The scientific way to measure
Ubiquitous, but problematic
The simplicity of last-touch attribution models comes at a price. By incentivizing vendors to push for as many low-cost clicks and views as possible, this model creates an environment prone to fraud in which everybody is fighting to get credit for user behavior, whether they are truly responsible for it or not.
It can't provide you with a clear understanding of the actual value your vendors are contributing because it doesn't account for brand equity, organic behavior, email marketing, push notifications or other external factors. This also makes it difficult to answer a common concern for performance teams: are multiple partners being paid for users who would have converted anyway?
Incrementality provides a way to test this directly. If a conversion would have happened without the campaign, it appears in the control group's baseline. Only the difference between exposed and unexposed groups counts as incremental value.
Everything you’d expect from a mobile DSP, but done better.
The automated process of buying and selling advertising space through digital platforms.
Refer to: Attribution Methodology
A randomized control trial test conducted by Remerge to measure the incremental impact of one or more campaigns.
See also: Randomized Controlled Trial
A report by Remerge showing the results of an uplift test. It presents the incremental revenue generated, on top of organic and other marketing-driven conversions. Also contains observed values such as ad spend, group sizes, amount of conversions, converters, and revenues per group, plus other metrics.
Stands for Store Kit Advertising Network. Apple’s measurement framework for tracking mobile attribution. Introduced in 2018 and widely implemented in 2020 with the iOS 14.5 update.
A group of users with common attributes such as location, demographics, activity level, value or amount of purchases, and how recently they last opened a specific app.
The share of users active in the app within certain time frames after install, reengagement, or other events.
A type of marketing channel used by an app owner to engage with their existing users through other channels within the same device. Usually, the aim is to encourage users to complete a particular task e.g. completing a purchase, buying in-game currency, placing a first order. The conventional way of retargeting relies on user IDs, such as AAID and IDFA.
Reshuffle indicates the randomization and marking of users when they were once part of a test or control group.
In incrementality measurement, reshuffling the group assignment for a specific application fights aggregated bias over time where one group doesn't see any ads while the other group is constantly exposed to them.
Reshuffling is relevant in cases where a test has been running for a long time and/or in campaigns the experience more extensive changes to the campaign setups, segmentation, or creative strategy.
The process by which individual ad placements are bought and sold via programmatic auctions that happen instantaneously. With real-time bidding, ad buyers bid on an ad space, which, if the auction is won, instantly displays the buyer's ad. This lets demand-side players such as advertisers or DSPs optimize the purchase of ad placements from multiple sources.
A method that randomly separates a specific population into two groups that are as similar to each other as possible, namely the test group and control group.
The number of ad placements a DSP is able to process in order to determine on how to bid on them.
Within the sphere of app marketing, a publisher is an App Developer that gets paid for placing ads within their app. For example, an advertiser wants to reach their users via App Y, so they pay App Y to display their ads.
An incrementality testing methodology where devices in the control group are shown PSA ads, like donation drives or road safety reminders. By serving real ads, information on the devices within the control group that would have been exposed can be obtained. Unexposed devices are excluded from the measurement to reduce noise.
Refer to: Attribution Methodology
A user’s behavior not directly attributable to specific marketing efforts.
Refer to: Attribution Methodology
Within the sphere of app marketing, MMPs are a service provider that specializes in measuring activities that are happening within and leading to the app. An app publisher may incorporate an MMP into their app to track activity and events e.g. time spent on a certain screen, sources of incoming traffic, app opening frequencies etc.
The amount of revenue generated by the user for the App Developer during the entire duration of the relationship with the user, beginning with the app install.
Refer to: Attribution Methodology
The key metrics used to assess the effectiveness of an effort in achieving its objective. In programmatic advertising, the common types of performance indicators depend on the goals and nature of each campaign. These can include ROAS, cost per action, and retention rate.
An incrementality testing methodology where no ads from the campaign are shown to devices within the control group. Also known as a ‘holdout test’. Cost-free and easy to implement, but with a relatively high level of noise.
This method compares the behavior of all users in both groups. In the test group, this includes both exposed and unexposed users
A method of measuring the impact of a specific activity, on top of organic and other activity.
The estimated revenue caused directly by the campaign.
Formula:Revenue from test group – revenue from control group = iRevenue
A KPI used in calculating how cost-efficient a campaign is. This is used to evaluate the relationship between incremental revenue and the amount of money spent on the campaign. The figure is typically represented in percentage.
Formula:
Percentage: [IRevenue ÷ ad spend] × 100 = IROAS%
Ratio: IRevenue ÷ ad spend = IROAS
A KPI used to evaluate the cost of incremental conversions.
Formula:Ad spend ÷ [test group actions – control group actions] = iCPA
The estimated amount of conversions caused directly by the campaign.
Formula:
Test group conversions – control group conversions (scaled) = Incremental conversions
Actions made by a user within the app, such as log-in, registration, completion of onboarding, or purchases. These events can be tracked with the help of an MMP.
The deployment of the ad to the ad placement. An impression might not necessarily mean that the ad has been viewed.
A unique random device identifier Apple generates and assigns to every iOS device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.
A testing methodology that shows devices in the control group an ad ran by another advertiser on the platform, therefore removing any additional cost for clicks and impressions. The control group behavior is then marked with a ‘ghost impression’, which gives the information on which control group users would have been exposed.
