Pinpoint vs. Floodlight Content and Keyword Research Strategies – Whiteboard Friday

Posted by randfish

When we’re doing keyword research and targeting, we have a choice to make: Are we targeting broader keywords with multiple potential searcher intents, or are we targeting very narrow keywords where it’s pretty clear what the searchers were looking for? Those different approaches, it turns out, apply to content creation and site architecture, as well. In today’s Whiteboard Friday, Rand illustrates that connection.

Pinpoint vs Floodlight Content and Keyword Research Strategy Whiteboard

For reference, here are stills of this week’s whiteboards. Click on it to open a high resolution image in a new tab!

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about pinpoint versus floodlight tactics for content targeting, content strategy, and keyword research, keyword targeting strategy. This is also called the shotgun versus sniper approach, but I’m not a big gun fan. So I’m going to stick with my floodlight versus pinpoint, plus, you know, for the opening shot we don’t have a whole lot of weaponry here at Moz, but we do have lighting.

So let’s talk through this at first. You’re going through and doing some keyword research. You’re trying to figure out which terms and phrases to target. You might look down a list like this.

Well, maybe, I’m using an example here around antique science equipment. So you see these various terms and phrases. You’ve got your volume numbers. You probably have lots of other columns. Hopefully, you’ve watched the Whiteboard Friday on how to do keyword research like it’s 2015 and not 2010.

So you know you have all these other columns to choose from, but I’m simplifying here for the purpose of this experiment. So you might choose some of these different terms. Now, they’re going to have different kinds of tactics and a different strategic approach, depending on the breadth and depth of the topic that you’re targeting. That’s going to determine what types of content you want to create and where you place it in your information architecture. So I’ll show you what I mean.

The floodlight approach

For antique science equipment, this is a relatively broad phrase. I’m going to do my floodlight analysis on this, and floodlight analysis is basically saying like, “Okay, are there multiple potential searcher intents?” Yeah, absolutely. That’s a fairly broad phase. People could be looking to transact around it. They might be looking for research information, historical information, different types of scientific equipment that they’re looking for.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b15fc96679b8.73854740.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Are there four or more approximately unique keyword terms and phrases to target? Well, absolutely, in fact, there’s probably more than that. So antique science equipment, antique scientific equipment, 18th century scientific equipment, all these different terms and phrases that you might explore there.

Is this a broad content topic with many potential subtopics? Again, yes is the answer to this. Are we talking about generally larger search volume? Again, yes, this is going to have a much larger search volume than some of the narrower terms and phrases. That’s not always the case, but it is here.

The pinpoint approach

For pinpoint analysis, we kind of go the opposite direction. So we might look at a term like antique test tubes, which is a very specific kind of search, and that has a clear single searcher intent or maybe two. Someone might be looking for actually purchasing one of those, or they might be looking to research them and see what kinds there are. Not a ton of additional intents behind that. One to three unique keywords, yeah, probably. It’s pretty specific. Antique test tubes, maybe 19th century test tubes, maybe old science test tubes, but you’re talking about a limited set of keywords that you’re targeting. It’s a narrow content topic, typically smaller search volume.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b160069eb6b1.12473448.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Now, these are going to feed into your IA, your information architecture, and your site structure in this way. So floodlight content generally sits higher up. It’s the category or the subcategory, those broad topic terms and phrases. Those are going to turn into those broad topic category pages. Then you might have multiple, narrower subtopics. So we could go into lab equipment versus astronomical equipment versus chemistry equipment, and then we’d get into those individual pinpoints from the pinpoint analysis.

How do I decide which approach is best for my keywords?

Why are we doing this? Well, generally speaking, if you can take your terms and phrases and categorize them like this and then target them differently, you’re going to provide a better, more logical user experience. Someone who searches for antique scientific equipment, they’re going to really expect to see that category and then to be able to drill down into things. So you’re providing them the experience they predict, the one that they want, the one that they expect.

It’s better for topic modeling analysis and for all of the algorithms around things like Hummingbird, where Google looks at: Are you using the types of terms and phrases, do you have the type of architecture that we expect to find for this keyword?

It’s better for search intent targeting, because the searcher intent is going to be fulfilled if you provide the multiple paths versus the narrow focus. It’s easier keyword targeting for you. You’re going to be able to know, “Hey, I need to target a lot of different terms and phrases and variations in floodlight and one very specific one in pinpoint.”

There’s usually higher searcher satisfaction, which means you get lower bounce rate. You get more engagement. You usually get a higher conversion rate. So it’s good for all those things.

For example…

I’ll actually create pages for each of antique scientific equipment and antique test tubes to illustrate this. So I’ve got two different types of pages here. One is my antique scientific equipment page.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b161fa871e32.54731215.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

This is that floodlight, shotgun approach, and what we’re doing here is going to be very different from a pinpoint approach. It’s looking at like, okay, you’ve landed on antique scientific equipment. Now, where do you want to go? What do you want to specifically explore? So we’re going to have a little bit of content specifically about this topic, and how robust that is depends on the type of topic and the type of site you are.

If this is an e-commerce site or a site that’s showing information about various antiques, well maybe we don’t need very much content here. You can see the filtration that we’ve got is going to be pretty broad. So I can go into different centuries. I can go into chemistry, astronomy, physics. Maybe I have a safe for kids type of stuff if you want to buy your kids antique lab equipment, which you might be. Who knows? Maybe you’re awesome and your kids are too. Then different types of stuff at a very broad level. So I can go to microscopes or test tubes, lab searches.

This is great because it’s got broad intent foci, serving many different kinds of searchers with the same page because we don’t know exactly what they want. It’s got multiple keyword targets so that we can go after broad phrases like antique or old or historical or 13th, 14th, whatever century, science and scientific equipment ,materials, labs, etc., etc., etc. This is a broad page that could reach any and all of those. Then there’s lots of navigational and refinement options once you get there.

Total opposite of pinpoint content.

