Tag Archive | "Search"

More signs that Amazon is attracting bigger search ad budgets

CPG brands, in particular, are investing more in Amazon ads, but the marketplace is still a long way from catching Google and Facebook.



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Google brings search to podcasts through automatic transcription

Better enunciate, your Google Podcasts visibility may depend on it.



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LinkedIn taps Bing search data for interest targeting

Advertisers can reach audiences based on business-oriented content they’ve engaged with on Bing.



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How Google Dishes Out Content by Search Intent

Posted by TheMozTeam

This post was originally published on the STAT blog.


In the STAT whitepaper, Using search intent to connect with consumers, we looked at how SERP features change with a searcher’s intent — informational, commercial, transactional, or local. It was so chock-full of research that it sparked oodles of other content inspiration — from the basics of building an intent-based keyword list to setting up your own search intent project, to Scott Taft’s guide to building your own search intent dashboard.

But while doing the research for the whitepaper, we found ourselves pondering another question: is there a similar relationship between search intent and the kind of page content that Google sources results from?

We know from our study that as searchers head down the intent funnel, the SERP feature landscape shifts accordingly. For example, Google serves up progressively more shopping boxes, which help close the deal, as a searcher moves from awareness to purchase.

So, as consumers hunt for that perfect product, does the content that Google serves up shift from, say, category pages to product pages? To get to the bottom of this mystery, we mounted a three-pronged attack.

Prong 1: Uncover the top SERP players

Since Google delivers the content they deem most helpful, figuring out who their SERP favs are ensured that we were analyzing the best performing content.

To do this, we used the same 6,422 retail keywords from our original research, segmented them by search intent, and then gathered the top 12 results (give or take a few) that appeared on each SERP.

This gave us:

  • 6,338 informational intent results,
  • 35,210 commercial intent results,
  • 24,633 transactional intent results,
  • and 10,573 local intent results

…to analyze the stink out of. (That’s 76,754 results all told.)

From there, we dug into root domains (e.g. eBay.com and Amazon.com) to uncover the four most frequently occurring businesses for each search intent category.

We made an executive decision to exclude Google, who claimed top billing across the board, from our analysis for two reasons. One, because we attribute shopping boxes and images to them, which show up a lot for retail keywords, and two, because they aren’t exactly a competitor you can learn from.

Prong 2: Identify content page managers

After compiling the winningest sites to snoop on, it was time to see what kind of content they were offering up to the Google gods — which should’ve been easy, right? Wrong. Unfortunately, examining URL structures for frequently occurring page markers is a somewhat painful process.

Some sites, like Homedepot.com (who we wish had made the list for this very reason), have clean, easy to decipher URL structures: all product and category pages are identified with a “/p/” and “/b/” that always show up in the same spot in the URL.

And then you have the Amazon.coms of the world that use a mix of seemingly random markers, like “/s?rh=” and “/dp” that appear all over the place.

In the end — thanks to Stack Overflow, SequelPro, and a lot of patience — we were able to classify our URLs, bringing us to our third and final prong.

Prong 3: Mash everything together and analyze

Once we got all of our ducks in a row, it was time to get our super-sleuth on.

Informational intent (6,338 results)

This is the very top of the intent funnel. The searcher has identified a need and is looking for information on the best solution — is a [laptop] or [desktop computer] the right choice for their home office; what’s the difference between a [blender] and a [food processor] when making smoothies?

Thanks to the retail nature of our keywords, three product powerhouses — Amazon, Walmart, and Best Buy — rose to the top, along with Wikipedia, whose sole purpose in life is to provide the kind of information that searchers usually want to see at this stage of intent.

Although Wikipedia doesn’t have page markers, we chose to categorize their search results as product pages. This is because each Wikipedia entry typically focuses on a single person, place, or thing. Also, because they weren’t important to our analysis: while Wikipedia is a search competitor, they’re not a product competitor. (We still love you though, Wikipedia!)

Diving into the type of content that Amazon, Walmart, and Best Buy served up (the stuff we were really after), category pages surfaced as the preferred choice.

Given the wide net that a searcher is casting with their informational query, it made sense to see more category pages at this stage — they help searchers narrow down their hunt by providing a wide range of options to choose from.

What did have us raising our eyebrows a little was the number of product pages that appeared. Product pages showcase one specific item and are typically optimized for conversion, so we expected to see these in large quantities further down the funnel — when a searcher has a better idea of what they want.

Commercial intent (35,210 results)

When it comes to a commercial intent query, the searcher is starting to dig deeper into the product they’re after — they’re doing comparative research, reading reviews, and looking into specific functionality.

Here, Amazon continued to rule the URL roost, Wikipedia dropped off, eBay judo-chopped Walmart out of second place, and Best Buy stayed put at the bottom.

In terms of the content that these sites offered up, we saw the addition of review pages from Amazon, and buyer guides from Amazon, eBay, and Best Buy. We figured this would be the case, seeing as how we used modifiers like “best,” “compare,” and “reviews” to apply commercial intent to our keywords.

