How to drive revenue from abandoned carts

Reasons to abandon are almost as unique as your customers. Maybe it wasn’t exactly what they were looking for. Perhaps it was more expensive then they were expecting. Or they hadn’t seen how much shipping would cost. They may have added items to their cart with no intention to buy or accidentally closed the tab.

Whatever the reason, every single one of these customers is
still an opportunity.  

The average rate of cart abandonment is around 76%. This means, for every 100 customers walking through your virtual door, 76 are leaving without making a purchase. You wouldn’t accept this in a brick-and-mortar store, so why put up with it online?

Not just for ecommerce

It’s important not to think of abandoned cart programs as
only applicable to B2C or ecommerce brands.

Abandoned browse emails can be used in a very similar way. For a B2B brand or service consider what your high intent actions are. Whether it’s filling in a form or viewing a pricing page, you can create abandoned browse emails to capture any of those who drop off without completing an action.

Program your abandoned carts

Abandoned carts or browse emails should be the backbone of your marketing strategy. Ultimately, it’ll help you recover more lost revenue and convert more visitors.

While the sound of a ‘program’ may feel complicated, the abandoned cart is a simple follow-up email. Browsers are reminded after exiting the window where they added items to their shopping carts.

But, to set this up, a couple of technical steps are necessary.

Integrate

Integrating your ecommerce store or CRM with Engagement Cloud is essential. Through this, Engagement Cloud will know when a shopper abandons your site. This action will trigger your abandoned cart or abandoned browse program. The program, built in Engagement Cloud, will send these emails to your contacts every time they fail to complete your desired action.

Build

Building your program is quick and painless, thanks to the product development team at dotdigital. Engagement Cloud comes fully equipped with over 30 pre-built programs, ready and waiting for you to switch on. Among these, you’ll find the abandoned cart and abandoned browse.

You can create more complex or specific abandonment programs in Engagement Cloud’s easy-to-use program builder. And, with our connectors like Magento and Shopify, you don’t even need to build a program. It’s ready and waiting for you to switch on in your account.

Create

Once your program is in place, you need to create the email you want to be sent to your contacts. Depending on the program, it may be one email, or it could be three. But your design will also be dictated, in part, by the strategy you adopt.

All the best, most successful abandonment emails are using one or more of these tactics to achieve its goals.

Don’t fire out discounts every time

We know it can be tempting to include discount codes, but
don’t send them out every time someone abandons an action on your site.
Shoppers are savvy and they’re clocking onto this tactic.

Instead, consider factoring in some stipulations:

  • Where does the shopper fit into your RFM personas? Are they Champions, Loyal or Need nurturing? These are the perfect targets to offer discounts. For your Champions and Loyal segments, you’re rewarding loyalty. Need nurturing customers will receive the incentive they need to tip into your Recent category.
  • How much are they spending? What’s the complete value of the cart they’ve abandoned? Consider offering discounts for carts worth over $100. Or make the code conditional on spending over $50.
  • Optimize the potential spending power of your new subscribers or members by offering them a discount on their first purchase. By stating it’s on their first purchase, you’re also setting expectations, so they know not to expect repeat discounts, every time they abandon a cart.

US wine subscription company, Winc, offer money off first orders, but only for first-time buyers. With a clear understanding of the value of its proposition and knowledge that shoppers tend to buy more than one bottle in an order, this email prompts new shoppers to take the first step on their journey with Winc.

Keep it super simple

The best thing about abandoned browse emails and abandoned carts is that they don’t have to be complicated.

Actually, the simpler they are the better the impact they have. Why over-complicate the message? All you’re trying to do is push them to complete their purchase, so packing the email full of additional items or CTAs reduces its impact.

Beardbrand personalized abandoned cart

Beardbrand makes its CTA big, and bold, and impossible to miss. The brand also adds a sense of urgency with its phrasing ‘reclaim your cart!’. The body copy of the email emphasizes this further, explaining the cart is about to expire. With a clear time pressure and a clear spotlight being shone on a single product, the reader feels compelled to complete the action.

Use social proof to tip them over

Social proof is one of the most powerful tools in your arsenal. According to reviews platform, Feefo, 95% of people are influenced by reading reviews.

Adding social proof elements to programs like abandoned carts or browse can help you convert first-time visitors or uncertain buyers.

