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Advertising attribution: how to measure the value of buying volume?

2022-07-07 04:31:00 Tencent lecture hall

The author of this article :Fengyu , tencent IEG Product planning

One 、 Basic concepts

( One ) Definition

attribution (Attribution) It refers to the use of identifiers to identify the conversion path of users , And judge the effective contact value . The advertising platform is based on attribution results , Pay for it ; Advertisers can be used to optimize the delivery strategy .

picture source :Google App Online advertising courses

( Two ) Implement the core logic : The match between advertising and transformation

Attribution of applied advertising , Rely on getting ads 、 Transform information , And realize the matching of the two :

  1. Advertising information : The user clicks / Browsed the advertisement , User information that will click on the advertisement ( For example, the user's IMEI perhaps IDFA etc. ) And advertising information ( Like advertising trackingID, Advertising application package name or IOS Of APP ID) Upload and record ;
  2. Transform information : After the advertisement is transformed , Convert this advertisement into user information 、 The attribute information of the advertisement is matched with the user information and advertisement information of the click advertisement to complete the attribution .

picture source : Zeng Rong's nonsense

As can be seen from the above figure , From advertising to transformation , The core depends on three steps :

  • The first 3 Step : When the user device clicks on the advertisement , Will the advertisement ID 、 Send user information to attribution platform ;
  • The first 7 Step : Newly installed APP After starting , Will the advertisement ID、 Send user information to attribution platform ;
  • The first 8 Step : Attribution platform for attribution analysis .

Two 、 Record point : Data required for attribution and how it is used

Users' watching advertisements and conversion usually do not happen in a closed loop APP Inside , To achieve cross user APP The tracking of is actually to connect the first 3 Step ( Advertisement reporting ) And the 7 Step ( Report of transformation ), And combine the two ID Match and identify the user .

( One ) Deterministic attribution :Android

Android Deterministic attribution at the user level , Include equipment ID( Such as Google Of GAID)、Google Play Referrer And consumer identifier ( Such as anonymous login data , Or the email address that has been hashed ) Multiple identifiers including , Accurately identify user equipment , And will click on the advertisement 、 User behaviors such as downloading applications or executing in app events match it .

  1. overseas : Google framework GAID and Google Play Referrer

Overseas Android platforms can be used to track users ID There are mainly :

  1. GAID: namely Google Advertising ID, yes Google Play App store and Android Devices installed by third-party app stores ID;
  2. Google Play Referrer: namely Google Play For the same SDK Assigned fixed value , Only for installation of Google Framework Android device , And only track Google Play Application behavior in

2. At home : Launched by mobile security alliance  OAID

OAID: By the domestic mobile security alliance (MSA) United Huawei , millet ,oppo,vivo And other anonymous device identifiers launched by terminal manufacturers .

In addition to clear users provided by interested parties ID outside , Fingerprint matching can also be used for Android's deterministic attribution . Fingerprint recognition uses device information ( System font settings 、 Hardware properties, etc ) Create continuous and unique ID, Used to identify specific users . This is not in line with Apple's privacy policy .

( Two ) Deterministic attribution :iOS

picture source : after IDFA Time : Buying volume put 、 advertisements 、 Marketing strategy of attribution analysis - GameRes Hot money

  1. iOS 14.4 front :IDFA

IDFA yes iOS Advertisements ID, stay 14.4 It was in the default on state , Both self attribution platforms and third-party attribution platforms can be used for the deterministic attribution of advertising .

  1. iOS 14.5 in the future :SKAN

The apple in iOS14.5 Upgraded privacy policy (ATT,App Tracking Transparency ), User IDFA It is off by default , Applications need to track user behavior , You need to send two request pop ups ( Advertisement broadcast and download ), whole IDFA Low availability .Vungle The test data is 95% The user of opened LAT(Limit Ad Tracking), Limit advertising tracking .

So apple is IDFA In addition to providing SKAdNetwork( SK finger StoreKit) , Can be used for non user level deterministic attribution : Without identifying the user's identity , Measure the application installation and advertising effect .

picture source :Apple Developer Documentation

Click on the advertisement to download 、 After activation ,SKAN Will start 24 Hour countdown , Count user behavior ; After installation 0-24 After hours, the installation and conversion information will be sent back to the advertising platform ( Such as 24 Hours have conversion events , Then the statistics can be extended 24 Hours , at most 48 Hours ).

SKAN built-in 64 A bit , It can be used to track different events , picture source :iOS 14 Growth support programs

SKAN The limitations of include : Only the early behavior data of users are counted , Cannot measure accurately LTV; Only data at the advertising series level , Excluding advertising materials and other data , Cannot optimize alone ; The data has at least 24 Hour delay, etc .

