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In spite of all the new social networks, instant messaging tools, and mobile apps, email continues to grow in influence as a sales channel. Consider the following:

However, in spite of email’s effectiveness many marketers continue to overlook ways to take full advantage of the opportunity email marketing presents. For example, 83% of marketers aren’t tracking and measuring campaigns, and only 17% are driving email list growth.

There’s no sign that email is going to stop being effective, but marketers need to upgrade the way they execute their campaigns if they want to take full advantage of the opportunity email marketing offers. Here’s how:

Get the basics right

Testing is a vital component to keeping your email marketing strategy ahead of changing consumer behavior. Subject line testing is a simple way to glean insights into open rates and learn what language resonates with your customers, and small tweaks can often lead to big results. MarketingExperiments did a test with subject lines. They found just by changing and testing words they were able to get a 125.9% higher click through rate.

A comprehensive study on subject lines by Retention Science, looking at 267 emails across 543 campaigns during a 6 month period, uncovered a lot of useful actionable tips for marketing and e-commerce professionals.

  • Keep it short. Most emails (52%) have subject lines in the 11-15 word range. The most successful are in the 6 to 10 word range, with a 21% higher open rate.
  • Make it personal. Including a customer first name gets their attention. It also produced a 2.6% higher click through rate, according to the same survey.
  • Not Every Week Needs a Sale. Most flash sales (think Groupon) emails fair pretty badly. It might be due to the long subject lines (20 words) or high frequency (4 – 8 times weekly, compared to 2 – 4 at most for other brands); but either way, constantly bombarding customers with “Sales!” doesn’t increase conversion rates.

Pay attention to deliverability

Too often we overlook the most obvious opportunities. Turns out lots of brands with big lists are leaving large sums of money on the table due to deliverability issues. A study by Return Path found that 12% of emails go missing and 7% end in spam folders.

That’s nearly twenty percent of your emails. Cutting that number in half will give your email campaigns a 10% boost. There are numerous solutions to these issues, such as purging lists after the first hard bounce, after several soft bounces, after emails go unopened for a while, or using a double opt-in (also known as “confirmed opt-in”), rather than a single opt-in, as ISP’s give these email lists priority. These are just a few of the ways you can use to ensure emails are delivered consistently – there are dozens of others.


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Design for mobile

Since Q1 in 2011, the number of email opens on mobile devices has increased 180%. Yet, at the start of 2014 only 22% of marketers surveyed worldwide were using mobile-optimized templates. Hosting provider, BlueHornet, found that 70% of consumers delete emails that don’t render well on a phone, and even if the email does render then 61% will give up on trying to buy an item if a mobile store can’t be accessed on their phone. Even worse, according to the Google Mobile Ad Blog, 40% will go to a competitor to buy a similar item.

Mobile email optimization tactics range in complexity based on your marketing goals, but even basic tweaks such as reducing text length, increasing font size, and narrowing templates can make an impact. For example, Silverpop performed an A/B test for a client using mobile-optimized versus non optimized email which resulted in a 64% higher CTR on the version that was optimized for font size and template width and touch.

Email marketing is well worth investing in. Keeping track of returned emails or those which go into spam is also a lot more achievable than crossing your fingers hoping that Facebook won’t change its algorithms. Make your subject lines so enticing that customers have to open, then make the content interesting enough to click through. Once you’ve got them this far then its all down to the products, images, copy and design of your ecommerce store.


When a SaaS business is ramping up customer acquisition for the first time, they almost always try Adwords first. And with good reason. Search is still the #1 place where people go to get shit done and Google still dominates the primary intent-based advertising channel on the internet.

But quickly, most SaaS marketers building out a set of pay-per-click (PPC) campaigns will realize that they can’t live on AdWords alone. Why? There simply isn’t enough search volume to grow most businesses on. Most people don’t know that products like yours exist, so how could they possibly be searching for one?

If you’re like most SaaS businesses, you need to generate demand, not just capture it. You need to educate your market that:

  1. It has a problem
  2. Your product is the solution

This is when content marketing comes into play. Webinars, blog posts, drip email courses, and videos are great ways to help customers solve their problems while building awareness for your product.

