Just today, I found out what SOV means.

Researching this post, in fact, was the first time I discovered the definition (SOV = “Share of Voice,” by the way!). I had seen it on social media articles and updates and never knew what it meant.

Do you have similar acronyms or abbreviations you’ve wondered about?

Our social media shorthand is amazingly extensive. We have acronyms and abbreviations for not only the marketing terms that we use but also the way that we chat back …

The post The Definitive List of Social Media Acronyms and Abbreviations, Defined appeared first on Social.

Do you remember seeing your first emoticon?

The first documented use of “:-)” dates back to 1982, when Scott Fahlman proposed that it be used as a “joke marker” on a message board for Carnegie Mellon University computer scientists. Here’s his Internet-changing message:

“I propose that the following character sequence for joke markers:

“:-)”
Read it sideways.”

Today, emoticons need a bit less explanation. As social media has grown (and character counts have shrunk), these pictorial representations of feelings are playing a significant role in communication.

If you’re still not …

The post 7 Reasons to Use Emoticons in Your Writing and Social Media, According to Science appeared first on Social.

Posted by randfish

Today I’m going to make a crazy claim—that in modern SEO, there are times, situations, and types of analyses where correlation is actually MORE interesting and useful than causality. I know that sounds insane, but stick with me until the end and at least give the argument a chance. And for those of you who like visuals, our friend AJ Ghergich and his intrepid team of designers created some nifty graphics to accompany the piece.

Once upon a time, SEO professionals had a reasonable sense of many (or perhaps even most) of the inputs into the search engine’s ranking systems. We leveraged our knowledge of how Google interpreted various modifications to keywords, links, content, and technical aspects to hammer on the signals that produced results.

But today, there can be little argument—Google’s ranking algorithm has become so incredibly complex, nuanced, powerful, and full-featured, that modern SEOs have all but given up on hammering away at individual signals. Instead, we’re becoming more complete marketers, with greater influence on all of the elements of our organizations’ online presence.

Web marketers operate in a world where Google:

• Uses machine learning to identify editorial endorsements vs. spam (e.g. Penguin)
• Measures and rewards engagement (e.g. pogo-sticking)
• Rewards signals that correlate with brands (and attempts to remove/punish non-brand entities)
• Applies thousands of immensely powerful and surprisingly accurate ways to analyze content (e.g. Hummingbird)
• Punishes sites that produce mediocre content (intentionally or accidentally) even if the site has good content, too (e.g. Panda)
• Rapidly recognizes and accounts for patterns of queries and clicks as rank boosting signals (e.g. this recent test)
• Makes 600+ algorithmic updates each year, the vast majority of which are neither announced nor known by the marketing/SEO community

Given this frenetic ecosystem, the best path forward isn’t to exclusively build to the signals that are recognized and accepted as having a direct impact on rankings (keyword-matching, links, etc). Those who’ve previously pursued such a strategy have mostly failed to deliver on long-term results. Many have found their sites in serious trouble due to penalization, more future-focused competitors, and/or a devaluing of their tactics.

Instead, successful marketers have been engaging in the tactics that Google’s own algorithms are chasing—popularity, relevance, trust, and a great overall experience for visitors. Very frequently, that means looking at correlation rather than causation.

[Via Moz’s 2013 Ranking Factors – the new 2015 version is coming this summer!]

We’ll engage in a thought experiment to help highlight the issue:

Let’s say you discover, as a signal of quality, Google directly measures the time a given searcher spends on a page visited from the SERPs. Sites with pages searchers spend more time on get a rankings boost, while those with quick abandonment find their pages falling in the rankings. You decide to press your advantage with this knowledge by using some clever hacks to keep visitors on your page longer and to make clicking the back button more difficult. Sure, it may suck for some visitors, but those are the ones you would have lost anyway (and they would have hurt your rankings!), so you figure they’re not worth worrying about. You’ve identified a metric that directly impacts Google’s algorithm, and you’re going to make the most of it.

Meanwhile, your competitor (who has no idea about the algorithmic impact of this factor) has been working on a new design that makes their website content easier, faster, and more pleasurable to consume. When the new design launches, they initially see a fall in rankings, and don’t understand why. But you’re pretty sure you know what’s happened. Google’s use of the time-on-site metric is hurting them because visitors are now getting the information they want from your competitor’s new design faster than before, and thus, they’re leaving more quickly, hurting the site’s rankings. You cackle with delight as your fortune swells.

But what happens long term? Google’s quality testers see diminished happiness among searchers. They rework their algorithms to reward sites that successfully deliver great experiences more quickly. At the same time, competitors gain more links, amplification, social sharing, and word of mouth because real users are deriving more positive experiences from their site than yours. You found an algorithmic loophole and exploited it briefly, but by playing the “where’s Google weak?” game rather than the “where’s Google going?” game, you’ve ultimately lost.

