The Importance of Being Different: Creating a Competitive Advantage With Your USP

Posted by TrentonGreener

“The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

While this quote has been credited to everyone from Francis Phillip Wernig, under the pseudonym Alan Ashley-Pitt, to Einstein himself, the powerful message does not lose its substance no matter whom you choose to credit. There is a very important yet often overlooked effect of not heeding this warning. One which can be applied to all aspects of life. From love and happiness, to business and marketing, copying what your competitors are doing and failing to forge your own path can be a detrimental mistake.

While as marketers we are all acutely aware of the importance of differentiation, we’ve been trained for the majority of our lives to seek out the norm.

We spend the majority of our adolescent lives trying desperately not to be different. No one has ever been picked on for being too normal or not being different enough. We would beg our parents to buy us the same clothes little Jimmy or little Jamie wore. We’d want the same backpack and the same bike everyone else had. With the rise of the cell phone and later the smartphone, on hands and knees, we begged and pleaded for our parents to buy us the Razr, the StarTAC (bonus points if you didn’t have to Google that one), and later the iPhone. Did we truly want these things? Yes, but not just because they were cutting edge and nifty. We desired them because the people around us had them. We didn’t want to be the last to get these devices. We didn’t want to be different.

Thankfully, as we mature we begin to realize the fallacy that is trying to be normal. We start to become individuals and learn to appreciate that being different is often seen as beautiful. However, while we begin to celebrate being different on a personal level, it does not always translate into our business or professional lives.

We unconsciously and naturally seek out the normal, and if we want to be different—truly different in a way that creates an advantage—we have to work for it.

The truth of the matter is, anyone can be different. In fact, we all are very different. Even identical twins with the same DNA will often have starkly different personalities. As a business, the real challenge lies in being different in a way that is relevant, valuable to your audience, and creates an advantage.

“Strong products and services are highly differentiated from all other products and services. It’s that simple. It’s that difficult.” – Austin McGhie, Brand Is a Four Letter Word

Let’s explore the example of Revel Hotel & Casino. Revel is a 70-story luxury casino in Atlantic City that was built in 2012. There is simply not another casino of the same class in Atlantic City, but there might be a reason for this. Even if you’re not familiar with the city, a quick jump onto Atlantic City’s tourism website reveals that of the five hero banners that rotate, not one specifically mentions gambling, but three reference the boardwalk. This is further illustrated when exploring their internal linking structure. The beaches, boardwalk, and shopping all appear before a single mention of casinos. There simply isn’t as much of a market for high-end gamblers in the Atlantic City area; in the states Las Vegas serves that role. So while Revel has a unique advantage, their ability to attract customers to their resort has not resulted in profitable earnings reports. In Q2 2012, Revel had a gross operating loss of $35.177M, and in Q3 2012 that increased to $36.838M.

So you need to create a unique selling proposition (also known as unique selling point and commonly referred to as a USP), and your USP needs to be valuable to your audience and create a competitive advantage. Sounds easy enough, right? Now for the kicker. That advantage needs to be as sustainable as physically possible over the long term.

“How long will it take our competitors to duplicate our advantage?”

You really need to explore this question and the possible solutions your competitors could utilize to play catch-up or duplicate what you’ve done. Look no further than Google vs Bing to see this in action. No company out there is going to just give up because your USP is so much better; most will pivot or adapt in some way.

Let’s look at a Seattle-area coffee company of which you may or may not be familiar. Starbucks has tried quite a few times over the years to level-up their tea game with limited success, but the markets that Starbucks has really struggled to break into are the pastry, breads, dessert, and food markets.

Other stores had more success in these markets, and they thought that high-quality teas and bakery items were the USPs that differentiated them from the Big Bad Wolf that is Starbucks. And while they were right to think that their brick house would save them from the Big Bad Wolf for some time, this fable doesn’t end with the Big Bad Wolf in a boiling pot.

Never underestimate your competitor’s ability to be agile, specifically when overcoming a competitive disadvantage.

If your competitor can’t beat you by making a better product or service internally, they can always choose to buy someone who can.

After months of courting, on June 4th, 2012 Starbucks announced that they had come to an agreement to purchase La Boulange in order to “elevate core food offerings and build a premium, artisanal bakery brand.” If you’re a small-to-medium sized coffee shop and/or bakery that even indirectly competed with Starbucks, a new challenger approaches. And while those tea shops momentarily felt safe within the brick walls that guarded their USP, on the final day of that same year, the Big Bad Wolf huffed and puffed and blew a stack of cash all over Teavana. Making Teavana a wholly-owned subsidiary of Starbucks for the low, low price of $620M.

Sarcasm aside, this does a great job of illustrating the ability of companies—especially those with deep pockets—to be agile, and demonstrates that they often have an uncanny ability to overcome your company’s competitive advantage. In seven months, Starbucks went from a minor player in these markets to having all the tools they need to dominate tea and pastries. Have you tried their raspberry pound cake? It’s phenomenal.

Why does this matter to me?

Ok, we get it. We need to be different, and in a way that is relevant, valuable, defensible, and sustainable. But I’m not the CEO, or even the CMO. I cannot effect change on a company level; why does this matter to me?

I’m a firm believer that you effect change no matter what the name plate on your desk may say. Sure, you may not be able to call an all-staff meeting today and completely change the direction of your company tomorrow, but you can effect change on the parts of the business you do touch. No matter your title or area of responsibility, you need to know your company’s, client’s, or even a specific piece of content’s USP, and you need to ensure it is applied liberally to all areas of your work.

Look at this example SERP for “Mechanics”:

While yes, this search is very likely to be local-sensitive, that doesn’t mean you can’t stand out. Every single AdWords result, save one, has only the word “Mechanics” in the headline. (While the top of page ad is pulling description line 1 into the heading, the actual headline is still only “Mechanic.”) But even the one headline that is different doesn’t do a great job of illustrating the company’s USP. Mechanics at home? Whose home? Mine or theirs? I’m a huge fan of Steve Krug’s “Don’t Make Me Think,” and in this scenario there are too many questions I need answered before I’m willing to click through. “Mechanics; We Come To You” or even “Traveling Mechanics” illustrates this point much more clearly, and still fits within the 25-character limit for the headline.

If you’re an AdWords user, no matter how big or small your monthly spend may be, take a look at your top 10-15 keywords by volume and evaluate how well you’re differentiating yourself from the other brands in your industry. Test ad copy that draws attention to your USP and reap the rewards.

Now while this is simply an AdWords text ad example, the same concept can be applied universally across all of marketing.

Title tags & meta descriptions

As we alluded to above, not only do companies have USPs, but individual pieces of content can, and should, have their own USP. Use your title tag and meta description to illustrate what differentiates your piece of content from the competition and do so in a way that attracts the searcher’s click. Use your USP to your advantage. If you have already established a strong brand within a specific niche, great! Now use it to your advantage. Though it’s much more likely that you are competing against a strong brand, and in these scenarios ask yourself, “What makes our content different from theirs?” The answer you come up with is your content’s USP. Call attention to that in your title tag and meta description, and watch the CTR climb.

I encourage you to hop into your own site’s analytics and look at your top 10-15 organic landing pages and see how well you differentiate yourself. Even if you’re hesitant to negatively affect your inbound gold mines by changing the title tags, run a test and change up your meta description to draw attention to your USP. In an hour’s work, you just may make the change that pushes you a little further up those SERPs.

Branding

Let’s break outside the world of digital marketing and look at the world of branding. Tom’s Shoes competes against some heavy hitters in Nike, Adidas, Reebok, and Puma just to name a few. While Tom’s can’t hope to compete against the marketing budgets of these companies in a fair fight, they instead chose to take what makes them different, their USP, and disseminate it every chance they get. They have labeled themselves “The One for One” company. It’s in their homepage’s title tag, in every piece of marketing they put out, and it smacks you in the face when you land on their site. They even use the call-to-action “Get Good Karma” throughout their site.

Now as many of us may know, partially because of the scandal it created in late 2013, Tom’s is not actually a non-profit organization. No matter how you feel about the matter, this marketing strategy has created a positive effect on their bottom line. Fast Company conservatively estimated their revenues in 2013 at $250M, with many estimates being closer to the $300M mark. Not too bad of a slice of the pie when competing against the powerhouses Tom’s does.

Wherever you stand on this issue, Tom’s Shoes has done a phenomenal job of differentiating their brand from the big hitters in their industry.

Know your USP and disseminate it every chance you get.

This is worth repeating. Know your USP and disseminate it every chance you get, whether that be in title tags, ad copy, on-page copy, branding, or any other segment of your marketing campaigns. Online or offline, be different. And remember the quote that we started with, “The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

The amount of marketing knowledge that can be taken from this one simple statement is astounding. Heed the words, stand out from the crowd, and you will have success.

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Reblogged 4 years ago from tracking.feedpress.it

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

Exposing The Generational Content Gap: Three Ways to Reach Multiple Generations

Posted by AndreaLehr

With more people of all ages online than ever before, marketers must create content that resonates with multiple generations. Successful marketers realize that each generation has unique expectations, values and experiences that influence consumer behaviors, and that offering your audience content that reflects their shared interests is a powerful way to connect with them and inspire them to take action.

We’re in the midst of a generational shift, with
Millennials expected to surpass Baby Boomers in 2015 as the largest living generation. In order to be competitive, marketers need to realize where key distinctions and similarities lie in terms of how these different generations consume content and share it with with others.

