What Do You Do When You Lose Organic Traffic to Google SERP Features?

Posted by Emily.Potter

Google’s increasing dominance of their own search engine results pages (SERPs) has kicked up a lot of panic and controversy in the SEO industry. As Barry Adams pointed out on Twitter recently, this move by Google is not exactly new, but it does feel like Google has suddenly placed their foot on the accelerator:

Follow that Twitter thread and you’ll see the sort of back-and-forth these changes have started to create. Is this an ethical move by Google? Did you deserve the business they’re taking in the first place? Will SEO soon be dead? Or can we do what we’ve always done and adapt our strategies in smart, agile ways?

It’s hard to think positive when Google takes a stab at you like it did with this move on Ookla:

But regardless of how you feel about what’s happening, local packs, featured snippets, and SERP features from Google, properties like Google News, Images, Flights, Videos, and Maps are riding on a train that has no plans on stopping.

To give you an idea of how rapid these changes are occurring, the image below is what the SERP rankings looked like in November 2016 for one of our client’s key head terms:

And this image is the SERP for the same keyword by early December 2017 (our client is in green):

Check out MozCast’s Feature Graph if you want to see the percentage of queries specific features are appearing on.

Who is this blog post for?

You’re likely reading this blog post because you noticed your organic traffic has dropped and you suspect it could be Google tanking you.

Traffic drops tend to come about from four main causes: a drop in rankings, a decrease in search volume, you are now ranking for fewer keywords, or because SERP features and/or advertising are depressing your CTRs.

If you have not already done a normal traffic drop analysis and ruled out the first three causes, then your time is better spent doing that first. But if you have done a traffic drop analysis and reached the conclusion that you’re likely to be suffering from a change in SERP features, then keep reading.

But I’m too lazy to do a full analysis

Aside from ruling everything else out, other strong indications that SERP features are to blame will be a significant drop in clicks (either broadly or especially for specific queries) in Google Search Console where average ranking is static, but a near consistent amount of impressions.

I’ll keep harping on about this point, but make sure that you check clicks vs impressions for both mobile and desktop. Do this both broadly and for specific key head terms.

When you spend most of your day working on a desktop computer, sometimes in this industry we forget how much mobile actually dominates the scene. On desktop, the impact these have on traffic there is not as drastic; but when you go over to a mobile device, it’s not uncommon for it to take around four full scrolls down before organic listings appear.

From there, the steps to dealing with a Google-induced traffic drop are roughly as follows:

  1. Narrow down your traffic drop to the introduction of SERP features or an increase in paid advertising
  2. Figure out what feature(s) you are being hit by
  3. Gain hard evidence from SEO tools and performance graphs
  4. Adapt your SEO strategy accordingly

That covers step one, so let’s move on.

Step 2.0: Figure out which feature(s) you are being hit by

For a comprehensive list of all the different enhanced results that appear on Google, Overthink Group has documented them here. To figure out which one is impacting you, follow the below steps.

Step 2.1

Based off of your industry, you probably already have an idea of which features you’re most vulnerable to.

  • Are you an e-commerce website? Google Shopping and paid advertising will be a likely candidate.
  • Do you tend to generate a lot of blog traffic? Look at who owns the featured snippets on your most important queries.
  • Are you a media company? Check and see if you are getting knocked out of top news results.
  • Do you run a listings site? Maybe you’re being knocked by sponsored listings or Google Jobs.

Step 2.2

From there, sanity check this by spot-checking the SERPs for a couple of the keywords you’re concerned about to get a sense for what changed. If you roughly know what you’re looking for when you dig into the data, it will be easier to spot. This works well for SERP features, but determining a change in the amount of paid advertising will be harder to spot this way.

Once again, be sure to do this on both mobile and desktop. What may look insignificant from your office computer screen could be showing you a whole different story on your mobile device.

Step 3.0: Gain hard evidence from SEO tools and performance graphs

Once you have a top level idea of what has changed, you need to confirm it with SEO tools. If you have access to one, a historical rank tracking tool will be the most efficient way to dig into how your SERPs are evolving. I most frequently use STAT, but other great tools for this are Moz’s SERP features report, SEOmonitor, and SEMRush.