Over the last decade, in case after case of marketers optimizing for the causal elements of Google’s algorithm, this pattern of short-term gain leading to long-term loss continually occurs. That’s why, today, I suggest marketers think about what correlates with rankings as much as what actually causes them.

If many high-ranking sites in your field are offering mobile apps for Android and iOS, you may be tempted to think there’s no point to considering an app-strategy just for SEO because, obviously, having an app doesn’t make Google rank your site any higher. But what if those mobile apps are leading to more press coverage for those competitors, and more links to their site, and more direct visits to their webpages from those apps, and more search queries that include their brand names, and a hundred other things that Google maybe IS counting directly in their algorithm?

And, if many high ranking sites in your field engage in TV ads, you may be tempted to think that it’s useless to investigate TV as a channel because there’s no way Google would reward advertising as a signal for SEO. But what if those TV ads drive searches and clicks, which could lead directly to rankings? What if those TV ads create brand-biasing behaviors through psychological nudges that lead to greater recognition and a higher likelihood of searchers click on, link to, share, talk about, write about, buy from, etc. your TV-advertising competitor?

Thousands of hard-to-identify, individual signals, mashed together through machine learning, are most likely directly responsible for your competitor’s website outranking yours on a particular search query. But even if you had a list of the potential inputs and the mathematical formulas Google’s process considers most valuable for that query’s ranking evaluation, you’d be little closer to competently beating them. You may feel smugly satisfied that your own SEO knowledge exceeded that of your competitor, or of their SEO consultants, but smug satisfaction does not raise rankings. In fact, I think some of the SEO field’s historic obsession with knowing precisely how Google works and which signals matter is, at times, costing us a broader, deeper understanding of big-picture marketing*.

Time and again, I’ve seen SEO professionals whom I admire, respect, and find to be brilliant analysts of Google’s algorithms lose out to less-hyper-SEO-aware marketers who combine that big picture knowledge with more-basic/fundamental SEO tactics. While I certainly wouldn’t advise anyone to learn less about their field nor give up their investigation of Google’s inner workings, I am and will continue to strongly advise marketers of all specialties to think about all the elements that might have a second-order or purely correlated effect on Google’s rankings, rather than just concentrate on what we know to be directly causal.

—————–

* No one’s guiltier than I am of obsessing over discovering and sharing Google’s operations. And I’ll probably keep being that way because that’s how obsession works. But, I’m trying to recognize that this obsession isn’t necessarily connected to being the most successful marketer or SEO I can be.

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!

Many of the social media tips we share—the ways to create a marketing plan from scratch, how to get more followers, how to get more clicks—often focus on the small business side of social media marketing.

Now imagine achieving social media success when the brand you’re trying to promote is you.

Personal branding on social media is a topic we’d love to dive into a bit deeper, starting with this overview of tips and strategies. I’m grateful to have found a number …

The post The 5 Keys to Building a Social Media Strategy for Your Personal Brand appeared first on Social.

Posted by ProfAlfonso

Being a digital marketer, I spend my day knee-deep in data. The time I don’t spend analysing it, I spend explaining its significance to a client or junior colleague or arguing its significance with a client or senior colleague.

But after many debates over the importance of bounce rate, time on site, mobile conversion rate and the colour grey for buttons (our designer partook in that last one), we’re never much closer to an agreement on significance.

Our industry is swimming in data (thanks Google Analytics), but at times we’re drowning in it.

Numbers without context mean nothing. Data in the hands of even the savviest marketer is useless without a context to evaluate its performance against competitors or the industry at large.

Which is why we need benchmarks.
Through benchmarking, marketers can contextualise data to identify under-performing elements and amplify what is over-performing. They can focus on the KPIs that are important, and recognise whether they are achievable.

Benchmarks also give context to those who aren’t familiar with data. One pain point that digital marketers face globally is communicating their performance upwards. There are very few ‘digital natives’ sitting in company boardrooms these days but plenty of executives who know their numbers inside out.

Industry benchmark data arms us with perspective and framework when we need to communicate upwards. It ensures we get pats on the back when deserved and additional budget released when required.

Google Analytics Benchmarking Reports

Google, you might argue, have already solved these problems.

The upgrade and roll-out of Google Analytics Benchmarking Reports has been met with plenty of excitement for these reasons. With its large data set and nifty options to chop up the data by geography and website size, for a minute it certainly seemed like the benchmarking of our dreams. And while we recognise its usefulness to benchmark against real-time data (comparing a surge of traffic from a particular location for example, or seasonal demands), it still left us short of the hard data insights we were looking for.

