Saturday, 30 April 2016

EP42: 2 Facebook Campaign Metrics that Drive ROI

In this episode, our experts explain how you can
increase your relevance score and click-through-rate on the
Facebook platform. Using these metrics correctly will have a
lasting impact on your campaign's ROI – both in the short-term and
the long-term.


Gain better understanding and
control of your Facebook ads following these simple strategies, and
Facebook will reward you with better impressions and cheaper
clicks.


 


IN THIS EPISODE YOU'LL
LEARN:



  • How to lower your cost-per-click
    by increasing relevance score and
    click-through-rate.


  • The simple (but rarely used) rule
    of thumb that can indicate if your ad is going to work in the
    long-run (< ROI).


  • How to use these 2 metrics to make
    the necessary changes to your campaign (< to your ROI goal).


  • The “Share to Like” Ratio that
    measures the success of your ad campaigns.
    "s2">


 


LINKS AND RESOURCES
MENTIONED IN THIS EPISODE:


"https://www.digitalmarketer.com/podcast/episode-03-facebook-video-ad-game-plan/"
target="_blank">Episode 3: Facebook Video Ad Game
Plan


"https://www.digitalmarketer.com/podcast/the-ad-grid/" target=
"_blank">Episode 33: The Ad Grid: How to Build Campaigns that
Convert and Scale


"https://www.digitalmarketer.com/podcast/persuasive-ad-copy/"
target="_blank">Episode 34: 14 Elements of Persuasive Ad
Copy


"https://www.digitalmarketer.com/podcast/facebook-ad-campaign-mistakes/"
target="_blank">Episode 37: The 5 Biggest Facebook Ad Campaign
Mistakes


"https://www.digitalmarketer.com/podcast/critical-facebook-metrics/"
target="_blank">Episode 40: 4 Facebook Metrics Critical to Your
Success


"https://www.digitalmarketer.com/podcast/landing-page-conversion-rate/"
target="_blank">Episode 41: 9 Ways to Increase Landing Page
Conversion Rate


 


Press and hold link to visit
the page


"http://www.digitalmarketer.com/podcast/facebook-metrics-roi/?utm_source=Perpetual%20Traffic&utm_medium=podcast&utm_content=episode42&utm_campaign=Podcast%20Descriptions"
target="_blank">Show Page Notes


Thanks for
Listening!

Snapchat Delivers 10 Billion Video Views Daily: This Week in Social Media

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Welcome to our weekly edition of what's hot in social media news. To help you stay up to date with social media, here are some of the news items that caught our attention. What's New This Week Snapchat Now Serves 10 Billion Daily Video Views: “Now users are watching 10 billion videos a day on [...]


This post Snapchat Delivers 10 Billion Video Views Daily: This Week in Social Media first appeared on .

- Your Guide to the Social Media Jungle

Friday, 29 April 2016

10 Snapchat features we wish existed

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We here at Mashable are obsessed with Snapchat - the filters, the live stories, and of course, the Discover channels.


But of course, it's not perfect. In addition to a confusing, non-intuitive interface - Snapchat makes you work just to find that black drawing tool - there's a lot we wish it had. Plus, there are older, now-removed features, we want back too, like the "best friends" feature which highlighted who you snapped with the most. 


See also: 7 hidden features in the latest Snapchat update


While the app is always improving and adding new features, we've rounded up our wish list of Snapchat features, from an eraser tool to different fonts. Let's take a look. Read more...


More about Snapchat, Apps, Social Media, and Tech


Google AMP: What Bloggers Need to Know

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Have you heard of Google AMP? Want to know how it will impact your blog? To discover more about Google AMP and the future of blogging, I interview Leslie Samuel. More About This Show The Social Media Marketing podcast is an on-demand talk radio show from Social Media Examiner. It's designed to help busy marketers [...]


This post Google AMP: What Bloggers Need to Know first appeared on .

- Your Guide to the Social Media Jungle

Thursday, 28 April 2016

The Local SEO Agency's Complete Guide to Client Discovery and Onboarding

Posted by MiriamEllis

[Estimated read time: 6 minutes]


Why proper onboarding matters


Imagine getting three months in on a Local SEO contract before realizing that your client's storefront is really his cousin's garage. From which he runs two other “legit” businesses he never mentioned. Or that he neglected to mention the reviews he bought last year. Worse yet, he doesn't even know that buying reviews is a bad thing.


