With conversion rates north of 50% and CPI averaging $1, Apple Search Ads are commanding more investment from UA Managers with an appetite for low CPI. There are a couple reasons for such resounding success on the platform that are important to consider:
Relevancy is King. Because Apple doesn’t rely on ad revenue for its business model, it can focus on relevancy over profitability leading to higher conversions.
The platform is maturing. Search ads are only recently beginning to expand to new markets meaning less competition - for now.
Video is lifting conversion. Early adopters of the app marketing darling are more likely to innovate in other areas including the emerging trend of using video for app preview.
However, similar to other keyword-driven platforms like Google AdWords, many marketers are running into problems when they need to generate more scale on the platform.[Contact us for a free consultation on how to add scale to your Apple Search campaigns]
The good news is that scale isn’t a platform limitation . Rather, it’s a symptom of an ill-equipt keyword methodology.
The problem with using traditional keyword strategies on Apple Search Ads
In its simplest form, your goal is to reach relevant users with the right message. As far as keyword strategies go, up to this point the vast majority of marketers use a traditional intent-based approach as a proxy for relevancy.
Essentially, UA marketers start with a hypothesis about what search queries would indicate a need (see: relevancy) and follow that thread of logic to build out as much scale as possible. For example, with the intent-based approach for marketing the Zillow Mortgage app, you might expect the beginning of a keyword list to look something like this:
The downside of this approach is that it reaches its ceiling for scale relatively quickly. Not only that, but because competing marketers benchmark against one another and have the same basic logic for obvious keywords, competition for a limited set of keywords makes acquisition more expensive for everyone.
Furthermore, once you realize your campaign isn’t scaling like you hoped it’s too late. Once you realize the need to add additional keywords it means you’ve already been accruing opportunity cost and those ad dollars could have been better spent elsewhere. To truly maximize your ad dollars, you need to target your complete audience from the very beginning.
Benefits of an affinity-based approach
Fortunately, intent is not the only proxy for relevancy available to marketers. An affinity-based approach looks for signals that would suggest an implicit or explicit need for a product or app.
Rather than focusing only on people actively searching for a mortgage app, for example, an affinity-based approach without the aid of technology like Appnique might focus on related scenarios that could trigger the need to refinance or find a mortgage. That might include focusing on signals that might suggest scenarios such as:
Searching for/ started a new job
The logic here is that any of these events are indicators of a possible move and need for a mortgage. These examples are purely illustrative, but you can begin to see how taking a different approach to keyword targeting allows you to reach relevant, quality users at a lower CPI thanks to lower competition keywords.
Without the right technology, however, the risk of relying on your own set of hypotheses is that your bets aren’t rooted in real data and therefore are subject to the same downside as traditional intent-based methods. Appnique analyzes data across app stores, the web and social to uncover high affinity users and maps them to the keywords (and interests on Facebook) to so you can target them on Apple Search Ads with confidence and ease.
The result is a larger and often less competitive set of keywords that can be used to target users with a high degree of relevancy. That translates to greater scale and improve CPI by up to 50%.
Example of an affinity-based Apple Search Ad Campaign
In theory, an affinity-based approach sounds great, but can sound a little abstract. Here’s an example to bring things into focus.
For this exercise, we used Appnique to analyze Zillow’s mortgage app. With Appnique you can analyze apps and search terms by analyzing affinity by user affinity, also installed, keyword affinity or functional similarity. We suggest using all four, but for this short example, we pulled results based on functional similarity.
With a traditional intent-based approach to Apple Search Ads or Google AdWords keyword targeting, you might expect to benchmark against other apps in the finance or real estate category. However, when you frame your keyword strategy based on affinity - which Appnique technology is able to objectively analyze using data from app stores and the web - you uncover correlations with hundreds of apps across multiple categories as you can see below.
In the example above the app, “Grades for Parents and Students” (a shared app for parents to monitor their students performance at school) is one interesting example to examine. While it’s not related to finance or mortgages, it does fulfill a similar need by giving consumers more power in important or complex scenarios.
When you extrapolate this one, fairly obscure, example across hundreds of other apps and keywords you begin to see how Appnique technology allows you to add significant scale to your campaign.
Build a winning Apple Search Ad campaign of your own
If you are looking to give Apple Search Ads a spin, take a look at our eBook or register for our upcoming free webinar for some pointers on using the first party tools already available on the platform.
If you are already having some success from a CPI perspective and need to really scale your acquisition efforts, schedule some time with us for an analysis of your app and free consultation. We’ll give you insight to enhance your campaign immediately and a demo of our technology to see if it’s a fit for your app marketing efforts.