Marketers and retailers can get more comparative brand data with the latest update.
Please visit Search Engine Land for the full article.
Popups are already the best way to collect email addresses. As Head of Customer Success at WisePops, an intelligent popup solution, I see customers convincing as many as 1 visitor out of 5 to subscribe to their newsletter with a simple email popup.
Our average subscription rate is 5.9%. In other words, out of 10,000 visitors seeing a popup, our customers collect on average 590 emails. And if you don’t believe me, there are plenty of case studies on the matter which go in the same direction.
5.9% is good. But are there any new techniques you could use to boost your popups’ performance? The reply is a resounding YES. As most online marketing tools, popups are evolving very quickly. Let’s review the latest developments and how they can help you collect more email subscribers.
Targeting plays a huge part in the success of an opt-in popup. Some of our customers’ popup A/B tests showed that a simple tweak in the targeting can double the number of emails collected. And in terms of targeting, lots of options have appeared in the past few months.
In recent months we’ve seen a new trend emerging where marketers design different campaigns for each of their categories or topics. Here’s a good ecommerce illustration from SohoHome, an Interior shop. The message perfectly matches the category where it’s displayed, thus driving a higher engagement.
Here’s another example from KlientBoost, a CRO and PPC agency. They create separate campaigns for their most popular blog articles. This campaign is displayed on one page only.
Popups are adapting to the context
Marketers are pushing this logic further with contextual targeting. In short, it’s a way to trigger a popup when some custom conditions are met by the page or the user. Popular examples include popups displayed depending on the cart value, popups adapted to the user’s loyalty status, or popups displayed to users who have visited a specific page before.
Here’s one example from Christy Dawn, a fashion retailer. They display a specific popup on out-of-stock products.
As you already guessed, the engagement rate is pretty high.
Earlier this year, Google shared new guidelines for mobile popups. Prior to this release, they invited webmasters to stop displaying popups which prevented users from accessing content on a landing page.
Since then, marketers have adapted their popup strategy to this change and mobile popups have never been this efficient and user-friendly.
Nike is leading the game with a popup displayed when a user clicks a call-to-action:
Another good illustration comes from Timberland. See how they shrank their popup’s size?
Why do they bother? Because mobile now makes up the majority of Internet traffic. And marketers need to adapt to this new paradigm.
With the rise of video marketing and GIFs, it seems only logical that marketers draw inspiration from this trend and add life to their popups. Why is it so important? Because it contributes to catching your visitor’s attention and boosts your popups’ CTR.
This is how Inkbox adds fun to their opt-in popups:
Here’s another example from Vivadogs, a company offering boxes for dogs. What’s so interesting about it is that:
(1) it makes the message very visible (could you miss it? no!)
(2) it gives a good sense of what customers can expect when they order a box for their pet
(3) it’s more fun than a basic popup
Popups have been an essential part of marketers’ toolboxes for a few years now. But like any marketing tool, you must renew it to make sure it’s adapted to the changing behavior of your visitors and leads.
As Einstein once said, “the measure of intelligence is the ability to change”. Let’s be more intelligent in 2018 and let’s work hard on our popups!
This partner guest post was written by Greg d’Aboville who’s Head of Customer Success at Wisepops, a tool that helps marketers build intelligent website popups.
Posted by Daniel_Marks
It seems like every couple months (weeks?) there’s a new post predicting the end of SEO:
But rather than proclaiming that SEO is already dead (it’s not), let’s look at 3 ways in which the SEO industry might eventually die and ways in which SEOs can prepare themselves.
Google is constantly experimenting with new ad formats that actually provide a better user experience than organic results. The better the ads, the less traffic that will be captured by organic listings. What are some examples of when PPC ads provide a better UX than organic results?
Google has rolled out home service ads to a few markets as a way of making it easier to find various contractors. Even sites with incredible local SEO will have a hard time competing with the convenience of these ads, not to mention the extremely valuable endorsement by Google.
This is a relatively simple example of Google providing a bunch of relevant information directly in the SERPs with a simple interface that will, inevitably, come at the expense of people clicking through to organic listings about car reviews, details, dealerships, etc. I suspect that Google will continue rolling out these rich, informational interfaces to high-value verticals such as credit cards, mortgages, legal services, and so on.
It’s hard to imagine such a prominent, compelling interface not sucking up a decent amount of organic clicks. Google Flights has also likely contributed to the fact that Google earns twice as much travel-related revenue as Expedia.
This is one of the most ambitious ad interfaces that Google offers — you can literally play apps directly within Google! No need to download the app or click through to a site. It’s incredible.
SEO hasn’t traditionally been a huge driver of app discovery, so app streaming for games specifically might not be that disruptive to SEOs, but you can imagine this functionality being rolled out for all sorts of purposes. For example, instead of building functionality similar to Google Flights, maybe Google could simply send you to the Kayak app interface within search results and piggyback off the improved user experience of apps dedicated to one specific purpose.
As interesting as the previous examples were, the most dangerous Google ad products for most SEOs would likely be the different ones related to product discovery.
Product Listing Ads (PLAs), for example, offer a fairly basic interface relative to the previously mentioned examples, but this simplicity makes them very dangerous because it makes them so easy to scale. Google doesn’t need to build a bespoke solution for different verticals or sets of queries.
It’s not hard to imagine a future where product queries on Google simply return a large set of product cards with the option to buy directly within the Google interface. The pieces are already there with PLAs and the limited rollout of the ability to buy directly on Google through select websites. All it would take is Google expanding the number of product cards and the number of sites that offer direct checkout through Google. The implications of a shift like this are hard to overestimate.
These product-related ad interfaces include:
More info: Winning the shopping micro-moments
There are other examples of rich ad interfaces (please share some below, as it’s always interesting to see more!) and Google has many reasons to continue rolling them out as far and wide as possible:
Ad-blocking continues to grow and these rich ads remain on a small percentage of overall queries because they’re difficult to scale.
What happens when people no longer find what they’re looking for by typing in a Google search and clicking through to a result? Some of these alternative interfaces include:
It’s difficult to rank for a search query that is heavily personalized and has only one result:
There are a lot of sites that arguably offer a better search experience for a specific type of query:
Will people shift more and more of their searches to sites that offer a search experience tailor-made to one specific type of query?