The story is equally bad if you're diligently working to build quality unique content around a Chicago client's business in Wicker Park but then realize their address (and customer base) is actually in neighboring Avondale.


What you don't know will hurt you. And your clients.


A hallmark of the professional Local SEO department or agency is its dedication to getting off on the right foot with a new client by getting their data beautifully documented for the whole team from the start. At various times throughout the life of the contract, your teammates and staff from complementary departments will be needing to access different aspects of a client's core NAP, known challenges, company history, and goals.


Having this information clearly recorded in shareable media is the key to both organization and collaboration, as well as being the best preventative measure against costly data-oriented mistakes. Clear and consistent data play vital roles in Local SEO. Information must not only be gathered, but carefully verified with the client.


This article will offer you a working Client Discovery Questionnaire, an Initial Discovery Phone Call Script, and a useful Location Data Spreadsheet that will be easy for any customer to fill out and for you to then use to get those listings up to date. You're about to take your client discovery process to awesome new heights!


Why agencies don't always get onboarding right


Lack of a clearly delineated, step-by-step onboarding process increases the potential for human error. Your agency's Local SEO manager may be having allergies on Monday and simply forget to ask your new client if they have more than one website, if they've ever purchased reviews, or if they have direct access to their Google My Business listings. Or they could have that information and forget to share it when they jump to a new agency.


The outcomes of disorganized onboarding can range from minor hassles to disastrous mistakes.


Minor hassles would include having to make a number of follow-up phone calls to fill in holes in a spreadsheet that could have been taken care of in a single outreach. It's inconvenient for all teammates when they have to scramble for missing data that should have been available at the outset of the project.


Disastrous mistakes can stem from a failure to fully gauge the details and scope of a client's holdings. Suddenly, a medium-sized project can take on gigantic proportions when the agency learns that the client actually has 10 mini-sites with duplicate content on them, or 10 duplicate GMB listings, or a series of call tracking numbers around the web.


It's extremely disheartening to discover a mountain of work you didn't realize would need to be undertaken, and the agency can end up having to put in extra uncompensated time or return to the client to renegotiate the contract. It also leads to client dissatisfaction.


Setting correct client expectations is completely dependent on being able to properly gauge the scope of a project, so that you can provide an appropriate timeline, quote, and projected benchmarks. In Local, that comes down to documenting core business information, identifying past and present problems, and understanding which client goals are achievable. With the right tools and effective communication, your agency will be making a very successful start to what you want to be a very successful project.


Professional client discovery made simple


There's a lot you want to learn about a new client up front, but asking (and answering) all those questions right away can be grueling. Not to mention information fatigue, which can make your client give shorter and shorter answers when they feel like they've spent enough time already. Meanwhile your brain reaches max capacity and you can't use all that valuable information because you can't remember it.


To prevent such a disaster, we recommend dividing your Local SEO discovery process into a questionnaire to nail down the basics, a follow-up phone call to help you feel out some trickier issues, and a CSV to gather the location data. And we've created templates to get you started...


Client Discovery Questionnaire


Use our Local SEO Client Discovery Questionnaire to understand your client's history, current organization, and what other consultants they might also be working with. We've annotated each question in the Google Doc template to help you understand what you can learn and potential pitfalls to look out for.


If you want to make collecting and preserving your clients' answers extra easy, use Google Forms to turn that questionnaire into a form like this:



You can even personalize the graphic, questions, and workflow to suit your brand.


Client Discovery Phone Script


Once you've received your client's completed questionnaire and have had time to process the responses and do any necessary due diligence (like using our Check Listings tool to check how aggregators currently display their information), it's time to follow up on the phone. Use our annotated Local SEO Client Discovery Phone Script to get you started.


local seo client discovery phone script


No form necessary this time, because you'll be asking the client verbally. Be sure to pay attention to the client's tone of voice as they answer and refer to the notes under each question to see what you might be in for.


Location Data CSV


Sometimes the hardest part of Local SEO is getting all the location info letter-perfect. Make that easier by having the client input all those details into your copy of the Location Data Spreadsheet.


local seo location data csv


Then use the File menu to download that document as a CSV.




You'll want to proof this before uploading it to any data aggregators. If you're working with Moz Local, the next step is an easy upload of your CSV. If you're working with other services, you can always customize your data collection spreadsheet to meet their standards.


