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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Why the Links You’ve Built Aren’t Helping Your Page Rank Higher – Whiteboard Friday

Posted by randfish

Link building can be incredibly effective, but sometimes a lot of effort can go into earning links with absolutely no improvement in rankings. Why? In today’s Whiteboard Friday, Rand shows us four things we should look at in these cases, help us hone our link building skills and make the process more effective.

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

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re chatting about why link building sometimes fails.

So I’ve got an example here. I’m going to do a search for artificial sweeteners. Let’s say I’m working for these guys, ScienceMag.org. Well, this is actually in position 10. I put it in position 3 here, but I see that I’m position 10. I think to myself, “Man, if I could get higher up on this page, that would be excellent. I’ve already produced the content. It’s on my domain. Like, Google seems to have indexed it fine. It’s performing well enough to perform on page one, granted at the bottom of page one, for this competitive query. Now I want to move my rankings up.”

So a lot of SEOs, naturally and historically, for a long time have thought, “I need to build more links to that page. If I can get more links pointing to this page, I can move up the rankings.” Granted, there are some other ways to do that too, and we’ve discussed those in previous Whiteboard Fridays. But links are one of the big ones that people use.

I think one of the challenges that we encounter is sometimes we invest that effort. We go through the process of that outreach campaign, talking to bloggers and other news sites and looking at where our link sources are coming from and trying to get some more of those. It just doesn’t seem to do anything. The link building appears to fail. It’s like, man, I’ve got all these nice links and no new results. I didn’t move up at all. I am basically staying where I am, or maybe I’m even falling down. Why is that? Why does link building sometimes work so well and so clearly and obviously, and sometimes it seems to do nothing at all?

What are some possible reasons link acquisition efforts may not be effective?

Oftentimes if you get a fresh set of eyes on it, an outside SEO perspective, they can do this audit, and they’ll walk through a lot of this stuff and help you realize, “Oh yeah, that’s probably why.” These are things that you might need to change strategically or tactically as you approach this problem. But you can do this yourself as well by looking at why a link building campaign, why a link building effort, for a particular page, might not be working.

1) Not the right links

First one, it’s not the right links. Not the right links, I mean a wide range of things, even broader than what I’ve listed here. But a lot of times that could mean low domain diversity. Yeah, you’re getting new links, but they’re coming from all the same places that you always get links from. Google, potentially, maybe views that as not particularly worthy of moving you up the rankings, especially around competitive queries.

It might be trustworthiness of source. So maybe they’re saying “Yeah, you got some links, but they’re not from particularly trustworthy places.” Tied into that maybe we don’t think or we’re sure that they’re not editorial. Maybe we think they’re paid, or we think they’re promotional in some way rather than being truly editorially given by this independent resource.

They might not come from a site or from a page that has the authority that’s necessary to move you up. Again, particularly for competitive queries, sometimes low-value links are just that. They’re not going to move the needle, especially not like they used to three, four, five or six years ago, where really just a large quantity of links, even from diverse domains, even if they were crappy links on crappy pages on relatively crappy or unknown websites would move the needle, not so much anymore. Google is seeing a lot more about these things.

Where else does the source link to? Is that source pointing to other stuff that is potentially looking manipulative to Google and so they discounted the outgoing links from that particular domain or those sites or those pages on those sites?

They might look at the relevance and say, “Hey, you know what? Yeah, you got linked to by some technology press articles. That doesn’t really have anything to do with artificial sweeteners, this topic, this realm, or this region.” So you’re not getting the same result. Now we’ve shown that off-topic links can oftentimes move the rankings, but in particular areas and in health, in fact, may be one of those Google might be more topically sensitive to where the links are coming from than other places.

Location on page. So I’ve got a page here and maybe all of my links are coming from a bunch of different domains, but it’s always in the right sidebar and it’s always in this little feed section. So Google’s saying, “Hey, that’s not really an editorial endorsement. That’s just them showing all the links that come through your particular blog feed or a subscription that they’ve got to your content or whatever it is promotionally pushing out. So we’re not going to count it that way.” Same thing a lot of times with footer links. Doesn’t work quite as well. If you’re being honest with yourself, you really want those in content links. Generally speaking, those tend to perform the best.

Or uniqueness. So they might look and they might say, “Yeah, you’ve got a ton of links from people who are republishing your same article and then just linking back to it. That doesn’t feel to us like an editorial endorsement, and so we’re just going to treat those copies as if those links didn’t exist at all.” But the links themselves may not actually be the problem. I think this can be a really important topic if you’re doing link acquisition auditing, because sometimes people get too focused on, “Oh, it must be something about the links that we’re getting.” That’s not always the case actually.