Google Now effectively tries to give you information you’re interested in before ever having to type in a query:
You can imagine quite a few verticals that could be disrupted by improvements to pre-search:
Last but not least, perhaps conversational commerce will actually take off and people will start finding products/information by using chatbots:
More examples here: 11 Examples of Conversational Commerce and Chatbots in 2016
This has been well-covered, but Google continues to aggressively enrich organic results such that the mythical “ten blue links” SERP only makes up about 3% of searches:
The expansion of ads above the fold for some queries hasn’t helped, either. It’s hard to capture organic clicks on a SERP that looks like this (cut off):
These changes are especially impactful on mobile devices which already have limited screen real estate as is.
These new interfaces consume a relatively small percentage of overall searches. They might continue to cover more and more informational queries (“when is Mother’s day 2017?”), and personal ones (“set an alarm for 7am”), but transactional queries (“new Jordan shoes”) remain on conventional screens because ultimately, finding products through voice search or a chatbot might not be the most enjoyable experience:
1/ Conversational commerce is unproven, even in Asia. If texting takes more time than clicking a button on a webview, why is it better?
— Connie Chan (@conniechan) April 1, 2016
Broaden your skillset and make sure you’re providing value beyond simply optimizing a website for Google.
Google will always need the help of SEOs to understand the Internet.
Despite these threats, I think it’s very unlikely that SEO disappears as a discipline anytime soon. I have yet to run into a site that doesn’t have large SEO opportunities to capture, given the right projects. I also believe the best-case scenario for each threat is actually the most likely scenario. That being said, it can still be helpful to think through future threats to the SEO industry. Can you think of any others?
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Posted by KelseyLibert
When it comes to job availability and security, the future looks bright for inbound marketers.
The Bureau of Labor Statistics (BLS) projects that employment for marketing managers will grow by 13% between 2012 and 2022. Job security for marketing managers also looks positive according to the BLS, which cites that marketing employees are less likely to be laid off since marketing drives revenue for most businesses.
While the BLS provides growth estimates for managerial-level marketing roles, these projections don’t give much insight into the growth of digital marketing, specifically the disciplines within digital marketing. As we know, “marketing” can refer to a variety of different specializations and methodologies. Since digital marketing is still relatively new compared to other fields, there is not much comprehensive research on job growth and trends in our industry.
To gain a better understanding of the current state of digital marketing careers, Fractl teamed up with Moz to identify which skills and roles are the most in demand and which states have the greatest concentration of jobs.
We analyzed 75,315 job listings posted on Indeed.com during June 2015 based on data gathered from job ads containing the following terms:
We chose the above keywords based on their likelihood to return results that were marketing-focused roles (for example, just searching for “social media” may return a lot of jobs that are not primarily marketing focused, such as customer service). The occurrence of each of these terms in job listings was quantified and segmented by state. We then combined the job listing data with U.S. Census Bureau population estimates to calculate the jobs per capita for each keyword, giving us the states with the greatest concentration of jobs for a given search query.
Using the same data, we identified which job titles appeared most frequently. We used existing data from Indeed to determine job trends and average salaries. LinkedIn search results were also used to identify keyword growth in user profiles.
As the marketing industry continues to evolve due to emerging technology and marketing platforms, marketers are expected to pick up new skills and broaden their knowledge more quickly than ever before. Many believe this rapid rate of change has caused a marketing skills gap, making it difficult to find candidates with the technical, creative, and business proficiencies needed to succeed in digital marketing.
The ability to combine analytical thinking with creative execution is highly desirable and necessary in today’s marketing landscape. According to an article in The Guardian, “Companies will increasingly look for rounded individuals who can combine analytical rigor with the ability to apply this knowledge in a practical and creative context.” Being both detail-oriented and a big picture thinker is also a sought-after combination of attributes. A report by The Economist and Marketo found that “CMOs want people with the ability to grasp and manage the details (in data, technology, and marketing operations) combined with a view of the strategic big picture.”
But well-rounded marketers are hard to come by. In a study conducted by Bullhorn, 64% of recruiters reported a shortage of skilled candidates for available marketing roles. Wanted Analytics recently found that one of the biggest national talent shortages is for marketing manager roles, with only two available candidates per job opening.
While recruiter frustrations may indicate a shallow talent pool, LinkedIn tells a different story—the number of U.S.-based marketers who identify themselves as having digital marketing skills is on the rise. Using data tracked by Rand and LinkedIn, we found the following increases of marketing keywords within user profiles.
The number of profiles containing “content marketing” has seen the largest growth, with a 168% increase since 2013. “Social media” has also seen significant growth with a 137% increase. “Social media” appears on a significantly higher volume of profiles than the other keywords, with more than 2.2 million profiles containing some mention of social media. Although “SEO” has not seen as much growth as the other keywords, it still has the second-highest volume with it appearing in 630,717 profiles.
Why is there a growing number of people self-identifying as having the marketing skills recruiters want, yet recruiters think there is a lack of talent?
While there may be a lot of specialists out there, perhaps recruiters are struggling to fill marketing roles due to a lack of generalists or even a lack of specialists with surface-level knowledge of other areas of digital marketing (also known as a T-shaped marketer).
The data we gathered from LinkedIn confirm this, as the 20 most common digital marketing-related job titles being advertised call for a broad mix of skills.
It’s no wonder that marketing manager roles are hard to fill, considering the job ads are looking for proficiency in a wide range of marketing disciplines including social media marketing, SEO, PPC, content marketing, Google Analytics, and digital marketing. Even job descriptions for specialist roles tend to call for skills in other disciplines. A particular role such as SEO Specialist may call for several skills other than SEO, such as PPC, content marketing, and Google Analytics.
Taking a more granular look at job titles, the chart below shows the five most common titles for each search query. One might expect mostly specialist roles to appear here, but there is a high occurrence of generalist positions, such as Digital Marketing Manager and Marketing Manager.
