Make The Most Of Preload Mobile Ad Spend With A Shift To Holistic Buying

For developers looking to monetize their apps and advertisers looking to optimize their mobile advertising spend, it can be hard to understand where preloaded software fits into the mobile app monetization ecosystem and choose the most profitable ways to buy and sell.

A vast and growing marketplace

For the uninitiated, preloaded software is the software installed on new mobile devices before they leave the original equipment manufacturer (OEM). Since Apple transformed our idea of what mobile devices could do with the release of the first iPhone in 2007, the smartphone market has exploded.

So has the volume and variety of preloaded software. What started as a limited number of simple proprietary apps – think iPhone’s Camera, Find My Phone, and Messages, or Google Maps, Gmail, and YouTube on Google devices, as well as the device makers’ app stores – has since grown to encompass a huge range of apps and developers.

On-device advertising in the preload market has also evolved dramatically, from unsophisticated app preloads to an increasing set of opportunities to provide ongoing and relevant recommendations to users. While Apple shows ads in the App Store, many Android devices show regular app recommendations and suggest new apps after OS updates. Meanwhile, platforms including Google, Facebook, and AppLovin now automate bid pricing based on predicted value for the seller, thereby negating the need to pay fixed sums upfront for on-device exposure.

But some of the models by which advertisers invest in this ecosystem are outdated by not making use of automated bidding and recommendation capabilities.

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CPP sacrifices quality and revenue

One popular method for buying preload inventory is cost per preload (CPP), where buyers pay for every preload. This sounds simple enough, but it isn’t the best path to optimized spend in today’s market.

This is because CPP is essentially a legacy buying model from the days when apps were exclusively preloaded at the factory months in advance of activation, and necessarily untargeted and distributed with guaranteed revenue for the OEM. There are multiple challenges associated with this model.

For one thing, mobile attribution is difficult with CPP, because marketers pay for app opens, or ‘installs’. In other words, if a user doesn’t open the app, the preload cannot be measured by an independent third party – meaning advertisers are missing out on critical data for scaled marketing. Buyers are left dependent on OEMs and advertising networks for data, which leads to challenges like double counting.

Also, on-device is inherently a contextless environment, showing the same recommendation over and over simply because it offers the highest CPP bid. The current paradigm has shifted to dynamic preloads that change and respond in new ways over time, offering new content, prompts, and suggestions in response to machine learning and user behaviors across the lifecycle of the device, or even across multiple devices.

These days, CPP is effectively the lowest common denominator: Advertisers are limited to targeted basic parameters like device models at best, and the absence of more nuanced and predictive data increases their risk. So they tend to pay a CPP that averages performance across all devices, minus a factor that accounts for that risk.

In other words, all of these stakeholders are leaving money on the table because they cannot effectively optimize their spend or sell inventory to the people who will find it most valuable. So what is the solution for stakeholders who expect more from their spend?

A better alternative

Overall, a CPP spend model positions preload inventory as an island unto itself and results in lower quality inventory and lower bids. For app developers and advertisers, this means less incentive to spend and less chance of winning at auction. For sellers, this means they are not effectively maximizing revenue.

Instead of thinking of preload spend in isolation according to the CPP model, the smartest solution is to view preload as another type of inventory on par with CPA and other more common buying strategies.

Buyers and sellers who want to refresh and strengthen their revenue strategy without losing opportunities to profit should work together to leverage the power of optimization from machine learning and automated bidding, and transition to spending smartly and holistically across all inventory types — including preload.

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