( 3、 ... and ) Probabilistic attribution

In the case that deterministic attribution cannot be carried out by means of identifiers, etc , Attribution platform can carry out probability attribution .

The specific implementation mode is : Attribution platform believes 80% The installation of all takes place within the first hour after the click of the advertisement , Therefore, the user click time will be collected 、 Limited parameters such as installation time and basic equipment information ( Temporary data ), Speculate the source of the installation within a few hours after clicking .

Branch And other third-party platforms call probability attribution 90% The above users realize probability attribution .

picture source :iOS14 after , You may ask about attribution

3、 ... and 、 Value point : Attribution model

From the previous attribution logic part , We assume that the attribution monitoring platform has obtained the required advertising behavior and APP Transform data , The problem to be solved in this step is the attribution model : How to base on the transformation behavior of users , Judge the value of advertising .

( One ) Basic model :

The basic attribution model refers to the set rules , Judge the advertising value . Advertising platforms usually pay and settle according to the rules in the basic model .

  1. Final installation (Last Install)/ Channel package

Application installation in domestic Android Market , It mainly depends on channel subcontracting : That is, different channels / Traffic properties , Put on a different APK package , Advertisers finally activate (Last Install) Which channel package comes from to evaluate the effect of traffic channels .AMS Statistical approx 50% Of game advertisers adopt this scheme , Such as headlines .

Possible problems :

  1. Statistics of advertisers and advertising platforms gap: Due to the influence of Android manufacturer hijacking , The transformation counted by advertisers will be less than that of advertising platforms , As a result, the effect and quality of traffic channels cannot be accurately evaluated ;

picture source : the B standing UP The master stood out , Reveal the secret of app store blocking mobile game installation | Game view | GameLook.com.cn

  1. Can't measure “ assists ” The value of : For example, users are watching effect advertisements , After content marketing, enter the app store to download , The app store can get all the scores ;
  2. Unable to accurately measure the material / The influence of copywriting on transformation : If the channel package is only subdivided into traffic , Instead of aiming at different copywriting 、 Distinguish between materials , It will be detrimental to the model optimization of advertising platform and the material copywriting optimization of advertisers .

2. Finally, click (Last Click)

Finally, the click model refers to that users have clicked on advertisements on multiple platforms before installation , Finally, it is attributed to the advertising platform that has clicked the most recently since the first installation .

Click attribution is the most common way , Click the attribution window, which is usually 7 God 、28 God ( A special case :SKAN by 24 Hours ).

picture source Adjust  Reference documents

Some domestic advertisers will re match based on the last click data from different sources , Do the second attribution of the last click .

Possible problems :

It is impossible to measure the value of assists : For example, users watch effect advertisements many times 、 Download after content marketing , The last click can get all the scores .

  1. Effective contact attribution (View-Through Ads)

Before talking about effective contact attribution , First of all, we should distinguish the types of advertising behavior data :

1. Click attribution : For example, users click on banners 、 video 、 Insert ads, etc . The attribution window is usually 7 God .

2. Exhibition / Exposure / Browsing attribution : Users see advertisements but do not click on them , The resulting installation can be attributed to the channel that displays the advertisement . thus , The attribution window is short , Only for 24 Hours .

Because clicking has the active behavior of users , Therefore, in the attribution model, click advertising takes priority over display advertising .

picture source Adjust  Reference documents  

At present, foreign Facebook、 Domestic platforms such as Kwai adopt effective contact attribution schemes , The specific logic is : The advertising platform will let users click + Effective contact ( Like playing 3 Seconds later ) They are sent to advertisers as advertising attribution information .

The effect of effective contact attribution :

  1. For advertisers : The original natural quantity is classified into the effective contact , Advertisers spend more ;

The picture is from 【 And advertising attribution 】 Analysis on the reason why the attribution of effective contact can be popularized

  1. For advertisers : Improve the conversion rate of Kwai and other buying channels , Will affect brand advertising 、 Search optimization (ASO) The natural quantity attribution of the team , Part of the natural quantity originally belonging to the team will be attributed to the display attribution ;
  2. For advertising platforms that use advertising contacts : Increase the original natural quantity , The effect on the advertising platform becomes better , The overall advertising cost will be reduced ;
  3. For advertising platforms that do not use effective contacts : For example, the buying team put advertisements in Kwai , Yes, click + Play 3s Attribution in two ways ( Grab the original natural quantity ), Advertising conversion rate increased ; To be specific :
    1. For flows that have used valid contacts , The flow value is incorporated into the original natural quantity ecpm Will rise , Advertising models can get more ( Click on + Exposure ); Therefore, for unused effective contacts 、 Just click / Install attributed traffic channels , The effect will get worse .
    2. For example, a Facebook The advertisement has 10w The exposure , CTR 1.2%,CVR 30%, Therefore, the available clicks are converted into 360; In the case of increasing the attribution of effective contacts , For example, exposure attribution has 80 individual , Holistic CVR Up to the 37%. Advertisers keep the same advertising budget as before ,CPA It will go down ;
    3. here , If the advertiser chooses to reduce the total advertising budget , You can also get the same amount of conversion as the original ( Click on + Exposure conversion ), Therefore, the advertising revenue obtained by the same traffic increases , namely ECPM promote .