To better understand the habits of each generation,
BuzzStream and Fractl surveyed over 1,200 individuals and segmented their responses into three groups: Millennials (born between 1977–1995), Generation X (born between 1965–1976), and Baby Boomers (born between 1946–1964). [Eds note: The official breakdown for each group is as follows: Millennials (1981-1997), Generation X (1965-1980), and Boomers (1946-1964)]

Our survey asked them to identify their preferences for over 15 different content types while also noting their opinions on long-form versus short-form content and different genres (e.g., politics, technology, and entertainment).

We compared their responses and found similar habits and unique trends among all three generations.

Here’s our breakdown of the three key takeaways you can use to elevate your future campaigns:

1. Baby Boomers are consuming the most content

However, they have a tendency to enjoy it earlier in the day than Gen Xers and Millennials.

Although we found striking similarities between the younger generations, the oldest generation distinguished itself by consuming the most content. Over 25 percent of Baby Boomers consume 20 or more hours of content each week. Additional findings:

  • Baby Boomers also hold a strong lead in the 15–20 hours bracket at 17 percent, edging out Gen Xers and Millennials at 12 and 11 percent, respectively
  • A majority of Gen Xers and Millennials—just over 22 percent each—consume between 5 and 10 hours per week
  • Less than 10 percent of Gen Xers consume less than five hours of content a week—the lowest of all three groups

We also compared the times of day that each generation enjoys consuming content. The results show that most of our respondents—over 30 percent— consume content between 8 p.m. and midnight. However, there are similar trends that distinguish the oldest generation from the younger ones:

  • Baby Boomers consume a majority of their content in the morning. Nearly 40 percent of respondents are online between 5 a.m. and noon.
  • The least popular time for most respondents to engage with content online is late at night, between midnight and 5 a.m., earning less than 10 percent from each generation
  • Gen X is the only generation to dip below 10 percent in the three U.S. time zones: 5 a.m. to 9 a.m., 6 to 8 p.m., and midnight to 5 a.m.

When Do We Consume Content

When it comes to which device each generation uses to consume content, laptops are the most common, followed by desktops. The biggest distinction is in mobile usage: Over 50 percent of respondents who use their mobile as their primary device for content consumption are Millennials. Other results reveal:

  • Not only do Baby Boomers use laptops the most (43 percent), but they also use their tablets the most. (40 percent of all primary tablet users are Baby Boomers).
  • Over 25 percent of Millennials use a mobile device as their primary source for content
  • Gen Xers are the least active tablet users, with less than 8 percent of respondents using it as their primary device

Device To Consume Content2. Preferred content types and lengths span all three generations

One thing every generation agrees on is the type of content they enjoy seeing online. Our results reveal that the top four content types— blog articles, images, comments, and eBooks—are exactly the same for Baby Boomers, Gen Xers, and Millennials. Additional comparisons indicate:

  • The least preferred content types—flipbooks, SlideShares, webinars, and white papers—are the same across generations, too (although not in the exact same order)
  • Surprisingly, Gen Xers and Millennials list quizzes as one of their five least favorite content types

Most Consumed Content Type

All three generations also agree on ideal content length, around 300 words. Further analysis reveals:

  • Baby Boomers have the highest preference for articles under 200 words, at 18 percent
  • Gen Xers have a strong preference for articles over 500 words compared to other generations. Over 20 percent of respondents favor long-form articles, while only 15 percent of Baby Boomers and Millennials share the same sentiment.
  • Gen Xers also prefer short articles the least, with less than 10 percent preferring articles under 200 words

Content Length PreferencesHowever, in regards to verticals or genres, where they consume their content, each generation has their own unique preference:

  • Baby Boomers have a comfortable lead in world news and politics, at 18 percent and 12 percent, respectively
  • Millennials hold a strong lead in technology, at 18 percent, while Baby Boomers come in at 10 percent in the same category
  • Gen Xers fall between Millennials and Baby Boomers in most verticals, although they have slight leads in personal finance, parenting, and healthy living
  • Although entertainment is the top genre for each generation, Millennials and Baby Boomers prefer it slightly more than than Gen Xers do

Favorite Content Genres

3. Facebook is the preferred content sharing platform across all three generations

Facebook remains king in terms of content sharing, and is used by about 60 percent of respondents in each generation studied. Surprisingly, YouTube came in second, followed by Twitter, Google+, and LinkedIn, respectively. Additional findings:

  • Baby Boomers share on Facebook the most, edging out Millennials by only a fraction of a percent
  • Although Gen Xers use Facebook slightly less than other generations, they lead in both YouTube and Twitter, at 15 percent and 10 percent, respectively
  • Google+ is most popular with Baby Boomers, at 8 percent, nearly double that of both Gen Xers and Millennials

Preferred Social PlatformAlthough a majority of each generation is sharing content on Facebook, the type of content they are sharing, especially visuals, varies by each age group. The oldest generation prefers more traditional content, such as images and videos. Millennials prefer newer content types, such as memes and GIFs, while Gen X predictably falls in between the two generations in all categories except SlideShares. Other findings:

  • The most popular content type for Baby Boomers is video, at 27 percent
  • Parallax is the least popular type for every generation, earning 1 percent or less in each age group
  • Millennials share memes the most, while less than 10 percent of Baby Boomers share similar content

Most Shared Visual ContentMarketing to several generations can be challenging, given the different values and ideas that resonate with each group. With the number of online content consumers growing daily, it’s essential for marketers to understand the specific types of content that each of their audiences connect with, and align it with their content marketing strategy accordingly.

Although there is no one-size-fits-all campaign, successful marketers can create content that multiple generations will want to share. If you feel you need more information getting started, you can review this deck of additional insights, which includes the preferred video length and weekend consuming habits of each generation discussed in this post.

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Reblogged 4 years ago from tracking.feedpress.it

Has Google Gone Too Far with the Bias Toward Its Own Content?

Posted by ajfried

Since the beginning of SEO time, practitioners have been trying to crack the Google algorithm. Every once in a while, the industry gets a glimpse into how the search giant works and we have opportunity to deconstruct it. We don’t get many of these opportunities, but when we do—assuming we spot them in time—we try to take advantage of them so we can “fix the Internet.”

On Feb. 16, 2015, news started to circulate that NBC would start removing images and references of Brian Williams from its website.

This was it!

A golden opportunity.

This was our chance to learn more about the Knowledge Graph.

Expectation vs. reality

Often it’s difficult to predict what Google is truly going to do. We expect something to happen, but in reality it’s nothing like we imagined.

Expectation

What we expected to see was that Google would change the source of the image. Typically, if you hover over the image in the Knowledge Graph, it reveals the location of the image.

Keanu-Reeves-Image-Location.gif

This would mean that if the image disappeared from its original source, then the image displayed in the Knowledge Graph would likely change or even disappear entirely.

Reality (February 2015)

The only problem was, there was no official source (this changed, as you will soon see) and identifying where the image was coming from proved extremely challenging. In fact, when you clicked on the image, it took you to an image search result that didn’t even include the image.

Could it be? Had Google started its own database of owned or licensed images and was giving it priority over any other sources?

In order to find the source, we tried taking the image from the Knowledge Graph and “search by image” in images.google.com to find others like it. For the NBC Nightly News image, Google failed to even locate a match to the image it was actually using anywhere on the Internet. For other television programs, it was successful. Here is an example of what happened for Morning Joe:

Morning_Joe_image_search.png

So we found the potential source. In fact, we found three potential sources. Seemed kind of strange, but this seemed to be the discovery we were looking for.

This looks like Google is using someone else’s content and not referencing it. These images have a source, but Google is choosing not to show it.

Then Google pulled the ol’ switcheroo.

New reality (March 2015)

Now things changed and Google decided to put a source to their images. Unfortunately, I mistakenly assumed that hovering over an image showed the same thing as the file path at the bottom, but I was wrong. The URL you see when you hover over an image in the Knowledge Graph is actually nothing more than the title. The source is different.

Morning_Joe_Source.png

Luckily, I still had two screenshots I took when I first saw this saved on my desktop. Success. One screen capture was from NBC Nightly News, and the other from the news show Morning Joe (see above) showing that the source was changed.

NBC-nightly-news-crop.png

(NBC Nightly News screenshot.)

The source is a Google-owned property: gstatic.com. You can clearly see the difference in the source change. What started as a hypothesis in now a fact. Google is certainly creating a database of images.

If this is the direction Google is moving, then it is creating all kinds of potential risks for brands and individuals. The implications are a loss of control for any brand that is looking to optimize its Knowledge Graph results. As well, it seems this poses a conflict of interest to Google, whose mission is to organize the world’s information, not license and prioritize it.

How do we think Google is supposed to work?

Google is an information-retrieval system tasked with sourcing information from across the web and supplying the most relevant results to users’ searches. In recent months, the search giant has taken a more direct approach by answering questions and assumed questions in the Answer Box, some of which come from un-credited sources. Google has clearly demonstrated that it is building a knowledge base of facts that it uses as the basis for its Answer Boxes. When it sources information from that knowledge base, it doesn’t necessarily reference or credit any source.

However, I would argue there is a difference between an un-credited Answer Box and an un-credited image. An un-credited Answer Box provides a fact that is indisputable, part of the public domain, unlikely to change (e.g., what year was Abraham Lincoln shot? How long is the George Washington Bridge?) Answer Boxes that offer more than just a basic fact (or an opinion, instructions, etc.) always credit their sources.