To execute this strategy, you’ll need to move beyond Adwords and start pursuing the long tail. You’ll want to utilize a broader variety of channels: Linkedin, Facebook, Twitter, Google Display, and many more. Each one of these channels lets you target visitors by demographic traits: job titles, interests, group membership, etc. Your goal is to identify people who should care about the problem you solve and then get in front of them with an educational, content-based CTA. Educate them on the need, nurture the long-term relationship, and they’ll buy from you when they’re ready.

This is a great strategy, and I can tell you from personal experience that it works really damn well. I’ve managed digital marketing for three SaaS companies over the past 5 years, with ad budgets ranging from $4k to $200k per month. But it’s hard to get the measurement right. You have to look at data from every ad network you use and link it with data in your marketing automation platform, your CRM, and your app.

In fact, in order to spend your money effectively, you’ll need 26 reports, updated regularly, consolidating results from 7 or more platforms. And you need to build the reports yourself. No ad platform can give you the reporting you need, because you’re the only one with all the data.

In this post, I’ll show you how to construct the ultimate dashboard for PPC campaign measurement. I’ll show you how to consolidate the data you need into a single place, what reports you need, and how to act on them.

Stop reading now if you’re not ready to get your hands dirty. This stuff is hard work. But if you want to learn everything you need to measure your PPC funnel and build a scalable marketing machine, let’s get started.

Understanding the Problem

Our Cautionary Tale

Last December, our PPC performance crashed. Our two primary metrics—leads and cost per lead—were down broadly across channels. We caught the problem in our weekly PPC status meeting, and everyone around the table was concerned. I don’t remember the numbers as I write this, but I distinctly remember being concerned enough that I was worried about the future of our fledgling PPC program. The sinking feeling in the pit of my stomach left an impression.

We performed a ton of analysis, testing one theory after another. It retrospect, that sounds like we were performing a calm examination of the evidence. But at the time, the process felt more like when you’re running late for the most important meeting of your life and desperately looking for your keys. The clock was ticking as we tried to solve the problem, and as every day went by we were throwing ad dollars down the drain. Eventually, though, we figured it out.

Our A/B testing efforts were still in our infancy back then, and the folks responsible for running tests and the folks responsible for executing PPC campaigns weren’t necessarily as coordinated as they should have been. It turns out that one of our primary PPC landing pages that was used across many campaigns and channels was going through a test. Typically that landing page converted at 20%+, and the variation was converting in the low single digits. That change was enough to blow up the metrics throughout our funnel.

The test had already achieved significance, so we trashed the alternative that had performed so poorly. Immediately, things returned to normal. All was right with the world again.

This experience is what drove me to create the Ultimate PPC Dashboard. Rather than looking for my car keys while I’m in a rush, I decided to track every single metric in our PPC funnel, from top to bottom, so that whenever anything was going particularly poorly (or particularly well!) we would immediately be able to zero in on what was causing the deviation.

Our cautionary tale (yikes!) on PPC performance measurement

The Complexity Problem: PPC is a Machine with Many Moving Parts

A sudden performance dip can be bewildering simply because there are so many reasons it could be happening. If you list the various parts of your funnel—impression, click, conversion, lead quality, and lead-to-customer conversion—and then list everything that could possibly go wrong with any of them, you’ll see that the list quickly becomes very lengthy. For example, let’s take a look at diagnosing problems with your impressions.

If you haven’t made any direct changes to your campaigns, your number of impressions can change for a bunch of reasons:

  • Changes in your quality score. All major ad networks calculate a form of quality score for their algorithms, whether they document it publicly or not. The biggest determinant is always the engagement rate with the ad.
  • Changes in CPCs, seasonal or otherwise. CPCs, as a rule, trend slowly upwards over time. But time of year, holidays, and other dynamics are always at play and will impact your MoM performance.
  • Changes in available inventory, seasonal or otherwise. During some parts of the year, there simply aren’t as many people on LinkedIn as other times. You’ll find patterns unique to your channels and audience.

Those are just some ideas on diagnosing problems in the very first step of your funnel. As you go further down the funnel, the troubleshooting only gets more complicated. I’ve seen a bad form field validation for telephone number cause a major kink in our funnel. It happens.

