How Machine Learning Is Dramatically Improving Paid And Organic Search

Machine learning is helping paid search to become insanely more effective. A recent report by Aquisio found that paid search accounts that are optimized for machine learning achieve 71 percent better conversion rates, using lower cost-per-click, or CPC. Not only are ML-supported accounts more effective at conversions, they have also been linked to a lower churn rate.

What this means is, once again, technology is leveling the playing field for marketers. Paid search is more effective, more affordable and is now a more accessible tool for both CMOs and small marketing teams.

While this insight makes paid search more appealing – you get better results for the same amount of effort – especially for marketing leaders who are desperately trying to stay within budget and to have more money to compete in more pricey marketing must-haves, like high-quality video production and influencer campaigns, it doesn't mean we can take our agile minds off the focus of organic search and creative, value-driven, original content.

Paid search may be an easy way to get on top of the search engines' results, especially as machine learning is doing even more of the work for us, creating campaigns that are that much more likely to be seen by the right searchers at the right time. However, organic search is where authenticity is established. It's where your content will remain, even when your paid search campaign ends. And it's where all the searchers who ignore paid search results will look.

The trick to taking advantage of this continuing evolution of machine learning is to embrace how more sophisticated technology is making both paid and organic search more worthwhile, as well as every other facet of modern marketing.

Paid vs. Organic Search over Time

There's been a war for years over which is more effective, paid or organic search? Both have come out ahead at times, with results often depending on how the research is being done and what other outside factors are doing to impact search results.

In a 2011 study, the results overwhelmingly pointed to the power of organic search. On both Google and Bing, 94 percent of clicks were organic, to paid search's 6 percent. This study looked at a sample of 28 million UK searchers, and 1.4 billion searches.

Fast forward to 2013, where a different study, albeit using a smaller sample and focusing on one industry (insurance), found that 81% of users click on Google Adwords listings instead of the organic results. Leaving marketers with confusing disparity and lots of room for opinion.

Andy Taylor, who, as Associate Director of Research at Merkle, spends his waking hours analyzing digital marketing trends, points out that paid search has outpaced organic over the past few years. One of the reasons for this isn't just that SEM is better than SEO but that Google's algorithm changes have, over time, consistently harmed organic search and have bolstered paid search click growth.

Taylor recommends businesses to embrace both. Instead of asking, 'what's better, one or the other?' the question is, 'what's ideal, for my business, at the moment?' It's about taking a critical thinking approach to what's working right now. And, looking at the technology that's available to make every facet of your overall strategy, better.

Because, as we all know, what is the most effective search strategy now, is going to look different than what will work in two months, and will be unrecognizable from your brand's search strategy in two years.

As technology keeps changing what we can do, and as customers keep changing their expectations, the only solution is to implement an agile approach, coming up with a strategy, implementing it, measuring it, and refining it until the end of time.

Machine Learning for Paid Search and Google Ads

What exactly is going on with ML right now that is making those paid results more valued? Google is going on. Earlier this year, Google announced it was rolling out in-market audiences for search ads. This tech technique basically makes it easier for businesses to reach potential customers who have already searched for their category of products or services.

As Google explains:

"It analyses trillions of search queries and activity across millions of websites to help figure out when people are close to buying and surface ads that will be more relevant and interesting to them."

And then there's Google Attribution. It combines data from Adwords, Analytics and DoubleClicks to more clearly measure intent at each stage of the customer journey. This is then used to figure out how much credit to assign at every stage, from that initial touch point to the final pre-purchase click.

Machine Learning Is Changing Much More than Paid Search

But machine learning isn't just being used to evolve non-organic traffic. It is what Google and other search engines use every time they make an algorithm change, plugging in more and more data, evaluating more or different characteristics of a website, with the intent of making the algorithm more sophisticated. Take, for example, Google's RankBrain, which began to make itself known in 2015. CTO of WordStream, Larry Kim, theorizes that RankBrain uses machine learning to assess if Google needs to revise its own rankings, every time a searcher puts in a search and either is 'satisfied' (based on a series of Google-known data inputs) or 'unsatisfied.'

It's also being used to create the tools marketers can use to better hone their content, improve SEO, and deliver a more appealing experience for customers.

Want a machine to write your professional white papers and reports in natural language so you have more downloadable content to offer? Yseop is writing professional content for enterprise level organizations today – in several languages.

Want to increase your customer experience and better understand movement in your net promoter score? ML does it again, through Luminoso.

More ML Means Marketers Need to Be More Human, Not Less

By creating more sophisticated algorithms and more advanced SEM capabilities, search engines, namely Google, are pressuring content producers and marketers to deliver more sophisticated content. It's safe to assume, as time goes by, search engines will possess an increasing amount of lucidity concerning the quality of your online content. Which means creating quality content that focuses specifically on what your target is looking for is more critical than ever before, not less. Favoring more paid search because conversion statistics are shooting up right now is taking a very myopic view of the really big picture that is unfolding at the moment.

Relying on SEM to attract paying customers is and always will be a short term strategy. It can drive some traffic. It can lead to more conversions. It can push sales. But it can't build your brand, establish trust, or fortify your market position for the long-term.

Seeing the power of ML for paid search is nothing more than one more reason every CMO and marketer out there needs to look the dragon directly in the eyes – face head-on the fact that machine learning is evolving every facet of marketing, from search to customer relationship building, faster than we can keep up with.

It's time to learn more about how machine learning and AI work. To understand the tools being created and to start applying them more. Not just the free tools Google offers, but the specialized ML-empowered tools that may better serve your business needs in ways you haven't even imagined. Guess what? There are already hundreds of them.

Better technology isn't a reason to neglect a piece of the marketing puzzle that works in favor of one that takes less work. It's a responsibility marketers now have to make every campaign that much more insightful, purposeful and relevant.

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Source: How Machine Learning Is Dramatically Improving Paid And Organic Search