There are four possibilities when it comes to Google referencing content:

  • Option 1: It credits the content because someone else owns the rights to it
  • Option 2: It doesn’t credit the content because it’s part of the public domain, as seen in some Answer Box results
  • Option 3: It doesn’t reference it because it owns or has licensed the content. If you search for “Chicken Pox” or other diseases, Google appears to be using images from licensed medical illustrators. The same goes for song lyrics, which Eric Enge discusses here: Google providing credit for content. This adds to the speculation that Google is giving preference to its own content by displaying it over everything else.
  • Option 4: It doesn’t credit the content, but neither does it necessarily own the rights to the content. This is a very gray area, and is where Google seemed to be back in February. If this were the case, it would imply that Google is “stealing” content—which I find hard to believe, but felt was necessary to include in this post for the sake of completeness.

Is this an isolated incident?

At Five Blocks, whenever we see these anomalies in search results, we try to compare the term in question against others like it. This is a categorization concept we use to bucket individuals or companies into similar groups. When we do this, we uncover some incredible trends that help us determine what a search result “should” look like for a given group. For example, when looking at searches for a group of people or companies in an industry, this grouping gives us a sense of how much social media presence the group has on average or how much media coverage it typically gets.

Upon further investigation of terms similar to NBC Nightly News (other news shows), we noticed the un-credited image scenario appeared to be a trend in February, but now all of the images are being hosted on gstatic.com. When we broadened the categories further to TV shows and movies, the trend persisted. Rather than show an image in the Knowledge Graph and from the actual source, Google tends to show an image and reference the source from Google’s own database of stored images.

And just to ensure this wasn’t a case of tunnel vision, we researched other categories, including sports teams, actors and video games, in addition to spot-checking other genres.

Unlike terms for specific TV shows and movies, terms in each of these other groups all link to the actual source in the Knowledge Graph.

Immediate implications

It’s easy to ignore this and say “Well, it’s Google. They are always doing something.” However, there are some serious implications to these actions:

  1. The TV shows/movies aren’t receiving their due credit because, from within the Knowledge Graph, there is no actual reference to the show’s official site
  2. The more Google moves toward licensing and then retrieving their own information, the more biased they become, preferring their own content over the equivalent—or possibly even superior—content from another source
  3. If feels wrong and misleading to get a Google Image Search result rather than an actual site because:
    • The search doesn’t include the original image
    • Considering how poor Image Search results are normally, it feels like a poor experience
  4. If Google is moving toward licensing as much content as possible, then it could make the Knowledge Graph infinitely more complicated when there is a “mistake” or something unflattering. How could one go about changing what Google shows about them?

Google is objectively becoming subjective

It is clear that Google is attempting to create databases of information, including lyrics stored in Google Play, photos, and, previously, facts in Freebase (which is now Wikidata and not owned by Google).

I am not normally one to point my finger and accuse Google of wrongdoing. But this really strikes me as an odd move, one bordering on a clear bias to direct users to stay within the search engine. The fact is, we trust Google with a heck of a lot of information with our searches. In return, I believe we should expect Google to return an array of relevant information for searchers to decide what they like best. The example cited above seems harmless, but what about determining which is the right religion? Or even who the prettiest girl in the world is?

Religion-and-beauty-queries.png

Questions such as these, which Google is returning credited answers for, could return results that are perceived as facts.

Should we next expect Google to decide who is objectively the best service provider (e.g., pizza chain, painter, or accountant), then feature them in an un-credited answer box? The direction Google is moving right now, it feels like we should be calling into question their objectivity.

But that’s only my (subjective) opinion.

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Reblogged 4 years ago from tracking.feedpress.it

​Inbound Lead Generation: eCommerce Marketing’s Missing Link

Posted by Everett

If eCommerce businesses hope to remain competitive with Amazon, eBay, big box brands, and other online retail juggernauts, they’ll need to learn how to conduct content marketing, lead generation, and contact nurturing as part of a comprehensive inbound marketing strategy.

First, I will discuss some of the ways most online retailers are approaching email from the bottom of the funnel upward, and why this needs to be turned around. Then we can explore how to go about doing this within the framework of “Inbound Marketing” for eCommerce businesses. Lastly, popular marketing automation and email marketing solutions are discussed in the context of inbound marketing for eCommerce.

Key differences between eCommerce and lead generation approaches to email

Different list growth strategies

Email acquisition sources differ greatly between lead gen. sites and online stores. The biggest driver of email acquisition for most eCommerce businesses are their shoppers, especially when the business doesn’t collect an email address for their contact database until the shopper provides it during the check-out process—possibly, not until the very end.

With most B2B/B2C lead gen. websites, the entire purpose of every landing page is to get visitors to submit a contact form or pick up the phone. Often, the price tag for their products or services is much higher than those of an eCommerce site or involves recurring payments. In other words, what they’re selling is more difficult to sell. People take longer to make those purchasing decisions. For this reason, leads—in the form of contact names and email addresses—are typically acquired and nurtured without having first become a customer.

Contacts vs. leads

Whether it is a B2B or B2C website, lead gen. contacts (called leads) are thought of as potential customers (clients, subscribers, patients) who need to be nurtured to the point of becoming “sales qualified,” meaning they’ll eventually get a sales call or email that attempts to convert them into a customer.

On the other hand, eCommerce contacts are often thought of primarily as existing customers to whom the marketing team can blast coupons and other offers by email.

Retail sites typically don’t capture leads at the top or middle of the funnel. Only once a shopper has checked out do they get added to the list. Historically, the buying cycle has been short enough that eCommerce sites could move many first-time visitors directly to customers in a single visit.
But this has changed.

Unless your brand is very strong—possibly a luxury brand or one with an offline retail presence—it is probably getting more difficult (i.e. expensive) to acquire new customers. At the same time, attrition rates are rising. Conversion optimization helps by converting more bottom of the funnel visitors. SEO helps drive more traffic into the site, but mostly for middle-of-funnel (category page) and bottom-of-funnel (product page) visitors who may not also be price/feature comparison shopping, or are unable to convert right away because of device or time limitations.

Even savvy retailers publishing content for shoppers higher up in the funnel, such as buyer guides and reviews, aren’t getting an email address and are missing a lot of opportunities because of it.

attract-convert-grow-funnel-inflow-2.jpg

Here’s a thought. If your eCommerce site has a 10 percent conversion rate, you’re doing pretty good by most standards. But what happened to the other 90 percent of those visitors? Will you have the opportunity to connect with them again? Even if you bump that up a few percentage points with retargeting, a lot of potential revenue has seeped out of your funnel without a trace.

I don’t mean to bash the eCommerce marketing community with generalizations. Most lead gen. sites aren’t doing anything spectacular either, and a lot of opportunity is missed all around.

There are many eCommerce brands doing great things marketing-wise. I’m a big fan of
Crutchfield for their educational resources targeting early-funnel traffic, and Neman Tools, Saddleback Leather and Feltraiger for the stories they tell. Amazon is hard to beat when it comes to scalability, product suggestions and user-generated reviews.

Sadly, most eCommerce sites (including many of the major household brands) still approach marketing in this way…

The ol’ bait n’ switch: promising value and delivering spam

Established eCommerce brands have gigantic mailing lists (compared with lead gen. counterparts), to whom they typically send out at least one email each week with “offers” like free shipping, $ off, buy-one-get-one, or % off their next purchase. The lists are minimally segmented, if at all. For example, there might be lists for repeat customers, best customers, unresponsive contacts, recent purchasers, shoppers with abandoned carts, purchases by category, etc.

The missing points of segmentation include which campaign resulted in the initial contact (sometimes referred to as a cohort) and—most importantly—the persona and buying cycle stage that best applies to each contact.

Online retailers often send frequent “blasts” to their entire list or to a few of the large segments mentioned above. Lack of segmentation means contacts aren’t receiving emails based on their interests, problems, or buying cycle stage, but instead, are receiving what they perceive as “generic” emails.

The result of these missing segments and the lack of overarching strategy looks something like this:

My, What a Big LIST You Have!

iStock_000017047747Medium.jpg

TIME reported in 2012 on stats from Responsys that the average online retailer sent out between five and six emails the week after Thanksgiving. Around the same time, the Wall Street Journal reported that the top 100 online retailers sent an average of 177 emails apiece to each of their contacts in 2011. Averaged out, that’s somewhere between three and four emails each week that the contact is receiving from these retailers.

The better to SPAM you with!

iStock_000016088853Medium.jpg

A 2014 whitepaper from SimpleRelevance titled
Email Fail: An In-Depth Evaluation of Top 20 Internet Retailer’s Email Personalization Capabilities (
PDF) found that, while 70 percent of marketing executives believed personalization was of “utmost importance” to their business…

“Only 17 percent of marketing leaders are going beyond basic transactional data to deliver personalized messages to consumers.”

Speaking of email overload, the same report found that some major online retailers sent ten or more emails per week!

simplerelevance-email-report-frequency.png

The result?

All too often, the eCommerce business will carry around big, dead lists of contacts who don’t even bother reading their emails anymore. They end up scrambling toward other channels to “drive more demand,” but because the real problems were never addressed, this ends up increasing new customer acquisition costs.

The cycle looks something like this:

  1. Spend a fortune driving in unqualified traffic from top-of-the-funnel channels
  2. Ignore the majority of those visitors who aren’t ready to purchase
  3. Capture email addresses only for the few visitors who made a purchase
  4. Spam the hell out of those people until they unsubscribe
  5. Spend a bunch more money trying to fill the top of the funnel with even more traffic

It’s like trying to fill your funnel with a bucket full of holes, some of them patched with band-aids.