Only one job title containing “SEO” cracked the top five. This indicates that SEO knowledge is a desirable skill within other roles, such as general digital marketing and development.
Recruiter was the third most common job title among job listings containing social media keywords, which suggests a need for social media skills in non-marketing roles.
Similar to what we saw with SEO job titles, only one job title specific to PPC (Paid Search Specialist) made it into the top job titles. PPC skills are becoming necessary for more general marketing roles, such as Marketing Manager and Digital Marketing Specialist.
Across all search queries, the most common jobs advertised call for a broad mix of skills. This tells us hiring managers are on the hunt for well-rounded candidates with a diverse range of marketing skills, as opposed to candidates with expertise in one area.
Marketers who cultivate diverse skill sets are better poised to gain an advantage over other job seekers, excel in their job role, and accelerate career growth. Jason Miller says it best in his piece about the new breed hybrid marketer:
Using data from Indeed, we identified which inbound skills have the highest demand and which jobs are seeing the most growth. Social media keywords claim the largest volume of results out of the terms we searched for during June 2015.
“Social media marketing” or “social media management” appeared the most frequently in the job postings we analyzed, with 46.7% containing these keywords. “PPC” returned the smallest number of results, with only 3.8% of listings containing this term.
Perhaps this is due to social media becoming a more necessary skill across many industries and not only a necessity for marketers (for example, social media’s role in customer service and recruitment). On the other hand, job roles calling for PPC or SEO skills are most likely marketing-focused. The prevalence of social media jobs also may indicate that social media has gained wide acceptance as a necessary part of a marketing strategy. Additionally, social media skills are less valuable compared to other marketing skills, making it cheaper to hire for these positions (we will explore this further in the average salaries section below).
Our search results also included a high volume of jobs containing “digital marketing” and “SEO” keywords, which made up 19.5% and 15.5% respectively. At 5.8%, “content marketing” had the lowest search volume after “PPC.”
While the number of job listings tells us which skills are most in demand today, looking at which jobs are seeing the most growth can give insight into shifting demands.
Digital marketing job listings have seen substantial growth since 2009, when it accounted for less than 0.1% of Indeed.com search results. In January 2015, this number had climbed to nearly 0.3%.
While social media marketing jobs have seen some uneven growth, as of January 2015 more than 0.1% of all job listings on Indeed.com contained the term “social media marketing” or “social media management.” This shows a significant upward trend considering this number was around 0.05% for most of 2014. It’s also worth noting that “social media” is currently ranked No. 10 on Indeed’s list of top job trends.
Despite its growth from 0.02% to nearly 0.09% of search volume in the last four years, “content marketing” does not make up a large volume of job postings compared to “digital marketing” or “social media.” In fact, “SEO” has seen a decrease in growth but still constitutes a higher percentage of job listings than content marketing.
On the other hand, search volume on Indeed has either decreased or plateaued for “SEO,” “PPC,” and “Google Analytics.”
As we see in the graph, the volume of “SEO job” listings peaked between 2011 and 2012. This is also around the time content marketing began gaining popularity, thanks to the Panda and Penguin updates. The decrease may be explained by companies moving their marketing budgets away from SEO and toward content or social media positions. However, “SEO” still has a significant amount of job listings, with it appearing in more than 0.2% of job listings on Indeed as of 2015.
“PPC” has seen the most staggered growth among all the search terms we analyzed, with its peak of nearly 0.1% happening between 2012 and 2013. As of January of this year, search volume was below 0.05% for “PPC.”
Despite a lack of growth, the need for this skill remains steady. Between 2008 and 2009, “Google Analytics” job ads saw a huge spike on Indeed. Since then, the search volume has tapered off and plateaued through January 2015.
So we know the number of social media, digital marketing, and content marketing jobs are on the rise. But which skills are worth the most? We looked at the average salaries based on keywords and estimates from Indeed and salaries listed in job ads.
Job titles containing “SEO” had an average salary of $102,000. Meanwhile, job titles containing “social media marketing” had an average salary of $51,000. Considering such a large percentage of the job listings we analyzed contained “social media” keywords, there is a much larger pool of jobs; therefore, a lot of entry level social media jobs or internships are probably bringing down the average salary.
Job titles containing “Google Analytics” had the second-highest average salary at $82,000, but this should be taken with a grain of salt considering “Google Analytics” will rarely appear as part of a job title. The chart below, which shows average salaries for jobs containing keywords anywhere in the listing as opposed to only in the title, gives a more accurate idea of how much “Google Analytics” job roles earn on average.
Looking at the average salaries based on keywords that appeared anywhere within the job listing (job title, job description, etc.) shows a slightly different picture. Based on this, jobs containing “digital marketing” or “inbound marketing” had the highest average salary of $84,000. “SEO” and “Google Analytics” are tied for second with $76,000 as the average salary.
“Social media marketing” takes the bottom spot with an average salary of $57,000. However, notice that there is a higher average salary for jobs that contain “social media” within the job listing as opposed to jobs that contain “social media” within the title. This suggests that social media skills may be more valuable when combined with other responsibilities and skills, whereas a strictly social media job, such as Social Media Manager or Social Media Specialist, does not earn as much.
Looking for a new job? Maybe it’s time to pack your bags for Boston.
Massachusetts led the U.S. with the most jobs per capita for digital marketing, content marketing, SEO, and Google Analytics. New York took the top spot for social media jobs per capita, while Utah had the highest concentration of PPC jobs. California ranked in the top three for digital marketing, content marketing, social media, and Google Analytics. Illinois appeared in the top 10 for every term and usually ranked within the top five. Most of the states with the highest job concentrations are in the Northeast, West, and East Coast, with a few exceptions such as Illinois and Minnesota.
But you don’t necessarily have to move to a new state to increase the odds of landing an inbound marketing job. Some unexpected states also made the cut, with Connecticut and Vermont ranking within the top 10 for several keywords.
Job listings containing “digital marketing” or “inbound marketing” were most prevalent in Massachusetts, New York, Illinois, and California, which is most likely due to these states being home to major cities where marketing agencies and large brands are headquartered or have a presence. You will notice these four states make an appearance in the top 10 for every other search query and usually rank close to the top of the list.