But while these two types of content fit perfectly with the intent behind a commercial query, especially reviews, oddly enough they still paled in comparison to the number of category and product pages. Weird, right?

Transactional intent (24,633 results)

At the transactional intent stage of the game, the searcher has narrowed their hunt down to a few best options and is ready to throw their hard-earned shekels at the winner.

As far as the most frequently appearing sites go, there was a little do-si-do between eBay and Walmart, but overall, these top four sites did an excellent job following searchers down the intent funnel.

In terms of the kind of pages appearing, once again, we saw a huge number of category pages. Product pages made a respectable showing, but given the readiness to buy at the bottom of the funnel, we expected to see the scales tip in their favor.

Alack and alas, no dice.

Local intent (10,573 results)

Technically, we categorize local intent as a subsection of transactional intent. It’s likely that the only reason a searcher would be considering an in-store visit is if the product is something they want to take home with them. But because local searches typically surface different results from our other transactional queries, we look at them separately.

Here, Amazon’s reign was finally usurped by its biggest competitor, Walmart, and Yelp made a stunning first appearance to knock Best Buy down and eBay off the list.

Given that local intent searchers are on the hunt for a brick-and-mortar store, it made sense that Walmart would win out over Amazon. That said, it’s an incredible feat that Amazon doesn’t let a lack of physical location derail its retail dominance, especially when local is the name of the game (a location is literally part of these queries).

As for Yelp, they’re a trusted source for people trying to find a business IRL — so it wasn’t surprising to see them jump on our local intent SERPs. Like Wikipedia, Yelp doesn’t have product or category pages per se, but they do have markers that indicate pages with multiple business listings (we classified these as category pages), as well as markers that indicate single business listings (our product pages). We also found markers for reviews, which were a perfect fit for our analysis.

Finally, when it came to content, category and product pages (again!) showed up the most on these SERPs. So what’s going on here?

The (unexpected) takeaway

When we set out to examine the type of content that appears for the different search intents, we expected to see far more variation from one level to the next. We thought we’d find lots of category pages for informational intent, more reviews and buyer guides for commercial intent, and mostly product pages for transactional intent.

Instead, we found that category pages are Google’s top choice for retail keywords throughout all levels of search intent. Regardless of how specific a query is, category pages seem to be the first point of access when hunting for retail items. So why might this be?

Looking to our winning sites for answers, it appears that intent-blended pages are the bomb dot com for Amazon, Walmart, eBay, and Best Buy.

Their category pages contain: an image of each type of product and short, descriptive copy to help searchers narrow down their options (informational intent); a review or rating system for quick comparisons (commercial intent); and pricing information and a clear way to make a purchase (transactional intent).

Following any of the items to their designated product page — the second most returned type of content — you’ll find a similar intent-blended approach. In fact, by having alternative suggestions, like “people also bought” and “similar products,” appear on them, they almost resemble category pages.

This product page approach is different from what we often see with smaller boutique-style shops. Take Stutterheim for example (they sell raincoats perfect for our Vancouver weather). Their product pages have a single focus: buy this one thing.

Since smaller shops don’t have a never-ending supply of goods, their product pages have to push harder for the transaction — no distractions allowed. Large retailers like Amazon? They have enough stuff to keep searchers around until they stumble across something they like.

To find out what type of content you should serve at each step of the intent funnel, segment your keywords by search intent and track which of your pages rank, as well as how well they convert. This will help reveal what your searchers find most useful.

Ready to get your mitts on even more intent-based insights? Grab the full whitepaper: Using search intent to connect with consumers.

What search-intent insights have you dug up? Let us know in the comments!

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Bing Ads brings 3D ads to Search with Samsung

Users can interact with the desktop ad format to inspect product features and details.



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How retailers can survive Amazon’s stronghold in Google search

Keep Amazon’s impression share in perspective because it shouldn’t directly drive strategy, but rather provide context around the advertiser competition in your market.



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Deliver a more relevant search experience

A practical guide to increase conversions, improve time-on-site- and deliver personalized experiences.



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The Influence of Voice Search on Featured Snippets

Posted by TheMozTeam

This post was originally published on the STAT blog.


We all know that featured snippets provide easy-to-read, authoritative answers and that digital assistants love to say them out loud when asked questions.

This means that featured snippets have an impact on voice search — bad snippets, or no snippets at all, and digital assistants struggle. By that logic: Create a lot of awesome snippets and win the voice search race. Right?

Right, but there’s actually a far more interesting angle to examine — one that will help you nab more snippets and optimize for voice search at the same time. In order to explore this, we need to make like Doctor Who and go back in time.

From typing to talking

Back when dinosaurs roamed the earth and queries were typed into search engines via keyboards, people adapted to search engines by adjusting how they performed queries. We pulled out unnecessary words and phrases, like “the,” “of,” and, well, “and,” which created truncated requests — robotic-sounding searches for a robotic search engine.