Using one of these tools, look back at historical data (either broadly or for specific important keywords) and find the date the SERP feature appeared if you can. Once you have this date, line it up with a dip in your organic traffic or other performance metric. If there’s a match, you can be pretty confident that’s to blame.

For example, here’s what this analysis looked like for one of our clients on a keyword with a regional search volume of 49,500. They got hit hard on mobile-first by the appearance of a local pack, then an events snippet 10 days later.

This was the clicks and impression data for the head term on mobile from Google Search Console:

As this case demonstrates, here’s another strong reminder that when you’re analyzing these changes, you must check both mobile and desktop. Features like knowledge panels are much more intrusive on mobile devices than they are on desktop, so while you may not be seeing a dramatic change in your desktop traffic, you may on mobile.

For this client we improved their structured data so that they showed up in the event snippet instead, and were able to recover a good portion of the lost traffic.

How to adapt your SEO strategy

You may not be able to fully recover, but here are some different strategies you can use depending on the SERP feature. Use these links to jump to a specific section:

Have you tried bidding to beat Google?

I cover what to do if you’re specifically losing out on organic traffic due to paid advertising (spoiler alert: you’re probably gonna have to pay), but paid advertising can also be used as a tactic to subvert Google SERP features.

For example, Sky Scanner has done this by bidding on the query “flights” so they appear above the Google Flights widget:

Accelerated Mobile Pages (AMP)

AMP is a project sponsored by Google to improve the speed of mobile pages. For a lot of these challenges, implementing AMP may be a way to improve your rankings as Google SERPs continue to change.

If you’ve noticed a number of websites with AMP implemented are ranking on the first page of SERPs you care about, it’s likely worth investigating.

If you are a news website, implementing AMP is absolutely a must.

Featured snippets and PAA boxes

If you’re losing traffic because one of your competitors owns the featured snippets on your SERPs, then you need to optimize your content to win featured snippets. I’ve already written a blog post for our Distilled blog on tactics to steal them before, which you can read here.

In summary, though, you have a chance to win a featured snippet if:

  • The ones you’re targeting are pretty volatile or frequently changing hands, as that’s a good indication the owner doesn’t have a strong hold on it
  • If you rank higher than the current owner, as this indicates Google prefers your page; the structure of your content simply needs some tweaking to win the snippet

If you’ve identified some featured snippets you have a good chance of stealing, compare what the current owner has done with their content that you haven’t. Typically it’s things like the text heading the block of content and the format of the content that differentiates a featured snippet owner from your content.

Local packs

At SearchLove London 2018, Rob Bucci shared data from STAT on local packs and search intent. Local SEO is a big area that I can’t cover fully here, but if you’re losing traffic because a local pack has appeared that you’re not being featured in, then you need to try and optimize your Google My Business listing for the local pack if you can. For a more in depth instruction on how you can get featured in a local pack, read here.

Unfortunately, it may just not be possible for you to be featured, but if it’s a query you have a chance at appearing in local pack for, you first need to get set up on Google My Business with a link to your website.

Once you have Google My Business set up, make sure the contact and address information is correct.

Reviews are incredibly important for anyone competing within a local pack, and not just high reviews but also the number of reviews you’ve received is important. You should also consider creating Google Posts. In a lot of spaces this feature is yet to have been taken advantage of, which means you could be able to get a jumpstart on your competitors.

More queries are seeing paid advertisements now, and there are also more ads appearing per query, as told in this Moz post.

If you’re losing traffic because a competitor has set up a PPC campaign and started to bid on keywords you’re ranking well for, then you may need to consider overbidding on these queries if they’re important to you.

Unfortunately, there’s no real secret here: either you gotta pay or you’re going to have to shift your focus to other target queries.

You should have already done so, but if you haven’t already included structured data on your website you need to, as it will help you stand out on SERPs with lots of advertising. Wrapped into this is the need to get good reviews for your brand and for your products.

Google Shopping

Similar to paid advertising, if the appearance of Google Shopping sponsored ads has taken over your SERPs, you should consider whether it’s worth you building your own Google Shopping campaign.

Again, structured data will be an important tactic to employ here as well. If you’re competing with Google Shopping ads, you’re competing with product listings that have images, prices, and reviews directly in the SERP results to draw in users. You should have the same.