More surprising to find in the top 10 were smaller states such as Connecticut and Vermont. Many major organizations are headquartered in Connecticut, which may be driving the state’s need for digital marketing talent. Vermont’s high-tech industry growth may explain its high concentration of digital marketing jobs.
Although content marketing jobs are growing, there are still a low volume overall of available jobs, as shown by the low jobs per capita compared to most of the other search queries. With more than three jobs per capita, Massachusetts and New York topped the list for the highest concentration of job listings containing “content marketing” or “content strategy.” California and Illinois rank in third and fourth with 2.8 and 2.1 jobs per capita respectively.
Again, Massachusetts and New York took the top spots, each with more than eight SEO jobs per capita. Utah took third place for the highest concentration of SEO jobs. Surprised to see Utah rank in the top 10? Its inclusion on this list and others may be due to its booming tech startup scene, which has earned the metropolitan areas of Salt Lake City, Provo, and Park City the nickname Silicon Slopes.
Compared to the other keywords, “social media” sees a much higher concentration of jobs. New York dominates the rankings with nearly 24 social media jobs per capita. The other top contenders of California, Massachusetts, and Illinois all have more than 15 social media jobs per capita.
The numbers at the bottom of this list can give you an idea of how prevalent social media jobs were compared to any other keyword we analyzed. Minnesota’s 12.1 jobs per capita, the lowest ranking state in the top 10 for social media, trumps even the highest ranking state for any other keyword (11.5 digital marketing jobs per capita in Massachusetts).
Due to its low overall number of available jobs, “PPC” sees the lowest jobs per capita out of all the search queries. Utah has the highest concentration of jobs with just two PPC jobs per 100,000 residents. It is also the only state in the top 10 to crack two jobs per capita.
Regionally, the Northeast and West dominate the rankings, with the exception of Illinois. Massachusetts and New York are tied for the most Google Analytics job postings, each with nearly five jobs per capita. At more than three jobs per 100,000 residents, California, Illinois, and Colorado round out the top five.
Overall, our findings indicate that none of the marketing disciplines we analyzed are dying career choices, but there is a need to become more than a one-trick pony—or else you’ll risk getting passed up for job opportunities. As the marketing industry evolves, there is a greater need for marketers who “wear many hats” and have competencies across different marketing disciplines. Marketers who develop diverse skill sets can gain a competitive advantage in the job market and achieve greater career growth.
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Posted by Casey_Meraz
Competition in local search is fierce. While it’s typical to do some surface level research on your competitors before entering a market, you can go much further down the SEO rabbit hole. In this article we will look at how you can find more competitors, pull their data, and use it to beat them in the search game.
Since there are plenty of resources out there on best practices, this guide will assume that you have already followed the best practices for your own listing and are looking for the little things that might make a big difference in putting you over your competition. So if you haven’t already read how to perform the Ultimate Local SEO Audit or how to Find and Build Citations then you should probably start there.
Disclaimer: While it’s important to mention that correlation does not mean causation, we can learn a lot by seeing what the competition has done.
Some of the benefits of conducting competitive research are:
Once you isolate trends that seem to make a positive difference, you can create a hypothesis and test. This allows you to constantly be testing, finding out what works, and growing those positive elements while eliminating the things that don’t produce results. Instead of making final decisions off of emotion, make your decisions off of the conversion data.
A good competition analysis will give you a strong insight into the market and allow you to test, succeed, or fail fast. The idea behind this process is to really get a strong snapshot of your competition at a glance to isolate factors you may be missing in your company’s online presence.
Disclaimer 2: It’s good to use competitors’ ideas if they work, but don’t make that your only strategy.
Below I will cover a process I commonly use for competition analysis. I have also created this Google Docs spreadsheet for you to follow along with and use for yourself. To make your own copy simply go to File > Make A Copy. (Don’t ask me to add you as an owner please 🙂
Whether you work internally or were hired as an outside resource to help with your client’s SEO campaign, you probably have some idea of who the competition is in your space. Some companies may have good offline marketing but poor online marketing. If you’re looking to be the best, it’s a good idea to do your own research and see who you’re up against.
In my experience it’s always good to find and verify 5-10 online competitors in your space from a variety of sources. You can use tools for this or take the manual approach. Keep in mind that you have to screen the data tools give you with your own eye for accuracy.
We’re going to look at some tools you can use to find competitors here in a second, but keep in mind you want to record everything you find.
Make sure to capture the basic information for each competitor including their company name, location, and website. These tools will be useful at a later time. Record these in the “competitor research” tab of the spreadsheet.
This is pointing out the obvious, but if you have a set of keywords you want to rank for, you can look for trends and see who is already ranking where you want to be. Don’t limit this to just one or two keywords, instead get a broader list of the competitors out there.
To do this, simply come up with a list of several keywords you want to rank for and search for them in your geographic area. Make sure your Geographic preference is set correctly so you get accurate data.
To start we’re just going to collect the data and enter it into the spreadsheet. We will revisit this data shortly.
Outside of the basics, I always find it’s good to see who else is out there. Since organic and local rankings are more closely tied together than ever, it’s a good idea to use 3rd party tools to get some insight as to what else your website could be considered related to.
This can help provide hidden opportunities outside of the normal competition you likely look at most frequently.
SEMRush is a pretty neat competitive analysis tool. While it is a paid program, they do in fact have a few free visits a day you can check out. It’s limited but it will show you 10 competitors based on keyword ranking data. It’s also useful for recording paid competition as well.
To use the tool, visit www.SEMRush.com and enter your website in the provided search box and hit search. Once the page loads, you simply have to scroll down to the area that says “main competitors”. If you click the “view full report” option you’ll be taken to a page with 10 competition URLs.
Put these URLs into the spreadsheet so we can track them later.
This is a cool tool that will show your top 5 competitors in paid and organic search. Just like SEMRush, it’s a paid tool that’s easy to use. On the home page, you will see a box that loads where you can enter your URL. Once you hit search, a list of 5 websites will populate for free.
Enter these competitors into your spreadsheet for tracking.