Adidas abandoned browse

Adidas tap into its audiences’ fear of missing out, and desire for anything unique and personalized with this abandoned browse campaign. By adding a CTA to customize they’re giving shoppers even more incentive to resume their browse. Combined with its incorporated reviews, the reader is really inspired to return to the site.

Give them a reason to choose you

80% of consumers are willing to pay more or try a new brand if they offer a better customer experience.

You’ve already captured the shopper’s attention. They’ve been on your site, signed up for an account or subscribed to your newsletter. They’ve even gone as far as to look at a specific page or add items to their cart. But now, you need to show them why they should choose you, over your competitors.

Whisky Loot abandoned browse

While you may not think these programs are the place for this, Whisky Loot, a whiskey-themed subscription box, did just that. Using an abandoned browse email, it perfectly demonstrated the brands fun and quirky personality. It even addresses key issues that may have stopped the customer from completing their purchase. Overall, the email leaves readers smiling and thinking, ‘yeah, I will treat myself’.

Keep the revenue rolling by reviewing your abandoned carts

The strategy behind your abandoned carts or browse emails is essential.

To keep this program driving revenue, it’s important you
keep a close eye on it. If you see your ROI stagnating, it’s time to change it
up.

Never be afraid to be different and try something new. How
else are you going to stand out from your competition? Make every experience as
unique as your customers.

In the immortal words of T.S. Eliot: “Only those who will risk going too far can possibly find out how far one can go.”


Suggested reading

Product recommendations blog suggestion
B2B automation blog suggestion
Social proof blog suggestion

The post How to drive revenue from abandoned carts appeared first on dotdigital blog.

Reblogged 2 weeks ago from blog.dotdigital.com

Reputation, Rankings, and Revenue: Navigating Local for Non-Technical People

Posted by MiriamEllis

Your local SEO agency needs new clients in 2017. Your department needs to convince management to earmark robust resources for local SEM this year. What if the only thing standing in your way is presentation?

In the 10+ years I’ve been consulting with local businesses, I’ve watched our industry grow to absorb an incredibly diverse set of disparate-seeming tasks. The breadth of the lingo alone is on the verge of becoming a dialect of its own. Here, supporting our Moz Local product, some of my internal communications with team members read like a code, packed with acronyms, abbreviations, and shorthand references that encapsulate large concepts which, while perfectly understood between local SEOs, would likely mean little to many CEOs or local business owners. In other words: shoptalk. Every industry has it.

The ability to codify and convey a complex concept by distilling it down to its essence is critical to the art of the pitch. Tell your new lead or your all-hands meeting that the company’s NAP is inconsistent on FB and YP, their DA is weak, and their owner responses are painfully MIA and watch their eyes glaze over. Today, I’d like to help you get meaningful attention by translating your local SEO work into 3 terms that almost any non-technical party will not only understand, but care about tremendously: reputation, rankings and revenue.

How to explain the main components of local SEO

1. Guideline compliance

Step One: Determine that the business qualifies as local via Google’s definition in their guidelines.

Step Two: Adhere to all guidelines to ensure that the business isn’t spamming Google. The same applies to other major local business data platforms.

How does it impact the 3 Rs?

This protects reputation, in that the business conducts itself in an above-board fashion and doesn’t come across as spammy to search engines or consumers. It protects rankings in that penalties are avoided. It protects revenue in that resources are not wasted on risky practices and funds are being devoted to appropriate forms of marketing for the business model; money and time aren’t being spent on dubious work that can fall apart at any moment.

Further reading:

2. Website

Step One: Develop a technically clean website with good UX for all users/devices. If the site already exists, audit it for problems/penalties and resolve them.

Step Two: Develop the best possible website content in the business’ geo-industry.

Step Three: Properly optimize the site for local search + organic search.

Step Four: Optimize for conversions. All four goals should be a simultaneous effort.

How does it impact the 3 Rs?

This protects reputation in that the website delivers excellent customer service and establishes the business as an authoritative resource. It protects rankings in that penalties and filters are avoided, excellent content rises in visibility, and both local and organic results are won and held. It protects revenue in that conversions are not being lost to unsatisfactory user experiences.

