Single Touch Attribution Report, understanding and interpretation
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outline
Single Touch Attribution is a report for analyzing advertising performance measured through tracking links.
This document provides example settings and metric definitions for analysis purposes.
Single Touch Attribution
Analysis Purpose | ||
#1 | Advertisement inflow analysis by date | look |
#2 | Analysis of advertisement inflow and purchase conversion by date | look |
#1
Analysis of advertisement inflow results by date
This is the most basic advertising inflow analysis report. You can analyze ad clicks and execution figures by date and advertising channel.
Table Design
- Group by : Daily > Ad Campaign > Ad Partner > Tracking Link
- Metrics: New Install (by Click), Re-Install (by Click), Deeplink Open (Total), Click (Total)
Conditions
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Attribution Conditions
Attribution Conditions는 In-App Event Attribution결과에만 영향을 미칩니다.
본 예시의 Metric에 In-App Event는 포함되지 않기 때문에 이 설정은 분석에 어떠한 영향도 미치지 않습니다.
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Data Range
리포트 분석 기간을 선택합니다.
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Time Zone
분석이 수행될 타임존을 선택합니다.
(Korea standard time = UTC+9) (Korea standard time = UTC+9)
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Audience
_ If you have a predefined audience in Audience Studio, you can select it to start cohort analysis.
_ Audience Studio feature guide / Cohort Analysis: Escaping the trap of averages
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Analytics Conditions
광고 채널을 통해 유입된 결과만을 리포트하기 위해 Ad Partner 중 Organic(자연 유입) 채널을 제외했습니다.
_ Although Organic inflow is not an advertising channel, DFINERY recognizes it as an independent inflow channel and is included in Ad Partner properties.
#2
Analysis of daily advertisement inflow and purchase conversion
We create a report that checks everything from advertisement inflow to purchase conversion at once.
Table Design
Set ‘KPI Metrics’ as follows.
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Set KPI name
: Enter the metric name to add to the report.
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In what units should we count?
Total: Select the total number of times the event occurred.
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What are your campaign goals?
Customer purchases a product: The purchase completion event is analyzed.
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You can further segment your product purchases.
Track purchases of all products. : Analyze overall purchase results, not specific products. -
How many hours or days will it take to achieve the goal after the campaign inflows?
광고를 통한 앱 실행 후 구매 전환까지의 룩백윈도우(Lookback Window)를 선택합니다.
유입 기준은 New Install, Re-Install, Deeplink Open을 포함한 Last Open 입니다.
Conditions
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Attribution Conditions
Attribution Conditions는 In-App Event Attribution 결과에만 영향을 미칩니다. 따라서 기본 설정을 그대로 유지합니다.
본 예시의 Metric에 In-App Event는 포함되지 않기 때문에 이 설정은 분석에 어떠한 영향도 미치지 않습니다.
본 예시의 'KPI Metrics'는 Attribution Conditions 설정에 영향을 받지 않습니다.
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Data Range
리포트 분석 기간을 선택합니다.
-
Time Zone
분석이 수행될 타임존을 선택합니다.
(Korea standard time = UTC+9) (Korea standard time = UTC+9)
-
Audience
_ If you have a predefined audience in Audience Studio, you can select it to start cohort analysis.
_ Audience Studio feature guide / Cohort Analysis: Escaping the trap of averages
-
Analytics Conditions
광고채널을 통해 유입된 결과만을 리포트하기 위해 Ad Partner 중 Organic(자연유입) 채널을 제외했습니다.
Organic 유입이 광고 채널은 아니지만 디파이너리는 독립된 유입 채널로 인정하고, Ad Partner 속성에 포함됩니다.
Metric Definition
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Learn about the basic structure of Data Explorer
Data Explorer - Please check additional documentation for understanding of the analysis structure and examples
.
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Using tracking parameters
Supports further refinement of performance analysis by adding parameters to the DFINERY tracking link.
You can additionally refer to
the advertising performance measurement segmentation document using parameters
.
