This is the final week of a 5-week series on Configurable Attribution. We looked at Lookback Windows in week #1, Probabilistic in week #2 (see iOS 14+ restrictions), Device Reconciliation in week #3 and View-Through Attribution in week #4. Now that you have a handle on how to leverage configuration, let’s look at the resulting reports and how you can use them to further optimize your configurations.

If you’re doing active user acquisition or monetization, you are already looking at reports on a daily basis. However, there are a few specific metrics you may not be considering including mean time to install (MTTI), mean time to action (MTTA), and Influencer reporting.

MTTI and MTTA are two of the most interesting and under-leveraged metrics in the user acquisition and monetization space. These data points reveal the statistical mean for how long you can expect users to take between click and install/action to reach the desired conversion point. This analysis can be at the app level to give you insight into your audience’s behavior, and at the network level to fine-tune your attribution configuration. Now you can optimize your lookback windows to reach the maximum audience, while avoiding the point of diminishing returns.

A sampling of average MTTI by vertical

For all intents and purposes MTTI and MTTA give the same insight: MTTI for install campaigns and MTTA for reengagement campaigns. In the following examples, we’ll look at MTTI, but the same examples exist for MTTA.

For example, if the MTTI for your app is 30 hours, that tells me that the majority of my users wait approximately between 1 and 2 days to launch the app, after downloading. You can then compare your MTTI for the app to MTTI by network to start tuning your lookback windows. If your MTTI or MTTA for Network A is comparable to your app (~30 hours) then you can set your lookback window to 3 days without missing much traffic. Now you can look at a 3-day cycle on campaigns for quicker optimization.

Conversely, if you see a network with an MTTI much lower than your app, it means that users are launching the app much more quickly on this network, than on average. This may indicate that there is a publisher within the network which is mixing incentivized traffic in with non-incentivized traffic. This also happens to be a simple example of what Kochava Fraud Detection uncovers.

Two other critical pieces of intel to review are the Influencers and Influencer Summary reports. These reports show which networks are driving traffic and clicks, despite not having the winning click. Influencer reporting gives data about the click including the reason it did not win. Reasons may include being outside the lookback window, not being the last click, or being superseded by a higher match-type. You can consume Influencer data at an aggregate level (Influencer Summary) or at the device level (Influencer Report). These reports give you the actionable intelligence needed to manage your budget and ROI based on networks that are driving traffic, independently of whether they are winning attribution for installs.

The image below demonstrates the importance of influencing networks.

 

 

For more information on available reports and how to make the most of them, email your account manager or sign up now.

This is the 4th in a 5-week series on Configurable Reconciliation. So far, we’ve covered Lookback Windows in week #1, Probabilistic Attribution in week #2 (see iOS 14+ restrictions) and Device Attribution in week #3. This week we’re going to look at the power of configurable View-Through Attribution.

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While the mobile ad space is certainly still developing, most advertisers understand the importance of tracking clicks and installs. Savvy advertisers use these data points to streamline and optimize their ad spend. Some of these same advertisers are also tracking impressions to understand their click-through rates (CTRs) as well as adding a layer of accountability to ad-network reporting.

Impression tracking is available through a few mobile attribution & analytics providers. Tracking impressions is only the first step. The real insight comes with impression attribution or view-through attribution. There are several reasons why view-through attribution is important.

View-through attribution is the capability to credit an install or in-app event to a specific impression. This also enables insight into the influence impressions have on downstream engagements.

The Problem

Attributing clicks to installs is the first step to understanding mobile ad effectiveness, but it does nothing to uncover the truth of click-through rate (CTR) or effective frequency. The only way to understand these metrics is by tracking impressions in addition to clicks, installs, and events.

Part of what creates a healthy ecosystem is transparency. Historically, only ad networks have had any insight into impressions served. By introducing 3^rd-party tracking into the process, an independent source can verify impression volume and effectiveness.

In the same way that click attribution rules can vary between networks, impression tracking and view-through attribution include a variety of configurable aspects. Ad networks count impressions in different ways and attribute based on different criteria.

The Solution

Configurable view-through attribution puts the controls around defining an impression and the rules for attribution into the hands of the advertiser. There are several reasons why advertisers should be leveraging view-through attribution.

Gaining insight into brand lift.
As marketing budgets continue to shift toward mobile, brands need a way to measure lift outside of installs and post-install events. For example, Vizio may have a companion app for their devices, but they really want to sell hardware. By simply tracking impressions, the Vizio team can understand the reach of their ad budget, independent of driving installs. Additionally, view-through attribution with Kochava includes full influencer reporting. This insight can tie impressions which happened outside the configured lookback window, to an app install. Thus, Vizio can see how many times a user saw their ad impressions before installing the companion app for their device(s), which means they made a purchase.