Further reading:

3. Citations

Step One: Audit the existing citation landscape and correct inconsistent, incomplete and duplicate listings.

Step Two: Ensure listings have been developed on core local business data platforms.

Step Three: Develop geo/industry-specific citations.

Step Four: Manage citations on an on-going basis to catch emerging inconsistencies/duplicates/third party edits.

Step Five: Seek out unstructured citation opportunities (news, blogs, etc.).

How does it impact the 3 Rs?

This protects reputation in that the business is accurately listed in consumers’ preferred places, establishing identity and professionalism — citations are simply publishing and no business wants wrong information to be published about it. It protects rankings in that search engines’ trust in the validity of the business’ basic data is being augmented. It protects revenue in that transactions are not being lost due to the misdirection and frustration of consumers via inaccurate basic data around the web.

Further reading:

4. Reviews

Step One: Perfect and reinforce customer service policies and staff training.

Step Two: Implement a review acquisition strategy for key citation platforms and for the company website.

Step Three: Respond to reviews.

How does it impact the 3 Rs?

This protects reputation in that incoming customers derive trust from previous customers and the business’ reputation is being carefully managed from in-store service to online sentiment by the owner or agency department, including the improvement/resolution of negative sentiment via owner responses. It protects rankings by dint of surpassing competitors with a larger number of positive reviews on the major platforms. It protects revenue by winning trust-based transactions from new customers who are influenced by previous customers’ sentiment, while ensuring that neglect of negative sentiment or a simple lack of reviews isn’t turning potential consumers away. Actively managed reviews are one of the very best indicators of a responsive, reliable brand.

Further reading:

5. Links

Step One: Audit the existing link landscape for problem links and disavow or otherwise resolve them.

Step Two: Earn voluntary links via the publication and promotion of exceptional materials.

Step Three: Carefully seek out relevant link opportunities via safe methods such as local sponsorships, editorial contributions, or other vehicles on quality geo/industry sites.

How does it impact the 3 Rs?

This protects reputation in that the business is associating with the best-of-the-best and isn’t being lumped in by search engines or consumers with shady actors or practices. It protects the website’s rankings in that links are growing the brand’s renown over time, making it an active and visible competitor and proving its relevance to search engines. It protects the website’s revenue both in fostering traffic and conversions from new sources, and in utilizing allowed practices to safeguard against sudden plunges in visibility.

Further reading:

6. Social

Step One: Identify the social hubs preferred by your specific geo/industry consumers.

Step Two: Based on the culture of each platform, develop a policy and strategy for participation.

Step Three: Participate on these platforms in a spirit of sharing rather than selling.

Step Four: Given that Social is an extension of customer service, monitor all social accounts for consumer needs/complaints and enact your policy for resolution.

How does it impact the 3 Rs?

This protects reputation in that you are both contributing to and managing the online discussion of your brand, providing accessibility in a modern vein. It protects rankings in that some social results (like Twitter) will appear directly within the organic results of search engines like Google, establishing a sense of both company activity and consumer sentiment. It protects revenue in that neglected consumer sentiment does not lead to lost transactions or permanent negative reviews.

Further reading:

7. Offline

Step One: Recognize that anything that happens offline may be published online, whether this relates to company activity driving online content development or consumer in-store experiences driving online sentiment.

Step Two: Take whatever steps necessary to create a cohesive offline-to-online experience, including branding, messaging, signage, promotions, in-store apps or kiosks, and transactional support.

Step Three: Seek out real-world opportunities for establishing your brand as a community resource via traditional methods like print, radio, and television, as well as by participation in appropriate community organizations and events.

How does it impact the 3 Rs?

This protects reputation by cementing for consumers that they will enjoy a specific type of desired experience interacting with your brand, whether on the Internet or offline — it’s all about consistency, and it carries over into reviews. It protects rankings by creating the active, real-world company culture that contributes to both your own online publication strategy and the acquisition of third-party media mentions (online news, blogs, social, etc.). It protects revenue in that the most-desired end of the funnel of all of the above is the transaction, and today, most consumers will arrive at that moment via a combination of both on- and offline influences. By being present in what Google calls its four micro-moments, revenue is safeguarded and, ideally, improved.