2) Not the right content

Sometimes it’s not the right content. So that could mean things like it’s temporally focused versus evergreen. So for different kinds of queries, Google interprets the intent of the searchers to be different. So it could be that when they see a search like “artificial sweeteners,” they say, “Yeah, it’s great that you wrote this piece about this recent research that came out. But you know what, we’re actually thinking that searchers are going to want in the top few results something that’s evergreen, that contains all the broad information that a searcher might need around this particular topic.”

That speaks to it might not answer the searchers questions. You might think, “Well, I’m answering a great question here.” The problem is, yeah you’re answering one. Searchers may have many questions that they’re asking around a topic, and Google is looking for something comprehensive, something that doesn’t mean a searcher clicks your result and then says, “Well, that was interesting, but I need more from a different result.” They’re looking for the one true result, the one true answer that tells them, “Hey, this person is very happy with these types of results.”

It could be poor user experience causing people to bounce back. That could be speed things, UI things, layout things, browser support things, multi-device support things. It might not use language formatting or text that people or engines can interpret as on the topic. Perhaps this is way over people’s heads, far too scientifically focused, most searchers can’t understand the language, or the other way around. It’s a highly scientific search query and a very advanced search query and your language is way dumbed down. Google isn’t interpreting that as on-topic. All the Hummingbird and topic modeling kind of things that they have say this isn’t for them.

Or it might not match expectations of searchers. This is distinct and different from searchers’ questions. So searchers’ questions is, “I want to know how artificial sweeteners might affect me.” Expectations might be, “I expect to learn this kind of information. I expect to find out these things.” For example, if you go down a rabbit hole of artificial sweeteners will make your skin shiny, they’re like, “Well, that doesn’t meet with my expectation. I don’t think that’s right.” Even if you have some data around that, that’s not what they were expecting to find. They might bounce back. Engines might not interpret you as on-topic, etc. So lots of content kinds of things.

3) Not the right domain

Then there are also domain issues. You might not have the right domain. Your domain might not be associated with the topic or content that Google and searchers are expecting. So they see Mayo Clinic, they see MedicineNet, and they go, “ScienceMag? Do they do health information? I don’t think they do. I’m not sure if that’s an appropriate one.” It might be perceived, even if you aren’t, as spammy or manipulative by Google, more probably than by searchers. Or searchers just won’t click your brand for that content. This is a very frustrating one, because we have seen a ton of times when search behavior is biased by the brand itself, by what’s in this green text here, the domain name or the brand name that Google might show there. That’s very frustrating, but it means that you need to build brand affinity between that topic, that keyword, and what’s in searchers’ heads.

4) Accessibility or technical issues

Then finally, there could be some accessibility or technical issues. Usually when that’s the case, you will notice pretty easily because the page will have an error. It won’t show the content properly. The cache will be an issue. That’s a rare one, but you might want to check for it as well.

But hopefully, using this kind of an audit system, you can figure out why a link building campaign, a link building effort isn’t working to move the needle on your rankings.

With that, we will see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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​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.

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

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Leveraging Panda to Get Out of Product Feed Jail

Posted by MichaelC

This is a story about Panda, customer service, and differentiating your store from others selling the same products.

Many e-commerce websites get the descriptions, specifications, and imagery for products they sell from feeds or databases provided by the
manufacturers. The manufacturers might like this, as they control how their product is described and shown. However, it does their retailers
no good when they are trying to rank for searches for those products and they’ve got the exact same content as every other retailer. If the content
in the feed is thin, then you’ll have pages with…well….thin content. And if there’s a lot of content for the products, then you’ll have giant blocks of content that
Panda might spot as being the same as they’ve seen on many other sites. To throw salt on the wound, if the content is really crappy, badly written,
or downright wrong, then the retailers’ sites will look low-quality to Panda and users as well.

Many webmasters see Panda as a type of Google penalty—but it’s not, really. Panda is a collection of measurements Google
is taking of your web pages to try and give your pages a rating on how happy users are likely to be with those pages.
It’s not perfect, but then again—neither is your website.

Many SEO folks (including me) tend to focus on the kinds of tactical and structural things you can do to make Panda see
your web pages as higher quality: things like adding big, original images, interactive content like videos and maps, and
lots and lots and lots and lots of text. These are all good tactics, but let’s step back a bit and look at a specific
example to see WHY Panda was built to do this, and from that, what we can do as retailers to enrich the content we have
for e-commerce products where our hands are a bit tied—we’re getting a feed of product info from the manufacturers, the same
as every other retailer of those products.

