Machine Learning and Marketing Attribution
Technology with independent learning capabilities has long been the stuff of sci-fi fantasy. Today, we’re starting to see it in action… and although the world of data may be overlooked by Hollywood scriptwriters, it’s going to be heavily impacted by these new advances.
So what actually is machine learning? Put simply, machine leaning (ML) is a subset of artificial intelligence that uses statistical techniques to give computers the ability to independently ‘learn’ using data. From an analytics perspective ML is useful for a number of reasons. Chief among them are its abilities to identify data anomalies and classify large amounts of data.
Most of us see these benefits on an almost daily basis… ML helps banks zero in fraudulent activities in our accounts and filter dubious emails into out spam boxes. It’s also able to push us news articles, flights and products that we may be interested in at the time, and just generally pick up on things that often are less obvious to the human eye.
All of this throws up some pretty interesting questions in the realm of digital marketing, and notably in marketing attribution.
All digital businesses know that online marketing activities create huge volumes of data, with companies raking in literally millions of data points for every £1000 spent. On top of this, 46% of marketers see measuring digital ROI as their No.1 challenge. The abilities afforded to them by ML therefore represent a significant step towards the digital marketer’s ultimate goal of seamless 100% attribution in their online spend.
Firstly, ML removes human bias. Those who arbitrarily assign how much credit to give different marketing activities are inherently biasing their results. ML factors this out of the equation. Insights are taken solely from the data without external influence, meaning the numbers can speak for themselves.
Secondly, ML helps to automate marketing efforts. By stripping away a lot of the laborious manual efforts involved in adjusting marketing activities and spend, marketers are able to up their productivity. Algorithmic processes can crunch the numbers unaided and make adjustment decisions based on their findings. For example, when a customer clicks on a display ad, a ML algorithm is able to associate that action with the appropriate marketing strategy use to develop that creative, and optimize accordingly.
Developments in hardware are also significant here. It’s widely accepted that hardware capabilities now double every two years. Already, ML algorithms are able to exploit advances in GPUs and CPUs, allowing them to make real-time changes to advertising campaigns and budget allocations. With reactiveness being such an asset to the digital marketer, it’s easy to see why ML is such an exciting prospect.
Today, rather than marveling at their capabilities, consumers quickly come to expect the benefits presented to them by technological advances. It’s therefore not surprising that the average consumer is already accustomed to the advantages of ML, regardless of whether they’re aware of them. It’s therefore crucial that online marketers fully harness ML’s attribution capabilities, so as to ensure they deliver optimal customer experiences and ultimately improve overall Customer Lifetime Value.