Summary of section pages

• Dimensions

  • Dimensions in Adloop

  • Dimensions management page

  • Normalized dimensions

  • Conditional dimensions

• Metrics

  • Metrics in Adloop

  • Metrics management page

  • Normalized metrics

  • Calculated metrics

  • Calculated metrics definition

• Matching

• Matching for API or Custom Sources

• Matching for Organic Sources

• Unknown channels - How to create new channels?

• Cookie consent auto-correction

The “Data Management” pages can only be accessed by admins.

• Dimensions in Adloop

• Dimensions management page

• Normalized dimensions

• Conditional dimensions

On the same topic, see Reports > Dimensions menu

Dimensions are the names, categories or characteristics of digital marketing campaigns.

Each Platform has its own dimensions, but some are common to most of them, like campaigns name , ads name , objectives etc.

1 - Imported dimensions from Data Sources

For each source in the data Sources list, Adloop team has selected the most relevant dimensions:

• Some are imported by default ( default Dimensions ) - you can’t remove them

• Some are optional and you can select the ones you want to have in Adloop and ignore the others ( other Dimensions ).

Facebook Ads example:

Dimensions always have a logo in front of their name, so you can identify the data source they are coming from. Google Ads dimensions will have a logo in front of them, those from Facebook Ads a and so on.

2 - Normalised dimensions ( logo)

Some dimensions are common to all sources: the normalized dimensions in Adloop. They have a :Ad_centric: logo in front of them.

a - Why those normalized dimensions?

Some dimensions are common to most of the Ad platforms . So instead of having to choose several times the same dimension, we grouped them within the normalized dimensions.

Example:

• Google Ads campaigns names exist in Adloop under the dimension :Google_Ads: Campaign name

• Bing Ads campaigns names exist in Adloop under the dimension :Bing_ads: Campaign name

• Both are grouped under the normalized dimension :Ad_centric: Campaign name (SEA)

In a Report , you have Dimensions from Google Ads and Bing Ads and you want to see the performances of both those channels. Instead of having two columns :Google_Ads: Campaign name and :Bing_ads: Campaign name , just use the normalized dimension :Ad_centric: Campaign name (SEA) , you will only have one column.

b - Which are the normalized dimensions?

Our normalized dimensions in Adloop are:

c - Where to find the normalized dimensions?

You can find them under the Dimensions menu in the Reports .

Dimensions menu:

Dimensions menu when opened:

3 - Adloop dimensions ( :adloop: icon)

Some dimensions were created by Adloop to help marketing professionals to make a better use of data. The icon :adloop: is in front of them.

The Adloop dimensions are:

Adloop dimensions can mostly be found in the “General” category of the Dimensions menu:

4 - Dimensions categories

Dimensions are organized into categories defined by the Adloop teams.

The default categories cannot be deleted but their names can still be edited in the Dimensions management page. You can add as many dimensions as you want to organize the menus the way you want.

Managing dimensions

The Dimensions management page is available from the menu.


This page allows you to view and manage :

1. Standard dimensions

2. Source dimensions

3. The categories of the Dimensions

Visibility management options

Managing dimensions

Affiliate

Display

Email

Retargeting

Search

Shopping

Social Ads

Video

The conditional dimensions can be found in the Dimensions menu.

Want to know more about Dimensions? The Dimensions in Adloop


Conditional dimensions are very useful if you want to see your data another way than what’s offered natively in Adloop. For example, you want to check the performances of your Social Ads per campaign objectives. Or you want to compare your branded and non-branded Search performances.

To create a conditional dimension you click on a conditional dimension

1. Give it a name

2. Select visibility

3. Choose category

4. Select conditions


Step 1.

First, You have to give a name to your conditional dimension. Give it a clear name so you can identify it easily when creating a report.

Step 2.

You have to select a visibility. The options you can choose from are: Hidden, Primary, Secondary

Ideally, you would chose Primary for those dimensions that are the most important and Secondary for those that will appear below the line.

Step 3.

Choose the category based on the dimensions used for the conditional dimension. The categories you can choose from are the following: Affiliation, Analytics, Custom, Direct, Display, Email, Retargeting, SEA, Shopping, Social Ads, Social organic, Spot TV, Video

Step 4.

You have to set the rules for each value that your dimensions will have. For example here, Facebook Ads campaigns can have one of the following objectives : Reach, Interaction, View, Traffic

You can combine several rules for each value. For example here, there is an added rule: or facebook campaign name contains interaction in small letters.