The real problems

  1. Lack of a cohesive strategy across marketing channels
  2. Lack of a cohesive content strategy throughout all stages of the buying cycle
  3. Lack of persona, buying cycle stage, and cohort-based list segmentation to nurture contacts
  4. Lack of tracking across customer touchpoints and devices
  5. Lack of gated content that provides enough value to early-funnel visitors to get them to provide their email address

So, what’s the answer?

Inbound marketing allows online retailers to stop competing with Amazon and other “price focused” competitors with leaky funnels, and to instead focus on:

  1. Persona-based content marketing campaigns designed to acquire email addresses from high-quality leads (potential customers) by offering them the right content for each stage in their buyer’s journey
  2. A robust marketing automation system that makes true personalization scalable
  3. Automated contact nurturing emails triggered by certain events, such as viewing specific content, abandoning their shopping cart, adding items to their wish list or performing micro-conversions like downloading a look book
  4. Intelligent SMM campaigns that match visitors and customers with social accounts by email addresses, interests and demographics—as well as social monitoring
  5. Hyper-segmented email contact lists to support the marketing automation described above, as well as to provide highly-customized email and shopping experiences
  6. Cross-channel, closed loop reporting to provide a complete “omnichannel” view of online marketing efforts and how they assist offline conversions, if applicable

Each of these areas will be covered in more detail below. First, let’s take a quick step back and define what it is we’re talking about here.

Inbound marketing: a primer

A lot of people think “inbound marketing” is just a way some SEO agencies are re-cloaking themselves to avoid negative associations with search engine optimization. Others think it’s synonymous with “internet marketing.” I think it goes more like this:

Inbound marketing is to Internet marketing as SEO is to inbound marketing: One piece of a larger whole.

There are many ways to define inbound marketing. A cursory review of definitions from several trusted sources reveals some fundamental similarities :

Rand Fishkin

randfishkin.jpeg

“Inbound Marketing is the practice of earning traffic and attention for your business on the web rather than buying it or interrupting people to get it. Inbound channels include organic search, social media, community-building content, opt-in email, word of mouth, and many others. Inbound marketing is particularly powerful because it appeals to what people are looking for and what they want, rather than trying to get between them and what they’re trying to do with advertising. Inbound’s also powerful due to the flywheel-effect it creates. The more you invest in Inbound and the more success you have, the less effort required to earn additional benefit.”


Mike King

mikeking.jpeg

“Inbound Marketing is a collection of marketing activities that leverage remarkable content to penetrate earned media channels such as Organic Search, Social Media, Email, News and the Blogosphere with the goal of engaging prospects when they are specifically interested in what the brand has to offer.”

This quote is from 2012, and is still just as accurate today. It’s from an
Inbound.org comment thread where you can also see many other takes on it from the likes of Ian Lurie, Jonathon Colman, and Larry Kim.


Inflow

inflow-logo.jpeg

“Inbound Marketing is a multi-channel, buyer-centric approach to online marketing that involves attracting, engaging, nurturing and converting potential customers from wherever they are in the buying cycle.”

From Inflow’s
Inbound Services page.


Wikipedia

wikipedia.jpeg

“Inbound marketing refers to marketing activities that bring visitors in, rather than marketers having to go out to get prospects’ attention. Inbound marketing earns the attention of customers, makes the company easy to be found, and draws customers to the website by producing interesting content.”

From
Inbound Marketing – Wikipedia.


Larry-Kim.jpeg

Larry Kim

“Inbound marketing” refers to marketing activities that bring leads and customers in when they’re ready, rather than you having to go out and wave your arms to try to get people’s attention.”

Via
Marketing Land in 2013. You can also read more of Larry Kim’s interpretation, along with many others, on Inbound.org.


Hubspot

“Instead of the old outbound marketing methods of buying ads, buying email lists, and praying for leads, inbound marketing focuses on creating quality content that pulls people toward your company and product, where they naturally want to be.”

Via
Hubspot, a marketing automation platform for inbound marketing.

When everyone has their own definition of something, it helps to think about what they have in common, as opposed to how they differ. In the case of inbound, this includes concepts such as:

  • Pull (inbound) vs. push (interruption) marketing
  • “Earning” media coverage, search engine rankings, visitors and customers with outstanding content
  • Marketing across channels
  • Meeting potential customers where they are in their buyer’s journey

Running your first eCommerce inbound marketing campaign

Audience personas—priority no. 1

The magic happens when retailers begin to hyper-segment their list based on buyer personas and other relevant information (i.e. what they’ve downloaded, what they’ve purchased, if they abandoned their cart…). This all starts with audience research to develop personas. If you need more information on persona development, try these resources:

Once personas are developed, retailers should choose one on which to focus. A complete campaign strategy should be developed around this persona, with the aim of providing the “right value” to them at the “right time” in their buyer’s journey.

Ready to get started?

We’ve developed a quick-start guide in the form of a checklist for eCommerce marketers who want to get started with inbound marketing, which you can access below.

inbound ecommerce checklist

Hands-on experience running one campaign will teach you more about inbound marketing than a dozen articles. My advice: Just do one. You will make mistakes. Learn from them and get better each time.

Example inbound marketing campaign

Below is an example of how a hypothetical inbound marketing campaign might play out, assuming you have completed all of the steps in the checklist above. Imagine you handle marketing for an online retailer of high-end sporting goods.

AT Hiker Tommy campaign: From awareness to purchase

When segmenting visitors and customers for a “high-end sporting goods / camping retailer” based on the East Coast, you identified a segment of “Trail Hikers.” These are people with disposable income who care about high-quality gear, and will pay top dollar if they know it is tested and reliable. The top trail on their list of destinations is the
Appalachian Trail (AT).

Top of the Funnel: SEO & Strategic Content Marketing

at-tommy.jpg

Tommy’s first action is to do “top of the funnel” research from search engines (one reason why SEO is still so important to a complete inbound marketing strategy).

A search for “Hiking the Appalachian Trail” turns up your article titled “What NOT to Pack When Hiking the Appalachian Trail,” which lists common items that are bulky/heavy, and highlights slimmer, lighter alternatives from your online catalog.

It also highlights the difference between cheap gear and the kind that won’t let you down on your 2,181 mile journey through the wilderness of Appalachia, something you learned was important to Tommy when developing his persona. This allows you to get the company’s value proposition of “tested, high-end, quality gear only” in front of readers very early in their buyer’s journey—important if you want to differentiate your site from all of the retailers racing Amazon to the bottom of their profit margins.

So far you have yet to make “contact” with AT Hiker Tommy. The key to “acquiring” a contact before the potential customer is ready to make a purchase is to provide something of value to that specific type of person (i.e. their persona) at that specific point in time (i.e. their buying cycle stage).

In this case, we need to provide value to AT Hiker Tommy while he is getting started on his research about hiking the Appalachian Trail. He has an idea of what gear not to bring, as well as some lighter, higher-end options sold on your site. At this point, however, he is not ready to buy anything without researching the trail more. This is where retailers lose most of their potential customers. But not you. Not this time…

Middle of the funnel: Content offers, personalization, social & email nurturing

at-hiker-ebook.png

On the “What NOT to Pack When Hiking the Appalachian Trail” article (and probably several others), you have placed a call-to-action (CTA) in the form of a button that offers something like:

Download our Free 122-page Guide to Hiking the Appalachian Trail

This takes Tommy to a landing page showcasing some of the quotes from the book, and highlighting things like:

“We interviewed over 50 ‘thru-hikers’ who completed the AT and have curated and organized the best first-hand tips, along with our own significant research to develop a free eBook that should answer most of your questions about the trail.”

By entering their email address potential customers agree to allow you to send them the free PDF downloadable guide to hiking the AT, and other relevant information about hiking.

An automated email is sent with a link to the downloadable PDF guide, and several other useful content links, such as “The AT Hiker’s Guide to Gear for the Appalachian Trail”—content designed to move Tommy further toward the purchase of hiking gear.

If Tommy still has not made a purchase within the next two weeks, another automated email is sent asking for feedback about the PDF guide (providing the link again), and to again provide the link to the “AT Hiker’s Guide to Gear…” along with a compelling offer just for him, perhaps “Get 20% off your first hiking gear purchase, and a free wall map of the AT!”

Having Tommy’s email address also allows you to hyper-target him on social channels, while also leveraging his initial visit to initiate retargeting efforts.

Bottom of the funnel: Email nurturing & strategic, segmented offers

Eventually Tommy makes a purchase, and he may or may not receive further emails related to this campaign, such as post-purchase emails for reviews, up-sells and cross-sells.

Upon checkout, Tommy checked the box to opt-in to weekly promotional emails. He is now on multiple lists. Your marketing automation system will automatically update Tommy’s status from “Contact” or lead, to “Customer” and potentially remove or deactivate him from the marketing automation system database. This is accomplished either by default integration features, or with the help of integration tools like
Zapier and IFTTT.

You have now nurtured Tommy from his initial research on Google all the way to his first purchase without ever having sent a spammy newsletter email full of irrelevant coupons and other offers. However, now that he is a loyal customer, Tommy finds value in these bottom-of-funnel email offers.

