Recently, one of the hot topics in marketing has been machine learning. It seems like everyone is talking about it; shareholders inquire about it, while CEOs demand their CTOs and CMOs utilize it to support their current IT infrastructure and improve their marketing campaigns.
What is Machine Learning and How Does It Power Better Marketing?
Despite all the talk about machine learning, there remain several misunderstandings about what it is. It might be valuable to offer a quick definition.
Machine learning is the process of reading, analyzing, and labeling huge sets of data, very quickly and efficiently. Even the most data-savvy humans are incapable of managing the huge volumes of data currently available, much less converting that data into actionable insights. The best we can do is to take a small sample of data, then come up with conclusions and strategy based on that small sample. Our strategies are often mixed with our intuition, past experiences, and personal biases, which can tarnish or complicate the results.
Machine learning can augment our ability to absorb, synthesize, and extrapolate data, enabling data-backed problem solving. Here’s one example of a problem that machine learning can help address: A recent study provided by Think with Google mentions that only 37 percent of downloaded apps remain in use after a week. In other words, around 63 percent of all apps downloaded drop 100 percent in engagement just after a few days.
But why is that?
One possible answer is that the audience targeting is not optimal, or perhaps marketing campaigns are running in siloes and the insights aren’t being shared across departments. Or perhaps the marketing team is not looking at the data holistically, but rather is focusing on last click attribution and giving the credit to the last channel or campaign that brought in the download, not focusing on the big-picture view of the consumer journey. Finally, the ad copies may be unintentionally disingenuous, leading users to download an app they really don’t need.
Machine learning can help app developers zero in on the specific causes for their apps being discarded; in addition, there are four unique ways in which machine learning can enhance the work of digital marketers.
4 Ways Machine Learning Improves Digital Marketing
1) Better Audience: Machine Learning Can Better Discover Your Most Valuable Customers
In the app example provided earlier, let’s assume that you are trying to reach out to a more targeted audience; an audience that is more likely to pay for your app and use it for a longer period, and remain your customer indefinitely. Machine learning, when connected to Big Data, can better identify those consumers, understand the different stages of the consumer journey they are on, and label them properly, enabling you to fully understand which users to target. This in turn helps you maximize your budget by showing ads to the right users.
2) Better Optimization: Machine Learning Improves Ad Personalization
Machine learning can also help you provide uniquely tailored ads to the right audience. For example, Google’s responsive-search ads mix and match multiple headlines and ad descriptions for the best possible combination for each person. This has the effect of simplifying ad creation while producing a stronger ad response.
3) Better Engagement: Machine Learning Can Engage the Right People in the Right Moment
Consumers search differently in varied phases of their consumer journey and respond to offers and ads differently. That’s why it’s more important than ever that marketers have the right offers, bids, landing pages, etc.
Google is at the forefront of machine learning advertising by adopting Smart Bidding, which utilizes insights analyzed from millions of signals to adjust in real time, delivering better campaign results.
4) Better Insights: Machine Learning Provides Better Measurements and Insights
Marketers tend to attribute credit to the last-touch marketing channel. In the case of our example, if someone downloads your app from a Facebook ad, then that ad is the one that usually gets the full credit; most marketers tend not to look beyond that or to ask themselves where the journey truly started. Indeed, it is a very time-consuming task to look at each conversion to try to identify the real origin of the consumer’s decision.
But with machine learning, that task is a lot easier. Machine learning can look at where the consumer journey began, tracing the full history of the conversion. In other words, machine learning allows you to optimize your ads according to people’s interactions with your brand and product.