This website is a goldmine of data if you’re trying to learn about a startup. In addition to the basic information we’re looking for, you can also find out things like how much money they’ve raised, staff members, past employee history, and so much more.
Crunchbase also works pretty similarly to the prior tools in the sense that you you just enter your website URL and hit the search button. Once the page loads, you can scroll down the page to the competitors section for some data.
While Crunchbase is cool, it’s not too useful for smaller companies as it doesn’t seem to have too much data outside of the startup world.
This tool seems to have limited data for smaller websites but it’s worth a shot. It can also be a little bit more high-level than I prefer, but you should still check it out.
To use the tool visit www.compete.com and enter the URL you want to examine in the box provided then hit search.
Click the “Find more sites like” box to get list of three related sites. Enter these in the provided spreadsheet.
SimilarWeb provides a cool tool with a bunch of data to check out websites. After entering your information, you can scroll down to the similar sites section which will show websites it believes to be related.
The good news about SimilarWeb is that it seems to have data no matter how big or small your site is.
Now that we have a list of competitors, we can really do a deep dive to see who is ranking and what factors might be contributing to their success. To start, make sure to pick your top competitors from the spreadsheet and then look for and record the information below about each business on the Competitor Analysis tab.
You will want to to pull this information from their Google My Business page.
If you know the company’s name, it’s pretty easy to find them just by searching the brand. You can add the geographic location if it’s a multi-location business.
For example if I was searching for a Wendy’s in Parker, Colorado, I could simply search this: “Wendy’s Parker, CO” and it will pull up the location(s).
Make sure to take and record the following information from their local listings. Get the data from their Google My Business (Google + Page) and record it in the spreadsheet!
** Record this information on the spreadsheet. A sample is below.
Since you’ve already optimized your own listing for best practices, we want to see if there is any particular trends that seem to be working better in a certain area. We can then create a hypothesis and test it to see if any gains are losses are made. While we can’t isolate factors, we can get some insight as to what’s working the more you change it.
In my experience, examining trends is much easier when the data is side by side. You can easily pick out data that stands out from the rest.
You already know the ins and outs of your landing page. Now let’s look at each competitor’s landing page individually. Let’s look at the factors that carry the most weight and see if anything sticks out.
Record the following information into the spreadsheet and compare side by side with your company vs. the successful ones.
|Page title of landing page|
|City present? – Is the city present in the landing page meta title?|
|State present? – Is the state present in the landing page meta title?|
|Major KW in title? Is there a major keyword in the landing page meta title?|
|Content length on landing page – Possibly minor but worth examining. Copy/paste into MS Word|
|H1 present? – Is the H1 tag present?|
|City in H1? – Does the H1 contain the city name?|
|State in H1? – Does the H1 have the state or abbreviation in the heading?|
|Keyword in H1? – Do they use a keyword in the H1?|
|Local business schema present? – Are they using schema? Find out using the Google structured data testing tool here.|
|Embedded map present? – Are they embedding a Google map?|
|GPS coordinates present? – Are they using GPS coordinates via schema or text?|
Recently, I was having a conversation with a client who was super-excited about the efforts his staff was making. He proudly proclaimed that his office was building 10 new citations a day and added over 500 within the past couple of months!
His excitement freaked me out. As I suspected, when I asked to see his list, I saw a bunch of low quality directory sites that were passing little or no value. One way I could tell they were not really helping (besides the fact that some were NSFW websites), was that the citations or listings were not even indexed in Google.
I think it’s a reasonable assumption that you should test to see what Google knows about your business. Whatever Google delivers about your brand, it’s serving because it has the most relevance or authority in its eyes.
It’s actually pretty simple. Just do a Google Search. One of the ways that I try to evaluate and see whether or not a citation website is authoritative enough is to take the competition’s NAP and Google it. While you’ve probably done this many times before for citation earning, you can prioritize your efforts based off of what’s recurring between top ranked competitor websites.
As you can see in the example below where I did a quick search for a competitor’s dental office (by pasting his NAP in the search bar), I see that Google is associating this particular brand with websites like:
Pro Tip: Amazon local is relatively new, but you can see that it’s going to carry a citation benefit in local search. If your clients are willing, you should sign up for this.
Don’t want to copy and paste the NAP in a variety of formats? Use Andrew Shotland’s NAP Hunter to get your competitor’s variants. This tool will easily open multiple window tabs in your browser and search for combinations of your competitor’s NAP listings. It makes it easy and it’s kind of fun.
With citations, I’m generally in the ballpark of quality over quantity. That being said, if you’re just getting the same citations that everyone else has, that doesn’t really set you apart does it? I like to tell clients that the top citation sources are a must, but it’s good to seek out opportunities and monitor what your competition does so you can keep up and stay ahead of the game.
You need to check the top citations and see where you’re listed vs. your competition. Tools like Whitespark’s local citation finder make this much easier to get an easy snapshot.
If you’re looking to see which citations you should find and check, use these two resources below:
Just like in the example in the section above, you can find powerful hidden gems and also new website opportunities that arise from time to time.
A common mistake I see is businesses thinking it’s ok to just turn things off when they get to the top.That’s a bad idea. If you’re serious about online marketing, you know that someone is always out to get you. So in addition to tracking your brand mentions through the Fresh Web Explorer, you also need to be tracking your competition at least once a month! The good news is that you can do this easily with Fresh Web Explorer from Moz.
Plus track anything else you can think of related to your brand. This will help the on-going efforts get a bit easier.
Did you know some citation sources have dofollow links which mean they pass link juice to your website? Now while these by themselves likely won’t pass a lot of juice, it adds an incentive for you to be proactive with recording and promoting these listings.
When reviewing my competition’s citations and links I use a simple Chrome plugin called NoFollow which simply highlights nofollow links on pages. It makes it super easy to see what’s a follow vs. a nofollow link.
But what’s the benefit of this? Let’s say that I have a link on a city website that’s a follow link and a citation. If it’s an authority page that talks highly about my business, it would make sense for me to link to it from time to time. If you’re getting links from websites other than your own and linking to these high quality citations you will pass link juice to your page. It’s a pretty simple way of increasing the authority of your local landing pages.
