When I first heard the concept of “machine learning” my mind conjured up images of Robert Patrick repairing his body in Terminator 2, or the HAL’s famous line from 2001: A Space Odyssey. While these outcomes of artificial-intelligence-gone-awry still worry me from time to time, I’ve come to love what machine learning does for us in our modern age.
Machine learning is great at discovering anomalies in data - finding needles in haystacks - very quickly. This helps authorities uncover insider trading or fraudulent transactions on your credit cards. It helps Google identify spam and remove it from your email. Amazon uses machine learning to predict what other items you might find useful.
Machine learning is also very helpful for classification - bucketing things that are similar. Humans can do this too, but when the similarities aren’t obvious, machine learning picks up the slack. News aggregators use machine learning to comb through millions of articles to curate personal experiences on Google News, Flipboard, and other apps. Machine learning helps Kayak organize flight pricing and routes to deliver personalized results. Real-estate appraisers use machine learning to accurately determine housing values. It’s no wonder that Stanford’s most popular class is machine learning.
Recently, there has been an increase in press coverage on how machine learning impacts marketing. Good articles from CMO and Econsultancy help us marketers understand the role that machine learning can provide in building more relevant creative and automating marketing processes.
Here, though, I’d like to speak specifically about how machine learning can help marketers in calculating the true effectiveness of their marketing.
Marketing activity creates a deluge of data. For every $1,000 spent, you may generate anywhere from a million to over ten million data points crossing impressions, clicks, interactions, users, geo-demographics, psychographics, conversions, viewability, GRPs, subscribers, products preferences and configurations, content preferences, and revenue. Attribution providers seek to award credit for converting purchasers to individual marketing activities, so making sense of this deluge of data is critical.
Machine learning can be applied to attribution in two ways:
- Helping to Automatically Sort Marketing Efforts. Instead of manually determining which buckets to classify your marketing, a machine learning algorithm will crunch through all the possibilities and sort the marketing automatically. For example, when Marin executes a search ad which is clicked by the user, machine learning can associate that action with the appropriate marketing strategy used in the development of that ad.
- Removing Human Bias. Machine learning is critical to removing human bias from how we will weigh the results of that search ad against everything else - from print ads, to broadcast ads, to catalogs. Marketers who arbitrarily assign how much credit to give search activity vs social vs. display vs. TV are immediately biasing their results. It’s important to let the data speak for itself, and applying machine learning algorithms help do this more efficiently.
How does this all happen? As each generation of computer hardware and software advances, machine learning algorithms are written that exploit hardware advances like parallel processing Central Processing Units (CPUs) and Graphics Processing Units (GPUs) to perform a range of general statistical learning problems at scale. Advertising in today’s tumultuous media landscape requires near-real time instruments which can quickly identify changes across many, many variables.
Our modern day consumer has come to expect the benefits provided by machine learning. It helps us get answers more quickly, more accurately and with more personalization. CMOs and marketers are coming to know how to activate more personal ad experiences using machine learning, but I would encourage us not to stop there. Holistic omni-channel measurement and optimization can also benefit from these advanced data science techniques.
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This post was written by Michael Lamb, Director, Product Marketing.