Keep up to date on any business moves or changes in hours by designing a data update form like this one from SEER and periodically reminding your client contact to use it.


Why mutual signals of commitment really matter


There are two sides to every successful client project: one half belongs to the agency and the other to the company it serves. The attention to detail your agency displays via clean, user-friendly forms and good phone sessions will signal your professionalism and commitment to doing quality work. At the same time, the willingness of the client to take the necessary time to fill out these documents and have these conversations signals their commitment to receiving value from their investment.


It's not unusual for a new client to express some initial surprise when they realize how many questions you're asking them to answer. Past experience may even have led them to expect half-hearted, sloppy work from other SEO agencies. But, what you want to see is a willingness on their part to share everything they can about their company with you so that you can do your best work.


Anecdotally, I've fully refunded the down payments of a few incoming clients who claimed they couldn't take the time to fill out my forms, because I detected in their unwillingness a lack of genuine commitment to success. These companies have, fortunately, been the exception rather than the rule for me, and likely will be for your agency, too.


It's my hope that, with the right forms and a commitment to having important conversations with incoming clients at the outset, the work you undertake will make your Local team top agency and client heroes!


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!

Wednesday, 27 April 2016

How to Use the Pinterest Bulk Editor to Create Promoted Pins

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Looking for a faster way to create promoted pins on Pinterest? Have you tried the Pinterest bulk editor tool? Pinterest's bulk editor tool makes it easier to create and edit promoted pins and optimize multiple promoted pins at one time. In this article you'll discover how to create promoted pins in less time with Pinterest's [...]


This post How to Use the Pinterest Bulk Editor to Create Promoted Pins first appeared on .

- Your Guide to the Social Media Jungle

Measuring Content: You're Doing it Wrong

Posted by MatthewBarby

[Estimated read time: 10 minutes]


The traditional ways of measuring the success or failure of content are broken. We can't just rely on metrics like the number of pageviews/visits or bounce rate to determine whether what we're creating has performed well.


“The primary thing we look for with news is impact, not traffic,” says Jonah Peretti, Founder of BuzzFeed. One of the ways that BuzzFeed have mastered this is with the development of their proprietary analytics platform, POUND.

POUND enables BuzzFeed to predict the potential reach of a story based on its content, understand how effective specific promotions are based on the downstream sharing and traffic, and power A/B tests - and that's just a few examples.


Just because you've managed to get more eyeballs onto your content doesn't mean it's actually achieved anything. If that were the case then I'd just take a few hundred dollars and buy some paid StumbleUpon traffic every time.


Yeah, I'd generate traffic, but it's highly unlikely to result in me achieving some of my actual business goals. Not only that, but I'd have no real indication of whether my content was satisfying the needs of my visitors.


The scary thing is that the majority of content marketing campaigns are measured this way. I hear statements like “it's too difficult to measure the performance of individual pieces of content” far too often. The reality is that it's pretty easy to measure content marketing campaigns on a micro level - a lot of the time people don't want to do it.


Engagement over entrances


Within any commercial content marketing campaign that you're running, measurement should be business goal-centric. By that I mean that you should be determining the overall success of your campaign based on the achievement of core business goals.


If your primary business goal is to generate 300 leads each month from the content that you're publishing, you'll need to have a reporting mechanism in place to track this information.


On a more micro-level, you'll want to be tracking and using engagement metrics to enable you to influence the achievement of your business goals. In my opinion, all content campaigns should have robust, engagement-driven reporting behind them.


Total Time Reading (TTR)


One metric that Medium uses, which I think adds a lot more value than pageviews, is "Total Time Reading (TTR)." This is a cumulative metric that quantifies the total number of minutes spent reading a piece of content. For example, if I had 10 visitors to one of my blog articles and they each stayed reading the article for 1 minute each, the total reading time would be 10 minutes.


“We measure every user interaction with every post. Most of this is done by periodically recording scroll positions. We pipe this data into our data warehouse, where offline processing aggregates the time spent reading (or our best guess of it): we infer when a reader started reading, when they paused, and when they stopped altogether. The methodology allows us to correct for periods of inactivity (such as having a post open in a different tab, walking the dog, or checking your phone).” (source)

The reason why this is more powerful than just pageviews is because it takes into account how engaged your readers are to give a more accurate representation of its visibility. You could have an article with 1,000 pageviews that has a greater TTR than one with 10,000 pageviews.