Look into getting your pages implemented in Accelerated Mobile Pages (AMP), which is sponsored by Google. Not only has Google shown it favors pages that are in AMP, better site speed will lead to better conversion rates for your site.

To see if implementing AMP may be beneficial to your business, you can read some case studies of other businesses that have done so here.

Knowledge panels and carousels

Knowledge panels such as the one below appear for broad informational searches, and rarely on highly converting keywords. While they are arguably the most imposing of all the SERP features, unless you’re a content site or CelebrityNetWorth.com, they probably steal some of your less valuable traffic.

If you’re losing clicks due to knowledge panels, it’s likely happening on queries that typically can be satisfied by quick answers and therefore are by users who might have bounced from your site anyway. You won’t be able to beat a knowledge panel for quick answers, but you can optimize your content to satisfy affiliated longer-tail queries that users will still scroll to organic listings to find.

Create in-depth content that answers these questions and make sure that you have strong title tags and meta descriptions for these pages so you can have a better chance of standing out in the SERP.

In some cases, knowledge panels may be something you can exploit for your branded search queries. There’s no guaranteed way to get your content featured in a knowledge panel, and the information presented in them does not come from your site, so they can’t be “won” in the same way as a featured snippet.

To get into a knowledge panel, you can try using structured data markup or try to get your brand on Wikipedia if you haven’t already. The Knowledge Graph relies heavily on existing databases like Wikipedia that users directly contribute to, so developing more Wikipedia articles for your brand and any personal brands associated with it can be one avenue to explore.

Search Engine Journal has some tips on how to implement both of these strategies and more in their blog post here.

Google Jobs

Google Jobs has taken up huge amounts of organic real estate from listing sites. It will be tough to compete, but there are strategies you can employ, especially if you run a niche job boards site.

Shifting your digital strategy to integrate more paid advertising so you can sit above Google and to generating content in other areas, like on news websites and advice boards, can help you.

For more details on how to employ some of these strategies, you can read Search Engine Journal’s Google Jobs survival tips.

To conclude

Look, I’d be lying to you if I said this was good news for us SEOs. It’s not. Organic is going to get more and more difficult. But it’s not all doom and gloom. As Rand Fishkin noted in his BrightonSEO speech this September, if we create intelligent SEO strategies with an eye towards the future, then we have the opportunity to be ahead of the curve when the real disruption hits.

We also need to start integrating our SEO strategies with other mediums; we need to be educated on optimizing for social media, paid advertising, and other tactics for raising brand awareness. The more adaptable and diverse your online marketing strategies are, the better.

Google will always be getting smarter, which just means we have to get smarter too.

To quote Jayson DeMers,

“If you define SEO as the ability to manipulate your way to the top of search rankings, then SEO will die. But if you define SEO as the practice of improving a website’s visibility in search results, then SEO will never die; it will only continue to evolve.”

Search, like nearly every other industry today, will continue to come against dramatic unanticipated changes in the future. Yet search will also only continue to grow in importance. It may become increasingly more difficult to manipulate your way to the top of search results, but there will always be a need to try, and Google will continue to reward content that serves its users well.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 1 month ago from tracking.feedpress.it

​​Measure Your Mobile Rankings and Search Visibility in Moz Analytics

Posted by jon.white

We have launched a couple of new things in Moz Pro that we are excited to share with you all: Mobile Rankings and a Search Visibility score. If you want, you can jump right in by heading to a campaign and adding a mobile engine, or keep reading for more details!

Track your mobile vs. desktop rankings in Moz Analytics

Mobilegeddon came and went with slightly less fanfare than expected, somewhat due to the vast ‘Mobile Friendly’ updates we all did at super short notice (nice work everyone!). Nevertheless, mobile rankings visibility is now firmly on everyone’s radar, and will only become more important over time.

Now you can track your campaigns’ mobile rankings for all of the same keywords and locations you are tracking on desktop.

For this campaign my mobile visibility is almost 20% lower than my desktop visibility and falling;
I can drill down to find out why

Clicking on this will take you into a new Engines tab within your Keyword Rankings page where you can find a more detailed version of this chart as well as a tabular view by keyword for both desktop and mobile. Here you can also filter by label and location.

Here I can see Search Visibility across engines including mobile;
in this case, for my branded keywords.