Click attribution to upgrade to click + Exposure attribution effect improved , This case refers to :

【 On advertising attribution 】 Forced the “ assists ” Count as “ Direct score ”, Do you want to be shameless ?

The effect of adding exposure attribution logic to a platform is improved

Other common basic models include : First click 、 Time decay 、 Location attribution and other models .

( Two ) Algorithm model :

A differentiation model based on user data , Algorithm model attribution , Also known as data-driven attribution (Data-Driven Attribution, abbreviation DDA).

It is different from the basic model based on the set rules ,DDA Use all available path data , Including path length , Exposure sequence and advertising materials , To understand how the existence of a specific marketing touchpoint affects the possibility of user conversion to better allocate credit to any touchpoint .

The commonly used algorithm is Shapley value 、 Markov chain 、Harsanyi Dividend And survival analysis .

  1. Shapley Value :Google Analytics

“Shapley value ” By the Nobel Laureate in Economics Lloyd S. Shapley Put forward , It's a team member ( Marketing contacts ) Distribute team results fairly ( Conversion results ) Methods .

The specific implementation method is :1) By comparing the conversion probability of similar users who have contacted these contact points with the probability when a contact point does not appear in the path ;2) And the system will compare all the different arrangements of the contact points , Assign different credits to different path locations .

Simplified version understanding :

adopt “Facebook-Google” Linked CVR yes 2%; adopt “Facebook-TikTok-Google” Linked CVR yes 3%, That can be considered “TikTok” The value that contacts add to conversion is 50%.

meanwhile Shapley The value model will also change the order of links , in the light of TikTok stay Google front 、 The latter cases are modeled separately .

The picture is from :markov model vs shapley value marketing attribution

Google Analytics Examples of applications :

Display and transform each channel through the hierarchical diagram of the model 、 Weighted average contribution value at each path position .

Look vertically , The percentage represents at the specified path location , The actual percentage weight of a channel in the current node , for example The contribution of paid search to users' first conversion path is 31%, You can promote users to start transforming by putting such advertisements .

  1. Markov chain model

Markov chain assumes that the probability of state transition at a certain time only depends on its previous state . there “ state ” It can be understood as weather : For example, today's weather only depends on yesterday's weather , It doesn't depend on the weather the day after tomorrow ; In the advertisement , Status is understood as channel , The access channel of the user this time only depends on the last access channel , It doesn't depend on the last visited channel .

The model can remove a certain state ( channel ), Calculate the contribution value of this state to the final result .

Simplified version understanding :

  1. There is Facebook In the case of channels , adopt “Facebook-TikTok” The probability of link conversion is 30%*20%*80%, adopt “Google” The probability of link conversion is 60%, The total conversion rate is ;
  2. remove Facebook after , The overall conversion rate is only Google channel , namely 50%*60%, Judge the value of the channel by the declining conversion rate .

The picture is from :markov model vs shapley value marketing attribution

  1. Uplift: Tencent advertising

Uplift The model predicts the causal effect of some intervention on individual state or behavior , Specifically expressed as the difference between two conditional probabilities .

Specific implementation method : Divide the target population randomly , Advertising from one group of users , The other group did not intervene ( No advertising ), Then count the difference between the two groups in conversion rate , This difference can be approximated as the average possible causal effect of people with the same characteristics .

Example of Tencent Advertising Application

Compare the number of orders placed by the processing group and the control group 、 Order quantity 、 Conversion rate and other data , Analyze the effect of processing group data improvement , That is to say Uplift Incremental conversion .

Besides ,Adobe It uses Harsanyi Dividend Algorithm ,Facebook Data attribution model has been introduced, but the specific algorithm has not been published , And this year 4 Monthly deactivation .

Four 、 Attribution platform

In the attribution analysis of step 8 , Attribution monitoring platform can play two roles :

  1. Advertising platform : for example Google Ads Advertising can be launched , Advertisers are also required to report it APP Behavioral data for attribution . This kind of advertisement is used to launch , The platform that also carries advertising attribution analysis is called self attribution platform ;
  2. Third party monitoring platform : for example Adjust,Appsflyer, as well as Kochava And other third-party attribution platforms .