Scroll depth & time on page


A related and simpler metric to acquire is the average time on page (available within Google Analytics). The average time spent on your webpage will give a general indication of how long your visitors are staying on the page. Combining this with 'scroll depth' (i.e. how far down the page has a visitor scrolled) will help paint a better picture of how 'engaged' your visitors are. You'll be able to get the answer to the following:


“How much of this article are my visitors actually reading?”


“Is the length of my content putting visitors off?”


“Are my readers remaining on the page for a long time?”


Having the answers to these questions is really important when it comes to determining which types of content are resonating more with your visitors.


Social Lift


BuzzFeed's “Social Lift” metric is a particularly good way of understanding the 'virality' of your content (you can see this when you publish a post to BuzzFeed). BuzzFeed calculates “Social Lift” as follows:


((Social Views)/(Seed Views)+1)

Social Views: Traffic that's come from outside BuzzFeed; for example, referral traffic, email, social media, etc.


Seed Views: Owned traffic that's come from within the BuzzFeed platform; e.g. from appearing in BuzzFeed's newsfeed.


BuzzFeed Social Lift


This is a great metric to use when you're a platform publisher as it helps separate out traffic that's coming from outside of the properties that you own, thus determining its "viral potential."


There are ways to use this kind of approach within your own content marketing campaigns (without being a huge publisher platform) to help get a better idea of its "viral potential."


One simple calculation can just involve the following:


((social shares)/(pageviews)+1)

This simple stat can be used to determine which content is likely to perform better on social media, and as a result it will enable you to prioritize certain content over others for paid social promotion. The higher the score, the higher its "viral potential." This is exactly what BuzzFeed does to understand which pieces of content they should put more weight behind from a very early stage.


You can even take this to the next level by replacing pageviews with TTR to get a more representative view of engagement to sharing behavior.


The bottom line


Alongside predicting "viral potential" and "TTR," you'll want to know how your content is performing against your bottom line. For most businesses, that's the main reason why they're creating content.


This isn't always easy and a lot of people get this wrong by looking for a silver bullet that doesn't exist. Every sales process is different, but let's look at the typical process that we have at HubSpot for our free CRM product:



  1. Visitor comes through to our blog content from organic search.

  2. Visitor clicks on a CTA within the blog post.

  3. Visitor downloads a gated offer in exchange for their email address and other data.

  4. Prospect goes into a nurturing workflow.

  5. Prospect goes through to a BOFU landing page and signs up to the CRM.

  6. Registered user activates and invites in members of their team.


This is a simple process, but it can still be tricky sometimes to get a dollar value on each piece of content we produce. To do this, you've got to understand what the value of a visitor is, and this is done by working backwards through the process.


The first question to answer is, “what's the lifetime value (LTV) of an activated user?” In other words, “how much will this customer spend in their lifetime with us?”


For e-commerce businesses, you should be able to get this information by analyzing historical sales data to understand the average order value that someone makes and multiply that by the average number of orders an individual will make with you in their lifetime.


For the purposes of this example, let's say each of our activated CRM users has an LTV of $100. It's now time to work backwards from that figure (all the below figures are theoretical)…


Question 1: “What's the conversion rate of new CRM activations from our email workflow(s)?”


Answer 1: “5%”


Question 2: “How many people download our gated offers after coming through to the blog content?”


Answer 2: “3%”


Knowing this would help me to start putting a monetary value against each visitor to the blog content, as well as each lead (someone that downloads a gated offer).


Let's say we generate 500,000 visitors to our blog content each month. Using the average conversion rates from above, we'd convert 15,000 of those into email leads. From there we'd nurture 750 of them into activated CRM users. Multiply that by the LTV of a CRM user ($100) and we've got $75,000 (again, these figures are all just made up).


Using this final figure of $75,000, we could work backwards to understand the value of a single visitor to our blog content:


 ((75,000)/(500,000))

Single Visitor Value: $0.15


We can do the same for email leads using the following calculation:


(($75,000)/(15,000))

Individual Lead Value: $5.00


Knowing these figures will help you be able to determine the bottom-line value of each of your pieces of content, as well as calculating a rough return on investment (ROI) figure.


Let's say one of the blog posts we're creating to encourage CRM signups generated 500 new email leads; we'd see a $2,500 return. We could then go and evaluate the cost of producing that blog post (let's say it takes 6 hours at $100 per hour – $600) to calculate a ROI figure of 316%.