We have given an extra engine to all campaigns

We’ve given customers an extra engine for each campaign, increasing the number from 3 to 4. Use the extra slot to add the mobile engine and unlock your mobile data!

We will begin to track mobile rankings within 24 hours of adding to a campaign. Once you are set up, you will notice a new chart on your dashboard showing visibility for Desktop vs. Mobile Search Visibility.

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

Along with this change we have also added a Search Visibility score to your rankings data. Use your visibility score to track and report on your overall campaign ranking performance, compare to your competitors, and look for any large shifts that might indicate penalties or algorithm changes. For a deeper drill-down into your data you can also segment your visibility score by keyword labels or locations. Visit the rankings summary page on any campaign to get started.

How is Search Visibility calculated?

Good question!

The Search Visibility score is the percentage of clicks we estimate you receive based on your rankings positions, across all of your keywords.

We take each ranking position for each keyword, multiply by an estimated click-thru-rate, and then take the average of all of your keywords. You can think of it as the percentage of your SERPs that you own. The score is expressed as a percentage, though scores of 100% would be almost impossible unless you are tracking keywords using the “site:” modifier. It is probably more useful to measure yourself vs. your competitors rather than focus on the actual score, but, as a rule of thumb, mid-40s is probably the realistic maximum for non-branded keywords.

Jeremy, our Moz Analytics TPM, came up with this metaphor:

Think of the SERPs for your keywords as villages. Each position on the SERP is a plot of land in SERP-village. The Search Visibility score is the average amount of plots you own in each SERP-village. Prime real estate plots (i.e., better ranking positions, like #1) are worth more. A complete monopoly of real estate in SERP-village would equate to a score of 100%. The Search Visibility score equates to how much total land you own in all SERP-villages.

Some neat ways to use this feature

  • Label and group your keywords, particularly when you add them – As visibility score is an average of all of your keywords, when you add or remove keywords from your campaign you will likely see fluctuations in the score that are unrelated to performance. Solve this by getting in the habit of labeling keywords when you add them. Then segment your data by these labels to track performance of specific keyword groups over time.
  • See how location affects your mobile rankings – Using the Engines tab in Keyword Rankings, use the filters to select just local keywords. Look for big differences between Mobile and Desktop where Google might be assuming local intent for mobile searches but not for desktop. Check out how your competitors perform for these keywords. Can you use this data?

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

The Importance of Being Different: Creating a Competitive Advantage With Your USP

Posted by TrentonGreener

“The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

While this quote has been credited to everyone from Francis Phillip Wernig, under the pseudonym Alan Ashley-Pitt, to Einstein himself, the powerful message does not lose its substance no matter whom you choose to credit. There is a very important yet often overlooked effect of not heeding this warning. One which can be applied to all aspects of life. From love and happiness, to business and marketing, copying what your competitors are doing and failing to forge your own path can be a detrimental mistake.

While as marketers we are all acutely aware of the importance of differentiation, we’ve been trained for the majority of our lives to seek out the norm.

We spend the majority of our adolescent lives trying desperately not to be different. No one has ever been picked on for being too normal or not being different enough. We would beg our parents to buy us the same clothes little Jimmy or little Jamie wore. We’d want the same backpack and the same bike everyone else had. With the rise of the cell phone and later the smartphone, on hands and knees, we begged and pleaded for our parents to buy us the Razr, the StarTAC (bonus points if you didn’t have to Google that one), and later the iPhone. Did we truly want these things? Yes, but not just because they were cutting edge and nifty. We desired them because the people around us had them. We didn’t want to be the last to get these devices. We didn’t want to be different.

Thankfully, as we mature we begin to realize the fallacy that is trying to be normal. We start to become individuals and learn to appreciate that being different is often seen as beautiful. However, while we begin to celebrate being different on a personal level, it does not always translate into our business or professional lives.

We unconsciously and naturally seek out the normal, and if we want to be different—truly different in a way that creates an advantage—we have to work for it.

The truth of the matter is, anyone can be different. In fact, we all are very different. Even identical twins with the same DNA will often have starkly different personalities. As a business, the real challenge lies in being different in a way that is relevant, valuable to your audience, and creates an advantage.