We wanted reliable KPI data that went beyond user behaviour. We wanted average conversion rates and average transaction values as well as ‘softer’ engagement metrics such as bounce rate and time on site.

Most importantly, we wanted to know which engagement metrics actually correlated with the conversion rate, so we could narrow our field of analysis and efforts in pursuit of a healthier bottom line.

Which is why we went out and got our own and generated this e-commerce KPI report.

Data and methodology

We analysed the 56 million visits and approximately $252 million (€214 million) in revenue that flowed through 30 participating websites between August 1, 2013 and July 30, 2014. The websites were in the retail and travel sectors and included both online-only and those with a physical store as well as an e-commerce site.

We averaged stats on a per-website basis, so that websites with high levels of traffic didn’t skew the stats. We had more retail participants than travel participants so the average e-commerce figures are not the midpoint between travel and retail but the average figure across all study participants. Revenue is attributed on a last-click basis.

Results

Here is a highlight of some of our most relevant and interesting findings. For all the data and results, download the full report on
WolfgangDigital.com.

Average KPIs: Bounce rate, time on site, and conversion rate

First, we calculated some averages across engagement KPIs and commercial KPIs. If you are an e-commerce website in the travel or retail business, you can use these numbers to evaluate how your website is performing when set against a broad swath of your industry peers.

Well, remember the conversion measured here is a sale. If your conversion rate is lower than the study average don’t fire your CMO straight away; check if your average transaction value (ATV) is higher. If they balance each other out you are all good – if they don’t, it’s time to start digging deeper. Does the 1.4% conversion rate give you a smug tingly feeling or a stab of panic?

We often break down conversion rate into two parts: website-to-basket and basket-to-checkout. Industry norms tell us expect about 5% CR on website-to-basket and 30% on basket-to-checkout. Check which one of these conversion rates is most out of kilter on your site, then focus your attention there. This exercise will often give greater visibility on where the hole in your bucket is, Dear Liza.

Another factor in this analysis is that online-only retailers tend to enjoy higher conversion rates as the consumer
must transact via the website. If you have an offline presence, a lower conversion rate comes with the physical territory as your site visitors may convert in store.

KPIs by device: Mobile under scrutiny

Next, we segmented the data by device: desktop, tablet and mobile.

We found that although mobile and tablet together accounted for nearly half of website traffic (43%), they contributed to just over a quarter of revenue (26%).

Mobile alone accounted for 26% of traffic but only 10% of revenue. This suggests that while mobile is a favoured device for browsing and researching, it’s the desktop where users are more likely to whip out the credit card.

When we looked at conversion rates by device, this confirmed it.

What data matters: The correlations

We wanted to know which engagement figures had an influence (if any) on commercial ones.

Then we’d know which behavioural metrics were worth trying to improve to lift conversion rate, and which metrics we could finally label insignificant.

We did this by calculating correlations. A correlation ranges from 0 to 1, so 0 indicates on no correlation at all, while 1 signifies a clear correlation. A negative correlation indicates that as one variable increases the other decreases.

Time on site (0.34) and pages viewed (0.35) both had positive correlations with conversion rate, so our advice is to look at how to improve these metrics for your site to benefit from a higher conversion rate.

We delved into the device data and found mobile was the only device with positive traffic (0.29) and revenue (0.45) correlations to overall conversion rate. In fact, that 0.45 correlation rate between mobile revenue % and conversion rate was actually the strongest correlation rate across all factors we measured.

We infer that while the mobile conversion rate is depressingly low, a mobile user is still somebody with purchase intent who is likely to convert later on another device. The lesson we took from this is to make sure your website is mobile-optimised, particularly for ease of research and browsing content.

Finally, the time came to talk about bounce rate. Our Excel wizard had converted the data to an ‘un-bounce rate’ (1 minus the bounce rate) for consistency with positive time on site and pages viewed metrics. We gathered round the spreadsheet.

He revealed
there is actually a negative correlation (-0.12) between un-bounce rate and conversion rate. This correlation signals that it couldn’t be less influential on conversion rate, so for those unable to sleep at night for bounce anxiety, we’re delighted to let you sleep easy.

Increasing your conversion rate may not be as complex a task as it seems.

Our KPI study shows that if you can increase pages viewed and time on site it will push up your conversion rate (content marketing for conversion optimisation anybody?).

We’ve also proved that mobile matters. Don’t be discouraged if your mobile conversion rate pales against desktop’s performance; keep driving mobile traffic and revenue (however minor) and you’ll see the difference in your bottom line.

Read the full results broken down by industry level by downloading from the Wolfgang Digital e-commerce KPI Study.

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!