Fred Maude, head of product development at NMPi by Incubeta, explains how AI is empowering marketers to unify PPC and SEO data to optimize budgets and drive up ROI by 25%.
Every search marketer has done it. You type in a search term only to find the brand you are looking for is near the top of organic results and also in a prominent paid-for position.
We have all then wondered whether the company concerned needed to pay for a click they may well have got ranked anyway? In fact, we have all done this for the brand names we represent. Was it a smart move to be towards the top for both paid and organic clicks, or has someone, perhaps ourselves, wasted precious ad budget?
All SEO and PPC marketers have pondered over these questions, which have remained unanswerable – until now. As data has been siloed, there has been no combined insight into the two disciplines.
Available tools generally fall into either the SEO or PPC camp. They also either offer reports and insights on previous activity or facilitate campaign actions, such as bid management. Considering eMarketerOpens a new window calculates search marketing accounts for 41% of global spend on digital advertising, it would seem fair to say a lot of budget is at risk of being suboptimal.
That point becomes even more pressing when you factor in eMarketer’s finding that CPC rates have doubled between 2010 to 2020 from 0.07 Euros to 0.15 Euros. With an anticipated 51% in search budgets over the next three years, this CPC inflation is likely only set to continue.
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AI Answers the Big Questions
Budget is flowing into search, but without a holistic view on activity, practitioners are left wondering if they should pay for a certain keyword if it is performing well organically. They will only be able to make educated guesses how much their paid activity is cannibalizing their organic strategy. It can also be unclear if they should be paying for both generic and brand terms and, if they are, what paid position they should be taking?
Like everyone else in search marketing, we have been grappling with these questions for years for individual clients. It is only with recent developments in machine learning and data processing that we have been able to build a unified tool that can work for any client, which we call Seamless Search.
It gathers a holistic picture of what you are achieving in both organic and paid search to establish which keywords and phrases budget should and should not be bid on or the maximum a brand should bid on them. This involves crunching through a vast array of current and past PPC and SEO data, as well as a mountain of additional internal and external data for machine learning to factor in.
AI technology can start by looking at how paid search terms have worked in the past, taking into account a brand’s SEO positioning at the time.
That is only half the story, of course. These records need to be considered alongside current targeting plans and audience profiles as well as how other channels, such as Google Ads, are performing and whether a brand is currently running promotional activity. This needs to be factored in against what competitors are doing in paid and organic search, what they are bidding on, and what success they are having. Time of day and the location of a potential click need to be considered, as does the time of year.
All these considerations will be known to search marketers; it has just proven impossible to crunch through all the data points through one tool, until now.
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Unified Insight
The result is a single window on a computer screen that groups keywords so paid, and organic spending can be optimized. Marketers are informed when they will drive up value by bidding and how far they can go before the cost of a click is no longer worth it and even risks cannibalizing organic success.
Rather than have to work on educated guesswork, search marketers can align bidding strategy to real-time data based on current SEO performance and the known likely impact of a bid. Not all keywords are automated within the process. The system can look at groups of terms and decide which ones to make the best impact on.
This optimized, holistic approach has been proven to lift ROI in search revenue by 25% and drive up the effectiveness of clients’ search in terms of Return on Ad Spend (ROAS) of 20%.
The idea is that marketers have the educated guesswork taken out of the equation for them. With the siloes between channels removed, they can rest assured that AI has looked at the internal and external factors affecting the final bidding decision and made a decision based on what will happen to ROI. They can also rest assured that because this is AI, it keeps learning. So results should only improve as gut feelings are supplemented by cross-channel, data-driven optimization.