“Strong products and services are highly differentiated from all other products and services. It’s that simple. It’s that difficult.” – Austin McGhie, Brand Is a Four Letter Word

Let’s explore the example of Revel Hotel & Casino. Revel is a 70-story luxury casino in Atlantic City that was built in 2012. There is simply not another casino of the same class in Atlantic City, but there might be a reason for this. Even if you’re not familiar with the city, a quick jump onto Atlantic City’s tourism website reveals that of the five hero banners that rotate, not one specifically mentions gambling, but three reference the boardwalk. This is further illustrated when exploring their internal linking structure. The beaches, boardwalk, and shopping all appear before a single mention of casinos. There simply isn’t as much of a market for high-end gamblers in the Atlantic City area; in the states Las Vegas serves that role. So while Revel has a unique advantage, their ability to attract customers to their resort has not resulted in profitable earnings reports. In Q2 2012, Revel had a gross operating loss of $35.177M, and in Q3 2012 that increased to $36.838M.

So you need to create a unique selling proposition (also known as unique selling point and commonly referred to as a USP), and your USP needs to be valuable to your audience and create a competitive advantage. Sounds easy enough, right? Now for the kicker. That advantage needs to be as sustainable as physically possible over the long term.

“How long will it take our competitors to duplicate our advantage?”

You really need to explore this question and the possible solutions your competitors could utilize to play catch-up or duplicate what you’ve done. Look no further than Google vs Bing to see this in action. No company out there is going to just give up because your USP is so much better; most will pivot or adapt in some way.

Let’s look at a Seattle-area coffee company of which you may or may not be familiar. Starbucks has tried quite a few times over the years to level-up their tea game with limited success, but the markets that Starbucks has really struggled to break into are the pastry, breads, dessert, and food markets.

Other stores had more success in these markets, and they thought that high-quality teas and bakery items were the USPs that differentiated them from the Big Bad Wolf that is Starbucks. And while they were right to think that their brick house would save them from the Big Bad Wolf for some time, this fable doesn’t end with the Big Bad Wolf in a boiling pot.

Never underestimate your competitor’s ability to be agile, specifically when overcoming a competitive disadvantage.

If your competitor can’t beat you by making a better product or service internally, they can always choose to buy someone who can.

After months of courting, on June 4th, 2012 Starbucks announced that they had come to an agreement to purchase La Boulange in order to “elevate core food offerings and build a premium, artisanal bakery brand.” If you’re a small-to-medium sized coffee shop and/or bakery that even indirectly competed with Starbucks, a new challenger approaches. And while those tea shops momentarily felt safe within the brick walls that guarded their USP, on the final day of that same year, the Big Bad Wolf huffed and puffed and blew a stack of cash all over Teavana. Making Teavana a wholly-owned subsidiary of Starbucks for the low, low price of $620M.

Sarcasm aside, this does a great job of illustrating the ability of companies—especially those with deep pockets—to be agile, and demonstrates that they often have an uncanny ability to overcome your company’s competitive advantage. In seven months, Starbucks went from a minor player in these markets to having all the tools they need to dominate tea and pastries. Have you tried their raspberry pound cake? It’s phenomenal.

Why does this matter to me?

Ok, we get it. We need to be different, and in a way that is relevant, valuable, defensible, and sustainable. But I’m not the CEO, or even the CMO. I cannot effect change on a company level; why does this matter to me?

I’m a firm believer that you effect change no matter what the name plate on your desk may say. Sure, you may not be able to call an all-staff meeting today and completely change the direction of your company tomorrow, but you can effect change on the parts of the business you do touch. No matter your title or area of responsibility, you need to know your company’s, client’s, or even a specific piece of content’s USP, and you need to ensure it is applied liberally to all areas of your work.

Look at this example SERP for “Mechanics”:

While yes, this search is very likely to be local-sensitive, that doesn’t mean you can’t stand out. Every single AdWords result, save one, has only the word “Mechanics” in the headline. (While the top of page ad is pulling description line 1 into the heading, the actual headline is still only “Mechanic.”) But even the one headline that is different doesn’t do a great job of illustrating the company’s USP. Mechanics at home? Whose home? Mine or theirs? I’m a huge fan of Steve Krug’s “Don’t Make Me Think,” and in this scenario there are too many questions I need answered before I’m willing to click through. “Mechanics; We Come To You” or even “Traveling Mechanics” illustrates this point much more clearly, and still fits within the 25-character limit for the headline.

