How machine learning can optimize digital advertising
Advertising is essentially a customer centric endeavor and hence, customer personalization lies at the heart of a successful ad. The steps towards creating a brilliant digital ad campaign seem to be straight forward: Gather up data about the prospects, perform a customer segmentation analysis based on the data to group the potential customer in clusters, understand their challenges, and put some great content together.
The vastness of the digital advertising analytics data that needs to be analyzed to achieve this is what necessitates machine learning. Machine learning refers to the process of training a machine to perform as well as improving at a certain task by exposing it to data instead of programming it explicitly. Before we move further into how machine learning can transform the advertising sector, let us quickly understand,
How machine learning works
Let us suppose you want a machine learning algorithm that recognizes mangoes. Build a programme and expose it to five hundred images of mangoes of different shapes, sizes, and colors and label them. Now, you take some random new images of fruits, one of which has a mango in it. Expose your programme to the new images and it is likely to detect the unlabeled mango. While this is a child’s play for human beings, it still requires multiple intelligent human faculties. Machine learning trains a machine to emulate those human functions.
Well, this was as simple an example of machine learning as you would find. Now, let us focus on the initial question of advertising.
Customer segmentation analysis
Grouping customers into segments based on various features is a necessity when it comes to targeted ads. Demographic information as well as other forms of data is factored in to cluster the consumers in bands so that each segment can be understood, analyzed and catered with personalized ad content.
You can select some features and categorize customers based on those features. Let us say you want to find single men living in a city who have dogs. A machine learning algorithm can draw some correlations between unmarried men and men who buy dog food and you have your desired segment. A yet bigger challenge can be narrowing down on the specific features that you want to use for segmentation and luckily you can use machine learning to spot them as well. And that leads us to the next point.
Drawing correlations
In order to use customer personalization you need to know your customer and their specific preferences. This sort of understanding is often hard to achieve with traditional analytical methods. While social media has given an open platform to businesses to find and target their customers it has also made things difficult when it comes to investing in an ad campaign. Machine learning can help you up your game through correlations.
The machine learning algorithms can draw correlations that are nearly impossible to spot with the naked eye. Some data put through an algorithm may reveal that young women who like to watch cricket and also have an interest in cooking, are likely to download a certain app. You may not make too much sense of these correlations but they work more often than not. So, you can base your ad campaign on these insights.
Uncovering unlikely revenue opportunities
Let us say you want to advertise a video game. You are likely to target your ads towards a younger audience. While you would like to analyze every data point related to your offer, you are forced to work with some assumptions owing to constricted time and a tight budget. This way you may miss on some older customers who could probably have spent more money on the game you are advertising. The efficiency, and cost effectiveness brought by machine learning helps you fix these issues. It can help you look beyond the obvious and strengthen your advertising strategy.
While 95% of the advertisers have access to petabytes of demographic data about their prospects, most of them do not have the technological and computational prowess required to process that massive amount of data. Moreover, most businesses are forced to depend only upon structured data, missing out on vast resources of unstructured yet infinitely valuable data that sits idle as dark data. 85% of these businesses fail to capitalize dark data due to a lack of tools. Machine learning can and will change the scenario over time.
