Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

How to Elevate Your Social and Search Advertising for App Install

With mobile use still growing globally, a smartphone application is a great way to drive engagement. But how do you convince customers to download it? One of the best ways is by leveraging predictive segmentation and keyword targeting powered by artificial intelligence (AI) on paid search and social media platforms.

Mobile apps are more popular than ever, and they are not going away anytime soon. According to App Annie, annual mobile app downloads are expected to reach 258 billion in 2022, up from 205 billion in 2018. Due to the impact of COVID-19, consumer spending in apps hit a record high of US$27 billion in the second quarter of 2020.

With app revenue following a similar trajectory, it is no wonder that global app install ad spend will more than double in the coming years, from US$57.8 billion in 2019 to US$118 billion by 2022, according to AppsFlyer.

 

The Key Drivers of Increasing App Install Ad Spend

One of the biggest drivers behind this growth is competition. Brands are fighting to deliver personalized customer engagement and increase conversions, and a mobile app is one of the best channels to achieve that. The latest data from App Annie shows that mobile app usage jumped 40 percent year-over-year in the second quarter of 2020.

There are some other reasons behind the continued growth in app install ad spend. More non-tech-literate companies have realized the advantages of an app experience for customers, with its ability to foster long-term loyalty. In addition, the growing popularity of mobile gaming has also driven app install ad spend, which is expected to account for approximately 40 percent of all app install acquisition budgets by 2022. The increased demand resulting from millions more people in developing countries coming online has also had a big part to play.

 

Marketers’ Challenges With App Install Advertising

App install ads provide a tremendous opportunity for marketers, but it is not as straightforward as putting out a few ads and watching app installs start to grow.

Paid search and social media platforms – such as the major ones like Facebook, Apple Search Ads and Google Ads – are a complex terrain on which advertisers operate. Each platform has different specs and tendencies, which only complicates matters.

While the parameters these platforms provide give marketers granular control over their search criteria, it can be extremely difficult to know which user segments to target while keeping a low audience overlapping rate. It is also virtually impossible to exhaust all possible combinations of parameters, and endlessly tweaking and refining your combinations can lead you round in circles with no definitive answer of what works and what does not.

When leveraging keyword targeting in search advertising, marketer wouldn’t know that a keyword is getting fatigued and when to explore new keywords for search engine optimization.

The result? A lot of trial and error, and a lot of wasted ad budget.

 

How Ad Targeting Currently Works

In the current process, marketers will first define their target audience when deploying a campaign. Apart from using rough demographics like age and gender for segmentation, you also need to select interests or behavior on Facebook, or choose a number of keywords that you want to target in Apple Search.

However, broad keywords like ‘casino’ will not generate good enough results, meaning wasted ad spend. The trick is to come up with as many combinations of specific interests and keywords as possible, but this is a lot harder than it sounds. Even the most experienced marketers cannot know all the possible combinations, which means potential customers will slip through the net.

Even if you managed to find a segment that performed well, it would be tricky to know what the next best segment should be. So, the real challenge is to continuously come up with effective segments for precise targeting. The conventional way to find out is endless testing, which could mean even more wasted budget.

 

Increase ROAS With AI-Driven Segmentation and Keyword Targeting

To address those issues, marketers can use AI-powered tools to remove guesswork and trial and error, reducing the cost per install (CPI). Artificial intelligence is adept at predictive segmentation and smart keyword generation: by leveraging a massive database to uncover your users’ hidden interests and provide a much more holistic picture of them and their priorities. Here is how it works in practice.

1. Uncover hidden interests for predictive segmentation

By taking in data points from numerous sources (including first- and third-party data), AI can discover lots of granular and highly specific interests of your users within the app. It can then rank these interests based on their potential value and the likelihood of those interests being ad-responsive for each social media platform. This will give you an exhaustive and daily-updated ‘leaderboard’ of best segments even before the campaign starts. From this, you can set budgets for each segment.

2. Minimize overlap

Apart from defining the best segments, you also want to minimize audience overlap between your segments to guarantee the maximum reach. A dedicated AI platform will group the uncovered interests into ‘clusters’ using semantic keyword grouping – this involves grouping the similar interests together in order to make your content more relevant to what your target audience are looking for. AI can then show the overlap between each group – maybe the terms ‘high stakes gambling’ and ‘casino’ return very similar results, for example. In which case you can pivot your campaign to include different combinations of keywords to optimize reach.