Since the Pigeon update almost a year ago, links started to make a bigger impact in local search. You have to be earning links and you have to earn high quality links to your website and especially your Google My Business Landing page.
If the factors show you’re on the same playing field as your competition except in domain authority or page authority, you know your primary focus needs to be links.
Now here is where the research gets interesting. Remember the data sources we pulled earlier like compete, spyfu.com, etc? We are now going to get a bigger picture on the link profile because we did this extra work. Not only are we just going to look at the links that our competition in the pack has, we’ve started to branch out of that for more ideas which will potentially pay off big in the long run.
Now we want to take every domain we looked at when we started and run Open Site Explorer on each and every domain. Once we have these lists of links, we can then sort them out and go after the high quality ones that you don’t already have.
Typically, when I’m doing this research I will export everything into Excel or Google Docs, combine them into one spreadsheet and then sort from highest authority to least authority. This way you can prioritize your road map and focus on the bigger fish.
Keep in mind that citations usually have links and some links have citations. If they have a lot of authority you should make sure you add both.
If you feel like you’ve gone above and beyond your competition and yet you’re not seeing the gains you want, there is more you have to look at. Sometimes as an SEO it’s easy to get in a paradigm of just the technical or link side of things. But what about user behavior?
It’s no secret and even some recent tests are showing promising data. If your users visit your site and then click back to the search results it indicates that they didn’t find what they were looking for. Through our own experiments we have seen listings in the SERPs jump a few positions in hours just based off of user behavior.
You need to make sure your pages are answering the users queries as they land on your page, preferably above the fold. For example, if I’m looking for a haircut place and I land on your page, I might be wanting to know the hours, pricing, or directions to your store. Making information prevalent is essential.
Make sure that if you’re going to make these changes you test them. Come up with a hypothesis, test the results, and come to conclusion or another test based off of the data. If you want to know more about your users, I say that you need to find as much about them as human possible. Some services you can use for that are:
1. Inspectlet – Record user sessions and watch how they navigate your website. This awesome tool literally allows you to watch recorded user sessions. Check out their site.
2. LinkedIn Tracking Script – Although I admit it’s a bit creepy, did you know that you can see the actual visitors to your website if they’re logged into LinkedIn while browsing your website? You sure can. To do this complete the following steps:
1. Sign up for a LinkedIn Premium Account
2. Enter this code into the body of your website pages:
<img src="https://www.linkedin.com/profile/view?authToken=zRgB&authType=name&id=XXXXX" />
3. Replace the XXXXX with your account number of your profile. You can get this by logging into your profile page and getting the number present after viewid?=
4. Wait for the visitors to start showing up under “who’s viewed your profile”
3. Google Analytics – Watch user behavior and gain insights as so what they were doing on your website.
Speaking of user behavior, is your listing the only one without reviews? Does it have fewer or less favorable reviews? All of these are negative signals for user experience. Do you competitors have more positive reviews? If so you need to work getting more.
While this post was mainly geared towards local SEO as in Google My Business rankings, you have to consider that there are a lot of localized search queries that do not generate pack results. In these cases they’re just standard organic listings.
If you’ve been deterred to add these by Google picking its own meta descriptions or by their lack of ranking benefit, you need to check yourself before you wreck yourself. Seriously. Customers will make a decision on which listing to click on based on this information. If you’re not thinking about optimizing these for user intent on the corresponding page then you’re just being lazy. Spend the time, increase CTR, and increase your rankings if you’re serving great content.
The key to success here is realizing that this is a marathon and not a sprint. If you examine the competition in the top areas mentioned above and create a plan to overcome, you will win long term. This of course also assumes you’re not doing anything shady and staying above board.
While there were many more things I could add to this article, I believe that if you put your focus on what’s mentioned here you’ll have the greatest success. Since I didn’t talk too much about geo-tagged media in this article, I also included some other items to check in the spreadsheet under the competitor analysis tab.
Remember to actively monitor what those around you are doing and develop a pro-active plan to be successful for your clients.
What’s the most creative thing you have seen a competitor do successfully local search? I would love to hear about it in the comments below.
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Posted by randfish
To get a sense for the potential value of keywords in a certain niche, we need to do more than just look at the number of searches those keywords get each month. In today’s Whiteboard Friday, Rand explains what else we should be looking at, and how we can use other data to prioritize some groups over others.
Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week I want to chat about how you can estimate the total volume and value of a large set of keywords in a market or a niche.
Look, we’re going to try and simplify this and reduce it to something that is actually manageable, because you can go way, way deep down a well. You could spend a year trying to figure out whether Market A or Market B is better to enter or better to chase keywords in, better to create content in. But I want to try and make it a little simple without reducing it to something that is of no value whatsoever, which unfortunately can be how some marketers have looked at this in the past.
So let’s try this thought exercise. Let’s say I’m a recipe site or a food site and I’m thinking I want to get into the Asian noodles scene. There’s a lot of awesome Asian noodles out there. I, in fact, had Chow fun for lunch from Trove on Capitol Hill. When you come to MozCon, you have to try them. It’s awesome.
So maybe I’m looking at Chow fun and sort of all the keyword sets around those, that Chinese noodle world. Maybe I’m looking at pad Thai, a very popular Thai noodle, particularly in the U.S., and maybe Vietnamese rice noodles or bun. I’m trying to figure out which of these is the one that I should target. Should I start creating a lot of pad Thai recipes, a lot of Chow fun recipes? Should I go research one or the other of these? Am I going to chase the mid and long tail keywords?
I’m about to invest a large amount of effort and really build up a brand around this. Which one of these should I do?
Side note, this is getting more and more important as Google is moving to these topic modeling and sight specific, topic authority models. So if Google starts to identify my site as being an authority on Chow fun, I can expect to rank for all sorts of awesome stuff around it, versus if I just kind of dive in and out and have one-offs of Chow fun and 50 different other kinds of noodles. So this gets really important.