Then the dimension value will be, in this case, a fixed value : “ interaction ”.

If you want to set up additional dimension values you can, by clicking on the add a new dimension value button.

Finally, all of your campaigns have an objective, so nothing should be left blank, but in case that happens you can put “N/A” for not available in the “else the dimension value will be” field.

Then click on the Create/Modify button and you can already use your newly created dimensions in all your reports.

• Metrics in Adloop

• Metrics management page

• Normalized metrics

• Calculated metrics

• Calculated metrics definition

On the same topic, see Report > Metrics menu

Metrics are units or measure indicators used to evaluate the efficiency of web marketing campaigns.

Each platform has its own metrics but some of them are common to most of the platforms, like clicks , impressions , conversions or revenue .

1 - Metrics imported from Data sources

For each source in the data Sources list, Adloop team has selected the most relevant metrics:

• Some are imported by default ( default Metrics ) - you can’t remove them

• Some are optional and you can select the ones you want to have in Adloop and ignore the others ( other Metrics ).

Facebook Ads example:

Metrics always have a logo in front of their name, so you can identify the data source they are coming from. Google Ads metrics will have a logo in front of them, those from Facebook Ads a and so on.

2 - Normalized metrics ( logo)

Some metrics are common to all sources: the normalized metrics in Adloop. They have a logo in front of them.

a - Why those normalized metrics?

Some metrics are common to most of the Ad platforms . So, instead of having to choose several times the same metric, we grouped them within the normalized metrics.

Example:

• Google Ads impressions exist in Adloop under the dimension Impressions

• Bing Ads impressions exist in Adloop under the dimension Impressions

• Both are grouped under the normalized dimension Impressions

In a Report , you have Metrics from Google Ads and Bing Ads and you want to see the performances of both those channels. Instead of having two columns Impressions and Impressions , just use the normalized metric Impressions , you will only have one column.

b - Which are the normalized metrics?

Our normalized metrics in Adloop are:

c - Where can I find the normalized Metrics?

Normalized metrics can be found in the Metrics menu of the Reports.

The Metrics can be found there:

Opened Metrics menu:

3 - Calculated Metrics

Another exceptional feature offered by Adloop. Create an unlimited number of calculated metrics from all available metrics. The calculated metrics are - like other metrics - put in a category with a visibility criteria to choose.

To create/modify a metric nothing could be simpler: the interface is so intuitive and user-friendly with its formula editor that there is no need to give more explanations: there are + and -, * and / and ( ).

If you do percentages calculations, don't forget to divide by 100

Some precisions about the icons linked to the calculated metrics :

• If the formula is only made up of metrics from advertising sources (Ad-Centric) : icon

• If the formula is only made of analytics source metrics (Site-Centric) : icon

• If the formula is only made of Adloop source metrics (Ad-Centric) : icon

• If the formula is made of a mix of metrics : icon

4 - Metrics categories

Metrics are categorized by the Adloop teams.

The default categories cannot be deleted but their names can be edited in the metrics management page. You can add as many categories of metrics as you want to organize the menus the way you want.

Managing metrics

Awareness

Costs

Traffic

Conversion

Engagement

It is important to understand how calculated metrics are measured and their exact meanings.

To help you with that down below you can find a glossary for each calcualated metrics describing their meaning and calculation method.

Arrival rate

Proportion of landed clicks compared to the advertising clicks.

It gives you insight as to the proportion of clicks “lost”: the ones that you paid for but never arrived to the website.

{adloop-clicks} / {ad-clicks}

Average cart

The average cart value is calculated by dividing the revenue divided by the number of conversions.

{ad revenue} / {ad conversion}

Bounce rate

It represents the percentage of visitors who enter the site and then leave ("bounce") rather than continuing to view other pages within the same site. Bounce rate is calculated by counting the number of single page visits and dividing that by the total visits. It is then represented as a percentage of total visits.

({site-bounces)} / {site-sessions}) * 100

Conversion rate

The conversion rate is the number of conversions divided by the total number of visitors . For example, if an ecommerce site receives 200 visitors in a month and has 50 sales, the conversion rate would be 50 divided by 200, or 25%.

For our conversion rates, we use the main conversion metric added in the data source.

We have a conversion rate based on Analytics' data and another based on Adloop’s data.

({site-transactions} / {site-sessions}) * 100

({adloop-transactions} / {adloop-clicks}) * 100

Cost per completed videos

The cost per completed videos shows on average how much money you paid for a user to watch a video entirely. We use the metrics completed videos (also called videos 100% viewed on some platform) to calculate this KPI.

