Artificial intelligence and machine learning technology have the potential to revolutionise marketing as much as mobile, the internet and television did in the past.
Forward-thinking companies are using machine learning tools to supercharge their marketing. These early adopters take advantage of the technology’s ability to streamline data, unlock user insights and engage users in highly relevant ways. In fact, 85% of executives believe AI will allow their companies to obtain or sustain a competitive advantage, according to the The Boston Consulting Group.1
App marketers and developers are using machine learning to disrupt traditional business models, redefine categories and challenge the way we think about marketing. We’re seeing it benefit them in a few ways.
Excavating mountains of data
Today, people juggle multiple devices and are active across an array of digital media. Mobile apps provide value to customers while allowing marketers to deepen their relationships with them.
But marketers still have to excavate piles of data to answer their most pressing questions: Who are my most profitable users? Where are they coming from? How do I keep them coming back?
Faced with all of this data – and the ever-growing ways to parse it – it’s becoming difficult for marketers to know exactly how to get the best return on every dollar invested. According to McKinsey & Company, across all occupations in the US economy, one third of the time spent in the workplace involves collecting and processing data.2
The time and resources spent on digging through data means less time devoted to high-level tasks such as refining the marketing strategy or improving the product experience.
By analysing millions of data points in real time and making smart, optimised decisions to improve business performance, machine learning enables marketers to do more at scale – and frees up time to focus on more strategic tasks.
It also simplifies the marketing process. You determine the business objectives you want to achieve, you define your qualified audience – for instance, gamers who are likely to reach level 10 of your game or shoppers who are likely to spend more than $50 a month in your app – then you let the system figure out where and how to reach and engage the relevant prospects.
Optimising for customer value
As a marketer, one of your biggest challenges is to determine who cares about your brand enough to keep coming back. But even more important is finding users who are going to drive profitability. Machine learning makes it easier to find and engage the customers that are most valuable to your business by letting you look for them in places you may otherwise miss.
Rather than narrowly defining a target segment simply as “35-54 year-old woman”, machine learning lets you cast a much wider net, taking into account your business outcome – a sale, an in-app purchase, a completed level in a game, etc. It then looks at millions of signals to find people at scale who are likely to complete those actions.
With machine learning, your first step is analysing the people you know: your most valuable or profitable app users. The system then looks for other similar profiles. If you’ve developed a travel app, for example, it may look for individuals who have made purchases in other travel apps, are watching travel videos online or who are in the market for flight and hotel deals.
Take Trivago, for example. The online travel company wanted to drive in-app transactions from high-quality users. It used Universal App campaigns, which are powered by Google machine learning, to optimise for shoppers likely to make in-app conversions. As a result, the company saw a 20% increase in high-quality users across iOS and Android.
Machine learning technology doesn’t just find high-value users, it can also figure out the most effective ways to engage them. It essentially does this by matching the right message to the right creative to the right user at the right time. In fact, 81% of leading marketers agree that a capability in machine learning will be critical to provide personalised experiences along the consumer journey.3
Mobile gaming company, Pocket Gems, uses video app ads to appeal to its audience. The company matches users of its Episode storytelling app with highly relevant affinity groups (e.g. "fashionistas", "beauty mavens", "romance and drama movie fans"). This combination of the right message, right creative and right audience resulted in a 50% higher lifetime value for people on YouTube.
New controls for marketers and agencies
Although machine learning is streamlining the marketing process, people still play a critical role. Machine learning is only as good as the information you feed it. It’s incumbent on marketers and agencies to access the right data, identify the most critical business objectives – such as customer lifetime value – and continuously optimise the end-to-end customer journey. This includes improving your app store landing page, your app’s homepage, the user flow and in-app events.
Digital agency Incipia is using machine learning to help app clients grow their business. The agency helped gaming app, WordScapes, beat its seven-day retention goal by 60%. They optimised the bidding to focus on high-quality users and honed the creative process to develop variations of video ads that Google’s Universal App campaigns could optimise.
Companies like Incipia recognise that growth is not simply a product or marketing challenge – it's a business opportunity. App marketers are using technology to make smarter decisions with data, find and engage more profitable users and deliver more effective creative.
The marketers that invest in intelligent growth are setting themselves up for success in the new era of marketing.