Throughout the last decade, the professional life of the modern-day search engine marketer has unquestionably become centered around data and artificial intelligence applications. Debates and dialogues around Al subsets, machine learning and data science, and how exactly these buzzwords affect the workings of the industry, continue to multiply in their frequency. This trend cannot be surprising, though, when taking into consideration the truly staggering amounts of data we’re creating every minute of every day, and the pace with which we’re doing so is only accelerating with the growth of the Internet of Things (IoT). With everything from clicks to swipes, tweets to likes, we are today compiling information at an unprecedented rate.
For companies, all of this data brings new opportunities. It can be utilized and exploited by marketers to develop trend recognition, attract new customers, and ultimately create previously unforeseen efficiencies in their programs. Up until just a few years ago, businesses – both large and small – were in a position to decide whether they wanted to use data to gain a competitive advantage in their respective marketplaces. Today in the world of business, where there is a constant vying and jostling for customers, data is everything and more. Executives and practitioners uncover analytical techniques to turn the data available to them into actionable insights. The more one knows about their business, the better the decision-making and performance.
The sheer power of a data-driven marketing approach has been a much-covered subject. In a study conducted by Andrew McAfee and Erik Brynjolfsson with Harvard Business Review and MIT, it was revealed that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.” Pretty compelling numbers.
The Welcome Problem of too much Data
What does all this mean, then?
Do marketers need to quickly enroll in night courses covering statistical programming and computation techniques to help them explore, and decode, large data sets? Well, the answer, in short, is no. No, they don’t.
Thankfully, with this abundance of data has come the emergence of strategies and technologies that performance marketers can take advantage of to automate some of their processes and drive significantly better business outcomes. The multi-disciplinary field of data science is chief among them, empowering marketers to combine various data sets and decipher the variables in their campaigns that are having the biggest impact on performance. To paraphrase Steve Jobs, it’s like a “bicycle for the mind”; essentially helping humans increase productivity and output. As the sphere and practice of search engine marketing have matured and expanded, managing a program and making bids manually with spreadsheets has become immensely inefficient. Even the first-generation platforms that have dominated the ecosystem for years, those with legacy foundational infrastructure, are falling behind the innovative new solutions that come fully-equipped with data science techniques on a larger, more sophisticated scale.
So, just what is this magical data science?
Let’s define it as the “art of uncovering trends”. It is, of course, infinitely more complex than that once you dig under the surface, and features a blend of Bayesian statistics, predictive modeling, time-series analysis, clustering algorithms, and regression modeling to solve analytically advanced pains. And lying at the core of all that is data. Troves of the stuff.
SEM has always been about data – we can talk about the metrics we live and breathe every day: conversion rate percentages, cost-per-click (CPC), cost-per-acquisition (CPA), revenue-per-click (RPC), return-on-ad-spend (ROAS), and I could go on and on. And these are just the outcomes.
Starting at the beginning, where all this information originates, each ad click is home to an extraordinary wealth of data when taking into account modifiers such as location, time (broken itself down into time-of-day and day-of-week), and device (desktop, mobile, and tablet). Then you can throw in on top of that other existing data points like the user’s past browsing history, purchase history, age, gender, income, and a whole lot more. We’re talking about an unfathomable number of potential permutations for every one of your keywords. This raises the question of how do I parse and act on this information.
This is where data science comes into play.
The Key to Unlocking SEM Efficiency
Employing data science, whether that’s through a third party platform or proprietary in-house tools, will undoubtedly lead to a direct improvement in the performance of SEM campaigns. Here’s how it creates compelling value:
Superior Audience Targeting
Every click on a paid search ad contains vast riches of information – all sorts of demographic, psychographic, and behavioral data. Through the application of data science, marketers are empowered to parse through this information to better identify the make-up of their customers and then target them with increased accuracy accordingly. Reaching the right audience, at the right time, with the right message is paramount to any prosperous SEM campaign.
A Predictor of Success
The digital footprint that customers leave behind through their day-to-day searching habits paints an accurate portrait of their wants, needs, and interests. Predictive analysis encompasses the use of data science and statistical algorithms to translate this data and segment customer behavior, which can then be used to predict the probability of a conversion, whether it’s the buying of a product or the filling in of a form. Armed with this information, marketers can bid with more accuracy and eliminate pockets of wasted spend.
Automatically Create New Keywords
One of the many branches under the data science umbrella is natural language processing (NLP). In SEM terms, NLP is most aptly used as a keyword expansion tool whereby practitioners can leverage the technology to analyze search queries, detect associated keywords, and then suggest semantically similar keywords. This helps considerably in the expansion of your portfolio and presents areas of growth that were hitherto hidden.
Every keyword in a given SEM program has a unique, optimal bid value at which it drives the highest ROAS at the lowest possible price, otherwise known as the ideal CPC. Data science has made it possible to calculate this, unlocking efficiency on a scale not previously possible with manual bidding and legacy tools. The end result? A program that automatically and programmatically adjusts bids at the individual keyword level to ensure the best investments are being realized and new opportunities are being uncovered.
The Art of Data Science – Wrapping Up
Through the introduction of data science into marketing stacks across the world, SEM managers have become empowered with significantly more knowledge about the workings and intricacies of their campaigns. With that, companies in this digital age can now reach performance levels that the executives of yesteryear could only imagine. As these technologies continue to become widespread, challenges will arise, management tactics will change, and customers will demand more personalization from brands in the searching experience. The evidence is clear, though: data science trends are showing no signs of slowing down and when data science does meet SEM, advertising ROI improves quite considerably.