And this is just the start

Every inbound marketing campaign will have its own mix of appropriate channels. This post has focused mostly on email because acquiring the initial permission to contact the person is what fuels most of the other features offered by marketing automation systems, including:

  • Personalization of offers and other content on the site.
  • Knowing exactly which visitors are interacting on social media
  • Knowing where visitors and social followers are in the buying cycle and which persona best represents them, among other things.
  • Smart forms that don’t require visitors to put in the same information twice and allow you to build out more detailed profiles of them over time.
  • Blogging platforms that tie into email and marketing automation systems
  • Analytics data that isn’t blocked by Google and is tied directly to real people.
  • Closed-loop reporting that integrates with call-tracking and Google’s Data Import tool
  • Up-sell, cross-sell, and abandoned cart reclamation features
Three more things…
  1. If you can figure out a way to get Tommy to “log in” when he comes to your site, the personalization possibilities are nearly limitless.
  2. The persona above is based on a real customer segment. I named it after my friend Tommy Bailey, who actually did write the eBook
    Guide to Hiking the Appalachian Trail, featured in the image above.
  3. This Moz post is part of an inbound marketing campaign targeting eCommerce marketers, a segment Inflow identified while building out our own personas. Our hope, and the whole point of inbound marketing, is that it provides value to you.

Current state of the inbound marketing industry

Inbound has, for the the most part, been applied to businesses in which the website objective is to generate leads for a sales team to follow-up with and close the deal. An examination of various marketing automation platforms—a key component of scalable inbound marketing programs—highlights this issue.

Popular marketing automation systems

Most of the major marketing automation systems can be be used very effectively as the backbone of an inbound marketing program for eCommerce businesses. However, only one of them (Silverpop) has made significant efforts to court the eCommerce market with content and out-of-box features. The next closest thing is Hubspot, so let’s start with those two:

Silverpop – an IBMⓇ Company

silver-pop.jpeg

Unlike the other platforms below, right out of the box Silverpop allows marketers to tap into very specific behaviors, including the items purchased or left in the cart.

You can easily segment based on metrics like the Recency, Frequency and Monetary Value (RFM) of purchases:

silverpop triggered campaigns

You can automate personalized shopping cart abandonment recovery emails:

silverpop cart abandonment recovery

You can integrate with many leading brands offering complementary services, including: couponing, CRM, analytics, email deliverability enhancement, social and most major eCommerce platforms.

What you can’t do with Silverpop is blog, find pricing info on their website, get a free trial on their website or have a modern-looking user experience. Sounds like an IBMⓇ company, doesn’t it?

HubSpot

Out of all the marketing automation platforms on this list, HubSpot is the most capable of handling “inbound marketing” campaigns from start to finish. This should come as no surprise, given the phrase is credited to
Brian Halligan, HubSpot’s co-founder and CEO.

While they don’t specifically cater to eCommerce marketing needs with the same gusto they give to lead gen. marketing, HubSpot does have
an eCommerce landing page and a demo landing page for eCommerce leads, which suggests that their own personas include eCommerce marketers. Additionally, there is some good content on their blog written specifically for eCommerce.

HubSpot has allowed some key partners to develop plug-ins that integrate with leading eCommerce platforms. This approach works well with curation, and is not dissimilar to how Google handles Android or Apple handles their approved apps.

magento and hubspot

The
Magento Connector for HubSpot, which costs $80 per month, was developed by EYEMAGiNE, a creative design firm for eCommerce websites. A similar HubSpot-approved third-party integration is on the way for Bigcommerce.

Another eCommerce integration for Hubspot is a Shopify plug-in called
HubShoply, which was developed by Groove Commerce and costs $100 per month.

You can also use HubSpot’s native integration capabilities with
Zapier to sync data between HubSpot and most major eCommerce SaaS vendors, including the ones above, as well as WooCommerce, Shopify, PayPal, Infusionsoft and more. However, the same could be said of some of the other marketing automation platforms, and using these third-party solutions can sometimes feel like fitting a square peg into a round hole.

HubSpot can and does handle inbound marketing for eCommerce websites. All of the features are there, or easy enough to integrate. But let’s put some pressure on them to up their eCommerce game even more. The least they can do is put an eCommerce link in the footer:

hubspot menus

Despite the lack of clear navigation to their eCommerce content, HubSpot seems to be paying more attention to the needs of eCommerce businesses than the rest of the platforms below.

Marketo

Nothing about Marketo’s in-house marketing strategy suggests “Ecommerce Director Bob” might be one of their personas. The description for each of
their marketing automation packages (from Spark to Enterprise) mentions that it is “for B2B” websites.

marketo screenshot

Driving Sales could apply to a retail business so I clicked on the link. Nope. Clearly, this is for lead generation.

marketo marketing automation

Passing “purchase-ready leads” over to your “sales reps” is a good example of the type of language used throughout the site.

Make no mistake, Marketo is a top-notch marketing automation platform. Powerful and clean, it’s a shame they don’t launch a full-scale eCommerce version of their core product. In the meantime, there’s the
Magento Integration for Marketo Plug-in developed by an agency out of Australia called Hoosh Marketing.

magento marketo integration

I’ve never used this integration, but it’s part of Marketo’s
LaunchPoint directory, which I imagine is vetted, and Hoosh seems like a reputable agency.

Their
pricing page is blurred and gated, which is annoying, but perhaps they’ll come on here and tell everyone how much they charge.

marketo pricing page

As with all others except Silverpop, the Marketo navigation provides no easy paths to landing pages that would appeal to “Ecommerce Director Bob.”

Pardot

This option is a
SalesForce product, so—though I’ve never had the opportunity to use it—I can imagine Pardot is heavy on B2B/Sales and very light on B2C marketing for retail sites.

The hero image on their homepage says as much.

pardot tagline

pardot marketing automationAgain, no mention of eCommerce or retail, but clear navigation to lead gen and sales.

Eloqua / OMC

eloqua-logo.jpeg

Eloqua, now part of the Oracle Marketing Cloud (OMC), has a landing page
for the retail industry, on which they proclaim:

“Retail marketers know that the path to lifelong loyalty and increased revenue goes through building and growing deep client relationships.”

Since when did retail marketers start calling customers clients?

eloqua integration

The Integration tab on OMC’s “…Retail.html” page helpfully informs eCommerce marketers that their sales teams can continue using CRM systems like SalesForce and Microsoft Dynamics but doesn’t mention anything about eCommerce platforms and other SaaS solutions for eCommerce businesses.

Others

There are many other players in this arena. Though I haven’t used them yet, three I would love to try out are
SharpSpring, Hatchbuck and Act-On. But none of them appear to be any better suited to handle the concerns of eCommerce websites.

Where there’s a gap, there’s opportunity

The purpose of the section above wasn’t to highlight deficiencies in the tools themselves, but to illustrate a gap in who they are being marketed to and developed for.

So far, most of your eCommerce competitors probably aren’t using tools like these because they are not marketed to by the platforms, and don’t know how to apply the technology to online retail in a way that would justify the expense.

The thing is, a tool is just a tool

The
key concepts behind inbound marketing apply just as much to online retail as they do to lead generation.

In order to “do inbound marketing,” a marketing automation system isn’t even strictly necessary (in theory). They just help make the activities scalable for most businesses.

They also bring a lot of different marketing activities under one roof, which saves time and allows data to be moved and utilized between channels and systems. For example, what a customer is doing on social could influence the emails they receive, or content they see on your site. Here are some potential uses for most of the platforms above:

Automated marketing uses

  • Personalized abandoned cart emails
  • Post-purchase nurturing/reorder marketing
  • Welcome campaigns for the newsletter (other free offer) signups
  • Winback campaigns
  • Lead-nurturing email campaigns for cohorts and persona-based segments

Content marketing uses

  • Optimized, strategic blogging platforms, and frameworks
  • Landing pages for pre-transactional/educational offers or contests
  • Social media reporting, monitoring, and publishing
  • Personalization of content and user experience

Reporting uses

  • Revenue reporting (by segment or marketing action)
  • Attribution reporting (by campaign or content)

Assuming you don’t have the budget for a marketing automation system, but already have a good email marketing platform, you can still get started with inbound marketing. Eventually, however, you may want to graduate to a dedicated marketing automation solution to reap the full benefits.

Email marketing platforms

Most of the marketing automation systems claim to replace your email marketing platform, while many email marketing platforms claim to be marketing automation systems. Neither statement is completely accurate.

Marketing automation systems, especially those created specifically for the type of “inbound” campaigns described above, provide a powerful suite of tools all in one place. On the other hand, dedicated email platforms tend to offer “email marketing” features that are better, and more robust, than those offered by marketing automation systems. Some of them are also considerably cheaper—such as
MailChimp—but those are often light on even the email-specific features for eCommerce.

A different type of campaign

Email “blasts” in the form of B.O.G.O., $10 off or free shipping offers can still be very successful in generating incremental revenue boosts — especially for existing customers and seasonal campaigns.

The conversion rate on a 20% off coupon sent to existing customers, for instance, would likely pulverize the conversion rate of an email going out to middle-of-funnel contacts with a link to content (at least with how CR is currently being calculated by email platforms).

Inbound marketing campaigns can also offer quick wins, but they tend to focus mostly on non-customers after the first segmentation campaign (a campaign for the purpose of segmenting your list, such as an incentivised survey). This means lower initial conversion rates, but long-term success with the growth of new customers.

Here’s a good bet if works with your budget: Rely on a marketing automation system for inbound marketing to drive new customer acquisition from initial visit to first purchase, while using a good email marketing platform to run your “promotional email” campaigns to existing customers.