If you’re spending $1,000/day on your PPC campaigns, a sudden performance drop can become an emergency quickly. You need to be able monitor performance in real-time to find the source of the problem.

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The Ultimate PPC Dashboard

You need a dashboard that will help you monitor your PPC channel performance and quickly identify the cause of any deviations. In order to pinpoint the root cause, you need to start with a broad overview of your funnel and then quickly be able to identify and drill into the areas causing the problem. Here is the process that I use, in this order:

  1. At what stage in the funnel is the deviation occurring? Is it impressions? Clicks? Leads? Look at the raw counts at each funnel stage, starting backwards from number of customers. Trace the problem from the bottom of the funnel to the top.
  2. What specific channel is having a problem? Is Facebook a problem, or are all of your ad channels showing similar performance deviations at the same time? For every aggregated chart, you need one broken down by channel.
  3. What type of deviation are you seeing? There are three core types of problems: raw count, conversion rate, and cost per unit. Knowing which type of problem you’re having will direct your troubleshooting.

We’ve organized our dashboard, all 26 charts of it, to reflect this troubleshooting process (click to enlarge). FYI, numbers have been changed to protect the innocent:


Using this dashboard and these three questions, you know exactly where to look to diagnose and correct the root cause. You’ve just reduced a complex, unbounded problem down to a very specific problem that can be acted on quickly.

Creating a dashboard structure based on your funnel also has a second important benefit: it helps you stay focused on the big picture. It’s all too easy to get fooled into optimizing your PPC channels for each individual step of the process without keeping an eye on your ultimate goal: new recurring customers.

Check out our 26-chart dashboard for measuring PPC campaign performance

How We Built This Dashboard

To set up our dashboard, we used RJMetrics to combine the data for each of our PPC ad platforms—including impression counts, click counts, and spend by campaign—with our internal sales and marketing data. We’re an inside sales company, so we found the internal data we needed in our CRM (we use Salesforce) and our marketing automation tool (we use Pardot).

If you have a self-service model, like Squarespace, or have a freemium model, like Hootsuite, you’ll probably be more focused on analyzing data from your transactional database.

Because we used RJMetrics to build this dashboard, every chart on it gets automatically updated automatically. That’s right: I have a completely customized dashboard to measure my entire PPC funnel and it updates automatically, with no work on my part.

How You Can Build Your Own

It’s possible to combine the necessary data and create a dashboard using Excel, and I have personally done this before at prior companies. I’m an Excel geek, and a part of me loved that day I spent once a month updating my funnel spreadsheets. But the busier I got, the more of a headache this became. And my Excel solution was completely inflexible: any changes in the way that I wanted to analyze the data would take far too long to make.

When you’re trying to diagnose problems in real-time, you need your data to be up-to-date, and you need to be able to answer questions fast.

Excel just doesn’t cut it. I’ll never go back. Setting up this dashboard in RJMetrics has given me exactly what I need to feel confident spending real money on advertising. Having used this dashboard to diagnose a number of performance issues over the past 18 months, I wouldn’t do it any other way.

I’m actually working right now to put some test data together in our demo account so that you can play around with this dashboard yourself. When I do, I’ll update this post. For now, if you’d like to build a dashboard like this for your own business, we can absolutely make that happen. Just sign up for a trial to get started.

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One of the reasons why loyalty programs exist is to gather better data on customer behavior: if a customer is incentivized to identify themselves when they interact with our brand (presenting their loyalty card/info to collect rewards) we have a much clearer view of that customer’s interactions and purchases.

Which loyalty metrics are you tracking? Here are three you might be missing.

We have been a part of over 3,500 loyalty programs at Sweet Tooth. In that time we’ve used some cool metrics and gathered some pretty strong data to analyze; it’s a data nerd’s paradise! So when I knew I was writing a guest blog post for RJMetrics, I had to do it on data.

Here are three loyalty program metrics that most retailers won’t know, or use, but should. Where appropriate, I’ll include what we have found in our data from over 3,500 merchants.