3. Optimize the keyword life cycle

In order to optimize your keyword usage in search advertising, you need to determine the life cycle of each keyword, to ensure that the ones you are using are up to date. This is complicated further when you have multiple campaigns with several ad groups and countless keywords. The challenge is, how do you automate all the settings on each campaign, ad group and keyword?

AI can help you constantly frame and optimize your keywords, 24/7. It can detect when your keywords’ performance and volume are sufficient to return good results, and keep them in the positive keyword pool for the duration. As soon as it detects a particular keyword is becoming fatigued, it will move it to the detention pool, and if it is in a bad performance loop, it will move it to the negative keyword pool so it is not used during the campaign. When a fatigued keyword starts performing well again, it will be returned to the positive keyword pool.

  

When it comes to social and search advertising for app install, AI represents a shift from a ‘hit and hope’ approach to one that is science-led and backed up by data. It is the smart way to optimize your app install marketing, drive customers to your app and maximize your ROAS.

 

* Looking to take your social and search ads for app install to the next level with AI? We can help! Get in touch with our team today for an exclusive consultation.

WE ARE HERE TO HELP

Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.

YOU MIGHT ALSO LIKE

Is AI the Remedy for Brand Safety Woes?

When it comes to programmatic advertising today, companies are focusing on brand safety as much as they are on impressions, click-throughs and revenue generation, but it is virtually impossible to monitor brand safety due to the scale and speed that programmatic offers. However, with the increasing adoption of artificial intelligence (AI) in digital marketing, such technology will not only help marketers better target their ideal audience, it might just be the cure for protecting advertiser dollars.   Programmatic Could Compromise Your Brand Safety Brand safety came under the spotlight following a number of advertising mishaps in early 2017. Alexi Mostrous from The Times revealed that many household brands were unwittingly supporting terrorism on YouTube by placing their ads on hate and Islamic state videos. Later the same year, ads from some of the world’s biggest brands were seen to be running alongside videos that sexually exploited children. This led to widespread panic, with many brands pulling programmatic spend until they could be assured by publishers like Google that measures were being taken to filter out such content. In 2018, brand safety has broadened to cover any offensive, illegal or inappropriate content that appears next to a brand’s assets, thus threatening

What Is the Role of Deep Learning AI in Marketing?

It’s no secret to marketers that effective use of data is critical. It is the key to executing successful campaigns that engage consumers and drive towards long-term, profitable relationships, and artificial intelligence (AI) and machine learning are vital parts of analyzing and optimizing data at scale. The amount of available data on consumers and their habits, preferences and behaviors continues to grow. It is therefore increasingly challenging to make sense of the data and make accurate predictions. Consumers sharing information about themselves via social media and e-commerce using mobile devices and personal computers are leaving behind data that can prove extremely valuable if marketers are able to look at all the data points to build holistic profiles of past, current and prospective customers. A new conversation has started among marketers about Deep Learning. Deep Learning is the most advanced branch of artificial intelligence. Deep Learning uses multi-layered, ‘neural networks’ (computer systems modelled on the human brain and nervous system) to process large volumes of scattered data. At Appier, we have seen Deep Learning work particularly well for in-app marketing. In-app spend is expected to increase by 105% in APAC by 2021 (according to AppAnnie), and marketers are increasingly relying on

Why a Data-Driven Approach Is Vital to Business Recovery During Uncertain Times

The value of data to businesses is difficult to overestimate, especially now. Data might once have been seen as a nice-to-have to make brands more profitable, but now leveraging data to make critical decisions is widely viewed as essential to keeping a business afloat.   The New Normal To say these are uncertain times would be an understatement: around the world, the level of uncertainty related to the coronavirus is more than three times higher than during the 2002-3 severe acute respiratory syndrome (SARS) epidemic and about 20 times higher than during the Ebola outbreak, according to the IMF. This has had a huge impact on markets, with the OECD estimating global growth in GDP at as little as 1.5 percent, compared to 2.9 percent in 2019. That would be half of its previous estimate (3 percent) for 2020. It is also impacting global production and supply chains. The World Trade Organization predicts that global trade volume could shrink by between 13 and 32 percent in 2020 compared to 2019. Unsurprisingly, consumer confidence has also been hit. While confidence about an economy’s ability to recover varies greatly by region, there is a definite shift to consumer spending on essentials and