( One ) Advertising platform / Self attribution platform (Self Attributing Networks, SANs)

When the advertisement is exposed on the traffic , The advertising platform will be exposed 、 Click data is recorded in the background of its advertising attribution . When users download and activate apps from the app store , Advertisers need to send back the activation data of their applications to the advertising background for attribution . install 、 Conversion and other data will be used for advertising billing and subsequent advertising model optimization .

( notes :CPC、CPA and CPM It is the mainstream advertising billing mode , therefore , Media channels charge for all clicks that occur during the attribution window period set by themselves , Whether or not this click is the last click . however , Attribution provider sends installation data to channel , Channels can use this data for their own optimization .)

  1. Google Ads:

1) Record point :

be based on Google Click ID Follow up

2) Value point :
  • Attribution model : Click attribution + Exposure attribution
  • Attribution window :
    • Application advertisements aimed at increasing installation (ACi):
      • Click on : Default 30 God , It can be adjusted to 7 God /30 God ;
      • Watch with interest ( Click on or watch 10 Video ads of more than seconds ): Default 2 God , It can be adjusted to 1 God -3 God
      • Exhibition :1 God , It's not adjustable ;
      • install : Default 90 God , It can be adjusted to 1 God - 90 God .
    • Application advertising aimed at improving interactive events (ACe):
      • Click on : Default 90 God , It can be adjusted to 1 God -90 God ;
      • Watch with interest : Default 2 God , It can be adjusted to 1 God -30 God
      • Exhibition :1 God , It's not adjustable .

The picture is from : Introduction to the transformation time range of application advertising series

2. Facebook Ads:

1) Record point :Facebook Account

The user is in A、B Log in the same on the device Facebook Account , Users on Android A I have seen advertisements on the device , stay iOS Of B Install... In the device ,Facebook This installation will also be attributed to .

2) Value point :

  • Attribution model : Click attribution + Exposure attribution
  • Attribution window :1 Day Click +1 Day exposure ;1 Day Click ;7 Day Click ;7 Day Click +1 Day exposure . There are different attribution windows for different types of advertisements .
    • AEO(App Event Optimization): Target installation events
    • Conversion: Target in application conversion events

3. Apple Search Ads:

1) Record point : Do not track users
Advertising orientation : According to the crowd ( 5000+ user ) clustering , At least 5000+ user

Extended data :https://searchads.apple.com/privacy

2) Value point :

  • Attribution model : Finally, click
  • Attribution window :30 God

Extended reading : How mobile measurement service providers help monitor Apple Search Ads Advanced result

( Two ) Third party attribution platform (Mobile Measurement Providers, MMPs)

Self attribution platforms usually only have the advertising data of this channel , Ideally , Through the third-party attribution platform, the advertising data of the whole platform can be displayed . The specific process is as follows :

  1. Advertisers report the transformation information to the attribution platform ;
  2. Attribution platform sends transformation information to advertising platform ;
  3. The advertising platform returns the interactive information between users and advertisements .

( notes :MMP It will also provide other capabilities such as traffic anti cheating , For details, please check the introduction of corresponding products )

  1. Record point
A. Deterministic attribution data :
  • IDFA(iOS equipment ) and GPS ADID( Android devices ) etc.   advertisement ID
  • equipment ID , for example IDFV ( in the light of iOS), Android ID ( For Android ) and OAID ( For unusable Goolge Play Service Android devices )
  • MMPs self-built ID, Such as Adjust Created on Android reftag id.
B. Probabilistic attribution data :
  • Device type ( Mobile phone, for example ); Equipment name ( for example Samsung Galaxy S7) etc. ;IP Address ( Such as 77.185.208.234)

This section is referenced from Adjust  Reference documents  

  1. Value point
  • Attribution window : Click on 7 God , Exhibition 1 God , It can be set by yourself
  • Attribution model : By default, click the model last , It can be set by yourself

3. SAN And MMP The data difference

  • Attribution windows are different :ASA Default 30 God , other SAN(FB/Google) and MMP Adjustable ,
  • Attribution data source / Different models :MMP Record all you have seen 、 Click the advertisement to generate the installed data ,SAN Only single platform data is recorded .

appendix : It is applicable to the re attribution of reflow users

Re attribution refers to not using for a period of time / Uninstalled apps , However, the reinstallation or opening event of users who return after re marketing and promotion activities , Attribution .

For example, the user is uninstalling the application N God ( The window is set by the advertiser , Usually it is 90 God ) after , Click on the advertisement again , Re Download , It is regarded as re attribution .

picture source Adjust  Reference documents  

other :

  1. Basic concepts can be found MMP Attribution help document ( Adjust  and  AppsFlyer) 
  2. Advertising platform 、MMP The specific rules of are updated from time to time , Please refer to the official help documents .

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