Realized Customer Lifetime Value

Customer Lifetime Value (CLV) is the total profit (some use revenue) that a customer will generate over their entire lifetime. There are two ways to look at CLV:

  • Predicted CLV: How much profit you expect to come from a customer
  • Realized CLV: How much of the lifetime profit you have already made

Realized CLV is an extremely effective way to segment and target customers.

How to calculate it

To calculate realized CLV, take a given customer’s predicted CLV and divide by their actual lifetime profit. This will show how much of a customer’s lifetime value they have achieved, as a percent.


How to use it to improve your business

  • Use it to find your brand promoters. If a customer has realized close to 100% of a high predicted CLV, then it is very likely that they are happy with you. This is the perfect time to introduce a referral campaign. Remember, the total value that a customer brings isn’t just in profit that they directly generate; brand promoters can help find more high-value customers just like them.
  • Use it to measure the accuracy of predicted CLV. If a significant number of customers are regularly not realizing their predicted CLV, it means that the method being used to predict CLV is not accurate. Keep in mind that it takes time for a customer to realize their full CLV, so be sure to only analyze customers who have had sufficient time to do so.
  • Combine it with cohort analysis to improve your customer experience. When combined with cohort analysis, realized CLV can point you to cohorts of customers that likely won’t realize their full CLV potential. You can determine what is causing this low CLV, and improve the overall customer experience. The same can be said for a cohort of customers who have high CLVs.

How does your realized CLV compare to your predicted CLV?

How do you compare to other ecommerce retailers?

An average or range for this figure isn’t very useful, as this metric usually has a lot of variance. In general, retailers should aim for their customers to reach 90% of their (individually) predicted CLV. Note that if you’re using a general CLV metric for all of your customers (instead of a CLV for each customer or segment), then you should be aiming for approximately 70% accuracy.

A more practical way to measure realized CLV performance is to set intervals where customers should be at a certain realized CLV. For example, after a year we might set a target realized CLV of 30%.


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Breakage Rate

A loyalty program’s breakage rate is the ratio of points that do not get spent to the total number of points earned. Put simply: what percent of points aren’t spent?

Any retailer with a loyalty program can make use of this metric. But it can also be applied to other promotions as well. Any time you offer a customer some sort of discount or gift you can use a metric similar to breakage. The percent of unused abandoned cart coupons, the ratio of customers who didn’t accept a free gift during checkout, etc, are all useful situations for calculating breakage.

How to calculate it

To calculate breakage, find the total number of points that have not been spent, then divide this by the total number of points issued. This can be calculated for retailer’s lifetime, or during a particular time period. For example: the calendar year.


How to use it to improve your business

  • Determine the financial liability of loyalty points. Accountants will use breakage to determine the outstanding financial liability of points for a loyalty program, usually towards their fiscal year end. Accountants love breakage (free money!), but it often indicates that something is wrong with your loyalty program.


  • Use it to determine how engaged customers are with a loyalty program. If customers aren’t spending points to get rewards, then they are not likely to be active, loyal customers. It might also signal that your customers don’t know how to participate in the loyalty program.

How do you compare to other ecommerce retailers?

From our data, the average breakage rate is about 30%, but there is a ton of variance. Breakage will vary according to how frequently a customer visits or purchases, how engaging your program is, if you have point expiration, and if you have frequent communication with the customer, to name a few.

Average Time to First Spend

The average time to first spend is a measure of long it takes for customers to go from creating a new loyalty account to the first time they spend their loyalty points. This could be days, weeks, months, or (hopefully not) years.

The average time to spend metric is used to determine how long a customer takes to go through one “full cycle” of a rewards program. Basically, how long does it take for a customer to spend points and feel rewarded?

How long until your customers first start spending loyalty program points?

Knowing where a customer is in this rewards cycle is extremely useful for creating promotions and segmentation.

The average time to spend is directly related to a customer’s purchase frequency as well as what actions are rewarded. If a customer is making regular purchases, they’ll accrue points faster and will have a lower average time to spend. Similarly, if a loyalty program rewards customers for several actions, such as reviews or social sharing, then the customer will take less time to reach a spending threshold.