If you’re an AdWords user, no matter how big or small your monthly spend may be, take a look at your top 10-15 keywords by volume and evaluate how well you’re differentiating yourself from the other brands in your industry. Test ad copy that draws attention to your USP and reap the rewards.

Now while this is simply an AdWords text ad example, the same concept can be applied universally across all of marketing.

Title tags & meta descriptions

As we alluded to above, not only do companies have USPs, but individual pieces of content can, and should, have their own USP. Use your title tag and meta description to illustrate what differentiates your piece of content from the competition and do so in a way that attracts the searcher’s click. Use your USP to your advantage. If you have already established a strong brand within a specific niche, great! Now use it to your advantage. Though it’s much more likely that you are competing against a strong brand, and in these scenarios ask yourself, “What makes our content different from theirs?” The answer you come up with is your content’s USP. Call attention to that in your title tag and meta description, and watch the CTR climb.

I encourage you to hop into your own site’s analytics and look at your top 10-15 organic landing pages and see how well you differentiate yourself. Even if you’re hesitant to negatively affect your inbound gold mines by changing the title tags, run a test and change up your meta description to draw attention to your USP. In an hour’s work, you just may make the change that pushes you a little further up those SERPs.

Branding

Let’s break outside the world of digital marketing and look at the world of branding. Tom’s Shoes competes against some heavy hitters in Nike, Adidas, Reebok, and Puma just to name a few. While Tom’s can’t hope to compete against the marketing budgets of these companies in a fair fight, they instead chose to take what makes them different, their USP, and disseminate it every chance they get. They have labeled themselves “The One for One” company. It’s in their homepage’s title tag, in every piece of marketing they put out, and it smacks you in the face when you land on their site. They even use the call-to-action “Get Good Karma” throughout their site.

Now as many of us may know, partially because of the scandal it created in late 2013, Tom’s is not actually a non-profit organization. No matter how you feel about the matter, this marketing strategy has created a positive effect on their bottom line. Fast Company conservatively estimated their revenues in 2013 at $250M, with many estimates being closer to the $300M mark. Not too bad of a slice of the pie when competing against the powerhouses Tom’s does.

Wherever you stand on this issue, Tom’s Shoes has done a phenomenal job of differentiating their brand from the big hitters in their industry.

Know your USP and disseminate it every chance you get.

This is worth repeating. Know your USP and disseminate it every chance you get, whether that be in title tags, ad copy, on-page copy, branding, or any other segment of your marketing campaigns. Online or offline, be different. And remember the quote that we started with, “The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

The amount of marketing knowledge that can be taken from this one simple statement is astounding. Heed the words, stand out from the crowd, and you will have success.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

Your Daily SEO Fix: Week 5

Posted by Trevor-Klein

We’ve arrived, folks! This is the last installment of our short (< 2-minute) video tutorials that help you all get the most out of Moz’s tools. If you haven’t been following along, these are each designed to solve a use case that we regularly hear about from Moz community members.

Here’s a quick recap of the previous round-ups in case you missed them:

  • Week 1: Reclaim links using Open Site Explorer, build links using Fresh Web Explorer, and find the best time to tweet using Followerwonk.
  • Week 2: Analyze SERPs using new MozBar features, boost your rankings through on-page optimization, check your anchor text using Open Site Explorer, do keyword research with OSE and the keyword difficulty tool, and discover keyword opportunities in Moz Analytics.
  • Week 3: Compare link metrics in Open Site Explorer, find tweet topics with Followerwonk, create custom reports in Moz Analytics, use Spam Score to identify high-risk links, and get link building opportunities delivered to your inbox.
  • Week 4: Use Fresh Web Explorer to build links, analyze rank progress for a given keyword, use the MozBar to analyze your competitors’ site markup, use the Top Pages report to find content ideas, and find on-site errors with Crawl Test.

We’ve got five new fixes for you in this edition:

  • How to Use the Full SERP Report
  • How to Find Fresh Links and Manage Your Brand Online Using Open Site Explorer
  • How to Build Your Link Profile with Link Intersect
  • How to Find Local Citations Using the MozBar
  • Bloopers: How to Screw Up While Filming a Daily SEO Fix

Hope you enjoy them!