Metrics list
Index | Metric | Definition |
Ad-Touch | Unique Impression (Identifier) | Number of Impression Uniques generated through Identifier ID Matching method (based on Advertising ID) |
Ad-Touch | Unique Impression (Fingerprint) | Number of Impression Uniques generated by fingerprint method (based on Advertising ID) |
Ad-Touch | Unique Impression (IP) | Number of Impression Uniques generated by IP method (based on Advertising ID) |
Ad-Touch | Impression (Total) | Total Number of Impressions |
Ad-Touch | Unique Click (Identifier) | Number of Click Uniques generated through Identifier ID Matching method (based on Advertising ID) |
Ad-Touch | Unique Click (Fingerprint) | Number of Click Uniques generated through fingerprint method (based on Advertising ID) |
Ad-Touch | Unique Click (IP) | Number of Click Uniques generated by IP method (based on Advertising ID) |
Ad-Touch | Click (Total) | Total number of clicks |
Attribution | New Install (by Impression) | Number of new executions caused by Impression Ad-Touch |
Attribution | New Install (by Click) | Number of new executions generated by Click Ad-Touch |
Attribution | New Install (Total) | Total number of new executions |
Attribution | Re-Install (by Impression) | Number of executions after reinstallation caused by Impression Ad-Touch |
Attribution | Re-Install (by Click) | Number of executions after reinstallation caused by Click Ad-Touch |
Attribution | Re-Install (Unique) | Unique number of executions after reinstallation caused by all Ad-Touch (based on Advertising ID) |
Attribution | Re-Install (Total) | Number of executions after full reinstallation |
Attribution | Deeplink Open (Unique) | Unique number of deep link open executions generated by all Ad-Touches (based on Advertising ID) |
Attribution | Deeplink Open (Total) | Total number of deep link open executions |
Attribution | CTIT (10 Sec) | Number of cases where the time taken to install and run the app after Ad-Touch was less than 10 seconds |
Attribution | CTIT (10~60 Sec) | Number of cases where the time taken to install and run the app after Ad-Touch was more than 10 seconds and less than 60 seconds |
Attribution | CTIT (60~1,800 Sec) | Number of cases where the time taken from installing to running the app after Ad-Touch was more than 60 seconds but less than 1,800 seconds (30 minutes) |
Attribution | CTIT (1,800~3,600 Sec) | Number of cases where the time taken from installing to running the app after Ad-Touch was between 1,800 seconds (30 minutes) and less than 3,600 seconds (1 hour) |
Attribution | CTIT (3,600~ Sec) | The time taken from installing to running the app after Ad-Touch is more than 3,600 seconds (1 hour) |
Fraud Index | Traffic Control (Impression) | Total number of impressions judged to be Ad-Fraud by Traffic Control Rule |
Fraud Index | Traffic Control (Click) | Total number of clicks judged as Ad-Fraud by Traffic Control Rule |
Fraud Index | Traffic Control (New Install) | Total number of New Install cases judged to be Ad-Fraud by Traffic Control Rule |
Fraud Index | Traffic Control (Re-Install) | Total number of Re-Install cases judged as Ad-Fraud by Traffic Control Rule |
Fraud Index | Traffic Control (Deeplink Open) | Total number of Deeplink Open cases judged as Ad-Fraud by Traffic Control Rule |
Fraud Index | Click Injection (CTIT Rule) | Total number of Ad-Touch cases judged to be Ad-Fraud by Click Injection-CTIT Rule |
Fraud Index | Click Spamming (Impression) | Total number of impressions judged as Ad-Fraud by Click Spamming Rule |
Fraud Index | Click Spamming (Click) | Total number of clicks judged as Ad-Fraud by Click Spamming Rule |
Fraud Index | Device Conflict (Device Info) | Among Ad-Touch and app execution information, the number of all Attribution (New Install, Re-Install, Deeplink Open) cases determined to be Ad-Fraud due to mismatch of device information |
Fraud Index | Device Conflict (GEO) | Among Ad-Touch and app execution information, the number of all Attribution (New Install, Re-Install, Deeplink Open) cases determined to be Ad-Fraud due to inconsistent regional information |
Fraud Index | Device Conflict (Resolution) | Among Ad-Touch and app execution information, the number of all Attribution (New Install, Re-Install, Deeplink Open) cases judged to be Ad-Fraud due to mismatch in resolution information. |
Fraud Index | Black List by First-Party (Impression) | Total number of impressions judged as Ad-Fraud by Black List (First-Party) Rule |
Fraud Index | Black List by First-Party (Click) | Total number of clicks judged as Ad-Fraud by Black List (First-Party) Rule |
Fraud Index | Black List by First-Party (New Install) | Total number of New Installs judged to be Ad-Fraud by Black List (First-Party) Rule |
Fraud Index | Black List by First-Party (Re-Install) | Total number of Re-Install cases judged as Ad-Fraud by Black List (First-Party) Rule |
Fraud Index | Black List by First-Party (Deeplink Open) | Total number of Deeplink Open cases judged as Ad-Fraud by Black List (First-Party) Rule |
Fraud Index | Spoofing DSK (New Install) | Total number of New Installs judged to be Ad-Fraud by Spoofing DSK |
Fraud Index | Spoofing DSK (Re-Install) | Total number of Re-Install cases judged as Ad-Fraud by Spoofing DSK |
Fraud Index | Spoofing DSK (Deeplink Open) | Total number of Deeplink Open cases judged as Ad-Fraud by Spoofing DSK |
Performance | Purchase count | Number of purchases judged as performance by funnel according to Attribution Conditions |
Performance | Purchase amount | Purchase amount determined as performance by funnel according to Attribution Conditions |
Performance | Purchase User | Number of purchasing users |
Performance | ARPU | Average Revenue Per User |
Performance | ARPPU | Average Revenue Per Purchase User |
Performance | PPU | Purchase Per User |
Performance | Active User | Number of unique active users |
Performance | Refund count | Number of refunds judged as performance by funnel according to Attribution Conditions |
Performance | Refund amount | Refund amount determined based on performance by funnel according to Attribution Conditions |
Performance | n Day Retention | Number of users who launched the app on the +nth day from the app introduction date (based on Advertising ID) |