Data is the foundation of pay-per-click marketing; it’s both the by-product of every action undertaken, and the source of denoting which action is the most effective to take next. But not all PPC marketers are aware of the vast sources of keyword and search term data – local to their PPC campaigns and elsewhere – available for them to leverage. In this article, we discuss three really useful sources that you might not be aware of.
It’s very likely you’re aware of keyword targeting on Google Ads – even if you don’t work in the PPC world, ‘keywords’ and ‘AdWords’ are pretty synonymous – but a surprising amount of people seemingly don’t know that targeting a specific keyword doesn’t necessarily mean targeting that specific search on Google. Depending on the match type you’re using, your keyword could match almost anything.
While match types and how keyword matching works are a different conversation altogether, routinely checking what your keywords are matching to both prevent wasting budget on poor quality or irrelevant traffic, and to mine for new keyword ideas is an integral part of optimal account management.
Finding your search terms couldn’t be easier:
Now you’ve found them, what do you do with them? The two main uses for search term reports are:
While Shopping campaigns don’t rely on keyword targeting to work, you can still see what terms are matching your products/product groups and prevent irrelevant and poor performing traffic from matching them. This is significantly more important on Shopping than it is with standard search campaigns as you’re at the mercy of Google to determine which user searches are relevant to the products you’re advertising. Frequently Google will display your “HP Designjet 3200PS Wide Format Printer” for users searching for “printers” or “home printers” even though it’s a commercial printer used for printing big road-side adverts – this is just one example.
The process for this is very similar to finding the search terms for keywords:
The aforementioned main use for search terms on shopping is negative keyword discovery (used for preventing irrelevant, poor performing traffic matching your products and wasting your budget), but you can also use this data for finding new keywords to target on search.
While there isn’t a massively consistent correlation between the performance of terms on shopping campaigns and the same search terms on Google (due to the nature of the advert) it can still be a great way of uncovering new keyword sets you may have missed during previous passes of keyword research.
Another great way to mine for new keywords that is overlooked quite often is – essentially abusing – Google’s autocomplete system when you perform a search.
When you start typing a search, Google provides a list of the most common suggestions. You can use scripts to pull this data for you, but to explain the principles we can do a few manually.
As you can see in the image when searching for ‘vertical leap’, Google is suggesting that I’m looking for one of the four options below the search bar:
If I search for ‘vertical leap a’ Google will suggest searches where a word starting with ‘a’ was used after it.
We can repeat this process using the alphabet a through z, numbers 0 through 1 and combinations of both (aa, ab, ac, 1a, 1b, 1c, aa1, ab1 etc) – though I’d stick to just doing single letter alphabet searches and numbers if you’re doing it manually. Copy what Google suggests as you go into a notepad and eventually you’ll have a huge list of new terms to consider.
A few really good aspects of terms found via the autocomplete tool:
If you’re unsure about how to use the data available to you in order to get more from your PPC campaigns, speak to our experts today on 023 9283 0281. In the meantime, check out these related articles:
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Henry is a PPC and data science specialist at Vertical Leap and has worked in digital marketing since 2007. He specialises in data and analytics, enterprise PPC and design. In his spare time he likes golf, Photoshop battles, 3D printing, stencil graffiti, drone photography and brewing his own alcohol.
Categories: Data Science, Machine Learning, PPC
Categories: Data & Analytics, Martech
Categories: Machine Learning, Martech
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