Fix 1: How to Use the Full SERP Report

Moz’s Full SERP Report is a detailed report that shows the top ten ranking URLs for a specific keyword and presents the potential ranking signals in an easy-to-view format. In this Daily SEO Fix, Meredith breaks down the report so you can see all the sections and how each are used.

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Fix 2: How to Find Fresh Links and Manage Your Brand Online Using Open Site Explorer

The Just-Discovered Links report in Open Site Explorer helps you discover recently created links within an hour of them being published. In this fix, Nick shows you how to use the report to view who is linking to you, how they’re doing it, and what they are saying, so you can capitalize on link opportunities while they’re still fresh and join the conversation about your brand.


Fix 3: How to Build Your Link Profile with Link Intersect

The quantity and (more importantly) quality of backlinks to your website make up your link profile, one of the most important elements in SEO and an incredibly important factor in search engine rankings. In this Daily SEO Fix, Tori shows you how to use Moz’s Link Intersect tool to analyze the competitions’ backlinks. Plus, learn how to find opportunities to build links and strengthen your own link profile.


Fix 4: How to Find Local Citations Using the MozBar

Citations are mentions of your business and address on webpages other than your own such as an online yellow pages directory or a local business association page. They are a key component in search engine ranking algorithms so building consistent and accurate citations for your local business(s) is a key Local SEO tactic. In today’s Daily SEO Fix, Tori shows you how to use MozBar to find local citations around the web


Bloopers: How to Screw Up While Filming a Daily SEO Fix

We had a lot of fun filming this series, and there were plenty of laughs along the way. Like these ones. =)


Looking for more?

We’ve got more videos in the previous four weeks’ round-ups!

Your Daily SEO Fix: Week 1

Your Daily SEO Fix: Week 2

Your Daily SEO Fix: Week 3

Your Daily SEO Fix: Week 4


Don’t have a Pro subscription? No problem. Everything we cover in these Daily SEO Fix videos is available with a free 30-day trial.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

Reblogged 3 years ago from tracking.feedpress.it

The Colossus Update: Waking The Giant

Posted by Dr-Pete

Yesterday morning, we woke up to a historically massive temperature spike on MozCast, after an unusually quiet weekend. The 10-day weather looked like this:

That’s 101.8°F, one of the hottest verified days on record, second only to a series of unconfirmed spikes in June of 2013. For reference, the first Penguin update clocked in at 93.1°.

Unfortunately, trying to determine how the algorithm changed from looking at individual keywords (even thousands of them) is more art than science, and even the art is more often Ms. Johnson’s Kindergarten class than Picasso. Sometimes, though, we catch a break and spot something.

The First Clue: HTTPS

When you watch enough SERPs, you start to realize that change is normal. So, the trick is to find the queries that changed a lot on the day in question but are historically quiet. Looking at a few of these, I noticed some apparent shake-ups in HTTP vs. HTTPS (secure) URLs. So, the question becomes: are these anecdotes, or do they represent a pattern?

I dove in and looked at how many URLs for our 10,000 page-1 SERPs were HTTPS over the past few days, and I saw this:

On the morning of June 17, HTTPS URLs on page 1 jumped from 16.9% to 18.4% (a 9.9% day-over-day increase), after trending up for a few days. This represents the total real-estate occupied by HTTPS URLs, but how did rankings fare? Here are the average rankings across all HTTPS results:

HTTPS URLs also seem to have gotten a rankings boost – dropping (with “dropping” being a positive thing) from an average of 2.96 to 2.79 in the space of 24 hours.

Seems pretty convincing, right? Here’s the problem: rankings don’t just change because Google changes the algorithm. We are, collectively, changing the web every minute of the day. Often, those changes are just background noise (and there’s a lot of noise), but sometimes a giant awakens.

The Second Clue: Wikipedia

Anecdotally, I noticed that some Wikipedia URLs seemed to be flipping from HTTP to HTTPS. I ran a quick count, and this wasn’t just a fluke. It turns out that Wikipedia started switching their entire site to HTTPS around June 12 (hat tip to Jan Dunlop). This change is expected to take a couple of weeks.