If you have to choose one or the other, I’d go with a robust marketing automation system.

Some of the most popular email platforms used by eCommerce businesses, with a focus on how they handle various Inbound Marketing activities, include:

Bronto

bronto.jpeg

This platform builds in features like abandoned cart recovery, advanced email list segmentation and automated email workflows that nurture contacts over time.

They also offer a host of eCommerce-related
features that you just don’t get with marketing automation systems like Hubspot and Marketo. This includes easy integration with a variety of eCommerce platforms like ATG, Demandware, Magento, Miva Merchant, Mozu and MarketLive, not to mention apps for coupons, product recommendations, social shopping and more. Integration with enterprise eCommerce platforms is one reason why Bronto is seen over and over again when browsing the Internet Retailer Top 500 reports.

On the other hand, Bronto—like the rest of these email platforms—doesn’t have many of the features that assist with content marketing outside of emails. As an “inbound” marketing automation system, it is incomplete because it focuses almost solely on one channel: email.

Vertical Response

verticalresponse.jpeg

Another juggernaut in eCommerce email marketing platforms, Vertical Response, has even fewer inbound-related features than Bronto, though it is a good email platform with a free version that includes up to 1,000 contacts and 4,000 emails per month (i.e. 4 emails to a full list of 1,000).

Oracle Marketing Cloud (OMC)

Responsys (the email platform), like Eloqua (the marketing automation system) was gobbled up by Oracle and is now part of their “Marketing Cloud.”

It has been my experience that when a big technology firm like IBM or Oracle buys a great product, it isn’t “great” for the users. Time will tell.

Listrak

listrak.jpeg

Out of the established email platforms for eCommerce, Listrak may do the best job at positioning themselves as a full inbound marketing platform.

Listrak’s value proposition is that they’re an “Omnichannel” solution. Everything is all in one “Single, Integrated Digital Marketing Platform for Retailers.” The homepage image promises solutions for Email, Mobile, Social, Web and In-Store channels.

I haven’t had the opportunity to work with Listrak yet, but would love to hear feedback in the comments on whether they could handle the kind of persona-based content marketing and automated email nurturing campaigns described in the example campaign above.

Key takeaways

Congratulations for making this far! Here are a few things I hope you’ll take away from this post:

  • There is a lot of opportunity right now for eCommerce sites to take advantage of marketing automation systems and robust email marketing platforms as the infrastructure to run comprehensive inbound marketing campaigns.
  • There is a lot of opportunity right now for marketing automation systems to develop content and build in eCommerce-specific features to lure eCommerce marketers.
  • Inbound marketing isn’t email marketing, although email is an important piece to inbound because it allows you to begin forming lasting relationships with potential customers much earlier in the buying cycle.
  • To see the full benefits of inbound marketing, you should focus on getting the right content to the right person at the right time in their shopping journey. This necessarily involves several different channels, including search, social and email. One of the many benefits of marketing automation systems is their ability to track your efforts here across marketing channels, devices and touch-points.

Tools, resources, and further reading

There is a lot of great content on the topic of Inbound marketing, some of which has greatly informed my own understanding and approach. Here are a few resources you may find useful as well.

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Keyword Research How To – When Nothing Seems to Work

http://2webchicks.com/ reveals some of the most interesting facts about keyword research how. Check the link now to see other good SEO stuffs.

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12 Common Reasons Reconsideration Requests Fail

Posted by Modestos

There are several reasons a reconsideration request might fail. But some of the most common mistakes site owners and inexperienced SEOs make when trying to lift a link-related Google penalty are entirely avoidable. 

Here’s a list of the top 12 most common mistakes made when submitting reconsideration requests, and how you can prevent them.

1. Insufficient link data

This is one of the most common reasons why reconsideration requests fail. This mistake is readily evident each time a reconsideration request gets rejected and the example URLs provided by Google are unknown to the webmaster. Relying only on Webmaster Tools data isn’t enough, as Google has repeatedly said. You need to combine data from as many different sources as possible. 

A good starting point is to collate backlink data, at the very least:

  • Google Webmaster Tools (both latest and sample links)
  • Bing Webmaster Tools
  • Majestic SEO (Fresh Index)
  • Ahrefs
  • Open Site Explorer

If you use any toxic link-detection services (e.g., Linkrisk and Link Detox), then you need to take a few precautions to ensure the following:

  • They are 100% transparent about their backlink data sources
  • They have imported all backlink data
  • You can upload your own backlink data (e.g., Webmaster Tools) without any limitations

If you work on large websites that have tons of backlinks, most of these automated services are very likely used to process just a fraction of the links, unless you pay for one of their premium packages. If you have direct access to the above data sources, it’s worthwhile to download all backlink data, then manually upload it into your tool of choice for processing. This is the only way to have full visibility over the backlink data that has to be analyzed and reviewed later. Starting with an incomplete data set at this early (yet crucial) stage could seriously hinder the outcome of your reconsideration request.

2. Missing vital legacy information

The more you know about a site’s history and past activities, the better. You need to find out (a) which pages were targeted in the past as part of link building campaigns, (b) which keywords were the primary focus and (c) the link building tactics that were scaled (or abused) most frequently. Knowing enough about a site’s past activities, before it was penalized, can help you home in on the actual causes of the penalty. Also, collect as much information as possible from the site owners.

3. Misjudgement

Misreading your current situation can lead to wrong decisions. One common mistake is to treat the example URLs provided by Google as gospel and try to identify only links with the same patterns. Google provides a very small number of examples of unnatural links. Often, these examples are the most obvious and straightforward ones. However, you should look beyond these examples to fully address the issues and take the necessary actions against all types of unnatural links. 

Google is very clear on the matter: “Please correct or remove all inorganic links, not limited to the samples provided above.

Another common area of bad judgement is the inability to correctly identify unnatural links. This is a skill that requires years of experience in link auditing, as well as link building. Removing the wrong links won’t lift the penalty, and may also result in further ranking drops and loss of traffic. You must remove the right links.


4. Blind reliance on tools

There are numerous unnatural link-detection tools available on the market, and over the years I’ve had the chance to try out most (if not all) of them. Because (and without any exception) I’ve found them all very ineffective and inaccurate, I do not rely on any such tools for my day-to-day work. In some cases, a lot of the reported “high risk” links were 100% natural links, and in others, numerous toxic links were completely missed. If you have to manually review all the links to discover the unnatural ones, ensuring you don’t accidentally remove any natural ones, it makes no sense to pay for tools. 

If you solely rely on automated tools to identify the unnatural links, you will need a miracle for your reconsideration request to be successful. The only tool you really need is a powerful backlink crawler that can accurately report the current link status of each URL you have collected. You should then manually review all currently active links and decide which ones to remove. 

I could write an entire book on the numerous flaws and bugs I have come across each time I’ve tried some of the most popular link auditing tools. A lot of these issues can be detrimental to the outcome of the reconsideration request. I have seen many reconsiderations request fail because of this. If Google cannot algorithmically identify all unnatural links and must operate entire teams of humans to review the sites (and their links), you shouldn’t trust a $99/month service to identify the unnatural links.

If you have an in-depth understanding of Google’s link schemes, you can build your own process to prioritize which links are more likely to be unnatural, as I described in this post (see sections 7 & 8). In an ideal world, you should manually review every single link pointing to your site. Where this isn’t possible (e.g., when dealing with an enormous numbers of links or resources are unavailable), you should at least focus on the links that have the more “unnatural” signals and manually review them.

5. Not looking beyond direct links

When trying to lift a link-related penalty, you need to look into all the links that may be pointing to your site directly or indirectly. Such checks include reviewing all links pointing to other sites that have been redirected to your site, legacy URLs with external inbound links that have been internally redirected owned, and third-party sites that include cross-domain canonicals to your site. For sites that used to buy and redirect domains in order increase their rankings, the quickest solution is to get rid of the redirects. Both Majestic SEO and Ahrefs report redirects, but some manual digging usually reveals a lot more.

PQPkyj0.jpg

6. Not looking beyond the first link

All major link intelligence tools, including Majestic SEO, Ahrefs and Open Site Explorer, report only the first link pointing to a given site when crawling a page. This means that, if you overly rely on automated tools to identify links with commercial keywords, the vast majority of them will only take into consideration the first link they discover on a page. If a page on the web links just once to your site, this is not big deal. But if there are multiple links, the tools will miss all but the first one.

For example, if a page has five different links pointing to your site, and the first one includes a branded anchor text, these tools will just report the first link. Most of the link-auditing tools will in turn evaluate the link as “natural” and completely miss the other four links, some of which may contain manipulative anchor text. The more links that get missed this way the more likely your reconsideration request will fail.

7. Going too thin

Many SEOs and webmasters (still) feel uncomfortable with the idea of losing links. They cannot accept the idea of links that once helped their rankings are now being devalued, and must be removed. There is no point trying to save “authoritative”, unnatural links out of fear of losing rankings. If the main objective is to lift the penalty, then all unnatural links need to be removed.

Often, in the first reconsideration request, SEOs and site owners tend to go too thin, and in the subsequent attempts start cutting deeper. If you are already aware of the unnatural links pointing to your site, try to get rid of them from the very beginning. I have seen examples of unnatural links provided by Google on PR 9/DA 98 sites. Metrics do not matter when it comes to lifting a penalty. If a link is manipulative, it has to go.

In any case, Google’s decision won’t be based only on the number of links that have been removed. Most important in the search giant’s eyes are the quality of links still pointing to your site. If the remaining links are largely of low quality, the reconsideration request will almost certainly fail. 