<img src="http://d1avok0lzls2w.cloudfront.net/uploads/blog/55b1622740f0b5.73477500.jpg" rel="box-shadow: 0 0 10px 0 #999; border-radius: 20px;"

Pinpoint content, like this antique test tubes page, we’re still going to have some filtration options, but one of the important things to note is note how these are links that take you deeper. Depending on how deep the search volume goes in terms of the types of queries that people are performing, you might want to make a specific page for 17th century antique test tubes. You might not, and if you don’t want to do that, you can have these be filters that are simply clickable and change the content of the page here, narrowing the options rather than creating completely separate pages.

So if there’s no search volume for these different things and you don’t think you need to separately target them, go ahead and just make them filters on the data that already appears on this page or the results that are already in here as opposed to links that are going to take you deeper into specific content and create a new page, a new experience.

You can also see I’ve got my individual content here. I probably would go ahead and add some content specifically to this page that is just unique here and that describes antique test tubes and the things that your searchers need. They might want to know things about price. They might want to know things about make and model. They might want to know things about what they were used for. Great. You can have that information broadly, and then individual pieces of content that someone might dig into.

This is narrower intent foci obviously, serving maybe one or two searcher intents. This is really talking about targeting maybe one to two separate keywords. So antique test tubes, maybe lab tubes or test tube sets, but not much beyond that.

Ten we’re going to have fewer navigational paths, fewer distractions. We want to keep the searcher. Because we know their intent, we want to guide them along the path that we know they probably want to take and that we want them to take.

So when you’re considering your content, choose wisely between shotgun/floodlight approach or sniper/pinpoint approach. Your searchers will be better served. You’ll probably rank better. You’ll be more likely to earn links and amplification. You’re going to be more successful.

Looking forward to the comments, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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The Inbound Marketing Economy

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.

Methodology

We analyzed 75,315 job listings posted on Indeed.com during June 2015 based on data gathered from job ads containing the following terms:

  • “content marketing” or “content strategy”
  • “SEO” or “search engine marketing”
  • “social media marketing” or “social media management”
  • “inbound marketing” or “digital marketing”
  • “PPC” (pay-per-click)
  • “Google Analytics”

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.

Marketing skills are in high demand, but talent is hard to find

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.

Increase in marketers listing skills in content marketing, inbound marketing, and social media on LinkedIn profiles

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.

growth of marketing keywords in linkedin 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).

Popular job listings show a need for marketers to diversify their skill set

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.

20 most common marketing job titles

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.

5 most common job titles by search query

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:

future of marketing quote linkedin

Inbound job demand and growth: Most-wanted skills and fastest-growing jobs

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.

number of marketing job listings by keyword

“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.”

Digital marketing, social media, and content marketing experienced the most job growth

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 growth on  indeed.com

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%.

social media job growth on indeed.com

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.

content marketing job growth on indeed.com

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.

SEO, PPC, and Google Analytics job growth has slowed down

On the other hand, search volume on Indeed has either decreased or plateaued for “SEO,” “PPC,” and “Google Analytics.”

seo job growth on indeed.com

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 job growth on indeed.com

“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.”

google analytics job growth on indeed.com

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.

Most valuable skills are SEO, digital marketing, and Google Analytics

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.

national average marketing salaries

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.national salary averages marketing keywords

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.

Massachusetts, New York, and California have the most career opportunities for inbound marketers

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.

concentration of digital marketing jobs

marketing jobs per capita

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.

content marketing job concentration

per capita content 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.

seo job concentration

seo jobs per capita

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.

social media job concentration

social media jobs per capita

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).

ppc job concentration

ppc jobs per capita

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.

google analytics job concentration

google analytics 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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Creating Demand for Products, Services, and Ideas that Have Little to No Existing Search Volume – Whiteboard Friday

Posted by randfish

A lot of fantastic websites (and products, services, ideas, etc.) are in something of a pickle: The keywords they would normally think to target get next to no search volume. It can make SEO seem like a lost cause. In today’s Whiteboard Friday, Rand explains why that’s not the case, and talks about the one extra step that’ll help those organizations create the demand they want.

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about a particularly challenging problem in the world of SEO, and that is trying to do SEO or trying to do any type of web marketing when your product, service, or idea has no search volume around it. So nobody is already looking for what you offer. It’s a new thing, a new concept.

I’ll use the example here of a website that I’m very fond of, but which there’s virtually no search volume for, called Niice. It’s Niice.co.

It’s great. I searched for things in here. It brings me back all these wonderful visuals from places like Colossus and lots of design portals. I love this site. I use it all the time for inspiration, for visuals, for stuff that I might write about on blogs, for finding new artists. It’s just cool. I love it. I love the discovery aspect of it, and I think it can be really great for finding artists and designers and visuals.

But when I looked at the keyword research — and granted I didn’t go deep into the keyword research, but let’s imagine that I did — I looked for things like: “visual search engine” almost no volume; “search engine for designers” almost no volume; “graphical search engine” almost no volume; “find designer visuals” nada.

So when they look at their keyword research they go, “Man, we don’t even have keywords to target here really.” SEO almost feels like it’s not a channel of opportunity, and I think that’s where many, many companies and businesses make mistakes actually, because just because you don’t see keyword research around exactly around what you’re offering doesn’t mean that SEO can’t be a great channel. It just means we have to do an extra step of work, and that’s what I want to talk about today.

So I think when you encounter this type of challenge — and granted it might not be the challenge that there’s no keyword volume — it could be a challenge in your business, for your organization, for some ideas or products that you have or are launching that there’s just very little, and thus you’re struggling to come up with enough volume to create the quantity of leads, or free trials, or customers that you need. This process really can work.

Key questions to start.

1) Who’s the target audience?

In Niice’s case, that’s going to be a lot of designers. It might be people who are creating presentations. It might be those who are searching out designers or artists. It could be people seeking inspiration for all sorts of things. So they’re going to figure out who that is.