A regulation under the EU (European Union) law on data protection and privacy within the EU and the EEA (European Economic Area), that grants users control over how their data is stored and used by organizations. To comply with GDPR, programmatic sellers must clearly communicate to users how their data will be stored and used. When a user gives consent to an organization to process their data, it enables targeted advertising.
The percentage of devices within a test group that received at least one ad impression, versus the total number of devices within the test group targeted within a campaign during an uplift test. For example, if 900 out of 1,000 users are shown an ad, the exposure rate is 90%.
See also: Uplift Test
Refer to: Attribution Methodology
A link that sends users directly to a specific in-app location, instead of the app marketplace. Deep links bypass the steps needed to go through to reach a conversion point, bringing the user directly to where they can perform the intended action e.g. completing a purchase, buying coins, placing an order.
Within the sphere of app marketing, this refers to the group of devices that may be shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the control group.
Compare with: Control Group
Within the sphere of app marketing, this refers to the group of devices within the target audience that are not shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the test group.
Compare with: Test Group
A type of targeting that works with contextual signals only, such as location data (country, city, postal code), language setting, mobile operating system, device model, as well as publisher information.
A bill that enhances privacy rights and consumer protection for residents of California, United States. The CCPA took effect on January 1, 2020.
The CCPA provides these rights to consumers:
- Know what personal data is being collected about them.
- Know whether their personal data is sold or disclosed, and to whom.
- Say no to the sale of personal data.
- Access their personal data.
- Request a business to delete any personal information that was collected from that consumer.
- Equal service and price, even if they exercise their privacy rights.
A specific time frame that is taken into consideration when determining the source of a user’s action.
A role played by an MMP to credit the in-app activity of users to the correct media sources.
Refers to the process of identifying which conversions belong to which preceding click or impression. Common attribution methodologies include:
- Click-Through Attribution - Determines the source of a conversion based on the user’s click activity.
- View-Through Attribution - Determines the source of a conversion based on the ad impression delivered to the user.
- Deterministic Attribution - A model that establishes the origin of a user’s conversion from a specific click or impression, based on unique device IDs.
- Probabilistic Attribution - A model that establishes the likelihood of a user’s conversion originating from a specific click or impression, based on the data logged on both occasions, such as device language, timezone, IP address, and OS version.
- Last-Touch Attribution - A model that establishes a match between the action taken by a user (e.g. app open, purchase) and its corresponding ad click or impression. When a user converts from an ad, the DSP that delivered the respective ad is given full credit for that conversion event.
- Multi-Touch Attribution - Also known as multi-channel attribution. A model determines the value of every touchpoint on the way to a conversion. Rather than giving full credit to one ad, multi-touch attribution divides the credit among all advertising channels that the user has interacted with, leading to the conversion.
A method of identifying the touchpoints a user has encountered within a specified period before making a conversion.
The privacy framework from Apple that, among other things, manages the process of obtaining user consent before accessing their Identifier for Advertiser (IDFA).
The strategy a publisher employs to earn money from their app. This can be done through in-app advertisements, paid membership, and charging for premium features or an ad-free experience, among others. For example, some gaming apps are free to download and play, but users may need to pay in order to progress to the next level quickly.
Also known as Google Advertising Identifier. A unique device identifier that Android generates and assigns to every device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.
The advertiser is a person or legal entity focusing on generating sales and leads through serving ads that convey the right message to the right audience at the right time.
In mobile advertising, the advertiser is on the client-side and is the one interested in promoting an app.
A measurement framework developed by Google that works without device IDs. It measures the incremental uplift of one or more conversion events, removing the influence of other campaigns and organic conversions. Used to assess the effect of ID-less campaigns.
Similar to measuring the effect TV ads have, the principle is based on running campaigns on identifiable sub-markets (test group), while leaving other sub-markets unexposed (control group).
An incrementality testing methodology based on Ghost Ads, adapted for retargeting campaigns. The difference is that it removes all devices that are not seen on ad exchanges, or that would not be bid on, from both test and control groups, to reduce noise. A bid is placed as usual for the test group, while the control group is tracked with ‘ghost bids’ (bids that could have been placed, but weren’t in the end).
A KPI that measures the relationship between the revenue generated by specific advertising efforts and the money spent on them.
Formula
Percentage: [Revenue ÷ ad spend] × 100 = ROAS%
Ratio: Revenue ÷ ad spend = ROAS
See also: Incremental Return On Ad Spend
A company that works with publishers to sell ad inventory across ad networks.
A company that works with advertisers to purchase ad inventory across ad networks. Their platforms are built to identify a desired ad space and place bids on it.
Compare with: Supply-Side Platform
A digital marketplace where ad inventory from multiple publishers are available for advertisers to bid on in real time.
See also: Real-Time Bidding
An ad network like Meta, Snap, and Twitter, that attributes its traffic internally, without the involvement of third-party MMPs.
The dynamic adjustment of bid prices based on a user's in-app behavioral patterns, contextual information, time of day, and ad placement performance.
Also known as a dynamic ad. It is dynamically assembled based on the user’s behavior and information sourced from a feed. This type of ad delivers a tailored experience for individual users.
The division of an audience into distinct segments based on real-time events, thus enabling targeted advertising and alignment with a user's behavioral patterns and preferences.
A mobile marketing effort used to attract new users to an app. Paid UA may refer to ads shown in mobile ad networks or social media channels, while non-paid UA involves app store optimization and promotion on the advertiser’s own channels.
The automated process of buying and selling advertising space through digital platforms.