Further reading:

8. Other media

Depending on the business’ industry, other forms of media may contribute directly to reputation, rankings, and revenue. This could include email marketing, video marketing, or app, tool, or widget development. In essence, these are specialized forms of content development and social promotion that will need to be built into marketing strategies wherever appropriate.

Further reading:

How much do they need to know?

I’m a firm believer in full transparency and thorough documentation of all work performed so that clients, teams, or bosses can see exactly what is being done, even if the technicalities aren’t perfectly understood by them. As you undertake the various tasks of local SEM, you’ll want to both fully detail the steps you are taking and use every available means for measuring their outcomes. That’s how you keep clients and keep your department funded.

But initially, when first presenting your proposed strategic outline, paring it down to finite goals may greatly improve your communication with industry outsiders, establishing common ground where you are seeing eye-to-eye with confidence. I have yet to meet a business owner who doesn’t instinctively sense the importance of his company’s reputation, rankings, and revenue, so rather than risk losing him with complex jargon at the outset, why not signal that you are on the same wavelength with the simplest terms possible?

As a fellow local search marketer, I know that you, too, have your livelihood wrapped up in the 3 Rs, and I’m wishing you a highly converting 2017!

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

Reblogged 2 years ago from tracking.feedpress.it

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.

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

Reblogged 4 years ago from tracking.feedpress.it

How to Rid Your Website of Six Common Google Analytics Headaches

Posted by amandaecking

I’ve been in and out of Google Analytics (GA) for the past five or so years agency-side. I’ve seen three different code libraries, dozens of new different features and reports roll out, IP addresses stop being reported, and keywords not-so-subtly phased out of the free platform.

Analytics has been a focus of mine for the past year or so—mainly, making sure clients get their data right. Right now, our new focus is closed loop tracking, but that’s a topic for another day. If you’re using Google Analytics, and only Google Analytics for the majority of your website stats, or it’s your primary vehicle for analysis, you need to make sure it’s accurate.

Not having data pulling in or reporting properly is like building a house on a shaky foundation: It doesn’t end well. Usually there are tears.

For some reason, a lot of people, including many of my clients, assume everything is tracking properly in Google Analytics… because Google. But it’s not Google who sets up your analytics. People do that. And people are prone to make mistakes.

I’m going to go through six scenarios where issues are commonly encountered with Google Analytics.

I’ll outline the remedy for each issue, and in the process, show you how to move forward with a diagnosis or resolution.

1. Self-referrals

This is probably one of the areas we’re all familiar with. If you’re seeing a lot of traffic from your own domain, there’s likely a problem somewhere—or you need to extend the default session length in Google Analytics. (For example, if you have a lot of long videos or music clips and don’t use event tracking; a website like TEDx or SoundCloud would be a good equivalent.)

Typically one of the first things I’ll do to help diagnose the problem is include an advanced filter to show the full referrer string. You do this by creating a filter, as shown below:

Filter Type: Custom filter > Advanced
Field A: Hostname
Extract A: (.*)
Field B: Request URI
Extract B: (.*)
Output To: Request URI
Constructor: $A1$B1

You’ll then start seeing the subdomains pulling in. Experience has shown me that if you have a separate subdomain hosted in another location (say, if you work with a separate company and they host and run your mobile site or your shopping cart), it gets treated by Google Analytics as a separate domain. Thus, you ‘ll need to implement cross domain tracking. This way, you can narrow down whether or not it’s one particular subdomain that’s creating the self-referrals.

In this example below, we can see all the revenue is being reported to the booking engine (which ended up being cross domain issues) and their own site is the fourth largest traffic source:

I’ll also a good idea to check the browser and device reports to start narrowing down whether the issue is specific to a particular element. If it’s not, keep digging. Look at pages pulling the self-referrals and go through the code with a fine-tooth comb, drilling down as much as you can.

2. Unusually low bounce rate

If you have a crazy-low bounce rate, it could be too good to be true. Unfortunately. An unusually low bounce rate could (and probably does) mean that at least on some pages of your website have the same Google Analytics tracking code installed twice.

Take a look at your source code, or use Google Tag Assistant (though it does have known bugs) to see if you’ve got GA tracking code installed twice.

While I tell clients having Google Analytics installed on the same page can lead to double the pageviews, I’ve not actually encountered that—I usually just say it to scare them into removing the duplicate implementation more quickly. Don’t tell on me.