From there, they can look at the job title, interests, demographics of those people, and then you can do some cool stuff where you can figure out things like, “Oh, you know what? We could do some Facebook ad targeting to those right groups to help boost their interests in our product and potentially, well, create branded search volume down the road, attract direct visitors, build brand awareness for ourselves, and potentially get some traffic to the site directly as well. If we can convert some of that traffic, well, that’s fantastic.”

In their case, I think Niice is ad-supported right now, so all they really need is the traffic itself. But regardless, this is that same type of process you’d use.

2) What else do they search for?

What is that target audience searching for? Knowledge, products, tools, services, people, brands, whatever it is, if you know who the audience is, you can figure out what they’re searching for because they have needs. If they have a job title, if they have interests, if you have those profile features about the audience, you can figure out what else they’re going to be searching for, and in this case, knowing what designers are searching for, well, that’s probably relatively simplistic. The other parts of their audience might be more complex, but that one is pretty obvious.

From that, we can do content creation. We can do keyword targeting to be in front of those folks when they’re doing search by creating content that may not necessarily be exactly selling our tools, but that’s the idea of content marketing. We’re creating content to target people higher up in the funnel before they need our product.

We can use that, too, for product and feature inspiration in the product itself. So in this case, Niice might consider creating a design pattern library or several, pulling from different places, or hiring someone to come in and build one for them and then featuring that somewhere on the site if you haven’t done a search yet and then potentially trying to rank for that in the search engine, which then brings qualified visitors, the types of people who once they got exposed to Niice would be like, “Wow, this is great and it’s totally free. I love it.”

UX tool list, so list of tools for user experience, people on the design or UI side, maybe Photoshop tutorials, whatever it is that they feel like they’re competent and capable of creating and could potentially rank for, well, now you’re attracting the right audience to your site before they need your product.

3) Where do they go?

That audience, where are they going on the web? What do they do when they get there? To whom do they listen? Who are their influencers? How can we be visible in those locations? So from that I can get things like influencer targeting and outreach. I can get ad and sponsorship opportunities. I can figure out places to do partnership or guest content or business development.

In Niice’s case, that might be things like sponsor or speak at design events. Maybe they could create an awards project for Dribble. So they go to Dribble, they look at what’s been featured there, or they go to Colossus, or some of the other sites that they feature, and they find the best work of the week. At the end of the week, they feature the top 10 projects, and then they call out the designers who put them together.

Wow, that’s terrific. Now you’re getting in front of the audience whose work you’re featuring, which is going to, in turn, make them amplify Niice’s project and product to an audience who’s likely to be in their target audience. It’s sort of a win-win. That’s also going to help them build links, engagement, shares, and all sorts of signals that potentially will help them with their authority, both topically and domain-wide, which then means they can rank for all the content they create, building up this wonderful engine.

4) What types of content have achieved broad or viral distribution?

I think what we can glean from this is not just inspiration for content and keyword opportunities as we can from many other kinds of content, but also sites to target, in particular sites to target with advertising, sites to target for guest posting or sponsorship, or sites to target for business development or for partnerships, site to target in an ad network, sites to target psychographically or demographically for Facebook if we want to run ads like that, potentially bidding on ads in Google when people search for that website or for that brand name in paid search.

So if you’re Niice, you could think about contracting some featured artist to contribute visuals maybe for a topical news project. So something big is happening in the news or in the design community, you contract a few of the artists whose work you have featured or are featuring, or people from the communities whose work you’re featuring, and say, “Hey, we might not be able to pay you a lot, but we’re going to get in front of a ton of people. We’re going to build exposure for you, which is something we already do, FYI, and now you’ve got some wonderful content that has that potential to mimic that work.”

You could think about, and I love this just generally as a content marketing and SEO tactic, if you go find viral content, content that has had wide sharing success across the web from the past, say two, three, four, or five years ago, you have a great opportunity, especially if the initial creator of that content or project hasn’t continued on with it, to go say, “Hey, you know what? We can do a version of that. We’re going to modernize and update that for current audiences, current tastes, what’s currently going on in the market. We’re going to go build that, and we have a strong feeling that it’s going to be successful because it’s succeeded in the past.”

That, I think, is a great way to get content ideas from viral content and then to potentially overtake them in the search rankings too. If something from three or five years ago, that was particularly timely then still ranks today, if you produce it, you’re almost certainly going to come out on top due to Google’s bias for freshness, especially around things that have timely relevance.

5) Should brand advertisement be in our consideration set?

Then last one, I like to ask about brand advertising in these cases, because when there’s not search volume yet, a lot of times what you have to do is create awareness. I should change this from advertising to a brand awareness, because really there’s organic ways to do it and advertising ways to do it. You can think about, “Well, where are places that we can target where we could build that awareness? Should we invest in press and public relations?” Not press releases. “Then how do we own the market?” So I think one of the keys here is starting with that name or title or keyword phrase that encapsulates what the market will call your product, service or idea.

In the case of Niice, that could be, well, visual search engines. You can imagine the press saying, “Well, visual search engines like Niice have recently blah, blah, blah.” Or it could be designer search engines, or it could be graphical search engines, or it could be designer visual engines, whatever it is. You need to find what that thing is going to be and what’s going to resonate.

In the case of Nest, that was the smart home. In the case of Oculus, it was virtual reality and virtual reality gaming. In the case of Tesla, it was sort of already established. There’s electric cars, but they kind of own that market. If you know what those keywords are, you can own the market before it gets hot, and that’s really important because that means that all of the press and PR and awareness that happens around the organic rankings for that particular keyword phrase will all be owned and controlled by you.

When you search for “smart home,” Nest is going to dominate those top 10 results. When you search for “virtual reality gaming,” Oculus is going to dominate those top 10. It’s not necessarily dominate just on their own site, it’s dominate all the press and PR articles that are about that, all of the Wikipedia page about it, etc., etc. You become the brand that’s synonymous with the keyword or concept. From an SEO perspective, that’s a beautiful world to live in.