It’s just one site, though, right? Well, historically, this one site is the #1 largest land-holder across the SERP real-estate we track, with over 5% of the total page-1 URLs in our tracking data (5.19% as of June 17). Wikipedia is a giant, and its movements can shake the entire web.

So, how do we tease this apart? If Wikipedia’s URLs had simply flipped from HTTP to HTTPS, we should see a pretty standard pattern of shake-up. Those URLs would look to have changed, but the SERPS around them would be quiet. So, I ran an analysis of what the temperature would’ve been if we ignored the protocol (treating HTTP/HTTPS as the same). While slightly lower, that temperature was still a scorching 96.6°F.

Is it possible that Wikipedia moving to HTTPS also made the site eligible for a rankings boost from previous algorithm updates, thus disrupting page 1 without any code changes on Google’s end? Yes, it is possible – even a relatively small rankings boost for Wikipedia from the original HTTPS algorithm update could have a broad impact.

The Third Clue: Google?

So far, Google has only said that this was not a Panda update. There have been rumors that the HTTPS update would get a boost, as recently as SMX Advanced earlier this month, but no timeline was given for when that might happen.

Is it possible that Wikipedia’s publicly announced switch finally gave Google the confidence to boost the HTTPS signal? Again, yes, it’s possible, but we can only speculate at this point.

My gut feeling is that this was more than just a waking giant, even as powerful of a SERP force as Wikipedia has become. We should know more as their HTTPS roll-out continues and their index settles down. In the meantime, I think we can expect Google to become increasingly serious about HTTPS, even if what we saw yesterday turns out not to have been an algorithm update.

In the meantime, I’m going to melodramatically name this “The Colossus Update” because, well, it sounds cool. If this indeed was an algorithm update, I’m sure Google would prefer something sensible, like “HTTPS Update 2” or “Securageddon” (sorry, Gary).

Update from Google: Gary Illyes said that he’s not aware of an HTTPS update (via Twitter):

No comment on other updates, or the potential impact of a Wikipedia change. I feel strongly that there is an HTTPS connection in the data, but as I said – that doesn’t necessarily mean the algorithm changed.

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Reblogged 3 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

After seeing Rand’s “Mad Science Experiments in SEO” presented at last year’s MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO’s up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I’ve learned a thing or two about app store optimization and what goes into an app’s ranking. It’s been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website’s traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive’s recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I’ll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app’s ranking.

But first, a little context

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

  • Average app store rating
  • Rating/review volume
  • Download and install counts
  • Uninstalls (what retention and churn look like for the app)
  • App usage statistics (how engaged an app’s users are and how frequently they launch the app)
  • Growth trends weighted toward recency (how daily download counts changed over time and how today’s ratings compare to last week’s)
  • Keyword density of the app’s landing page (Ian did a great job covering this factor in a previous Moz post)

I’ve simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn’t say much. Over the next five studies, however, we’ll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I’ll stop here with the assumptions, but I’ve gone into far greater depth into how I’ve reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

  • They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
  • They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I’ll test in study #2)
  • They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
  • They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app’s current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they’re ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play’s Top 100 apps and very few blips within the App Store’s Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play’s 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these “lower-ranked” apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we’ll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it’s just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we’d see a downward trend in ratings. We’d expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

  1. The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
  2. The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play’s algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store’s calculation of an app’s current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I’m using the age of a current app’s version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I’m hypothesizing that the older the age of an app’s current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app’s rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app’s popularity on its ranking. In an ideal world, popularity would be measured by an app’s monthly active users (MAUs), but since few mobile app developers have released this information, I’ve settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app’s ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play’s Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store’s Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don’t have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I’ve applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

  1. Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
  2. Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store’s top charts.
  3. In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
  4. Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play’s top charts.
  5. The higher an app is rated, the less volatile its rankings are.
  6. The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
  7. An app’s update frequency is negatively correlated with Google Play’s ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app’s most recent ratings and reviews.
  8. The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
  9. In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
  10. Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores’ ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I’d now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It’s with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I’d hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app’s current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


Again, we’re oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let’s deconstruct the ranking formula together, one experiment at a time.

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Reblogged 3 years ago from tracking.feedpress.it