A massively oversimplified version, that a lot of people have done in the past, is to look broadly at kind of AdWords groups, the ones that AdWords selects for you, or individual keywords and say, “Oh, okay. Well, Chow fun gets 22,000 searches a month, Pad Thai gets 165,000, and rice noodles, which is the most popular version of that query — it could also be called Vietnamese noodles or bun noodles or something like that — gets 27,000. So there you go, one, two, three.
This is dead wrong. It’s totally oversimplified. It’s not taking into account all the things we have to do to really understand the market.
First off, this isn’t going to include all the variations, the mid and long tail keywords. So potentially there might be a ton of variations of rice noodles that actually add up to as much or more than pad Thai. Same thing with Chow fun. In fact, when I looked, it looked like there’s a ton of Chow fun modifications and different kinds of things that go in there. The Pad Thai list is a little short. It’s like chicken, vegetable, shrimp, and beef. Pretty simplistic.
There’s also no analysis of the competition going on here. Pad Thai, yeah it’s popular, but it also has 50 recipe sites all bidding for it, tons of online grocers bidding for it, tons of recipes books that are bidding on that. I don’t know. Then it could be that Chow fun has almost no competition whatsoever. So you’re really not considering that when you look in here.
Finally, and this can be important too, these numbers can be off by up to 200% plus or minus this number. So if you were to actually bid on Chow fun, you might see that you get somewhere in the 22,000 impressions per month, assuming your ad consistently shows up on page one, but you could see as little as 11,000. I’ve seen as much as 44,000, like huge variations and swings in either direction and not always totally consistent between these. You want them to be, but they’re not always.
So because of that, we have to go deeper. These problems mean that we have to expend a little more energy. Not a ton. It doesn’t have to be massive, but probably a week or two of work at least to try and figure this out. But it’s so important I think it’s worth it every time.
First off, we’re going to conduct a broad keyword research dive into each one of these. Not as much as we would do if we knew, hey, Chow fun is the one we’re going to target. We’re going to go deep. We’re going to find every possible keyword. We’re going to do kind of what I call a broad dive, not a deep dive into each market. So I might want to go, hey, I’m going to look at the AdWords suggestions and tally those up. I’m going to look at search suggest and related searches for some of the queries that I get from AdWords, some of the top ones anyway, and I’m going to do a brief competitive analysis. Maybe I’ll put the domains that I’m seeing most frequently around these specific topics into SEMrush or another tool like that — SpyFu, Key Compete or whatever your preference might be — and see what other terms and phrases they might be ranking on.
So now I’ve got a reasonable set. It probably didn’t take me more than a few hours to put that together, if that. If I’ve got an efficient process for this already, maybe even less.
Now comes the tricky part. I want you to take a small sample set, and we’ve done this a few times. Random might be not the right word. It’s a small considered set of keywords and bid on them through AdWords. When I say “considered,” what I mean is a few from the long tail, a few from the chunky middle, and a few from the head of the demand curve that are getting lots and lots of searches. Now I want to point each of those to some new, high-quality pages on your site as a test.
So I might make maybe one, two, or three different landing pages for each of these different sets. One of them might be around noodles. One might be around recipes. One might be around history or uses in cuisine or whatever it is.
Then I am going to know from that exercise three critically important things. I’m going to know accuracy of AdWords volume estimates, which is awesome. Now I know whether these numbers mean anything or not, how far off they were or not. I could probably run for between 10 and 15 days and get a really good sense for the accuracy of AdWords. If you’re feeling like being very comprehensive, run for a full month, especially if you have the budget, because you can learn even more over time, and you’ll rule out any inconsistencies due to a particular spike, like maybe The New York Times recipe section features Chow fun that week and suddenly there’s a huge spike or whatever it is.
You can also learn relative price competition in click-through rate. This is awesome. This means that I know it costs a lot more per visitor that I’m trying to get on pad Thai. There are two really good things to know there. When a click costs more money, that also usually means there are more advertisers willing to pay for that traffic.
If you’re on the other side of that, where you think, “Hey, look, we’re not going to compete organically right now. We just don’t have the domain authority to do it. It’s going to take us a while,” then a high price is a bad thing. You want that cheaper traffic so you can start to build up that brand through paid as you’re growing the organic side. So it really depends on who you are and what situation you’re in.
Then finally you can figure out some things around click-through rate as well, which is great to know. So you can build some true model estimates and then go into your board meeting or your client pitch or whatever it is and say, “Hey, here are the numbers.”
Lastly, you’re going to learn the difficulty of content creation, like how hard was it for you to create these kinds of things. Like, “Wow, when we write about Chow fun, it’s just easy. It just rolls off. Pad Thai we have a really hard time creating unique value because everything has been done in that world. We’re just not as passionate about those noodles as we are about Chow fun.” Cool. Great, you know that.
Also, assuming your test includes this, which it doesn’t always have to, you can guess from sort of engagement rate, browse rate, time on site, all those kinds of things, but you can look at search conversion as well. So let’s say you have some action to complete on the page — subscribe to our email newsletter, sign up to get updates when we send them out about this recipe, or create an account so you can sign in and save this recipe. All that kind of stuff or a direct ecommerce conversion, you can learn that through your bidding test.
Awesome. That’s great. Now we really, really know something. Based on that, we can do a true analysis, an accurate analysis of the different groups based on:
Growth rate might be an interpreted thing, but you can look at the Google trends to kind of figure out over time whether a broad group of terms is getting more or less popular. You could use something like Mention.net or Fresh Web Explorer from Moz to look at mentions as well.
Now, you can be happy here. I might have chosen chow fun because I looked and I said, “Hey, you know what, it did not have the most volume overall, but it did have the lightest competition, the highest return on investment. We were great at creating the content. We were able to engage our visitors there, had lots of mid and long tail terms. We think it’s poised for big growth with the growth of Chinese noodles overall and the fact that the American food scene hasn’t really discovered Chow fun the way they have Vietnamese noodles and pad Thai. So that is where we’re placing our bet.”
Great. Now you have a real analysis. You have numbers behind it. You have estimates you can make. This process, although a little heavy, is going to get you so much further than this kind of simplistic thinking.
All right, everyone, I look forward to hearing from you about how you’ve done analyses like these in the past, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.