{ad-spend} / {ad-video-played-actions}

Cost per interaction

The cost per interaction shows on average how much money you paid for a user to engage with your contact on the Paid Social media platforms. The definition of interaction varies accross the different platforms, but includes engagement or shares, comments and likes.

{ad-spend} / {interactions}

Cost per landed click

Average cost for clicks that arrived on your website, measured by us. It is to be compared with the Advertising CPC in order to optimize your digital marketing.

It shows you the real price you are paying to effectively drive traffic to your website.

{ad-spend} / {adloop-clicks}

Cost per useful click

Average cost for clicks that bring value to your business. It is to be compared with the Advertising CPC and the cost per landed click.

It shows you the real price you are paying to effectively engage users on your website.

It is a great KPI to evaluate traffic sources that are not necessarily converting.

{ad-spend} / {adloop-useful-clicks}

CPA

CPA in marketing stands for cost per acquisition or action and is a type of conversion rate marketing. Cost per acquisition refers to the fee a company will pay for an advertisement that results in a sale.

For our CPA, we use the main conversion metric added in the data source.

We have a CPA calculated using Advertising platforms' data, one using Analytics' data and another using Adloop’s data.

{ad-spend} / {ad-conversions}

{ad-spend} / {site-conversions}

{ad-spend} / {adloop-conversions}

CPC

Cost per click shows the average amount of money you are paying to get a user to your website, measured by the platforms. It is also the same for a bidding strategy (especially on Search) in which you define the maximum amount (Max CPC) you are willing to pay for a click.

{ad-spend} / {ad-clicks}

CPM

Cost per thousand (CPM), also called cost per mille , is a marketing term used to denote the price of 1,000 advertisement impressions on one web page. If a website publisher charges $2.00 CPM, that means an advertiser must pay $2.00 for every 1,000 impressions of its ad. The "M" in CPM represents the word "mille," which is Latin for "thousands."

({ad-spend} / {ad-impressions}) * 1000

CTR

A ratio showing how often people who see your ad or free product listing end up clicking it. Clickthrough rate (CTR) can be used to gauge how well your keywords and ads, and free listings, are performing. CTR is the number of clicks that your ad receives divided by the number of times your ad is shown: clicks ÷ impressions = CTR. For example, if you had 5 clicks and 100 impressions, then your CTR would be 5%.

({ad-clicks} / {ad-impressions}) * 100

Engagement rate

Proportion of useful clicks compared to the landed clicks. It gives you insight about the proportion of users that engaged with your website.

The higher the ratio, the more users engaged with your website coming from this traffic source.

({adloop-useful-clicks} / {adloop-clicks}) * 100

ROAS

ROAS is often expressed as a percentage and represents the revenue gained from each dollar spent on advertising.

For our ROAS, we use the main conversion metric added in the data source.

We have a ROAS calculated using Advertising platforms' data, one using Analytics' data and another using Adloop’s data.

{ad-revenue} / {ad-spend}

{site-revenue} / {ad-spend}

{adloop-revenue} / {ad-spend}

Views rate (Videos)

A ratio showing the number of paid views of a video ad to the number of started videos. It can also be called video completion rate.

({ad-videos-views} / {ad-video-played-actions}) * 100

Calculated metrics

The Matching is associating one by one Sources with Analytics or Attribution Sources in order to create relationships between all their Dimensions .

The Matching is associating one by one Sources with Analytics or Attribution Sources in order to create relationships between all their Dimensions .

Note : If you have the Adloop Tracking & Attribution Premium option, the matching is automatic - you don’t have to do anything to match your Ad Platform dimensions & Adloop data. You still need to do it for your Analytics Source though !

Thanks to the matching, Adloop can link Ad platforms dimensions (campaign name, keyword etc.) et Analytics dimensions (UTM). It helps marketers saving time, because they don’t have to go anymore to each platform to compare data and calculate all the ratios and KPI.

Matching for API or Custom Sources Matching for Organic Sources

Matching for API or custom sources

The Matching is associating one by one Ad Sources with Analytics or Attribution Sources in order to create relationships between all their Dimensions .

The Matching is associating one by one Ad Sources with Analytics or Attribution Sources in order to create relationships between all their Dimensions .

Note : If you have the Adloop Tracking & Attribution Premium option, the matching is automatic - you don’t have to do anything.