Understand the CTR of a campaign.
By tracking impressions as well as clicks, the advertiser can understand the relationship between how many times an ad is served to when a user clicks on the ad.

Catch users flying under the radar.
As hyper-personal targeting increases, a segment of users independently pursue a product without clicking on an ad to avoid retargeting and overwhelming ad personalization. View-through attribution allows you to attribute an impression to a user behavior without the user ever clicking on the ad.

Understanding the comprehensive impact of your mobile campaigns requires measuring every possible influencing aspect. View-through attribution provides insight into every aspect of user behavior connecting the dots from ad impression all the way through post-install activity. When it comes to your ad strategy, view-through attribution is the missing piece.

For a list of networks that support view-through attribution, check out https://www.kochava.com/new-network-integrations/.

Configurable Attribution #5 – Get the Reports You’re Missing

We’re now 3 weeks into a 5-week series on Configurable Reconciliation. We looked at Lookback Windows in week #1 and Probabilistic Attribution in week #2 (see iOS 14+ restrictions). This week, we’re going to dig into the bread-and-butter of attribution—Device Reconciliation.

Device reconciliation is the highest-integrity match type available. Device reconciliation happens when the device ID received on click or impression matches the device ID on install. This level of match integrity is attained in 3 ways—raw device matching, progressive reconciliation and Self-Attributing Network claims.

 

Raw Device Match

A raw device match happens when the device ID collected on install, which is always raw, matches with a raw device ID received on click or impression. This is the simplest scenario and is possible if a raw device ID is received on the click or impression. But what happens if the device ID received on click or impression is hashed unexpectedly through traffic rebrokering, or gets mangled in some way? For these cases, Kochava developed Progressive Reconciliation.

 

Progressive Reconciliation

Progressive Reconciliation is a process by which Kochava takes the raw device ID collected on install and creates over 20 variations of that device ID, based on typical alterations made by networks and publishers. The methods by which the variations are created include hashing and double hashing (SHA1 and MD5) adding and removing punctuation (dashes, colons, etc. depending on ID type), making the ID uppercase or lowercase, and creating combinations of all of these. These permutations ensure that all variations of a device ID—intended or unintended—are available for matching.  This combination of raw device matching and Progressive Reconciliation allows Kochava to attribute every available install on device ID.

 

SAN Claims

The final method for device reconciliation is via Self-Attributing Networks or SANs. Self-Attributing Networks are large networks with high-value traffic, which have unique integrations with Kochava. Each SAN receives a postback feed of all installs for apps running traffic on the network. If the SAN has a click that is eligible for attribution, the SAN responds with a claim and a click time. Kochava reports these installs as well as putting them into the larger context of all live campaigns. Self-Attributing Networks include Facebook, Twitter, Google and iAd.

The full attribution waterfall is based on match integrity, with device reconciliation at the top. The waterfall is as follows:

• Device Reconciliation
  • Raw device ID
  • Progressive reconciliation
  • SAN claims
• Probabilistic (see iOS 14+ restrictions)
  • IP address plus User Agent
    • Device ID not present on click/impression
    • Device ID present on click/impressions
  • IP Only (see iOS 14+ restrictions)
    • Device ID not present on click/impression
    • Device ID present on click/impressions
  • IP Range Only (see iOS 14+ restrictions)
    • Device ID not present on click/impression
    • Device ID present on click/impressions

In previous installments of the Configurable Attribution story, different configurations have been tied to specific goals. This Device Reconciliation story is more about exposing the inner-workings of the Attribution Engine than highlighting configurable options. Over the next 2 weeks we’re going to discuss how to digest reporting and the ins-and-outs of View-Through Attribution. Stay tuned.

Configurable Attribution #4 – View-Through Attribution

We looked at an overview of the Kochava Attribution Engine and talked about Lookback Windows in week #1 —how far back from the time of install, to consider clicks and impressions for attribution.

This week we are going to talk about Probabilistic Attribution (see iOS 14+ restrictions) – the only way** a device can be matched to a click or impression if there is no device identifier gathered at the time of click or impression. This may happen for a variety of reasons.  For example, the network may not be able to collect the device identifier, there may be legal reasons precluding the capture of device id, or the ad may be served on mobile web. In these cases, Kochava creates a profile based on the data that is collected, and matches that with the same collection of data points upon install or re-engagement.