3. Iframes anywhere

I’ve heard directly from Google engineers and Google Analytics evangelists that Google Analytics does not play well with iframes, and that it will never will play nice with this dinosaur technology.

If you track the iframe, you inflate your pageviews, plus you still aren’t tracking everything with 100% clarity.

If you don’t track across iframes, you lose the source/medium attribution and everything becomes a self-referral.

Damned if you do; damned if you don’t.

My advice: Stop using iframes. They’re Netscape-era technology anyway, with rainbow marquees and Comic Sans on top. Interestingly, and unfortunately, a number of booking engines (for hotels) and third-party carts (for ecommerce) still use iframes.

If you have any clients in those verticals, or if you’re in the vertical yourself, check with your provider to see if they use iframes. Or you can check for yourself, by right-clicking as close as you can to the actual booking element:

iframe-booking.png

There is no neat and tidy way to address iframes with Google Analytics, and usually iframes are not the only complicated element of setup you’ll encounter. I spent eight months dealing with a website on a subfolder, which used iframes and had a cross domain booking system, and the best visibility I was able to get was about 80% on a good day.

Typically, I’d approach diagnosing iframes (if, for some reason, I had absolutely no access to viewing a website or talking to the techs) similarly to diagnosing self-referrals, as self-referrals are one of the biggest symptoms of iframe use.

4. Massive traffic jumps

Massive jumps in traffic don’t typically just happen. (Unless, maybe, you’re Geraldine.) There’s always an explanation—a new campaign launched, you just turned on paid ads for the first time, you’re using content amplification platforms, you’re getting a ton of referrals from that recent press in The New York Times. And if you think it just happened, it’s probably a technical glitch.

I’ve seen everything from inflated pageviews result from including tracking on iframes and unnecessary implementation of virtual pageviews, to not realizing the tracking code was installed on other microsites for the same property. Oops.

Usually I’ve seen this happen when the tracking code was somewhere it shouldn’t be, so if you’re investigating a situation of this nature, first confirm the Google Analytics code is only in the places it needs to be.Tools like Google Tag Assistant and Screaming Frog can be your BFFs in helping you figure this out.

Also, I suggest bribing the IT department with sugar (or booze) to see if they’ve changed anything lately.

5. Cross-domain tracking

I wish cross-domain tracking with Google Analytics out of the box didn’t require any additional setup. But it does.

If you don’t have it set up properly, things break down quickly, and can be quite difficult to untangle.

The older the GA library you’re using, the harder it is. The easiest setup, by far, is Google Tag Manager with Universal Analytics. Hard-coded universal analytics is a bit more difficult because you have to implement autoLink manually and decorate forms, if you’re using them (and you probably are). Beyond that, rather than try and deal with it, I say update your Google Analytics code. Then we can talk.

Where I’ve seen the most murkiness with tracking is when parts of cross domain tracking are implemented, but not all. For some reason, if allowLinker isn’t included, or you forget to decorate all the forms, the cookies aren’t passed between domains.

The absolute first place I would start with this would be confirming the cookies are all passing properly at all the right points, forms, links, and smoke signals. I’ll usually use a combination of the Real Time report in Google Analytics, Google Tag Assistant, and GA debug to start testing this. Any debug tool you use will mean you’re playing in the console, so get friendly with it.

6. Internal use of UTM strings

I’ve saved the best for last. Internal use of campaign tagging. We may think, oh, I use Google to tag my campaigns externally, and we’ve got this new promotion on site which we’re using a banner ad for. That’s a campaign. Why don’t I tag it with a UTM string?

Step away from the keyboard now. Please.

When you tag internal links with UTM strings, you override the original source/medium. So that visitor who came in through your paid ad and then who clicks on the campaign banner has now been manually tagged. You lose the ability to track that they came through on the ad the moment they click on the tagged internal link. Their source and medium is now your internal campaign, not that paid ad you’re spending gobs of money on and have to justify to your manager. See the problem?

I’ve seen at least three pretty spectacular instances of this in the past year, and a number of smaller instances of it. Annie Cushing also talks about the evils of internal UTM tags and the odd prevalence of it. (Oh, and if you haven’t explored her blog, and the amazing spreadsheets she shares, please do.)