I’m going to use a real-live example that I suffered through about a month ago. I was looking for a replacement sink
stopper for a bathroom sink. I knew the brand, but there wasn’t a part number on the part I needed to replace. After a few Google
searches, I think I’ve found it on Amazon:


Don’t you wish online shopping was always this exciting?

What content actually teaches the customer

All righty… my research has shown me that there are standard sizes for plug stoppers. In fact, I initially ordered a
“universal fit sink stopper.” Which didn’t fit. Then I found 3 standard diameters, and 5 or 6 standard lengths.
No problem…I possess that marvel of modern tool chests, a tape measure…so I measure the part I have that I need to replace. I get about 1.5″ x 5″.
So let’s scroll down to the product details to see if it’s a match:

Kohler sink stopper product info from hell

Whoa. 1.2 POUNDS? This sink stopper must be made of
Ununoctium.
The one in my hand weighs about an ounce. But the dimensions
are way off as well: a 2″ diameter stopper isn’t going to fit, and mine needs to be at least an inch longer.

I scroll down to the product description…maybe there’s more detail there, maybe the 2″ x 2″ is the box or something.

I've always wanted a sink stopper designed for long long

Well, that’s less than helpful, with a stupid typo AND incorrect capitalization AND a missing period at the end.
Doesn’t build confidence in the company’s quality control.

Looking at the additional info section, maybe this IS the right part…the weight quoted in there is about right:

Maybe this is my part after all

Where else customers look for answers

Next I looked at the questions and answers bit, which convinced me that it PROBABLY was the right part:

Customers will answer the question if the retailer won't...sometimes.

If I was smart, I would have covered my bets by doing what a bunch of other customers also did: buy a bunch of different parts,
and surely one of them will fit. Could there
possibly was a clearer signal that the product info was lacking than this?

If you can't tell which one to buy, buy them all!

In this case, that was probably smarter than spending another 1/2 hour of my time snooping around online. But in general, people
aren’t going to be willing to buy THREE of something just to make sure they get the right one. This cheap part was an exception.

So, surely SOMEONE out there has the correct dimensions of this part on their site—so I searched for the part number I saw on the Amazon
listing. But as it turned out, that crappy description and wrong weight and dimensions were on every site I found…because they came from
the manufacturer.

Better Homes and Gardens...but not better description.

A few of the sites had edited out the “designed for long long” bit, but apart from that, they were all the same.

What sucks for the customer is an opportunity for you

Many, many retailers are in this same boat—they get their product info from the manufacturer, and if the data sucks in their feed,
it’ll suck on their site. Your page looks weak to both users and to Panda, and it looks the same as everybody else’s page for that product…to
both users and to Panda. So (a) you won’t rank very well, and (b) if you DO manage to get a customer to that page, it’s not as likely to convert
to a sale.

What can you do to improve on this? Here’s a few tactics to consider.

1. Offer your own additional description and comments

Add a new field to your CMS for your own write-ups on products, and when you discover issues like the above, you can add your own information—and
make it VERY clear what’s the manufacturer’s stock info and what you’ve added (that’s VALUE-ADDED) as well. My client
Sports Car Market magazine does this with their collector car auction reports in their printed magazine:
they list the auction company’s description of the car, then their reporter’s assessment of the car. This is why I buy the magazine and not the auction catalog.

2. Solicit questions

Be sure you solicit questions on every product page—your customers will tell you what’s wrong or what important information is missing. Sure,
you’ve got millions of products to deal with, but what the customers are asking about (and your sales volume of course) will help you prioritize as well as
find the problems opportunities.

Amazon does a great job of enabling this, but in this case, I used the Feedback option to update the product info,
and got back a total
bull-twaddle email from the seller about how the dimensions are in the product description thank you for shopping with us, bye-bye.
I tried to help them, for free, and they shat on me.

3. But I don’t get enough traffic to get the questions

Don’t have enough site volume to get many customer requests? No problem, the information is out there for you on Amazon :-).
Take your most important products, and look them up on Amazon, and see what questions are being asked—then answer those ONLY on your own site.

4. What fits with what?

Create fitment/cross-reference charts for products.
You probably have in-house knowledge of what products fit/are compatible with what other products.
Just because YOU know a certain accessory fits all makes and models, because it’s some industry-standard size, doesn’t mean that the customer knows this.

If there’s a particular way to measure a product so you get the correct size, explain that (with photos of what you’re measuring, if it seems
at all complicated). I’m getting a new front door for my house. 