8. Insufficient effort to remove links

Google wants to see a “good faith” effort to get as many links removed as possible. The higher the percentage of unnatural links removed, the better. Some agencies and SEO consultants tend to rely too much on the use of the disavow tool. However, this isn’t a panacea, and should be used as a last resort for removing those links that are impossible to remove—after exhausting all possibilities to physically remove them via the time-consuming (yet necessary) outreach route. 

Google is very clear on this:

m4M4n3g.jpg?1

Even if you’re unable to remove all of the links that need to be removed, you must be able to demonstrate that you’ve made several attempts to have them removed, which can have a favorable impact on the outcome of the reconsideration request. Yes, in some cases it might be possible to have a penalty lifted simply by disavowing instead of removing the links, but these cases are rare and this strategy may backfire in the future. When I reached out to ex-googler Fili Wiese’s for some advice on the value of removing the toxic links (instead of just disavowing them), his response was very straightforward:

V3TmCrj.jpg 

9. Ineffective outreach

Simply identifying the unnatural links won’t get the penalty lifted unless a decent percentage of the links have been successfully removed. The more communication channels you try, the more likely it is that you reach the webmaster and get the links removed. Sending the same email hundreds or thousands of times is highly unlikely to result in a decent response rate. Trying to remove a link from a directory is very different from trying to get rid of a link appearing in a press release, so you should take a more targeted approach with a well-crafted, personalized email. Link removal request emails must be honest and to the point, or else they’ll be ignored.

Tracking the emails will also help in figuring out which messages have been read, which webmasters might be worth contacting again, or alert you of the need to try an alternative means of contacting webmasters.

Creativity, too, can play a big part in the link removal process. For example, it might be necessary to use social media to reach the right contact. Again, don’t trust automated emails or contact form harvesters. In some cases, these applications will pull in any email address they find on the crawled page (without any guarantee of who the information belongs to). In others, they will completely miss masked email addresses or those appearing in images. If you really want to see that the links are removed, outreach should be carried out by experienced outreach specialists. Unfortunately, there aren’t any shortcuts to effective outreach.

10. Quality issues and human errors

All sorts of human errors can occur when filing a reconsideration request. The most common errors include submitting files that do not exist, files that do not open, files that contain incomplete data, and files that take too long to load. You need to triple-check that the files you are including in your reconsideration request are read-only, and that anyone with the URL can fully access them. 

Poor grammar and language is also bad practice, as it may be interpreted as “poor effort.” You should definitely get the reconsideration request proofread by a couple of people to be sure it is flawless. A poorly written reconsideration request can significantly hinder your overall efforts.

Quality issues can also occur with the disavow file submission. Disavowing at the URL level isn’t recommended because the link(s) you want to get rid of are often accessible to search engines via several URLs you may be unaware of. Therefore, it is strongly recommended that you disavow at the domain or sub-domain level.

11. Insufficient evidence

How does Google know you have done everything you claim in your reconsideration request? Because you have to prove each claim is valid, you need to document every single action you take, from sent emails and submitted forms, to social media nudges and phone calls. The more information you share with Google in your reconsideration request, the better. This is the exact wording from Google:

“ …we will also need to see good-faith efforts to remove a large portion of inorganic links from the web wherever possible.”

12. Bad communication

How you communicate your link cleanup efforts is as essential as the work you are expected to carry out. Not only do you need to explain the steps you’ve taken to address the issues, but you also need to share supportive information and detailed evidence. The reconsideration request is the only chance you have to communicate to Google which issues you have identified, and what you’ve done to address them. Being honest and transparent is vital for the success of the reconsideration request.

There is absolutely no point using the space in a reconsideration request to argue with Google. Some of the unnatural links examples they share may not always be useful (e.g., URLs that include nofollow links, removed links, or even no links at all). But taking the argumentative approach veritably guarantees your request will be denied.

54adb6e0227790.04405594.jpg
Cropped from photo by Keith Allison, licensed under Creative Commons.

Conclusion

Getting a Google penalty lifted requires a good understanding of why you have been penalized, a flawless process and a great deal of hands-on work. Performing link audits for the purpose of lifting a penalty can be very challenging, and should only be carried out by experienced consultants. If you are not 100% sure you can take all the required actions, seek out expert help rather than looking for inexpensive (and ineffective) automated solutions. Otherwise, you will almost certainly end up wasting weeks or months of your precious time, and in the end, see your request denied.

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Reblogged 4 years ago from moz.com

The Danger of Crossing Algorithms: Uncovering The Cloaked Panda Update During Penguin 3.0

Posted by GlennGabe

Penguin 3.0 was one of the most anticipated algorithm updates in recent years when it rolled out on October 17, 2014. Penguin hadn’t run for over a year at that point,
and there were many webmasters sitting in Penguin limbo waiting for recovery. They had cleaned up their link profiles, disavowed what they could, and were
simply waiting for the next update or refresh. Unfortunately, Google was wrestling with the algo internally and over twelve months passed without an
update.

So when Pierre Far finally
announced Penguin 3.0 a few days later on October 21, a few things
stood out. First, this was
not a new algorithm like Gary Illyes had explained it would be at SMX East. It was a refresh and underscored
the potential problems Google was battling with Penguin (cough, negative SEO).

Second, we were not seeing the impact that we expected. The rollout seemed to begin with a heavier international focus and the overall U.S impact has been
underwhelming to say the least. There were definitely many fresh hits globally, but there were a number of websites that should have recovered but didn’t
for some reason. And many are still waiting for recovery today.

Third, the rollout would be slow and steady and could take weeks to fully complete. That’s unusual, but makes sense given the microscope Penguin 3.0 was
under. And this third point (the extended rollout) is even more important than most people think. Many webmasters are already confused when they get hit
during an acute algorithm update (for example, when an algo update rolls out on one day). But the confusion gets exponentially worse when there is an
extended rollout.

The more time that goes by between the initial launch and the impact a website experiences, the more questions pop up. Was it Penguin 3.0 or was it
something else? Since I work heavily with algorithm updates, I’ve heard similar questions many times over the past several years. And the extended Penguin
3.0 rollout is a great example of why confusion can set in. That’s my focus today.


Penguin, Pirate, and the anomaly on October 24

With the Penguin 3.0 rollout, we also had
Pirate 2 rolling out. And yes, there are
some websites that could be impacted by both. That added a layer of complexity to the situation, but nothing like what was about to hit. You see, I picked
up a very a strange anomaly on October 24. And I clearly saw serious movement on that day (starting late in the day ET).

So, if there was a third algorithm update, then that’s
three potential algo updates rolling out at the same time. More about this soon,
but it underscores the confusion that can set in when we see extended rollouts, with a mix of confirmed and unconfirmed updates.


Penguin 3.0 tremors and analysis

Since I do a lot of Penguin work, and have researched many domains impacted by Penguin in the past, I heavily studied the Penguin 3.0 rollout. I 
published a blog post based on the first ten days of the update, which included some interesting findings for sure.

And based on the extended rollout, I definitely saw Penguin tremors beyond the initial October 17 launch. For example, check out the screenshot below of a
website seeing Penguin impact on October 17, 22, and 25.

But as mentioned earlier, something else happened on October 24 that set off sirens in my office. I started to see serious movement on sites impacted by
Panda, and not Penguin. And when I say serious movement, I’m referring to major traffic gains or losses all starting on October 24. Again, these were sites heavily dealing with Panda and had
clean link profiles. Check out the trending below from October 24 for several
sites that saw impact.


A good day for a Panda victim:



A bad day for a Panda victim:



And an incredibly frustrating day for a 9/5 recovery that went south on 10/24:

I saw this enough that I tweeted heavily about it and
included a section about Panda in my Penguin 3.0 blog post. And
that’s when something wonderful happened, and it highlights the true beauty and power of the internet.

As more people saw my tweets and read my post, I started receiving messages from other webmasters explaining that
they saw the same exact thing, and on their websites dealing with Panda and not Penguin. And not only
did they tell me about, they
showed me the impact.

I received emails containing screenshots and tweets with photos from Google Analytics and Google Webmaster Tools. It was amazing to see, and it confirmed
that we had just experienced a Panda update in the middle of a multi-week Penguin rollout. Yes, read that line again. Panda during Penguin, right when the
internet world was clearly focused on Penguin 3.0.

That was a sneaky move Google… very sneaky. 🙂

So, based on what I explained earlier about webmaster confusion and algorithms, can you tell what happened next? Yes, massive confusion ensued. We had the
trifecta of algorithm updates with Penguin, Pirate, and now Panda.


Webmaster confusion and a reminder of the algo sandwich from 2012

So, we had a major algorithm update during two other major algorithm updates (Penguin and Pirate) and webmaster confusion was hitting extremely high
levels. And I don’t blame anyone for being confused. I’m neck deep in this stuff and it confused me at first.

Was the October 24 update a Penguin tremor or was this something else? Could it be Pirate? And if it was indeed Panda, it would have been great if Google told
us it was Panda! Or did they want to throw off SEOs analyzing Penguin and Pirate? Does anyone have a padded room I can crawl into?

Once I realized this was Panda, and started to communicate the update via Twitter and my blog, I had a number of people ask me a very important question:


“Glenn, would Google really roll out two or three algorithm updates so close together, or at the same time?”