One clothing company I worked with tagged all of their homepage offers with UTM strings, which resulted in the loss of visibility for one-third of their audience: One million visits over the course of a year, and $2.1 million in lost revenue.

Let me say that again. One million visits, and $2.1 million. That couldn’t be attributed to an external source/campaign/spend.

Another client I audited included campaign tagging on nearly every navigational element on their website. It still gives me nightmares.

If you want to see if you have any internal UTM strings, head straight to the Campaigns report in Acquisition in Google Analytics, and look for anything like “home” or “navigation” or any language you may use internally to refer to your website structure.

And if you want to see how users are moving through your website, go to the Flow reports. Or if you really, really, really want to know how many people click on that sidebar link, use event tracking. But please, for the love of all things holy (and to keep us analytics lovers from throwing our computers across the room), stop using UTM tagging on your internal links.

Now breathe and smile

Odds are, your Google Analytics setup is fine. If you are seeing any of these issues, though, you have somewhere to start in diagnosing and addressing the data.

We’ve looked at six of the most common points of friction I’ve encountered with Google Analytics and how to start investigating them: self-referrals, bounce rate, iframes, traffic jumps, cross domain tracking and internal campaign tagging.

What common data integrity issues have you encountered with Google Analytics? What are your favorite tools to investigate?

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

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

Give It Up for Our MozCon 2015 Community Speakers

Posted by EricaMcGillivray

Super thrilled that we’re able to announce this year’s community speakers for MozCon, July 13-15th in Seattle!

Wow. Each year I feel that I say the pool keeps getting more and more talented, but it’s the truth! We had more quality pitches this year than in the past, and quantity-wise, there were 241, around 100 more entries than years previously. Let me tell you, many of the review committee members filled our email thread with amazement at this.

And even though we had an unprecedented six slots, the choices seemed even tougher!

241 pitches
Let that number sink in for a little while.

Because we get numerous questions about what makes a great pitch, I wanted to share both information about the speakers and their great pitches—with some details removed for spoilers. (We’re still working with each speaker to polish and finalize their topic.) I’ve also included my or Matt Roney‘s own notes on each one from when we read them without knowing who the authors were.

Please congratulate our MozCon 2015 community speakers!

Adrian Vender

Adrian is the Director of Analytics at IMI and a general enthusiast of coding and digital marketing. He’s also a life-long drummer and lover of music. Follow him at @adrianvender.

Adrian’s pitch:

Content Tracking with Google Tag Manager

While marketers have matured in the use of web analytics tools, our ability to measure how users interact with our sites’ content needs improvement. Users are interacting with dynamic content that just aren’t captured in a pageview. While there are JavaScript tricks to help track these details, working with IT to place new code is usually the major hurdle that stops us.

Finally, Google Tag Manager is that bridge to advanced content analysis. GTM may appear technical, but it can easily be used by any digital marketer to track almost any action on a site. My goal is to make ALL attendees users of GTM.

My talk will cover the following GTM concepts:

[Adrian lists 8 highly-actionable tactics he’ll cover.]

I’ll share a client example of tracking content interaction in GA. I’ll also share a link to a GTM container file that can help people pre-load the above tag templates into their own GTM.

Matt’s notes: Could be good. I know a lot of people have questions about Tag Manager, and the ubiquity of GA should help it be pretty well-received.


Chris DayleyChris Dayley

Chris is a digital marketing expert and owner of Dayley Conversion. His company provides full-service A/B testing for businesses, including design, development, and test execution. Follow him at @chrisdayley.

Chris’ pitch:

I would like to present a super actionable 15 minute presentation focused on the first two major steps businesses should take to start A/B testing:

1. Radical Redesign Testing

2. Iterative Testing (Test EVERYTHING)

I am one of the few CROs out there that recommends businesses to start with a radical redesign test. My reasoning for doing so is that most businesses have done absolutely no testing on their current website, so the current landing page/website really isn’t a “best practice” design yet.

I will show several case studies where clients saw more than a 50% lift in conversion rates just from this first step of radical redesign testing, and will offer several tips for how to create a radical redesign test. Some of the tips include:

[Chris lists three direct and interesting tips he’ll share.]

Next I suggest moving into the iterative phase.

