields when possible (like value, currency, contents...). You can specify in the tag your own parameters in integrations.facebook.custom_data.

Before starting

Each vendor solution identifies browsers via a proprietary COOKIE_ID. This prevents them from sharing user data with other solutions directly. The cookie synchronization allows vendors to share their Unique Identifiers with the Commanders Act CDP.

Commanders Act sends a request to the vendor server to get their COOKIE_ID for a user and then stores the mapping on the Commanders Act servers. Cookie synchronization maps different IDs together and thus enables user data to be shared.

Requirements

• Web service URL: The vendor solution provides Commanders Act with the URL to be called to trigger the webservice and get its COOKIE_ID for the current browser

• Commanders Act Token: Commanders Act provides the vendor with a 32-character unique ID (ex: KD04a85DjH1015yzuAu7pnaoTh5P53iL). This token will be used to send request to the Commanders Act web service.

Process

1) Commanders Act sends a request to the vendor's web service through its JavaScript tag (browser side).

2) The vendor API sends its COOKIE_ID to http://sync.commander1.com/#PARTNER_TOKEN#/#UID# by replacing on the fly the #UID# in the destination URL. The #TOKEN# is provided by Commanders Act and unique per vendor.

3) Commanders Act stores the vendor COOKIE_ID into its cookie sync mapping table. This database keep an history of Commanders Act unique ID & every synchronized vendor COOKIE_ID(s).

4) End users can create segments from DataCommander UI. The COOKIE_IDs will be translated into the vendor's one when extracting the data, so the vendor will be able to re-use them.

Commanders Act Data API v2.0.0

Scroll down for code samples, example requests and responses. Select a language for code samples from the tabs above or the mobile navigation menu.

It is highly recommended that you send multiple objects in one HTTP request. This API allows streaming using newline separated JSON format or ndjson (http://ndjson.org/)

Rate-limits

• You may send up to 30 requests per second

• You may have up to 30 concurrent connections

• If you send many conversions/products/etc. in bulk, the upload speed will be limited to 30 conversions/products/etc. per second

Rate limiting examples

• If you send 1 conversion per request you will be limited to 30 requests per second

• If you send 90 conversions in one request your upload will be completed in about 3 seconds

• If you send 40 requests, each with one conversion in the same second, 30 of them will be processed and 10 of them will be rejected

Limitations

• You can send up to 150 conversion items

Date formats

Use the long format with timezone for passing ISO-8601 dates. The following formats are accepted:

• "2019-04-29T13:47:47.315Z"

• "2019-04-29T13:47:47Z"

• "2019-04-29T13:47:47.315+02:00"

Errors

Errors are always returned as an array of objects in the top-level "errors" property.

Errors in bulk operations

For bulk operations you may have "errors" and "data" properties at the same time since some objects may have errors while others may not. Bulk errors are aggregated which means there won't be an error for each instance of an error but one error for each type of error with the number of occurrences and some examples of line numbers or ids.

Error object

Error objects have the following properties

Base URLs:

Authentication

• HTTP Authentication, scheme: bearer Token will be provided by our support/consulting team

Default

Upsert conversions

Code samples

POST /conversions/bulk

This endpoint creates and updates conversions. Your request will be processed asynchronously. It can take up to 24 hours until the request is processed and updates are made in the database.

Body parameter

Parameters

Example responses

202 Response

400 Cannot parse nd-json line

400 Missing required property

400 Invalid property type

400 Invalid property format

401 Authorization header is missing

401 Token type is missing

401 The token type is invalid

401 The provided token is unknown

403 Response

Too Many Requests

500 Response

Responses

Response Schema

Upsert products

Code samples

POST /products/bulk

This endpoint creates and updates products. Your request will be processed asynchronously. It can take up to 24 hours until the request is processed and updates are made in the database.

Body parameter

Parameters

Responses

Schemas

Conversion

It is recommended to use as many fields as you can in order to be able to build good segments with advanced conditions

Properties

Enumerated Values

ConversionItem

It is recommended to use as many fields as you can in order to be able to build good segments with advanced conditions

Properties

Address

It is recommended to use as many fields as you can in order to be able to build good segments with advanced conditions

Properties

Product

There are three ways to have product information in your conversion items. First method is to put product properties inline for each conversion item. Second method is to synchronize your product catalog with our database using "POST /products/bulk" endpoint and only send product ids in conversion items (our server will copy product properties from catalog). Third method is a combination of previous ones and implies having a product catalog and send the product information inline. In case a property is present in both catalog product and inline product, properties from inline product will overwrite properties from catalog. This method is useful when product information is incomplete or complementary in inline products. It is recommended to send products inline, except when you do not have all product information. In most cases you don't need to use the catalog. It is recommended to use as many fields as you can in order to be able to build good segments with advanced conditions. When you only send the id of the product in a conversion item, you need to make sure that your catalog already contains the product, otherwise product properties will not be added to your conversion item.

Properties

Enumerated Values