The data points on which probabilistic attribution (see iOS 14+ restrictions) relies include IP address and user agent. The combination of these creates a unique identity for a device in the absence of a device identifier. As an example, an IP address includes 4 stanzas of up to 3 digits each, and the user agent is a string of information identifying your browser, device version, etc. See an example here. None of this data is personally identifiable information (PII).

In Kochava, this form of attribution is configurable at 3 levels. The advertiser may choose from the following:

• IP plus User Agent – combination of complete IP address and user agent
• IP Only – only the IP address is considered, the user agent is ignored.
• IP Range – only the first 3 stanzas of the IP address are considered, and the user agent is ignored.

Each of these configurations has its unique advantages and impacts the relative integrity of an attributed match as well as the balance between attributed and unattributed installs and events.

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Goal #1: Get as Close to Device-Based Attribution as Possible

If attribution integrity is your primary goal, IP address plus user agent is the best option. This requires that users are on the same IP address and have the same user agent in order to be attributed. The addition of the user agent accounts for issues where many users’ are using the same corporate network and thus show the same public IP address.

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Goal #2: Measure Cross-Platform Conversions

If you want to serve ads on mobile web or even desktop traffic, but measure mobile engagement, IP Only is a good, balanced configuration. For example, if a user clicks on an ad on their home or office wireless network and then launches the app on their mobile device, connected to the same network, the public IP address will be the same, but the user agent will be ignored. Thus, the click from a desktop computer can be matched with the install on the mobile device simply by matching the IP address.

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Goal #3: Work Around Malformed User Agents

There are cases when the user agent may be mangled or changed based on the way some networks or site IDs handle it. This can result from issues between an app and a publisher SDK, or sometimes a network’s server-to-server integration can overwrite the device’s user agent with its own. In these cases, using IP Only allows you to continue attributing installs while the problem is remedied.

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Configurable attribution is one of the most powerful tools at an advertiser’s disposal, and Kochava has the most robust configuration available. This is part 2 in a 5-week series. Over the next 4 weeks, we will be unpacking the Kochava Attribution Engine: What it is, how it works, and the tactics to configure your attribution to support your strategy.

 

**In the case of native Android apps, Google Referrer can be used to do attribution without a device identifier

Configurable Attribution #3 – Device Reconciliation

Configurable attribution is one of the most powerful tools at an advertiser’s disposal, and Kochava has the most robust configuration available. It allows the advertiser to tailor the balance between attributed and unattributed installs and events to their attribution goals. Over the next 4 weeks we will be unpacking the Kochava Attribution Engine: What it is, how it works, and the tactics to configure your attribution to support your strategy.

The Kochava Attribution Engine is designed to be unbiased, comprehensive and immediate, considering every available data point to ensure authoritative attribution. Its objectivity results from considering all possible engagements—impressions, clicks, installs, events—and determining the winning engagement. The consideration of all engagements ensures that the attribution decision is final. Once the attribution is completed, it becomes actionable via real-time postback, data syndication, and reporting. The factors that figure into attribution, in order of priority, are lookback window, the integrity of the match, and the time of the click.

Lookback window defines how far back, from the time of install, to consider clicks and impressions for attribution. For example, a 1-day Lookback Window means the click must have been recorded < 24hrs prior to install.

Lookback window defaults are 30 days for device-based matching and 7 days for probabilistic-based (see iOS 14+ restrictions) matching, but they are fully configurable by media partner, as well as at the campaign and/or tracker level.

Configurable lookback windows are a powerful tool. By adjusting lookback windows advertisers can customize the balance between attributed and unattributed installs and events, to serve your advertising goals. Here are a few different goals and the strategies and tactics that can be employed to achieve your goals:

Goal #1:  Optimize campaign efficiency

By matching your lookback window configuration to your MTTI (mean time to install) by partner, you can ensure that your lookback window is set at the optimal point before your campaign reaches diminishing returns.   These can be set by campaign and by network. For example, games tend to have a relatively longer MTTI than other categories. If your MTTI on Network A is 30 hours, you can set your lookback window for Network A to 3 days to attribute the majority of new users to that campaign.  This allows you to optimize campaigns without wasting dollars waiting for the stragglers.

Goal #2:  Acquire High-Intent Users

Acquiring high-intent users can be expensive. By employing shorter lookback windows, you can ensure that you are only paying for the users with the highest intent.

Goal #3:  Gain network traffic prioritization

Getting up-to-speed with a new network and earning priority placement takes time. By increasing the lookback window, you can ensure that every possible conversion is attributed to that network, thus gaining traction and earning prioritized network traffic.

Read part 2 of this series.