How to calculate it

First, calculate the time in between a loyalty account being created and its first point spending event. Calculate this on a per account basis. If you have a large customer base, use a sample size (make sure it’s a statistically significant sample size). Take the average of these and you’re done.


How to use it to improve your business

  • Optimize customer communications. If a customer is approximately 90% through their average time to spend, it is a great time to ask them to perform an action that will earn a reward. They’ll be more motivated than usual to earn points because they’re close to having enough points to earn a reward. You could send them an email letting them know that they receive points for referring their friends, or send them a coupon giving “double points” on their next purchase, or any action that you determine valuable. These campaigns are often our most successful, in terms of engagement. All of this is possible because we understand the average time to spend.

How do you compare to other ecommerce retailers?

From our data we see an average time of 92 days to spend points. Note that we also see a lot of variance in this number because there are a lot of variables that affect the average time to spend.

Loyalty & Data: Best Friends Forever

Like I said, one of the main reasons why loyalty programs were created was to give a customer an incentive to identify themselves at each interaction. This incentive comes in the form of collecting reward points.

Loyalty programs are great for developing happy, loyal customers that spend more and refer their friends, but don’t forget that loyalty programs are also great at giving data-driven retailers better data.


You’re shopping online and go to check out, only to realize you’re $10 away from qualifying for free shipping. You can fork over $4.99 for shipping, or find another item to tack onto your order. No brainer, right?

By encountering a seemingly rational choice about shipping, you’ve been prompted to happily increase your order amount. This is why 60% of ecommerce companies cite “free shipping with conditions” as their most successful marketing tool.


If you are an ecommerce business, optimizing your free shipping threshold works because it plays on consumer psychology. Specifically, offers of free shipping tap into how we rationalize our shopping behaviors, the psychology of choice, and the value we place on the concept of “free.”

How to use the psychology of free shipping as a marketing tool

Rationalizing shopping online

Most shoppers are still more accustomed to the offline store than the online environment. Because of this, we lack the context for understanding how shipping costs factor into online shopping.

We are loss-averse and effort-averse


Free shipping helps us rationalize buying something online instead of going to a store. If shipping turns out to be too expensive for an item that we could just as easily get at the store down the street, the rationalization fails, and we abandon our carts. “Unexpected costs” is the number one reason shopping carts are abandoned.


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The fact that the costs are “unexpected” is significant. Etsy recently ran a round of experiments that prevented customers from seeing shipping costs until checkout, and sales dropped, sparking public outcry from both buyers and sellers. Whatever your shipping policy is, transparency is key.

Surprise! How unexpected shipping costs make your customers feel.

We don’t see shipping for online purchases as an extra service

If you buy something online, you have to have it shipped. There’s no way around it. This is very different from the traditional model, where having an item shipped to your house is an above-and-beyond, special service, because everyone else has to go to the store. Because shipping is required, we perceive shipping costs for online purchases as an annoying extra fee. Some are predicting that in the future, because of this new cost model, all shipping will be free.

The lesson here:

Customers view it as unreasonable to tack on an “extra fee” for something that is required, even though they understand that there is no such thing as truly free shipping. The key is to be as transparent about shipping costs as possible, so that the customer doesn’t feel “taken advantage of” at the point of purchase.

The illusion of choice

Consumers love making choices, even when they’re somewhat artificial

Seeing a free shipping option gives us a “choice”

If we’re going for cost-effectiveness in shipping, we’ll be looking at making a choice between free shipping, and the next cheapest option. A choice between two options (one of which appeals to our desire to acquire more stuff) is the simplest and most appealing kind of choice. We know companies aren’t taking a loss on shipping – they’ll get their money somehow – but when we see it presented as a choice, we feel a lot better about it.

By shopping online, we feel we are choosing the more convenient option


Choosing to shop online feels like a choice to avoid all the inconveniences of traditional shopping. Now we have two options to buy most things that we want, and when we pick the “convenient” one, the cost of shipping is the only bump in the road. Shipping costs generally feel like something we think we shouldn’t have to pay for, even though they are built into everything we buy traditionally (tomatoes at the grocery store are marked up to cover shipping costs), not to mention our personal transportation costs when we go to a store (gas, bus fare, etc.). We only feel the pain of these costs when we see them itemized.