So, hopefully, for those of you who are struggling around demand for your keywords, for your volume, this process can be something that’s really helpful. I look forward to hearing from you in the comments. We’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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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 4 years ago from tracking.feedpress.it

Inverse Document Frequency and the Importance of Uniqueness

Posted by EricEnge

In my last column, I wrote about how to use term frequency analysis in evaluating your content vs. the competition’s. Term frequency (TF) is only one part of the TF-IDF approach to information retrieval. The other part is inverse document frequency (IDF), which is what I plan to discuss today.

Today’s post will use an explanation of how IDF works to show you the importance of creating content that has true uniqueness. There are reputation and visibility reasons for doing this, and it’s great for users, but there are also SEO benefits.

If you wonder why I am focusing on TF-IDF, consider these words from a Google article from August 2014: “This is the idea of the famous TF-IDF, long used to index web pages.” While the way that Google may apply these concepts is far more than the simple TF-IDF models I am discussing, we can still learn a lot from understanding the basics of how they work.

What is inverse document frequency?

In simple terms, it’s a measure of the rareness of a term. Conceptually, we start by measuring document frequency. It’s easiest to illustrate with an example, as follows:

IDF table

In this example, we see that the word “a” appears in every document in the document set. What this tells us is that it provides no value in telling the documents apart. It’s in everything.

Now look at the word “mobilegeddon.” It appears in 1,000 of the documents, or one thousandth of one percent of them. Clearly, this phrase provides a great deal more differentiation for the documents that contain them.

Document frequency measures commonness, and we prefer to measure rareness. The classic way that this is done is with a formula that looks like this:

idf equation

For each term we are looking at, we take the total number of documents in the document set and divide it by the number of documents containing our term. This gives us more of a measure of rareness. However, we don’t want the resulting calculation to say that the word “mobilegeddon” is 1,000 times more important in distinguishing a document than the word “boat,” as that is too big of a scaling factor.

This is the reason we take the Log Base 10 of the result, to dampen that calculation. For those of you who are not mathematicians, you can loosely think of the Log Base 10 of a number as being a count of the number of zeros – i.e., the Log Base 10 of 1,000,000 is 6, and the log base 10 of 1,000 is 3. So instead of saying that the word “mobilegeddon” is 1,000 times more important, this type of calculation suggests it’s three times more important, which is more in line with what makes sense from a search engine perspective.

With this in mind, here are the IDF values for the terms we looked at before:

idf table logarithm values

Now you can see that we are providing the highest score to the term that is the rarest.

What does the concept of IDF teach us?

Think about IDF as a measure of uniqueness. It helps search engines identify what it is that makes a given document special. This needs to be much more sophisticated than how often you use a given search term (e.g. keyword density).

Think of it this way: If you are one of 6.78 million web sites that comes up for the search query “super bowl 2015,” you are dealing with a crowded playing field. Your chances of ranking for this term based on the quality of your content are pretty much zero.

massive number of results for broad keyword

Overall link authority and other signals will be the only way you can rank for a term that competitive. If you are a new site on the landscape, well, perhaps you should chase something else.

That leaves us with the question of what you should target. How about something unique? Even the addition of a simple word like “predictions”—changing our phrase to “super bowl 2015 predictions”—reduces this playing field to 17,800 results.

Clearly, this is dramatically less competitive already. Slicing into this further, the phrase “super bowl 2015 predictions and odds” returns only 26 pages in Google. See where this is going?

What IDF teaches us is the importance of uniqueness in the content we create. Yes, it will not pay nearly as much money to you as it would if you rank for the big head term, but if your business is a new entrant into a very crowded space, you are not going to rank for the big head term anyway

If you can pick out a smaller number of terms with much less competition and create content around those needs, you can start to rank for these terms and get money flowing into your business. This is because you are making your content more unique by using rarer combinations of terms (leveraging what IDF teaches us).

Summary

People who do keyword analysis are often wired to pursue the major head terms directly, simply based on the available keyword search volume. The result from this approach can, in fact, be pretty dismal.

Understanding how inverse document frequency works helps us understand the importance of standing out. Creating content that brings unique angles to the table is often a very potent way to get your SEO strategy kick-started.

Of course, the reasons for creating content that is highly differentiated and unique go far beyond SEO. This is good for your users, and it’s good for your reputation, visibility, AND also your SEO.

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Reblogged 4 years ago from tracking.feedpress.it

​The 3 Most Common SEO Problems on Listings Sites

Posted by Dom-Woodman

Listings sites have a very specific set of search problems that you don’t run into everywhere else. In the day I’m one of Distilled’s analysts, but by night I run a job listings site, teflSearch. So, for my first Moz Blog post I thought I’d cover the three search problems with listings sites that I spent far too long agonising about.

Quick clarification time: What is a listings site (i.e. will this post be useful for you)?

The classic listings site is Craigslist, but plenty of other sites act like listing sites:

  • Job sites like Monster
  • E-commerce sites like Amazon
  • Matching sites like Spareroom

1. Generating quality landing pages

The landing pages on listings sites are incredibly important. These pages are usually the primary drivers of converting traffic, and they’re usually generated automatically (or are occasionally custom category pages) .

For example, if I search “Jobs in Manchester“, you can see nearly every result is an automatically generated landing page or category page.

There are three common ways to generate these pages (occasionally a combination of more than one is used):

  • Faceted pages: These are generated by facets—groups of preset filters that let you filter the current search results. They usually sit on the left-hand side of the page.
  • Category pages: These pages are listings which have already had a filter applied and can’t be changed. They’re usually custom pages.
  • Free-text search pages: These pages are generated by a free-text search box.

Those definitions are still bit general; let’s clear them up with some examples:

Amazon uses a combination of categories and facets. If you click on browse by department you can see all the category pages. Then on each category page you can see a faceted search. Amazon is so large that it needs both.