Thanks to the matching, Adloop can link Ad platforms dimensions (campaign name, keyword etc.) et Analytics dimensions (UTM). It helps marketers saving time, because they don’t have to go anymore to each platform to compare data and calculate all the ratios and KPI.

To create a matching, click on the “Add” button in Matchings .

Matching on channel level

Choose in the list the data source for which you want to create the matching

Here we will create the matching for Google Ads.

Here, we will add matching rules to indicate Adloop when Google Search campaigns have to be recognized. In other words, what are the UTM parameters used for my Google Search campaigns?

Attention: We have separate connectors for Search, Shopping, Display, Video (Google). Campaigns must have different namings in order for the matching to work properly.

To do so, we advise you to open in a separate tab an Adloop report with the following UTM parameters: source, medium and campaign (in this order). For metrics, choose sessions. This will help you know what UTM parameteres are used for your channels.

• source : google

• medium : cpc

• campaign : contains “Search” (as opposed to Shopping campaigns)

For Adloop matching to work properly, you need to have a clear campaign tracking and to have UTM parameters correctly set-up. If this is not the case, you should work on your tracking before setting-up Adloop matching. Otherwise, you may have incoherent data.

I will add the UTM parameters above by adding 3 matching rules:

Validate and your channel matching is ready!

Matching on campaign level

Don’t stop there! We offer matching on more granular levels too, like campaign, ad group or even creative.

To get the most out of Adloop, we advise you to do the matching at least on the campaign level.

To do so, once the matching on the channel level is done, click on the “Pen” button on the right of your screen.

You arrive then on the dimensions list of your data source. Choose the dimension for which you want to set-up the matching by clicking on the “Pen” icon.

We advise you to proceed by descending granularity: first, campaign, then adgroup, then keyword. We will start here with the campaign (“Campaign name”).

On granular levels, we offer two types of matching: automatic or manual.

Automatic matching

You can use the automatic matching only if the campaign name is absolutely identical between the Ad platform and one of the UTM parameters.

Example : your Search campaign is named AD - Search - Campaign 1 in Google Search and the Campaign UTM parameter is also AD - Search - Campaign 1

This is the case, most of the time, for campaigns of the Google universe thanks to the native connection between Google Ads and Google Analytics. Thanks to this connection, the name of the campaign is sent to the UTM campaign (following the same principle, the keyword is sent to the UTM keyword). This is also the case when you use dynamic tracking parameters, like the {{campaign.name}} in Facebook Ads for example.

We encourage to use as much as possible dynamic parameters as they make Adloop matching easier but also tracking simpler.

To do so, just click on the “Automac matching” and indicate in which UTM parameters to find the identical value as well as the matching type.

Here my campaign name is found in the UTM campaign.

There are 3 types of matching:

• iso value: lowercases and uppercases are identical between the Ad source and the Analytics one.

• iso value in lowercases

• iso value in uppercases

Click on “Validate” and you are done with the matching! Easy, right?

Manual matching

If manual matching is a bit more complicated than the automatic one, we encourage you to set it up, at least on the campaign level. More granular levels are more complicated to maintain, especially if you have an important number of adgroups or keywords.

You will have to set up a manual matching for channels that don’t have direct connection with Google Analytics and those that don’t have dynamic tracking. This is the case for Facebook Ads or Criteo, for example.

The same way as for automatic matching, go to the dimension for which you want to set-up the matching.

For each campaign (“Value” column), you create a matching by indicating which UTM parameters are used for this campaign.

For that, we advise you to create two Adloop reports in two differents tabs:

• one with UTM source, medium and campaign (and/or keyword and/or content) (filter the report according to the source and medium values used for the matching on the channel level) and sessions

• one with the dimension chosen for the matching (here, “Campaign name Facebook”) and clicks.

Those two reports will help you find easily what the UTM parameters used. If you don’t find them or if the campaign naming is not explicit, you can check the tracking on the Ad platform or ask your traffic manager or your media agency.

Here I see that my campaign “Soldes Hiver” uses the UTM campaign “SoldesHiver_trafic”

I set up my matching like that:

Tip: when you click on the field to fill while it is still empty, a list with the available values will be displayed. You can then directly click on it.

Validate and the matching on the campaign level is done! The process is to be repeated for every campaigns and for other dimensions, if you choose to.

Matching for advertising sources

In Adloop you can have a 360 view of your digital channels, so this includes natural channels like Direct, SEO or referrers.

As there is no import of data related to these sources (no external data), only data from Analytics, there is no need to create custom data sources.