The lesson here:

You may not be able to offer free shipping on every purchase, but you should make it an option at some order value. By finding your ideal free shipping threshold you’ll be able to motivate customers to spend more and your customers will feel in control of what they’re spending on.

The power of “free”


We see something “free” and automatically overvalue it

According to Dan Ariely, behavioral economics researcher, people typically overvalue the benefits of “free” items even when compared to better-quality items that cost a small price (i.e. most overwhelmingly prefer a free Hershey’s Kiss to a 14¢ Lindt truffle). The concept of “free” is so powerful that when Amazon introduced free shipping in some European countries, the number of orders increased dramatically, everywhere but France. Instead of being reduced to zero, the shipping price in France was mistakenly reduced to 1 franc (about 10¢), and this was enough to prevent a jump in sales. People place such a huge value on “free” that they viewed 10-cent shipping as not a good enough deal to sway them.

How “free” things make your customers act irrationally.

We feel uncertainty around the benefits of options that cost money

People have a hard time measuring the utility they expect to receive from purchasing an item and struggle to translate its value into dollars and cents. How much is that designer t-shirt really worth? That latte? Is it worth that price? This uncertainty adds a negative element to our consumer experience. Free options, on the other hand, have no downside. Our emotional response to them is much more positive.

The lesson here:

When in doubt between offering a product discount or free shipping, go with free shipping. While the monetary amount might be exactly the same, customers will value “free” more.

Thinking about offering a product discount? Try free shipping instead.

Make it work for you

Free shipping influences consumer behavior on a deeply psychological (and often irrational) level, adding a powerful boost to your average order value. If you need help finding your ideal free shipping threshold, be sure sure to check out this how-to guide. It will help you find the ideal threshold to turn free shipping into your best marketing tool.


There is no shortage of top-down research telling us that the ecommerce market is enormous, growing extremely fast, and showing no signs of slowing down. According to sources like eMarketer, ecommerce is the only trillion-dollar industry growing at a double-digit percentage each year. And with the US Census Bureau estimating that only 7% of retail sales are done on the internet, ecommerce still has a lot of runway for growth.

Ecommerce is the only trillion-dollar industry growing at a double-digit percentage each year

Despite all this research, however, no one seems to be able to answer the key question: how many ecommerce companies are there?. The few estimates that exist vary by orders of magnitude, from tens of thousands to nearly a million.

We set out to answer this question for ourselves.


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How we did it

We have a secret ingredient that helped us build an estimate from the ground-up: proprietary data. Here at RJMetrics, we work with hundreds of online retailers who generously allow us to anonymize high-level data points for analyses like these.

By combining our proprietary data with size and revenue information from third-party sources like the Internet Retailer Top 500 Guide, Alexa, and BuiltWith, we’ve conducted a comprehensive bottoms-up analysis of the ecommerce industry.

Size matters

Obviously, the long tail is going to be very long here. Using BuiltWith to identify which websites have ecommerce technologies installed, we found 180,000 live websites with just the Magento shopping cart. When you extrapolate to include the full universe of competing ecommerce technologies, you can see how some estimates approach the one-million mark. As you might have guessed, however, the majority of these sites are not generating revenue on any meaningful scale.

In order to separate the wheat from the chaff, we needed to come up with revenue-based exclusion criteria.

Tying Alexa rank to revenue

Alexa rank is an easily-obtained proxy for traffic. Alexa ranks every website in the world based on traffic volume. A global rank of 1 represents the website with the most traffic in the world (currently Google). Since ecommerce revenue is directly correlated with the number of visitors to a site, we theorized that Alexa rank could serve as a proxy for revenue. To test this, we needed revenue data for a set of ecommerce companies that spanned a broad spectrum of Alexa ranks.