Posted by SamuelScott
It’s ten o’clock. Do you know where your logs are?
I’m introducing this guide with a pun on a common public-service announcement that has run on late-night TV news broadcasts in the United States because log analysis is something that is extremely newsworthy and important.
If your technical and on-page SEO is poor, then nothing else that you do will matter. Technical SEO is the key to helping search engines to crawl, parse, and index websites, and thereby rank them appropriately long before any marketing work begins.
The important thing to remember: Your log files contain the only data that is 100% accurate in terms of how search engines are crawling your website. By helping Google to do its job, you will set the stage for your future SEO work and make your job easier. Log analysis is one facet of technical SEO, and correcting the problems found in your logs will help to lead to higher rankings, more traffic, and more conversions and sales.
Here are just a few reasons why:
However, log analysis is something that is unfortunately discussed all too rarely in SEO circles. So, here, I wanted to give the Moz community an introductory guide to log analytics that I hope will help. If you have any questions, feel free to ask in the comments!
Computer servers, operating systems, network devices, and computer applications automatically generate something called a log entry whenever they perform an action. In a SEO and digital marketing context, one type of action is whenever a page is requested by a visiting bot or human.
127.0.0.1 user-identifier frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326
Note: A hyphen is shown in a field when that information is unavailable.
Every single time that you — or the Googlebot — visit a page on a website, a line with this information is output, recorded, and stored by the server.
Log entries are generated continuously and anywhere from several to thousands can be created every second — depending on the level of a given server, network, or application’s activity. A collection of log entries is called a log file (or often in slang, “the log” or “the logs”), and it is displayed with the most-recent log entry at the bottom. Individual log files often contain a calendar day’s worth of log entries.
Different types of servers store and manage their log files differently. Here are the general guides to finding and managing log data on three of the most-popular types of servers:
Log analysis (or log analytics) is the process of going through log files to learn something from the data. Some common reasons include:
Log analysis is rarely performed regularly. Usually, people go into log files only in response to something — a bug, a hack, a subpoena, an error, or a malfunction. It’s not something that anyone wants to do on an ongoing basis.
Why? This is a screenshot of ours of just a very small part of an original (unstructured) log file:
Ouch. If a website gets 10,000 visitors who each go to ten pages per day, then the server will create a log file every day that will consist of 100,000 log entries. No one has the time to go through all of that manually.
There are three general ways to make log analysis easier in SEO or any other context:
Tim Resnik’s Moz essay from a few years ago walks you through the process of exporting a batch of log files into Excel. This is a (relatively) quick and easy way to do simple log analysis, but the downside is that one will see only a snapshot in time and not any overall trends. To obtain the best data, it’s crucial to use either proprietary tools or the ELK Stack.
Splunk and Sumo-Logic are proprietary log analysis tools that are primarily used by enterprise companies. The ELK Stack is a free and open-source batch of three platforms (Elasticsearch, Logstash, and Kibana) that is owned by Elastic and used more often by smaller businesses. (Disclosure: We at Logz.io use the ELK Stack to monitor our own internal systems as well as for the basis of our own log management software.)
For those who are interested in using this process to do technical SEO analysis, monitor system or application performance, or for any other reason, our CEO, Tomer Levy, has written a guide to deploying the ELK Stack.
However you choose to access and understand your log data, there are many important technical SEO issues to address as needed. I’ve included screenshots of our technical SEO dashboard with our own website’s data to demonstrate what to examine in your logs.
It’s important to know the number of requests made by Baidu, BingBot, GoogleBot, Yahoo, Yandex, and others over a given period time. If, for example, you want to get found in search in Russia but Yandex is not crawling your website, that is a problem. (You’d want to consult Yandex Webmaster and see this article on Search Engine Land.)
Moz has a great primer on the meanings of the different status codes. I have an alert system setup that tells me about 4XX and 5XX errors immediately because those are very significant.
Temporary 302 redirects do not pass along the “link juice” of external links from the old URL to the new one. Almost all of the time, they should be changed to permanent 301 redirects.
Google assigns a crawl budget to each website based on numerous factors. If your crawl budget is, say, 100 pages per day (or the equivalent amount of data), then you want to be sure that all 100 are things that you want to appear in the SERPs. No matter what you write in your robots.txt file and meta-robots tags, you might still be wasting your crawl budget on advertising landing pages, internal scripts, and more. The logs will tell you — I’ve outlined two script-based examples in red above.
If you hit your crawl limit but still have new content that should be indexed to appear in search results, Google may abandon your site before finding it.
The addition of URL parameters — typically used in tracking for marketing purposes — often results in search engines wasting crawl budgets by crawling different URLs with the same content. To learn how to address this issue, I recommend reading the resources on Google and Search Engine Land here, here, here, and here.
Google might be ignoring (and not crawling or indexing) a crucial page or section of your website. The logs will reveal what URLs and/or directories are getting the most and least attention. If, for example, you have published an e-book that attempts to rank for targeted search queries but it sits in a directory that Google only visits once every six months, then you won’t get any organic search traffic from the e-book for up to six months.
If a part of your website is not being crawled very often — and it is updated often enough that it should be — then you might need to check your internal-linking structure and the crawl-priority settings in your XML sitemap.
Have you uploaded something that you hope will be indexed quickly? The log files will tell you when Google has crawled it.
One thing I personally like to check and see is Googlebot’s real-time activity on our site because the crawl budget that the search engine assigns to a website is a rough indicator — a very rough one — of how much it “likes” your site. Google ideally does not want to waste valuable crawling time on a bad website. Here, I had seen that Googlebot had made 154 requests of our new startup’s website over the prior twenty-four hours. Hopefully, that number will go up!
As I hope you can see, log analysis is critically important in technical SEO. It’s eleven o’clock — do you know where your logs are now?
Posted by AlexApptentive
This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author’s views are entirely his or her own and may not reflect the views of Moz, Inc.
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.
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:
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.
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:
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.
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.
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.
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).
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.
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).
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.
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).
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:
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.
This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.
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.
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.
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%).
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.
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.
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:
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.
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.
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.
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:
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:
Rating Count > Installs > Trends > Rating
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.