In the Matching > Organic Sources tab, you can manage your different organic sources.

By default, 4 are already created:

• SEO

• Direct

• Social organic

• Referral sites

To create a new organic source, click on the "Add" button. You must then fill in the name of your source and its type.

Example: Instagram organic / Social

You must then match this organic data source with the Analytics data (in other words, you must now declare the channel).

To do this, go back to the "Data Source" tab and click on "Add". You arrive in the interface for creating a match.

In the drop-down list, choose the organic source you just created. You can then follow the [[usual matching procedure.|Matching]]

Organic sources

In some cases, it is possible that you have to create organic sources to match some of the sources such as Email, SMS, and Newsletter that normally wouldn’t belong to any data source. By adding an Organic Source, it will appear in the 'Acquisition Channels' and 'Channel Group' menus in Adloop reports and you can associate dimensions with it through matching

1. Create the organic source

First, you have to create the organic source to do so go to the Data Sources menu and click on Organic sources. After clicking on +Add you have to name your organic source and name its type.

2. Create the matching for the source

To do so you have to go to the Matchings menu and click on +Add and select your created organic data source.

After selecting your data source you also have to select your related source which in this case will be the Adloop Tracking & Attribution source.

Here, we will add matching rules to indicate Adloop when the specific channel you created has to be recognized.

To do so, we advise you to open in a separate tab an Adloop report with the following parameters: Channel and Adloop code. For metrics, choose sessions. This will help you know what UTM parameters are used for your channels.

For Adloop matching to work properly, you need to have a clear campaign tracking and to have UTM parameters correctly set-up. If this is not the case, you should work on your tracking before setting-up Adloop matching. Otherwise, you may have incoherent data.

Validate and your channel matching is ready!

Cookie consent auto-correction

Having headaches lately on correcting your data to take into account the data lost due to cookie consent rate ? Well, we have you covered: you’ll find this functionality directly into the tool and your data will be corrected within seconds.

Which data do we correct?

Cookie consent affect data measurement happening on-site: number of page views, conversions, revenue and other on-site behaviours. In other words, it does not affect how much money you pay for a click on Google Search or how many reactions your Instagram ads is getting.

We collect data from 3 different types of data-sources :

• Advertising data-sources (Google Search, Google Perf Max, Meta etc.)

• Analytics data-sources (Google Analytics, Adobe Analytics, Piano etc.)

• Adloop Attribution data-source (well, this one is only coming from Adloop)

When cookie-consent auto correction is on in your the reports, this means that the following metrics are corrected:

• Conversions and Revenue metrics coming from Advertising sources . Therefore, KPI that include those metrics in their calculation, like ROAS or CPA, will be corrected as well. Metrics like Ad Spend, Clicks, Impressions, Reactions, Interactions or Completed Videos are not corrected.

• All metrics coming from Analytics data-sources

• All metrics coming from Adloop Attribution data-source

See your corrected data in your reports

You can switch between corrected and non-corrected data directly from our reports, within seconds and without reloading the reports.

You can correct your data on our Table report, Dashboards, Quick Dashboard, Cycle report and Chart report.

You cannot correct your data on paths-to-conversion reports: Order ID, Path Explorer and Channel Affinity reports.

You will find a button “Consent auto-correct” above your report, close to the “Help” button.

By clicking on this button, you can switch on and off the consent auto-correct.

When it is on, it means your data is corrected and it will look like this:

When it is off, it means your data is not corrected and it will look like this:

If you haven’t configured yet the consent auto-correct, the button will look like this:

You’ll see your data change instantly in your reports.

Pay attention: when you download your report, the consent auto-correct setting will be applied. If on, your data will be corrected in your export. If off, your data will not be corrected in your report.

Set-up your cookie consent rate

The cookie consent rate can be set-up directly in the platform and, good news, it only takes a couple of minutes.

To do so, go to the Metrics & KPIs menu, in the Data Management section and click on the Consent auto-correction .

You will arrive on this page:

First, activate the consent auto-correction by clicking on the switch.

Then enter, your average consent rate in the box and save it. It will be your default rate.

You can enter your cookie consent rate over the last year, past 6 months or past month.

If your consent rate changes a lot month to month, you can also apply specific consent rate for each month by filling up the boxes:

If no specific consent rate is filled in, the default consent rate will be applied. Don’t forget to click on the save button!

Finally, you can decide to exempt channels from consent. By default, the consent auto-correct is applied to all channels.

To do so, click next to channel you want to exempt to consent and choose the option in the menu: