by Alex Ho
(Image source: StorySet )
Table of Contents
- Advertising Attribution
- Common rules-based attribution methods
- The primary practice: Last-click Attribution
- Emerging Difficulties from the market trends
- Privacy policies formulated by tech firms
- Attribution is getting harder and harder
- Data-driven attributions methods: MTA & MMM
- MTA (Multi-Touch Attribution)
- MMM (Media Mix Modeling)
- Comparison between MTA and MMM
- Approaches related to ads attribution from tech Firms
- Meta
- Google
- References
Advertising Attribution
Advertising Attribution aims to determine the contribution of each individual ad campaign touch point for a certain marketing goal (e.g. make a purchase, click on a banner, install mobile App).
There 3 reasons that it is difficult to correctly measure the marketing performances across multiple touch points
<1> Each Ad platform tends to credit same individual conversions to themselves
- Advertisers tend to simultaneously place multiple Ads in multiple channels.
- Each Ad platform (e.g. Facebook, Google) provides different attribution methods for the advertisers.
- (Take e-commerce scenario as example) If we add the converted GMV from various ads in each channels, the subtotal number could be higher than 100% of the real total GMV.
<2> Much traffic could not be correctly attributed because of the measurement limitation across devices or operating systems
- It makes the attribution model falsely credit a chunk of conversions to “direct traffic”
- Then it could make advertiser underestimate the performance from certain marketing channels.
<3> Data signal loss due to emerging privacy policies
- More and more privacy policies formulated by tech firms has made ads platforms more difficult to collect 3rd-party user data (compared with what they were able to before).
Common rules-based attribution methods
- Last-click
- First-click
- Linear (equally credits every touch point)
- Time-decay (credits touch points by the duration between an impression and conversion)
- Position-based (40% credit each to the first and last impressions, and 20% spread over the rest)
The primary practice: Last-click Attribution
Last-click Attribution credits the conversion to only the last individual touch point that a user clicked (or saw) before taking the desired action (a.k.a driving the conversion). It is by far the most common and dominant attribution method.
There is one major drawback of Last-click Attribution: It could make advertisers (i.e. media buyers) ignore the value and incremental contribution of the touch points in the higher funnel (i.e. the marketing channels before the last touch point.)
Emerging Difficulties from the market trends
Privacy policies formulated by tech firms
Examples raised by the tech firms
- Apple ATT (App Tracking Transparency) policy
- Google will abandon 3rd-party cookie in Chrome browser soon (even if they keep delaying it)
- Google Tag Manager stops tracking tags from being fired for users who opted out the feature
- GA4 does not collect IP address information any more.
Some results
- Generally, less than 30% of iOS users are willing to let App developers track and use their device data.
- CPM cost in Meta Ads increased while impressions dropped.
- As the advertising performance drops, Advertiser (a.k.a Media Buyer) cut their overall online ads spending, and focus more on the Performance Marketing instead of Brand Marketing.
- Investors’ lack of confidence in platforms such as Meta and Snap were reflected in the stock prices.
Attribution is getting harder and harder
- Because of the privacy policies applied from the tech firms, there are more users (or visitors) are not able to be tracked by the advertisers and ad platforms.
- And as the majority of the advertisers are still relying on the
Last-click Attribution, it attributes even more conversions to the direct traffic (because we are not able to attribute it to the real campaign which should be credited). - That makes the marketing performance of those major ad platform looks worse than before.
Data-driven attributions methods: MTA & MMM
Before we dive deeper, keep these in mind:
- No attribution model can perfectly credit each conversion to the correct touch point
- Either MTA or MMM is designed to complement the primary attribution model - the Last-click Attribution.
- Any projection based on historical data could be unhelpful when the external world changes dramatically (e.g. COVID-19 pandemic in 2020, Apple announced ATT policy in 2021, new players entering advertising market).
Some known facts from the markets
- As of July 2022, 53% of large advertisers are using MTA to track and optimize their marketing spending across channels (source: MMA Global)
(The following focus on the general concepts of MTA and MMM and will not identify any specific products from existing tech firms)
MTA (Multi-Touch Attribution)
- MTA assigns fractional credit to each touch point in the overall customer journey.