ROI in its simplest form is calculated as:


(((($return)-($investment))/($investment))*100)

You don't necessarily need to follow these figures religiously when it comes to content performance on a broader level, especially when you consider that some content just doesn't have the primary goal of lead generation. That said, for the content that does have this goal, it makes sense to pay attention to this.


The link between engagement and ROI


So far I've talked about two very different forms of measurement:



  1. Engagement

  2. Return on investment


What you'll want to avoid is actually thinking about these as isolated variables. Return on investment metrics (for example, lead conversion rate) are heavily influenced by engagement metrics, such as TTR.


The key is to understand exactly which engagement metrics have the greatest impact on your ROI. This way you can use engagement metrics to form the basis of your optimization tests in order to make the biggest impact on your bottom line.


Let's take the following scenario that I faced within my own blog as an example…


The average length of the content across my website is around 5,000 words. Some of my content way surpasses 10,000 words in length, taking an estimated hour to read (my recent SEO tips guide is a perfect example of this). As a result, the bounce rate on my content is quite high, especially from mobile visitors.


Keeping people engaged within a 10,000-word article when they haven't got a lot of time on their hands is a challenge. Needless to say, it makes it even more difficult to ensure my CTAs (aimed at newsletter subscriptions) stand out.


From some testing, I found that adding my CTAs closer to the top of my content was helping to improve conversion rates. The main issue I needed to tackle was how to keep people on the page for longer, even when they're in a hurry.


To do this, I worked on the following solution: give visitors a concise summary of the blog post that takes under 30 seconds to read. Once they've read this, show them a CTA that will give them something to read in more detail in their own time.


All this involved was the addition of a "Summary" button at the top of my blog post that, when clicked, hides the content and displays a short summary with a custom CTA.


Showing Custom Summaries


This has not only helped to reduce the number of people bouncing from my long-form content, but it also increased the number of subscribers generated from my content whilst improving user experience at the same time (which is pretty rare).


I've thought that more of you might find this quite a useful feature on your own websites, so I packaged it up as a free WordPress plugin that you can download here.


Final thoughts


The above example is just one example of a way to impact the ROI of your content by improving engagement. My advice is to get a robust measurement process in place so that you're able to first of all identify opportunities, and then go through with experiments to take advantage of the opportunity.


More than anything, I'd recommend that you take a step back and re-evaluate the way that you're measuring your content campaigns to see if what you're doing really aligns with the fundamental goals of your business. You can invest in endless tools that help you measure things better, but if core metrics that you're looking for are wrong, then this is all for nothing.


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!

Tuesday, 26 April 2016

6 Instagram Tools to Improve Your Marketing

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Do you want to take your Instagram marketing to the next level? Have you considered using tools to support your efforts there? Adding the right Instagram tools into your marketing flow can help you project a more professional image and give you valuable analytic insights. In this article you'll discover six tools to improve your [...]


This post 6 Instagram Tools to Improve Your Marketing first appeared on .

- Your Guide to the Social Media Jungle

Meet Talkshow, the latest viral app the Internet is freaking out about

Talkshow2
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Look out, Internet. The new Peach has arrived - and we have Taylor Swift to thank. 


A new text messaging app called Talkshow became the buzzy new social platform du jour on Tuesday for its quirky concept: it's like “texting in public.”


The iOS-only app lets users host message-based “Talkshows” about various topics, from sports and politics to TV and music. People notify followers when a Talkshow is live, encouraging anyone who's watching to send messages, post reactions and GIFs or even join in as a co-host. It's like Periscope for texting.



Founder Michael Sippey said in a blog post that the concept is inspired by a conversation posted online of Taylor Swift and Ed Sheeran.   Read more...


More about Social Media, Social, Apps, and Tech


Monday, 25 April 2016

How to Simplify the Publishing of Curated Content on Facebook With Free Tools

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Looking for a way to simplify the process of publishing your curated content on Facebook? Want to schedule curated content posts and links to your own blog? By using If This Then That (IFTTT), Pocket, and Google Docs, you can create a cost-effective and time-saving workflow that will help you keep your Facebook page stocked [...]


This post How to Simplify the Publishing of Curated Content on Facebook With Free Tools first appeared on .

- Your Guide to the Social Media Jungle

Can We Predict the Google Weather?