Indeed generates its landing pages through free text search, for example if we search for “IT jobs in manchester” it will generate: IT jobs in manchester.

teflSearch generates landing pages using just facets. The jobs in China landing page is simply a facet of the main search page.

Each method has its own search problems when used for generating landing pages, so lets tackle them one by one.


Aside

Facets and free text search will typically generate pages with parameters e.g. a search for “dogs” would produce:

www.mysite.com?search=dogs

But to make the URL user friendly sites will often alter the URLs to display them as folders

www.mysite.com/results/dogs/

These are still just ordinary free text search and facets, the URLs are just user friendly. (They’re a lot easier to work with in robots.txt too!)


Free search (& category) problems

If you’ve decided the base of your search will be a free text search, then we’ll have two major goals:

  • Goal 1: Helping search engines find your landing pages
  • Goal 2: Giving them link equity.

Solution

Search engines won’t use search boxes and so the solution to both problems is to provide links to the valuable landing pages so search engines can find them.

There are plenty of ways to do this, but two of the most common are:

  • Category links alongside a search

    Photobucket uses a free text search to generate pages, but if we look at example search for photos of dogs, we can see the categories which define the landing pages along the right-hand side. (This is also an example of URL friendly searches!)

  • Putting the main landing pages in a top-level menu

    Indeed also uses free text to generate landing pages, and they have a browse jobs section which contains the URL structure to allow search engines to find all the valuable landing pages.

Breadcrumbs are also often used in addition to the two above and in both the examples above, you’ll find breadcrumbs that reinforce that hierarchy.

Category (& facet) problems

Categories, because they tend to be custom pages, don’t actually have many search disadvantages. Instead it’s the other attributes that make them more or less desirable. You can create them for the purposes you want and so you typically won’t have too many problems.

However, if you also use a faceted search in each category (like Amazon) to generate additional landing pages, then you’ll run into all the problems described in the next section.

At first facets seem great, an easy way to generate multiple strong relevant landing pages without doing much at all. The problems appear because people don’t put limits on facets.

Lets take the job page on teflSearch. We can see it has 18 facets each with many options. Some of these options will generate useful landing pages:

The China facet in countries will generate “Jobs in China” that’s a useful landing page.

On the other hand, the “Conditional Bonus” facet will generate “Jobs with a conditional bonus,” and that’s not so great.

We can also see that the options within a single facet aren’t always useful. As of writing, I have a single job available in Serbia. That’s not a useful search result, and the poor user engagement combined with the tiny amount of content will be a strong signal to Google that it’s thin content. Depending on the scale of your site it’s very easy to generate a mass of poor-quality landing pages.

Facets generate other problems too. The primary one being they can create a huge amount of duplicate content and pages for search engines to get lost in. This is caused by two things: The first is the sheer number of possibilities they generate, and the second is because selecting facets in different orders creates identical pages with different URLs.

We end up with four goals for our facet-generated landing pages:

  • Goal 1: Make sure our searchable landing pages are actually worth landing on, and that we’re not handing a mass of low-value pages to the search engines.
  • Goal 2: Make sure we don’t generate multiple copies of our automatically generated landing pages.
  • Goal 3: Make sure search engines don’t get caught in the metaphorical plastic six-pack rings of our facets.
  • Goal 4: Make sure our landing pages have strong internal linking.

The first goal needs to be set internally; you’re always going to be the best judge of the number of results that need to present on a page in order for it to be useful to a user. I’d argue you can rarely ever go below three, but it depends both on your business and on how much content fluctuates on your site, as the useful landing pages might also change over time.

We can solve the next three problems as group. There are several possible solutions depending on what skills and resources you have access to; here are two possible solutions:

Category/facet solution 1: Blocking the majority of facets and providing external links
  • Easiest method
  • Good if your valuable category pages rarely change and you don’t have too many of them.
  • Can be problematic if your valuable facet pages change a lot

Nofollow all your facet links, and noindex and block category pages which aren’t valuable or are deeper than x facet/folder levels into your search using robots.txt.

You set x by looking at where your useful facet pages exist that have search volume. So, for example, if you have three facets for televisions: manufacturer, size, and resolution, and even combinations of all three have multiple results and search volume, then you could set you index everything up to three levels.

On the other hand, if people are searching for three levels (e.g. “Samsung 42″ Full HD TV”) but you only have one or two results for three-level facets, then you’d be better off indexing two levels and letting the product pages themselves pick up long-tail traffic for the third level.

If you have valuable facet pages that exist deeper than 1 facet or folder into your search, then this creates some duplicate content problems dealt with in the aside “Indexing more than 1 level of facets” below.)

The immediate problem with this set-up, however, is that in one stroke we’ve removed most of the internal links to our category pages, and by no-following all the facet links, search engines won’t be able to find your valuable category pages.

In order re-create the linking, you can add a top level drop down menu to your site containing the most valuable category pages, add category links elsewhere on the page, or create a separate part of the site with links to the valuable category pages.

The top level drop down menu you can see on teflSearch (it’s the search jobs menu), the other two examples are demonstrated in Photobucket and Indeed respectively in the previous section.

The big advantage for this method is how quick it is to implement, it doesn’t require any fiddly internal logic and adding an extra menu option is usually minimal effort.

Category/facet solution 2: Creating internal logic to work with the facets

  • Requires new internal logic
  • Works for large numbers of category pages with value that can change rapidly

There are four parts to the second solution:

  1. Select valuable facet categories and allow those links to be followed. No-follow the rest.
  2. No-index all pages that return a number of items below the threshold for a useful landing page
  3. No-follow all facets on pages with a search depth greater than x.
  4. Block all facet pages deeper than x level in robots.txt

As with the last solution, x is set by looking at where your useful facet pages exist that have search volume (full explanation in the first solution), and if you’re indexing more than one level you’ll need to check out the aside below to see how to deal with the duplicate content it generates.


Aside: Indexing more than one level of facets

If you want more than one level of facets to be indexable, then this will create certain problems.

