Throughout the last decade, the life of the modern-day search engine marketer has become centered around data and artificial intelligence (AI) applications. Debates and dialogues around AI subsets, machine learning and data science, and how exactly they affect the workings of the industry continue to multiply. 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).
- Develop trend recognition.
- Attract new customers.
- Ultimately create previously unforeseen efficiencies in their programs.
“[C]ompanies 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 DataWhat 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. Performance marketers can take advantage of these 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 has matured and expanded, managing a program and making bids manually with spreadsheets have 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 infinitely more complex than that once you dig under the surface. Data science 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, which include:
- Conversion rate percentages.
- Cost-per-click (CPC).
- Cost-per-acquisition (CPA).
- Revenue-per-click (RPC).
- Return-on-ad-spend (ROAS).
- Time (broken itself down into time-of-day and day-of-week).
- Device (desktop, mobile, and tablet).
- Past browsing history.
- Purchase history.
- And a whole lot more.
The Key to Unlocking SEM EfficiencyEmploying 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 TargetingEvery 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 SuccessThe 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. This 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 KeywordsOne 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.
- Suggest semantically similar keywords.
Unrivaled EfficiencyEvery 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.
Wrapping UpThrough 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.
- Customers will demand more personalization from brands in the searching experience.