Artificial intelligence, machine learning, deep learning, neural networks. These are all part of the digital advertiser’s lexicon now as the algorithms power so much of the tactical mechanics of our campaigns. The purpose? To serve that ad combination on that impression at that bid to that audience member to achieve the campaign goal with greater efficiency and efficacy than we marketers could do manually. All by training algorithmic models to understand patterns and predict outcomes based on gobs of historical data.
Google, Microsoft, Facebook and all the other digital advertising platforms are using data and algorithms to identify intent and predict customer needs, behavior and marketing outcomes.
This is the Predictive Marketing Era. And it is changing how performance media strategists and managers work and the skills they and their teams need to prioritize to become smarter, nimbler and more effective PPC marketers. This was the topic of my keynote during our virtual SMX Next event on Tuesday (available on-demand with registration).
Companies have been using predictive analytics for things like anticipating inventory needs, pricing optimization and fraud prevention for years. Machine learning is now pervasive in many marketing tools, including media buying, with ad creation and serving, bidding and targeting increasingly powered by algorithms.
Search marketing, specifically, is evolving from keyword buying to audience buying to predictive intent buying, with automated campaigns served across surfaces based on predicted outcomes. Search marketing is no longer just about buying the right keyword at an efficient cost-per-click.
Now add accelerant. Lots of people have noted that COVID-19 has acted as a trend accelerator. We are certainly seeing that in digital advertising and marketing. The introductions of Facebook Shops and free listings in Google Shopping were both fast-tracked due to COVID, for example, as consumers’ shopping behavior trended further online. Many of the consumer habits formed in these months aren’t going to disappear.
And machine learning and artificial intelligence are at the heart of nearly every new feature in digital marketing. Ads are served wherever and whenever the systems anticipate the desired outcome. Keywords and/or audiences often play a role, but the platforms are now using data and algorithms to identify intent and predict or anticipate customer needs, behavior and marketing outcomes. As I wrote after Google Marketing Live last year, the company’s new campaign types deliver ads across multiple channels — to own every aspect of the customer journey, from the top to the bottom of the funnel.
The idea of running Search and Display together in one campaign will still make many advertisers shudder. But most of the new campaign types don’t give advertisers the option to opt out of channel inventory. That, Google will say, was a tactic necessary in a pre-machine learning powered world. Machine learning may be overhyped, but it underpins nearly every aspect of campaigns and will continue to grow in importance.
Understand how the systems are designed to work. To visualize how much of paid search uses machine learning now, we color-coded Search Engine Land’s Period Tables of PPC Elements. It’s critical to understand how these elements are designed to work before deploying them. Learn as much as you can about how these AI and ML systems are designed to work, what we know about the signals they use, their benefits and shortcomings.
The algorithms aren’t perfect. The models train on data, and those inputs matter (there are numerous examples of the unintended consequences of algorithmic bias). A healthy dose of skepticism will help you identify when things aren’t delivering the outcomes that matter to your business. But this requires understanding how an element is designed to work.
Take the seasonality adjustment feature, for example. Many people started using it in their Google Ads campaigns at the beginning of the COVID-19 pandemic. Seasonality adjustment was not designed to be used during a sustained period of change, though.
Take data stewardship seriously. None of this works without data. PPC pros are in a position to help inform data strategy in their organizations. In many ways, search marketers have been at the center of understanding how to use data to do better marketing and get better results.
How can you ensure you have systems and processes in place to catch early indicators and be able to segment and activate your own data quickly in your own campaigns? How can you build more direct relationships with users to give you more control and insights as browsers crack down on third-party cookies?
How can your own data or other data sources be used to improve pattern recognition and outcomes in your own campaigns — in ways that respect user privacy and regulations?
Focus on user experiences. Ideally, in the Predictive Marketing Era the algorithms prioritize good user experiences as experiences that are predicted to have the best outcomes based on historical data are weighted more heavily. I spoke last year at SMX Advanced about the relationship between branding and performance, and this also speaks to experience. Direct to consumer brands have put in stark light the importance of branding and experience on performance outcomes.
Pay attention to story, ad creatives, landing pages, retargeting experiences, and your visuals.
See the bigger strategic picture. Particularly with the algorithms increasingly dictating where and to whom ads are served in the Predictive Marketing era, strategic skills are going to be far more valuable than tactical or mechanical skills.
I’d argue that marketing fundamentals matter even more now. This requires a shift in focus to revenue optimization instead of simply channel optimization.
As customer journeys get more complicated, focus on the experiences you’re creating based on intent, not on the channel. To do this well takes strategic, creative thinking and planning.
If we’re not looking at the bigger picture, we can miss the interplay of marketing efforts and their combined impact on the bottom line. This might also require focusing on new key performance indicators and metrics.
Think about ways you can internalize Predictive Marketing into every aspect of your work to anticipate behaviors and outcomes, from the data you use to the experiences you create to the ways you’re measuring success.