  • How big is the door I need? 
  • Do I measure the width of the door itself, or the width of the
    opening (probably 1/8″ wider)? 
  • Or if it’s pre-hung, do I measure the frame too? Is it inswing or outswing?
  • Right or left hinged…am I supposed to
    look at the door from inside the house or outside to figure this out? 

If you’re a door seller, this is all obvious stuff,
but it wasn’t obvious to me, and NOT having the info on a website means (a) I feel stupid, and (b) I’m going to look at your competitors’ sites
to see if they will explain it…and maybe I’ll find a door on THEIR site I like better anyway.

Again, prioritize based on customer requests.

5. Provide your own photos and measurements

If examples of the physical products are available to you, take your own photos, and take your own measurements.

In fact, take your OWN photo of YOURSELF taking the measurement—so the user can see exactly what part of the product you’re measuring.
In the photo below, you can see that I’m measuring the diameter of the stopper, NOT the hole in the sink, NOT the stopper plus the rubber gasket.
And no, Kohler, it’s NOT 2″ in diameter…by a long shot.

Don't just give the measurements, SHOW the measurements

Keep in mind, you shouldn’t have to tear apart your CMS to do any of this. You can put your additions in a new database table, just tied to the
core product content by SKU. In the page template code for the product page, you can check your database to see if you have any of your “extra bits” to display
alongside the feed content, and this way keep it separate from the core product catalog code. This will make updates to the CMS/product catalog less painful as well.

Fixing your content doesn’t have to be all that difficult, nor expensive

At this point, you’re probably thinking “hey, but I’ve got 1.2 million SKUs, and if I were to do this, it’d take me 20 years to update all of them.”
FINE. Don’t update all of them. Prioritize, based on factors like what you sell the most of, what you make the best margin on, what customers
ask questions about the most, etc. Maybe concentrate on your top 5% in terms of sales, and do those first. Take all that money you used to spend
buying spammy links every month, and spend it instead on junior employees or interns doing the product measurements, extra photos, etc.

And don’t be afraid to spend a little effort on a low value product, if it’s one that frequently gets questions from customers.
Simple things can make a life-long fan of the customer. I once needed to replace a dishwasher door seal, and didn’t know if I needed special glue,
special tools, how to cut it to fit with or without overlap, etc.
I found a video on how to do the replacement on
RepairClinic.com. So easy!
They got my business for the $10 seal, of course…but now I order my $50 fridge water filter from them every six months as well.

Benefits to your conversion rate

Certainly the tactics we’ve talked about will improve your conversion rate from visitors to purchasers. If JUST ONE of those sites I looked at for that damn sink stopper
had the right measurement (and maybe some statement about how the manufacturer’s specs above are actually incorrect, we measured, etc.), I’d have stopped right there
and bought from that site.

What does this have to do with Panda?

But, there’s a Panda benefit here too. You’ve just added a bunch of additional, unique text to your site…and maybe a few new unique photos as well.
Not only are you going to convert better, but you’ll probably rank better too.

If you’re NOT Amazon, or eBay, or Home Depot, etc., then Panda is your secret weapon to help you rank against those other sites whose backlink profiles are
stronger than
carbon fibre (that’s a really cool video, by the way).
If you saw my
Whiteboard Friday on Panda optimization, you’ll know that
Panda tuning can overcome incredible backlink profile deficits.

It’s go time

We’re talking about tactics that are time-consuming, yes—but relatively easy to implement, using relatively inexpensive staff (and in some
cases, your customers are doing some of the work for you).
And it’s something you can roll out a product at a time.
You’ll be doing things that really DO make your site a better experience for the user…we’re not just trying to trick Panda’s measurements.

  1. Your pages will rank better, and bring more traffic.
  2. Your pages will convert better, because users won’t leave your site, looking elsewhere for answers to their questions.
  3. Your customers will be more loyal, because you were able to help them when nobody else bothered.

Don’t be held hostage by other peoples’ crappy product feeds. Enhance your product information with your own info and imagery.
Like good link-building and outreach, it takes time and effort, but both Panda and your site visitors will reward you for it.

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E-Commerce KPI Study: There’s (Finally) a Benchmark for That

Posted by ProfAlfonso

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

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

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

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

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

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

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

Google Analytics Benchmarking Reports

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

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

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

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

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

Data and methodology

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

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

Results

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

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

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

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

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

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

KPIs by device: Mobile under scrutiny

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

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

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

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

What data matters: The correlations

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

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

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

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

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

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

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

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

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

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

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

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

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