Ad spend measurement: 3 ways marketers tackle one of mobile’s biggest analytics challenges

Mobile marketers across the globe recognize the massive importance of ad spend measurement. The ability to effectively collect ad spend data from media providers directly affects a marketer’s success on mobile.

But various events can skew your ad spend data as it travels from your ad networks into your analytics, distorting metrics, destroying the ability to target your most profitable audiences, and interfering with vital activities like creative analytics. As a result, collection of accurate and detailed spend data from ad partners is a non-trivial task that trips many marketing teams up.

It is a problem that Singular set out to solve for marketers more than seven years ago. In that time we’ve pioneered numerous technologies to automate the collection of accurate and detailed ad spend data directly from media providers in just about any form imaginable: API, export, PDF, screen-scraping, and more.

As the industry matures, and other analytics platforms start to recognize the importance of ad spend and ROI analysis, the time feels right to review the various spend collection methods being utilized in the mobile marketing industry and highlight the advantages as well as the limitations of each method.

In doing so, we hope to advance the growing dialogue on ad spend collection in the analytics ecosystem and continue pushing the industry to improve the handoff of marketing data from media providers to advertisers.

Overview of spend collection methods

Currently there are three main types of methods for collecting ad spend:

• Direct: platform integrations
• Semi-direct: exports and reports
• Indirect: passing spend data in tracking link parameters (i.e. cost “macros”)
• Indirect: passing spend data in server-to-server postbacks

Platform Integrations

In this method, media providers such as mobile ad networks report rich metadata and performance information through some form of programmatic data reporting, commonly a reporting API. In many cases, networks have multiple API endpoints that may serve different granularities, breakdowns, formats, or audiences.

Advantages

• Platform integrations give marketers the ability to accurately match the media provider numbers, including cases in which data changes retroactively
• Platform integrations give marketers access to a wealth of information beyond ad spend, such as additional performance metrics, creative data, targeting options and more
• Platform integrations are the only way to integrate with the self-attributing networks (SANs): Facebook, Google, Twitter, Pinterest, Apple Search Ads and others
• Platform integrations pass sensitive data is securely,  server-to-server
• Platform integrations provide data as quickly as it is available, and therefore quicker than any other method

Limitations

• Platform integrations are harder to build and maintain
• Platform integrations must map media provider identifiers to user data, requiring coordination between tracking links and data collected
• Platform integrations can limit data update frequency – while some networks offer near real-time updates, others offer hourly or daily updates

Semi-direct

There are also cases where networks send data in email reports to complement some form of reporting that the API lacks. There are other cases in which dashboards and various types of exports (e.g., CSV via Amazon S3) complement reporting where an API is not available.

Advantages

• Semi-direct at least gives you data … always a good thing
• Semi-direct data is right from the ad network, so it should be accurate

Limitations

• Semi-direct data may not be timely
• Semi-direct data for one time period could be different in a later export as more data from extended attribution windows becomes accurate
• Semi-direct methods can be brittle

Passing Spend Data in Tracking Link Parameters

With this method, marketers attach a few additional macros for cost data to the tracking links they create in their attribution platform (e.g., cost={...}&cost_model={...}). These links are built such that additional cost information is appended on top of every ad click (and ad impression, when view tags are supported).

While most larger networks support passing spend data through tracking links, many networks do not support this method. In addition, we’ve found that relying solely on tracking links to transmit cost data frequently leads to inaccuracies, which is why we recommend marketers complement data from tracking links with data from Platforms integrations, side-by-side, to ensure 100% accuracy and consistency.

Advantages

• Tracking link parameters deliver a built-in capability to attach cost to individual user data
• Tracking link parameters update data in near real-time
• Tracking link parameters are simpler technology and relatively easy to maintain

Limitations

• Tracking link parameters have inherent discrepancies with media providers – tracking links don’t ensure a 100% match with the network’s spend figures, and spend could differ from the actual invoices marketers receive
• Tracking link parameters make it difficult to support cost reconciliations, retroactive data updates and discounts
• Tracking link parameters are not applicable for self-attributing networks (like Facebook, Google, Twitter, Snap and others) as tracking links aren’t supported in these networks
• Tracking link parameters make it challenging to support CPM & CPA campaigns:
  • CPM requires impression tags, which aren’t globally support yet, and due to sheer volume/inaccuracies will only increase discrepancies.
  • CPA is harder to support as cost is determined by a downstream metric or a set of downstream metrics, and there isn’t a clear way to define that at the link level

Passing spend data in postbacks

This method is similar to the tracking link method, however, instead of using tracking link parameters, media providers can send cost data through postbacks directly to the attribution provider. While we expect postbacks to deliver improvements over the tracking link method, other challenges (listed below) still remain unresolved.

Advantages

• Postbacks deliver a built-in capability to attach cost to individual user data
• Postbacks deliver data in near real-time
• Postbacks offer support for all campaign types (as opposed to tracking link parameters)

Limitations

• Postbacks suffer from inherent discrepancies with media providers – this method doesn’t ensure a 100% match with the network’s spend figures, and spend could differ from the actual invoices marketers receive
• Postbacks make it difficult to support cost reconciliations, retroactive data updates, and discounts
• Postbacks are not applicable for self-attributing networks like Facebook, Google, Twitter, Snap and others
• Postbacks require development from the network, and not all networks have the resources, ability, or desire to change their ad server to fit these requirements, and as a result, coverage is still limited

Summary

As pioneers in this field, we are excited to see the increased awareness of the problem of marketing data collection. This is a problem we have been solving for our customers for over four years, and along the way we have seen the impact of our work: better collection techniques, new interfaces with media providers, and overall increases in granularity, speed and accuracy.

Our fundamental belief is that the best solution to the problem is the most comprehensive one: one that combines all available methods of ad spend and marketing data collection into a hybrid approach. Singular’s customers are some of the largest marketers in the world, and as such, we are held to the highest standards of delivery for accuracy, coverage, speed, and granularity.

Our promise to our customers and our ecosystem is to keep innovating, and tackling the problems to come. In fact, we have some groundbreaking innovations we are excited to share with the world in the upcoming months, and we can’t wait to tell you more about them.

To learn how Singular can solve for marketing data collection in your business, request a demo now.