I will show several case studies of how to move through iterative testing so you eventually test every element on your page.

Erica’s notes: Direct, interesting, and with promise of multiple case studies.


Duane BrownDuane Brown

Duane is a digital marketer with 10 years’ experience having lived and worked in five cities across three continents. He’s currently at Unbounce. When not working, you can find Duane traveling to some far-flung location around the world to eat food and soak up the culture. Follow him at @DuaneBrown.

Duane’s pitch:

What Is Delightful Remarketing & How You Can Do It Too

A lot of people find remarketing creepy and weird. They don’t get why they are seeing those ads around the internet…. let alone how to make them stop showing.

This talk will focus on the different between remarketing & creating delightful remarketing that can help grow the revenue & profit at a company and not piss customers off. 50% of US marketers don’t use remarketing according to eMarketer (2013).

– [Duane’s direct how-to for e-commerce customers.] Over 60% of customers abandon a shopping cart each year: http://baymard.com/lists/cart-abandonment-rate (3 minute)

– Cover a SaaS company using retargeting to [Duane’s actionable item]. This remarketing helps show your products sticky features while showing off your benefits (3 minute)

– The Dos: [Duane’s actionable tip], a variety of creative & a dedicated landing page creates delightful remarketing that grows revenue (3 minute)

– Wrap up and review main points. (2 minutes)

Matt’s notes: Well-detailed, an area in which there’s a lot of room for improvement.


Gianluca FiorelliGianluca Fiorelli

Moz Associate, official blogger for StateofDigital.com and known international SEO and inbound strategist, Gianluca works in the digital marketing industry, but he still believes that he just know that he knows nothing. Follow him at @gfiorelli1.

Gianluca’s pitch:

Unusual Sources for Keyword and Topical Research

A big percentage of SEOs equal Keyword and Topical Research to using Keyword Planner and Google Suggest.

However, using only them, we cannot achieve a real deep knowledge of the interests, psychology and language of our target.

In this talk, I will present unusual sources and unnoticed features of very well-known tools, and offer a final example based on a true story.

Arguments touched in the speech (not necessarily in this order):

[Gianluca lists seven how-tos and one unique case study.]

Erica’s notes: Theme of Google not giving good keyword info. Lots of unique actionable points and resources. Will work in 15 minute time limit.


Ruth Burr ReedyRuth Burr Reedy

Ruth is the head of on-site SEO for BigWing Interactive, a full-service digital marketing agency in Oklahoma City, OK. At BigWing, she manages a team doing on-site, technical, and local SEO. Ruth has been working in SEO since 2006. Follow her at @ruthburr.

Ruth’s pitch:

Get Hired to Do SEO

This talk will go way beyond “just build your own website” and talk about specific ways SEOs can build evidence of their skills across the web, including:

[Ruth lists 7 how-tos with actionable examples.]

All in a funny, actionable, beautiful, easy-to-understand get-hired masterpiece.

Erica’s notes: Great takeaways. Wanted to do a session about building your resume as a marketer for a while.


Stephanie WallaceStephanie Wallace

Stephanie is director of SEO at Nebo, a digital agency in Atlanta. She helps clients navigate the ever-changing world of SEO by understanding their audience and helping them create a digital experience that both the user and Google appreciates. Follow her at @SWallaceSEO.

Stephanie’s pitch:

Everyone knows PPC and SEO complement one another – increased visibility in search results help increase perceived authority and drive more clickthroughs to your site overall. But are you actively leveraging the wealth of PPC data available to build on your existing SEO strategy? The key to effectively using this information lies in understanding how to test SEO tactics and how to apply the results to your on-page strategies. This session will delve into actionable strategies for using PPC campaign insights to influence on-page SEO and content strategies. Key takeaways include:

[Stephanie lists four how-tos.]

Erica’s notes: Nice and actionable. Like this a lot.


As mentioned, we had 241 entries, and many of them were stage quality. Notable runners up included AJ Wilcox, Ed Reese, and Daylan Pearce, and a big pat on the back to all those who tossed their hat in.

Also, a huge thank you to my fellow selection committee members for 2015: Charlene Inoncillo, Cyrus Shepard, Danie Launders, Jen Lopez, Matt Roney, Rand Fishkin, Renea Nielsen, and Trevor Klein.

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