The first ever dinosaur to use Google.

Of course, as search engines have evolved, so too has their ability to understand natural language patterns and the intent behind queries. Google’s 2013 Hummingbird update helped pave the way for such evolution. This algorithm rejigging allowed Google’s search engine to better understand the whole of a query, moving it away from keyword matching to conversation having.

This is good news if you’re a human person: We have a harder time changing the way we speak than the way we write. It’s even greater news for digital assistants, because voice search only works if search engines can interpret human speech and engage in chitchat.

Digital assistants and machine learning

By looking at how digital assistants do their voice search thing (what we say versus what they search), we can see just how far machine learning has come with natural language processing and how far it still has to go (robots, they’re just like us!). We can also get a sense of the kinds of queries we need to be tracking if voice search is on the SEO agenda.

For example, when we asked our Google Assistant, “What are the best headphones for $ 100,” it queried [best headphones for $ 100]. We followed that by asking, “What about wireless,” and it searched [best wireless headphones for $ 100]. And then we remembered that we’re in Canada, so we followed that with, “I meant $ 100 Canadian,” and it performed a search for [best wireless headphones for $ 100 Canadian].

We can learn two things from this successful tête-à-tête: Not only does our Google Assistant manage to construct mostly full-sentence queries out of our mostly full-sentence asks, but it’s able to accurately link together topical queries. Despite us dropping our subject altogether by the end, Google Assistant still knows what we’re talking about.

Of course, we’re not above pointing out the fumbles. In the string of: “How to bake a Bundt cake,” “What kind of pan does it take,” and then “How much do those cost,” the actual query Google Assistant searched for the last question was [how much does bundt cake cost].

Just after we finished praising our Assistant for being able to maintain the same subject all the way through our inquiry, we needed it to be able to switch tracks. And it couldn’t. It associated the “those” with our initial Bundt cake subject instead of the most recent noun mentioned (Bundt cake pans).

In another important line of questioning about Bundt cake-baking, “How long will it take” produced the query [how long does it take to take a Bundt cake], while “How long does that take” produced [how long does a Bundt cake take to bake].

They’re the same ask, but our Google Assistant had a harder time parsing which definition of “take” our first sentence was using, spitting out a rather awkward query. Unless we really did want to know how long it’s going to take us to run off with someone’s freshly baked Bundt cake? (Don’t judge us.)

Since Google is likely paying out the wazoo to up the machine learning ante, we expect there to be less awkward failures over time. Which is a good thing, because when we asked about Bundt cake ingredients (“Does it take butter”) we found ourselves looking at a SERP for [how do I bake a butter].

Not that that doesn’t sound delicious.

Snippets are appearing for different kinds of queries

So, what are we to make of all of this? That we’re essentially in the midst of a natural language renaissance. And that voice search is helping spearhead the charge.

As for what this means for snippets specifically? They’re going to have to show up for human speak-type queries. And wouldn’t you know it, Google is already moving forward with this strategy, and not simply creating more snippets for the same types of queries. We’ve even got proof.

Over the last two years, we’ve seen an increase in the number of words in a query that surfaces a featured snippet. Long-tail queries may be a nuisance and a half, but snippet-having queries are getting longer by the minute.

When we bucket and weight the terms found in those long-tail queries by TF-IDF, we get further proof of voice search’s sway over snippets. The term “how” appears more than any other word and is followed closely by “does,” “to,” “much,” “what,” and “is” — all words that typically compose full sentences and are easier to remove from our typed searches than our spoken ones.

This means that if we want to snag more snippets and help searchers using digital assistants, we need to build out long-tail, natural-sounding keyword lists to track and optimize for.

Format your snippet content to match

When it’s finally time to optimize, one of the best ways to get your content into the ears of a searcher is through the right snippet formatting, which is a lesson we can learn from Google.

Taking our TF-IDF-weighted terms, we found that the words “best” and “how to” brought in the most list snippets of the bunch. We certainly don’t have to think too hard about why Google decided they benefit from list formatting — it provides a quick comparative snapshot or a handy step-by-step.

From this, we may be inclined to format all of our “best” and “how to” keyword content into lists. But, as you can see in the chart above, paragraphs and tables are still appearing here, and we could be leaving snippets on the table by ignoring them. If we have time, we’ll dig into which keywords those formats are a better fit for and why.

Get tracking

You could be the Wonder Woman of meta descriptions, but if you aren’t optimizing for the right kind of snippets, then your content’s going to have a harder time getting heard. Building out a voice search-friendly keyword list to track is the first step to lassoing those snippets.

Want to learn how you can do that in STAT? Say hello and request a tailored demo.

Need more snippets in your life? We dug into Google’s double-snippet SERPs for you — double the snippets, double the fun.

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Not just for auto anymore: Google tests giant image search ads in new verticals.

The ads feature a carousel of images.



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Managing Sitemap XML with Google Search Console

What to look for in Search Console indexing reports, plus learn why and how to create a dynamic Sitemap file.



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