Why yes, they would. Anyone remember the algorithm sandwich from April of 2012? That’s when Google rolled out Panda on April 19, then Penguin 1.0 on April 24,
followed by Panda on April 27. Yes, we had three algorithm updates all within ten days. And let’s not forget that the Penguin update on April 24, 2012 was the
first of its kind! So yes, Google can, and will, roll out multiple major algos around the same time.

Where are we headed? It’s fascinating, but not pretty


Panda is near real-time now

When Panda 4.1 rolled out on September 23, 2014, I immediately disliked the title and version number of the update. Danny Sullivan named it 4.1, so it stuck. But for
me, that was not 4.1… not even close. It was more like 4.75. You see, there have been a number of Panda tremors and updates since P4.0 on May 20,
2014.

I saw what I was calling “tremors”
nearly weekly based on having access to a large amount of Panda data (across sites, categories, and countries).
And based on what I was seeing, I reached out to John Mueller at Google to clarify the tremors. John’s response was great and confirmed what I was seeing.
He explained that there
was not a set frequency for algorithms like Panda. Google can roll out an algorithm, analyze the
SERPs, refine the algo to get the desired results, and keep pushing it out. And that’s exactly what I was seeing (again, almost weekly since Panda 4.0).


When Panda and Penguin meet in real time…

…they will have a cup of coffee and laugh at us. 🙂 So, since Panda is near-real time, the crossing of major algorithm updates is going to happen.
And we just experienced an important one on October 24 with Penguin, Pirate, and Panda. But it could (and probably will) get more chaotic than what we have now.
We are quickly approaching a time where major algorithm updates crafted in a lab will be unleashed on the web in near-real time or in actual real time.

And if organic search traffic from Google is important to you, then pay attention. We’re about to take a quick trip into the future of Google and SEO. And
after hearing what I have to say, you might just want the past back…


Google’s brilliant object-oriented approach to fighting webspam

I have presented at the past two SES conferences about Panda, Penguin, and other miscellaneous disturbances in the force. More about those “other
disturbances” soon. In my presentation, one of my slides looks like this:

Over the past several years, Google has been using a brilliant, object-oriented approach to fighting webspam and low quality content. Webspam engineers can
craft external algorithms in a lab and then inject them into the real-time algorithm whenever they want. It’s brilliant because it isolates specific
problems, while also being extremely scalable. And by the way, it should scare the heck out of anyone breaking the rules.

For example, we have Panda, Penguin, Pirate, and Above the Fold. Each was crafted to target a specific problem and can be unleashed on the web whenever
Google wants. Sure, there are undoubtedly connections between them (either directly or indirectly), but each specific algo is its own black box. Again,
it’s object-oriented.

Now, Panda is a great example of an algorithm that has matured to where Google highly trusts it. That’s why Google announced in June of 2013 that Panda
would roll out monthly, over ten days. And that’s also why it matured even more with Panda 4.0 (and why I’ve seen tremors almost weekly.)

And then we had Gary Illyes explain that Penguin was moving along the same path. At SMX East,
Gary explained that the new Penguin algorithm (which clearly didn’t roll out on October 17) would be structured in a way where subsequent updates could be rolled out more easily.
You know, like Panda.

And by the way, what if this happens to Pirate, Above the Fold, and other algorithms that Google is crafting in its Frankenstein lab? Well my friends, then
we’ll have absolute chaos and society as we know it will crumble. OK, that’s a bit dramatic, but you get my point.

We already have massive confusion now… and a glimpse into the future reveals a continual flow of major algorithms running in real-time, each that
could pummel a site to the ground. And of course, with little or no sign of which algo actually caused the destruction. I don’t know about you, but I just
broke out in hives. 🙂


Actual example of what (near) real-time updates can do

After Panda 4.0, I saw some very strange Panda movement for sites impacted by recent updates. And it underscores the power of near-real time algo updates.
As a quick example,
temporary Panda recoveries can happen if you
don’t get out of the gray area enough. And now that we are seeing Panda tremors almost weekly, you can experience potential turbulence several times per
month.

Here is a screenshot from a site that recovered from Panda, didn’t get out of the gray area and reentered the strike zone, just five days later.

Holy cow, that was fast. I hope they didn’t plan any expensive trips in the near future. This is exactly what can happen when major algorithms roam the web
in real time. One week you’re looking good and the next week you’re in the dumps. Now, at least I knew this was Panda. The webmaster could tackle more
content problems and get out of the gray area… But the ups and downs of a Panda roller coaster ride can drive a webmaster insane. It’s one of the
reasons I recommend making
significant changes when
you’ve been hit by Panda. Get as far out of the gray area as possible.


An “automatic action viewer” in Google Webmaster Tools could help (and it’s actually being discussed internally by Google)

Based on webmaster confusion, many have asked Google to create an “automatic action viewer” in Google Webmaster Tools. It would be similar to the “manual
actions viewer,” but focused on algorithms that are demoting websites in the search results (versus penalties). Yes, there is a difference by the way.

The new viewer would help webmasters better understand the types of problems that are being impacted by algorithms like Panda, Penguin, Pirate, Above the
Fold, and others. Needless to say, this would be incredibly helpful to webmasters, business owners, and SEOs.

So, will we see that viewer any time soon? Google’s John Mueller
addressed this question during the November 3 webmaster hangout (at 38:30).

John explained they are trying to figure something out, but it’s not easy. There are so many algorithms running that they don’t want to provide feedback
that is vague or misleading. But, John did say they are discussing the automatic action viewer internally. So you never know…


A quick note about Matt Cutts

As many of you know, Matt Cutts took an extended leave this past summer (through the end of October). Well, he announced on Halloween that he is
extending his leave into 2015. I won’t go crazy here talking about his decision overall, but I will
focus on how this impacts webmasters as it relates to algorithm updates and webspam.

Matt does a lot more than just announce major algo updates… He actually gets involved when collateral damage rears its ugly head. And there’s not a
faster way to rectify a flawed algo update than to have Mr. Cutts involved. So before you dismiss Matt’s extended leave as uneventful, take a look at the
trending below:

Notice the temporary drop off a cliff, then 14 days of hell, only to see that traffic return? That’s because Matt got involved. That’s the
movie blog fiasco from early 2014 that I heavily analyzed. If
Matt was not notified of the drop via Twitter, and didn’t take action, I’m not sure the movie blogs that got hit would be around today. I told Peter from
SlashFilm that his fellow movie blog owners should all pay him a bonus this year. He’s the one that pinged Matt via Twitter and got the ball rolling.

It’s just one example of how having someone with power out front can nip potential problems in the bud. Sure, the sites experienced two weeks of utter
horror, but traffic returned once Google rectified the problem. Now that Matt isn’t actively helping or engaged, who will step up and be that guy? Will it
be John Mueller, Pierre Far, or someone else? John and Pierre are greatly helpful, but will they go to bat for a niche that just got destroyed? Will they
push changes through so sites can turn around? And even at its most basic level, will they even be aware the problem exists?

These are all great questions, and I don’t want to bog down this post (it’s already incredibly long). But don’t laugh off Matt Cutts taking an extended
leave. If he’s gone for good, you might only realize how important he was to the SEO community
after he’s gone. And hopefully it’s not because
your site just tanked as collateral damage during an algorithm update. Matt might be
running a marathon or trying on new Halloween costumes. Then where will you be?


Recommendations moving forward:

So where does this leave us? How can you prepare for the approaching storm of crossing algorithms? Below, I have provided several key bullets that I think
every webmaster should consider. I recommend taking a hard look at your site
now, before major algos are running in near-real time.

  • Truly understand the weaknesses with your website. Google will continue crafting external algos that can be injected into the real-time algorithm.
    And they will go real-time at some point. Be ready by cleaning up your site now.
  • Document all changes and fluctuations the best you can. Use annotations in Google Analytics and keep a spreadsheet updated with detailed
    information.
  • Along the same lines, download your Google Webmaster Tools data monthly (at least). After helping many companies with algorithm hits, that
    information is incredibly valuable, and can help lead you down the right recovery path.
  • Use a mix of audits and focus groups to truly understand the quality of your site. I mentioned in my post about

    aggressive advertising and Panda

    that human focus groups are worth their weight in gold (for surfacing Panda-related problems). Most business owners are too close to their own content and
    websites to accurately measure quality. Bias can be a nasty problem and can quickly lead to bamboo-overflow on a website.
  • Beyond on-site analysis, make sure you tackle your link profile as well. I recommend heavily analyzing your inbound links and weeding out unnatural
    links. And use the disavow tool for links you can’t remove. The combination of enhancing the quality of your content, boosting engagement, knocking down
    usability obstacles, and cleaning up your link profile can help you achieve long-term SEO success. Don’t tackle one quarter of your SEO problems. Address
    all of them.
  • Remove barriers that inhibit change and action. You need to move fast. You need to be decisive. And you need to remove red tape that can bog down
    the cycle of getting changes implemented. Don’t water down your efforts because there are too many chefs in the kitchen. Understand the changes that need
    to be implemented, and take action. That’s how you win SEO-wise.


Summary: Are you ready for the approaching storm?

SEO is continually moving and evolving, and it’s important that webmasters adapt quickly. Over the past few years, Google’s brilliant object-oriented
approach to fighting webspam and low quality content has yielded algorithms like Panda, Penguin, Pirate, and Above the Fold. And more are on their way. My
advice is to get your situation in order now, before crossing algorithms blend a recipe of confusion that make it exponentially harder to identify, and
then fix, problems riddling your website.

Now excuse me while I try to build a flux capacitor. 🙂

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