Suppose you have a facet for size:

  • Televisions: Size: 46″, 44″, 42″

And want to add a brand facet:

  • Televisions: Brand: Samsung, Panasonic, Sony

This will create duplicate content because the search engines will be able to follow your facets in both orders, generating:

  • Television – 46″ – Samsung
  • Television – Samsung – 46″

You’ll have to either rel canonical your duplicate pages with another rule or set up your facets so they create a single unique URL.

You also need to be aware that each followable facet you add will multiply with each other followable facet and it’s very easy to generate a mass of pages for search engines to get stuck in. Depending on your setup you might need to block more paths in robots.txt or set-up more logic to prevent them being followed.

Letting search engines index more than one level of facets adds a lot of possible problems; make sure you’re keeping track of them.


2. User-generated content cannibalization

This is a common problem for listings sites (assuming they allow user generated content). If you’re reading this as an e-commerce site who only lists their own products, you can skip this one.

As we covered in the first area, category pages on listings sites are usually the landing pages aiming for the valuable search terms, but as your users start generating pages they can often create titles and content that cannibalise your landing pages.

Suppose you’re a job site with a category page for PHP Jobs in Greater Manchester. If a recruiter then creates a job advert for PHP Jobs in Greater Manchester for the 4 positions they currently have, you’ve got a duplicate content problem.

This is less of a problem when your site is large and your categories mature, it will be obvious to any search engine which are your high value category pages, but at the start where you’re lacking authority and individual listings might contain more relevant content than your own search pages this can be a problem.

Solution 1: Create structured titles

Set the <title> differently than the on-page title. Depending on variables you have available to you can set the title tag programmatically without changing the page title using other information given by the user.

For example, on our imaginary job site, suppose the recruiter also provided the following information in other fields:

  • The no. of positions: 4
  • The primary area: PHP Developer
  • The name of the recruiting company: ABC Recruitment
  • Location: Manchester

We could set the <title> pattern to be: *No of positions* *The primary area* with *recruiter name* in *Location* which would give us:

4 PHP Developers with ABC Recruitment in Manchester

Setting a <title> tag allows you to target long-tail traffic by constructing detailed descriptive titles. In our above example, imagine the recruiter had specified “Castlefield, Manchester” as the location.

All of a sudden, you’ve got a perfect opportunity to pick up long-tail traffic for people searching in Castlefield in Manchester.

On the downside, you lose the ability to pick up long-tail traffic where your users have chosen keywords you wouldn’t have used.

For example, suppose Manchester has a jobs program called “Green Highway.” A job advert title containing “Green Highway” might pick up valuable long-tail traffic. Being able to discover this, however, and find a way to fit it into a dynamic title is very hard.

Solution 2: Use regex to noindex the offending pages

Perform a regex (or string contains) search on your listings titles and no-index the ones which cannabalise your main category pages.

If it’s not possible to construct titles with variables or your users provide a lot of additional long-tail traffic with their own titles, then is a great option. On the downside, you miss out on possible structured long-tail traffic that you might’ve been able to aim for.

Solution 3: De-index all your listings

It may seem rash, but if you’re a large site with a huge number of very similar or low-content listings, you might want to consider this, but there is no common standard. Some sites like Indeed choose to no-index all their job adverts, whereas some other sites like Craigslist index all their individual listings because they’ll drive long tail traffic.

Don’t de-index them all lightly!

3. Constantly expiring content

Our third and final problem is that user-generated content doesn’t last forever. Particularly on listings sites, it’s constantly expiring and changing.

For most use cases I’d recommend 301’ing expired content to a relevant category page, with a message triggered by the redirect notifying the user of why they’ve been redirected. It typically comes out as the best combination of search and UX.

For more information or advice on how to deal with the edge cases, there’s a previous Moz blog post on how to deal with expired content which I think does an excellent job of covering this area.

Summary

In summary, if you’re working with listings sites, all three of the following need to be kept in mind:

  • How are the landing pages generated? If they’re generated using free text or facets have the potential problems been solved?
  • Is user generated content cannibalising the main landing pages?
  • How has constantly expiring content been dealt with?

Good luck listing, and if you’ve had any other tricky problems or solutions you’ve come across working on listings sites lets chat about them in the comments below!

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Reblogged 4 years ago from tracking.feedpress.it

The Nifty Guide to Local Content Strategy and Marketing

Posted by NiftyMarketing

This is my Grandma.

She helped raised me and I love her dearly. That chunky baby with the Gerber cheeks is
me. The scarlet letter “A” means nothing… I hope.

This is a rolled up newspaper. 

rolled up newspaper

When I was growing up, I was the king of mischief and had a hard time following parental guidelines. To ensure the lessons she wanted me to learn “sunk in” my grandma would give me a soft whack with a rolled up newspaper and would say,

“Mike, you like to learn the hard way.”

She was right. I have
spent my life and career learning things the hard way.

Local content has been no different. I started out my career creating duplicate local doorway pages using “find and replace” with city names. After getting whacked by the figurative newspaper a few times, I decided there had to be a better way. To save others from the struggles I experienced, I hope that the hard lessons I have learned about local content strategy and marketing help to save you fearing a rolled newspaper the same way I do.

Lesson one: Local content doesn’t just mean the written word

local content ecosystem

Content is everything around you. It all tells a story. If you don’t have a plan for how that story is being told, then you might not like how it turns out. In the local world, even your brick and mortar building is a piece of content. It speaks about your brand, your values, your appreciation of customers and employees, and can be used to attract organic visitors if it is positioned well and provides a good user experience. If you just try to make the front of a building look good, but don’t back up the inside inch by inch with the same quality, people will literally say, “Hey man, this place sucks… let’s bounce.”