Posted by Dr-Pete

[Estimated read time: 7 minutes]


Four years ago, just weeks before the first Penguin update, the MozCast project started collecting its first real data. Detecting and interpreting Google algorithm updates has been both a far more difficult and far more rewarding challenge than I ever expected, and I've learned a lot along the way, but there's one nagging question that I've never been able to answer with any satisfaction. Can we use past Google data to predict future updates?

Before any analysis, I've always been a fan of using my eyes. What does Google algorithm "weather" look like over a long time-period? Here's a full year of MozCast temperatures:




Most of us know by now that Google isn't a quiet machine that hums along until the occasional named update happens a few times a year. The algorithm is changing constantly and, even if it wasn't, the web is changing constantly around it. Finding the signal in the noise is hard enough, but what does any peak or valley in this graph tell you about when the next peak might arrive? Very little, at first glance.


It's worse than that, though


Even before we dive into the data, there's a fundamental problem with trying to predict future algorithm updates. To understand it, let's look at a different problem - predicting real-world weather. Predicting the weather in the real world is incredibly difficult and takes a massive amount of data to do well, but we know that that weather follows a set of natural laws. Ultimately, no matter how complex the problem is, there is a chain of causality between today's weather and tomorrow's and a pattern in the chaos.


The Google algorithm is built by people, driven by human motivations and politics, and is only constrained by the rules of what's technologically possible. Granted, Google won't replace the entire SERP with a picture of a cheese sandwich tomorrow, but they can update the algorithm at any time, for any reason. There are no natural laws that link tomorrow's algorithm to today's. History can tell us about Google's motivations and we can make reasonable predictions about the algorithm's future, but those future algorithm updates are not necessarily bound to any pattern or schedule.


What do we actually know?


If we trust Google's public statements, we know that there are a lot of algorithm updates. The fact that only a handful get named is part of why we built MozCast in the first place. Back in 2011, Eric Schmidt testified before Congress, and his written testimony included the following data:


To give you a sense of the scale of the changes that Google considers, in 2010 we conducted 13,311 precision evaluations to see whether proposed algorithm changes improved the quality of its search results, 8,157 side-by-side experiments where it presented two sets of search results to a panel of human testers and had the evaluators rank which set of results was better, and 2,800 click evaluations to see how a small sample of real-life Google users responded to the change. Ultimately, the process resulted in 516 changes that were determined to be useful to users based on the data and, therefore, were made to Google's algorithm.

I've highlighted one phrase - "516 changes". At a time when we believed Google made maybe a dozen updates per year, Schmidt revealed that it was closer to 10X/week. Now, we don't know how Google defines "changes," and many of these changes were undoubtedly small, but it's clear that Google is constantly changing.


Google's How Search Works page reveals that, in 2012, they made 665 "improvements" or "launches" based on an incredible 118,812 precision evaluations. In August of 2014, Amit Singhal stated on Google+ that they had made "more than 890 improvements to Google Search last year alone." It's unclear whether that referred to the preceding 12 months or calendar year 2013.


We don't have a public number for the past couple of years, but it is incredibly unlikely that the rate of change has slowed. Google is making changes to search on the order of 2X/day.

Of course, anyone who has experience in software development realizes that Google didn't evenly divide 890 improvements over the year and release one every 9 hours and 51 minutes. That would be impractical for many reasons. It's very likely that releases are rolled out in chunks and are tied to some kind of internal process or schedule. That process or schedule may be irregular, but humans at Google have to approve, release, and audit every change.


In March of 2012, Google released a video of their weekly Search Quality meeting, which, at the time, they said occurred "almost every Thursday". This video and other statements since reveal a systematic process within Google by which updates are reviewed and approved. It doesn't take very advanced math to see that there are many more updates per year than there are weekly meetings.


Is there a weekly pattern?


Maybe we can't predict the exact date of the next update, but is there any regularity to the pattern at all? Admittedly, it's a bit hard to tell from the graph at the beginning of this post. Analyzing an irregular time series (where both the period between spikes and intensity of those spikes changes) takes some very hairy math, so I decided to start a little simpler.