- Input - It tracks all of the individual-level data that happens in advertising campaigns.
- Output - So that advertisers can measure the contribution of each touch factors (e.g. channels, creatives, audiences).
- Drawbacks of MTA
- MTA requires access to individual-level user data tied to trackable conversion events (this could be helped by using MMM. Will be explained soon)
- Hard to handle offline campaign measurement
MMM (Media Mix Modeling)
- MMM uses supervised machine learning model to attribute the marketing performance results to multiple factors (for easier understanding, I personally separate them into marketing factors and external factors)
- Marketing factors examples: spending in different marketing channels, big promotions, special marketing-related events in the local market
- External factors examples: seasonality, market trends, and etc.
- Machine learning input and output (let’s take e-commerce advertiser’s scenario for example)
- Data input: spending in each single channel
- Data output: converted GMV from each single channel
- MMM does not need to know “what each factor actually means in the real world” in order to process the analysis and projection
- This means - if you randomly rename the “factors” that you input into the MMM model, it does not affect the output results.
- What’s more, after digesting the historical data, MMM model can project the future marketing performance results based on optimized re-allocated marketing spending suggestions
- This can help advertisers to review the contribution from each single marketing channel regardless of being online or offline
- Accordingly, advertisers can revise their spending allocation across various channels in order to maximize the marketing performance.
- If you are already using MTA, then MMM can be seen as a complement.
- You can consider the former as micro and short-term analysis, while the latter as macro and long-term analysis
- Common drawbacks of MMM (more to be stated in the following table)
- MMM requires huge amount of data (maybe accumulated for at least 2 or 3 years) as input of the model to generate reliable results and projection. Such time-length is much longer than campaign time length in the real world, so it could not catch up with emerging external signals fast enough as new things happen. So it’s not realistic to use MMM to review the performance of short-term advertising campaigns.
Comparison between MTA and MMM
(And don’t forget. We can use both MTA and MMM simultaneously.)
MTA | MMM | |
Time-bound | Real-time measurement | Time-lagging measurement and projection
|
Suitable use cases | Measuring performances of short-term campaigns | Long-term and macro marketing spending optimization |
Level of attribution granularity | Able to measure the impact of a specific single touch point | Not able to measure the impact of a specific single touch point |
Data volume required for data input | Does not need huge amount of data | Needs huge amount of data as input of the model to generate reliable results and projection.
Generally it requires data accumulated for more than 2 or 3 years. |
Dependency on advertiser’s ability to track individual user identity | MTA requires access to individual-level data
Therefore, MTA could be severely affected by the restrictions from tech firms (Apple and Google) | MMM can be applied even when advertisers are unable to track individual user behavior.
Information of individual users are unnecessary. MMM uses aggregated date for modeling |
Offline Ads | Hard to measure offline channels Ads performances | Able to handle both online and offline channels |
Approaches related to ads attribution from tech Firms
Meta
Meta provides MMM Robyn, a free and open-sourced data model, for advertisers or software companies to implement and include it into their own analysis tools.
In September 2021, Google replaced the Last-click attribution by Data-driven Attribution as the default conversion model in Google Ads (buy-side ad network).
- It uses in-house algorithm to assign fractional credit to touch points
- Last-click attribution is still available, not deprecated.
Google Data-driven Attribution Methodology
References
In 2019, Amex started a project to replace MTA with MMM
About Alex
- Software Product Manager. Work experiences in Taipei, Singapore, and Shanghai.
- Currently based in Taipei City, Taiwan.
- Contact me via: alex.ho.helloworld@gmail.com
Notes & Articles
產品經理、思維策略
產品經理思維與方法論Thinking Framework of a Product Manager The North Star Metrics in Product Management底層邏輯, 思考問題, 解決問題 [Notes] 生態系競爭多模型思維 (運用多種思維模型去理解複雜的世界)[Notes] 網路效應 Network Effect 認知偏誤 (Cognitive Bias) & 行為金融學[Notes] AI / ML[Notes] 溝通、談判、衝突管理[Notes] 用戶運營Introduction of Advertising Attribution, MTA, and MMM經濟、投資
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