I had this experience proved to me recently while conducting an interview at
Nifty for our law division. Our office is a beautifully designed brick, mustache, animal on the wall, leg lamp in the center of the room, piece of work you would expect for a creative company.

nifty offices idaho

Anywho, for our little town of Burley, Idaho it is a unique space, and helps to set apart our business in our community. But, the conference room has a fluorescent ballast light system that can buzz so loudly that you literally can’t carry on a proper conversation at times, and in the recent interviews I literally had to conduct them in the dark because it was so bad.

I’m cheap and slow to spend money, so I haven’t got it fixed yet. The problem is I have two more interviews this week and I am so embarrassed by the experience in that room, I am thinking of holding them offsite to ensure that we don’t product a bad content experience. What I need to do is just fix the light but I will end up spending weeks going back and forth with the landlord on whose responsibility it is.

Meanwhile, the content experience suffers. Like I said, I like to learn the hard way.

Start thinking about everything in the frame of content and you will find that you make better decisions and less costly mistakes.

Lesson two: Scalable does not mean fast and easy growth

In every sales conversation I have had about local content, the question of scalability comes up. Usually, people want two things:

  1. Extremely Fast Production 
  2. Extremely Low Cost

While these two things would be great for every project, I have come to find that there are rare cases where quality can be achieved if you are optimizing for fast production and low cost. A better way to look at scale is as follows:

The rate of growth in revenue/traffic is greater than the cost of continued content creation.

A good local content strategy at scale will create a model that looks like this:

scaling content graph

Lesson three: You need a continuous local content strategy

This is where the difference between local content marketing and content strategy kicks in. Creating a single piece of content that does well is fairly easy to achieve. Building a true scalable machine that continually puts out great local content and consistently tells your story is not. This is a graph I created outlining the process behind creating and maintaining a local content strategy:

local content strategy

This process is not a one-time thing. It is not a box to be checked off. It is a structure that should become the foundation of your marketing program and will need to be revisited, re-tweaked, and replicated over and over again.

1. Identify your local audience

Most of you reading this will already have a service or product and hopefully local customers. Do you have personas developed for attracting and retaining more of them? Here are some helpful tools available to give you an idea of how many people fit your personas in any given market.

Facebook Insights

Pretend for a minute that you live in the unique market of Utah and have a custom wedding dress line. You focus on selling modest wedding dresses. It is a definite niche product, but one that shows the idea of personas very well.

You have interviewed your customer base and found a few interests that your customer base share. Taking that information and putting it into Facebook insights will give you a plethora of data to help you build out your understanding of a local persona.

facebook insights data

We are able to see from the interests of our customers there are roughly 6k-7k current engaged woman in Utah who have similar interests to our customer base.

The location tab gives us a break down of the specific cities and, understandably, Salt Lake City has the highest percentage with Provo (home of BYU) in second place. You can also see pages this group would like, activity levels on Facebook, and household income with spending habits. If you wanted to find more potential locations for future growth you can open up the search to a region or country.

localized facebook insights data

From this data it’s apparent that Arizona would be a great expansion opportunity after Utah.

Neilson Prizm

Neilson offers a free and extremely useful tool for local persona research called Zip Code Lookup that allows you to identify pre-determined personas in a given market.

Here is a look at my hometown and the personas they have developed are dead on.

Neilson Prizm data

Each persona can be expanded to learn more about the traits, income level, and areas across the country with other high concentrations of the same persona group.

You can also use the segment explorer to get a better idea of pre-determined persona lists and can work backwards to determine the locations with the highest density of a given persona.

Google Keyword Planner Tool

The keyword tool is fantastic for local research. Using our same Facebook Insight data above we can match keyword search volume against the audience size to determine how active our persona is in product research and purchasing. In the case of engaged woman looking for dresses, it is a very active group with a potential of 20-30% actively searching online for a dress.

google keyword planner tool

2. Create goals and rules

I think the most important idea for creating the goals and rules around your local content is the following from the must read book Content Strategy for the Web.

You also need to ensure that everyone who will be working on things even remotely related to content has access to style and brand guides and, ultimately, understands the core purpose for what, why, and how everything is happening.

3. Audit and analyze your current local content

The point of this step is to determine how the current content you have stacks up against the goals and rules you established, and determine the value of current pages on your site. With tools like Siteliner (for finding duplicate content) and ScreamingFrog (identifying page titles, word count, error codes and many other things) you can grab a lot of information very fast. Beyond that, there are a few tools that deserve a more in-depth look.

BuzzSumo

With BuzzSumo you can see social data and incoming links behind important pages on your site. This can you a good idea which locations or areas are getting more promotion than others and identify what some of the causes could be.

Buzzsumo also can give you access to competitors’ information where you might find some new ideas. In the following example you can see that one of Airbnb.com’s most shared pages was a motiongraphic of its impact on Berlin.

Buzzsumo

urlProfiler

This is another great tool for scraping urls for large sites that can return about every type of measurement you could want. For sites with 1000s of pages, this tool could save hours of data gathering and can spit out a lovely formatted CSV document that will allow you to sort by things like word count, page authority, link numbers, social shares, or about anything else you could imagine.

url profiler

4. Develop local content marketing tactics

This is how most of you look when marketing tactics are brought up.

monkey

Let me remind you of something with a picture. 

rolled up newspaper

Do not start with tactics. Do the other things first. It will ensure your marketing tactics fall in line with a much bigger organizational movement and process. With the warning out of the way, here are a few tactics that could work for you.

Local landing page content

Our initial concept of local landing pages has stood the test of time. If you are scared to even think about local pages with the upcoming doorway page update then please read this analysis and don’t be too afraid. Here are local landing pages that are done right.

Marriott local content

Marriot’s Burley local page is great. They didn’t think about just ensuring they had 500 unique words. They have custom local imagery of the exterior/interior, detailed information about the area’s activities, and even their own review platform that showcases both positive and negative reviews with responses from local management.

If you can’t build your own platform handling reviews like that, might I recommend looking at Get Five Stars as a platform that could help you integrate reviews as part of your continuous content strategy.