I started by assuming that a regular pattern was present and looking for a way to remove some of the noise based on that assumption. The simplest analysis that yielded results involved taking a 3-day moving average and calculating the Mean Standard Error (MSE). In other words, for every temperature (each temperature is a single day), take the mean of that day and the day on either side of it (a 3-day window) and square the difference between that day's temperature and the 3-day mean. This exaggerates stand-alone peaks, and smooths some of the noisier sequences, resulting in the following graph:




This post was inspired in part by February 2016, which showed an unusually high signal-to-noise ratio. So, let's zoom in on just the last 90 days of the graph:




See peaks 2–6 (starting on January 21)? The space between them, respectively, is 6 days, 7 days, 7 days, and 8 days. Then, there's a 2-week gap to the next, smaller spike (March 3) and another 8 days to the one after that. While this is hardly proof of a clear regular pattern, it's hard to believe the weekly pacing is entirely a coincidence, given what we know about the algorithm update approval process.


This pattern is less clear in other months, and I'm not suggesting that a weekly update cycle is the whole picture. We know Google also does large data refreshes (including Penguin) and sometimes rolls updates out over multiple days (or even weeks). There's a similar, although noisier, pattern in April 2015 (the first part of the 12-month MSE graph). It's also interesting to note the activity levels around Christmas 2015:




Despite all of our conspiracy theories, there really did seem to be a 2015 Christmas lull in Google activity, lasting approximately 4 weeks, followed by a fairly large spike that may reflect some catch-up updates. Engineers go on vacation, too. Notice that that first January spike is followed by a roughly 2-week gap and then two 1-week gaps.


The most frequent day of the week for these spikes seems to be Wednesday, which is odd, if we believe there's some connection to Google's Thursday meetings. It's possible that these approximately weekly cycles are related to naturally occurring mid-week search patterns, although we'd generally expect less pronounced peaks if change were related to something like mid-week traffic spikes or news volume.


Did we win Google yet?


I've written at length about why I think algorithm updates still matter, but, tactically speaking, I don't believe we should try to plan our efforts around weekly updates. Many updates are very small and even some that are large on average may not effect our employer or clients.


I view the Google weather as a bit like the unemployment rate. It's interesting to know whether that rate is, say, 5% or 7%, but ultimately what matters to you is whether or not you have a job. Low or high unemployment is a useful economic indicator and may help you decide whether to risk finding a new job, but it doesn't determine your fate. Likewise, measuring the temperature of the algorithm can teach us something about the system as a whole, but the temperature on any given day doesn't decide your success or failure.


Ultimately, instead of trying to predict when an algorithm update will happen, we should focus on the motivations behind those updates and what they signal about Google's intent. We don't know exactly when the hammer will fall, but we can get out of the way in plenty of time if we're paying attention.


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How 'bathroom bills' started an online war over transgender rights

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YouTube star Joey Salads recently watched a video entitled "What Pisses Me Off About Transgender Bathrooms" and came up with an idea for one of his signature "social experiments."


Salads, who declined to provide his legal name for privacy reasons, wanted to test how women react to seeing a visibly transgender woman in a public bathroom. 



He quickly researched gender identity online and chatted with two friends who he says represent opposite sides of the argument. 


Then Salads, 22, donned a dress, purse and blonde wig, and let the cameras roll as he followed women into a public bathroom in a north Hollywood apartment complex. The video, released on Monday, has since been watched more than one million times.  Read more...


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Sunday, 24 April 2016

Facebook Simplifies Mobile Video Ad Buying: This Week in Social Media

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Welcome to our weekly edition of what's hot in social media news. To help you stay up to date with social media, here are some of the news items that caught our attention. What's New This Week Facebook Introduces New Features for Purchasing and Planning Mobile Video Ads: Facebook introduced “new features to make it [...]


This post Facebook Simplifies Mobile Video Ad Buying: This Week in Social Media first appeared on .

- Your Guide to the Social Media Jungle

Saturday, 23 April 2016

Photo challenge: Show us your upside down photos

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Our community challenges are finally back and better than ever. We will be hosting a new challenge every Saturday morning, so be on the lookout for our weekend photo themes to put your photography skills to the test.


Turn your frowns upside down this week because we're challenging you to play with perspective and take your photos upside down for our community challenge.


Head stands, reflection photos and playful angles are all appreciated. Here are a few shots to check out for inspiration.



Image: tyler tronson


Image: TYLER TRONSON

To enter the challenge and potentially be featured on Mashable and our Instagram, share your photos using  #Mashpics_UpsideDown by Wednesday, April 27th at 12 p.m. GMT! Don't forget to follow us and stay up-to-date for more challenges. Read more...


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