Airbnb Neighborhood Guides

I not so secretly have a big crush on Airbnb’s approach to local. These neighborhood guides started it. They only have roughly 21 guides thus far and handle one at a time with Seoul being the most recent addition. The idea is simple, they looked at extremely hot markets for them and built out guides not just for the city, but down to a specific neighborhood.

air bnb neighborhood guides

Here is a look at Hell’s Kitchen in New York by imagery. They hire a local photographer to shoot the area, then they take some of their current popular listing data and reviews and integrate them into the page. This idea would have never flown if they only cared about creating content that could be fast and easy for every market they serve.

Reverse infographicing

Every decently sized city has had a plethora of infographics made about them. People spent the time curating information and coming up with the concept, but a majority just made the image and didn’t think about the crawlability or page title from an SEO standpoint.

Here is an example of an image search for Portland infographics.

image search results portland infographics

Take an infographic and repurpose it into crawlable content with a new twist or timely additions. Usually infographics share their data sources in the footer so you can easily find similar, new, or more information and create some seriously compelling data based content. You can even link to or share the infographic as part of it if you would like.

Become an Upworthy of local content

No one I know does this better than Movoto. Read the link for their own spin on how they did it and then look at these examples and share numbers from their local content.

60k shares in Boise by appealing to that hometown knowledge.

movoto boise content

65k shares in Salt Lake following the same formula.

movoto salt lake city content

It seems to work with video as well.

movoto video results

Think like a local directory

Directories understand where content should be housed. Not every local piece should be on the blog. Look at where Trip Advisor’s famous “Things to Do” page is listed. Right on the main city page.

trip advisor things to do in salt lake city

Or look at how many timely, fresh, quality pieces of content Yelp is showcasing from their main city page.

yelp main city page

The key point to understand is that local content isn’t just about being unique on a landing page. It is about BEING local and useful.

Ideas of things that are local:

  • Sports teams
  • Local celebrities or heroes 
  • Groups and events
  • Local pride points
  • Local pain points

Ideas of things that are useful:

  • Directions
  • Favorite local sports
  • Granular details only “locals” know

The other point to realize is that in looking at our definition of scale you don’t need to take shortcuts that un-localize the experience for users. Figure and test a location at a time until you have a winning formula and then move forward at a speed that ensures a quality local experience.

5. Create a content calendar

I am not going to get into telling you exactly how or what your content calendar needs to include. That will largely be based on the size and organization of your team and every situation might call for a unique approach. What I will do is explain how we do things at Nifty.

  1. We follow the steps above.
  2. We schedule the big projects and timelines first. These could be months out or weeks out. 
  3. We determine the weekly deliverables, checkpoints, and publish times.
  4. We put all of the information as tasks assigned to individuals or teams in Asana.

asana content calendar

The information then can be viewed by individual, team, groups of team, due dates, or any other way you would wish to sort. Repeatable tasks can be scheduled and we can run our entire operation visible to as many people as need access to the information through desktop or mobile devices. That is what works for us.

6. Launch and promote content

My personal favorite way to promote local content (other than the obvious ideas of sharing with your current followers or outreaching to local influencers) is to use Facebook ads to target the specific local personas you are trying to reach. Here is an example:

I just wrapped up playing Harold Hill in our communities production of The Music Man. When you live in a small town like Burley, Idaho you get the opportunity to play a lead role without having too much talent or a glee-based upbringing. You also get the opportunity to do all of the advertising, set design, and costuming yourself and sometime even get to pay for it.

For my advertising responsibilities, I decided to write a few blog posts and drive traffic to them. As any good Harold Hill would do, I used fear tactics.

music man blog post

I then created Facebook ads that had the following stats: Costs of $.06 per click, 12.7% click through rate, and naturally organic sharing that led to thousands of visits in a small Idaho farming community where people still think a phone book is the only way to find local businesses.

facebook ads setup

Then we did it again.

There was a protestor in Burley for over a year that parked a red pickup with signs saying things like, “I wud not trust Da Mayor” or “Don’t Bank wid Zions”. Basically, you weren’t working hard enough if you name didn’t get on the truck during the year.

Everyone knew that ol’ red pickup as it was parked on the corner of Main and Overland, which is one of the few stoplights in town. Then one day it was gone. We came up with the idea to bring the red truck back, put signs on it that said, “I wud Not Trust Pool Tables” and “Resist Sins n’ Corruption” and other things that were part of The Music Man and wrote another blog complete with pictures.

facebook ads red truck

Then I created another Facebook Ad.

facebook ads set up

A little under $200 in ad spend resulted in thousands more visits to the site which promoted the play and sold tickets to a generation that might not have been very familiar with the show otherwise.

All of it was local targeting and there was no other way would could have driven that much traffic in a community like Burley without paying Facebook and trying to create click bait ads in hope the promotion led to an organic sharing.

7. Measure and report

This is another very personal step where everyone will have different needs. At Nifty we put together very custom weekly or monthly reports that cover all of the plan, execution, and relevant stats such as traffic to specific content or location, share data, revenue or lead data if available, analysis of what worked and what didn’t, and the plan for the following period.

There is no exact data that needs to be shared. Everyone will want something slightly different, which is why we moved away from automated reporting years ago (when we moved away from auto link building… hehe) and built our report around our clients even if it took added time.

I always said that the product of a SEO or content shop is the report. That is what people buy because it is likely that is all they will see or understand.

8. In conclusion, you must refine and repeat the process

local content strategy - refine and repeat

From my point of view, this is by far the most important step and sums everything up nicely. This process model isn’t perfect. There will be things that are missed, things that need tweaked, and ways that you will be able to improve on your local content strategy and marketing all the time. The idea of the cycle is that it is never done. It never sleeps. It never quits. It never surrenders. You just keep perfecting the process until you reach the point that few locally-focused companies ever achieve… where your local content reaches and grows your target audience every time you click the publish button.

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Reblogged 4 years ago from tracking.feedpress.it