To get revenue data, we turned to the data in the Internet Retailer Top 500 guide and augmented it with our own proprietary benchmarking data set. The IR 500 includes the heaviest-hitters in ecommerce and our own data covered mid- and smaller-sized companies. Between these two data sets we had Alexa rank and revenue data on the full spectrum of ecommerce companies. Here’s what we saw:


Jackpot! There appears to be a pretty clear-cut link between revenue and Alexa rank. To be sure, let’s zoom in past the Walmarts and Amazons of the world and just look at the “long tail” of sites with Alexa ranks between 10,000 and 1,000,000:

Awesome. These combined data sets have given us visibility into the revenue of ecommerce companies throughout the Alexa top 1 million sites.

Meaningful scale

Note that, while the 500k-1M data point is quite low, it’s far from zero. The mean 2013 revenue for sites in that range is actually $1.5 Million and the median is around $500k. As evidenced by that discrepancy, average revenue drops meaningfully in this range.

For this reason, we’ve made an Alexa rank of 1,000,000 the cutoff for sites we include in our count.

While we are aware of many websites with an Alexa rank above 1,000,000 that are generating well into six and even seven figures of revenue, we believe there would be far more false positives than false negatives if we included sites beyond this mark. We’re comfortable concluding that the balance of false positives/negatives that exist on either side of the threshold are well balanced with a threshold at the Alexa one-million mark.

Defining ecommerce

Now that we had a way of estimating which ecommerce companies are actually generating meaningful revenue, we simply needed some way of figuring out which sites in the Alexa Top One Million are actually ecommerce.

Using the BuiltWith API, we were able to profile every website in the Alexa Top One Million by evaluating the technologies being used by those sites. BuiltWith can detect a whole universe of shopping carts, marketing tools, and other ecommerce-specific technology that makes a website a dead giveaway as ecommerce.

But this wasn’t good enough—we were still getting a lot of false positives and false negatives. We decided to go a step further. We scraped the HTML of each site’s home page and looked for certain words: “shop”, “buy”, “sell”. We also detected defunct pages and sites that looked more like linkspam. We ended up building an entire set of rules to automatically evaluate whether or not a given site was ecommerce.

And at every turn, we evaluated the rules against a set of websites that we had evaluated by hand. Eventually, our algorithm was actually able to predict whether a site was ecommerce with 95% accuracy.

After we had fine-tuned the algorithm, we turned it loose on the Alexa Global Top One Million sites. Here’s what we found:


There are approximately 110,000 ecommerce websites generating revenue of meaningful scale on the internet.

There are 110,000 ecommerce websites generating revenue of meaningful scale on the internet

More than 12% of the 100,000 highest-traffic websites are ecommerce, and that density clearly declines to about 10% for long tail. According to our data, ecommerce websites make up approximately 10-12% of the internet. And to our knowledge, we’re the first to actually attempt to count them.

Ecommerce websites make up 10-12% of the internet

I should point out that we include any online transactional business in our assessment. In addition to traditional online retail, this includes companies selling virtual goods, hosted software providers, marketplaces, travel sites, and even mobile apps with a commerce component. Basically, if you can spend money on their website, it qualifies.

It should also be noted that our detection methodology excludes non-English language websites and pornographic websites. When building our algorithm, we had to search for particular content on these pages. We didn’t have the resources to translate and test these rules in other languages, and we didn’t have the…inclination…to test them against pornographic websites. Both of these limitations of our analysis deflate the numbers we report.

Mid-market ecommerce companies generate a ton of revenue

Having just tagged every site on the Alexa Top One Million as ecommerce or not, and having figured out the underlying relationship between Alexa rank and revenue, we have our hands on a pretty interesting dataset. We’ll be exploring this data in several posts down the road, but here’s the first cut we wanted to share with you.

We looked at the revenue breakdown between the largest and smallest of these sites to try to figure out the industry landscape. Based on our dataset, ecommerce clearly breaks down into three distinct groups.

  • The largest ecommerce sites on the internet make up about 1% of the total population and generate 34% of the total revenue.
  • A distinct middle tier of ecommerce sites make up 51% of the total population and generate 63% of the total revenue.
  • Small ecommerce sites make up 48% of the total population and generate 3% of the total revenue.

The top 1% of ecommmerce sites generate 34% of total ecommerce revenue

Here’s the data:

Alexa Rank % of Total Ecommerce Businesses % of Total Revenue
Top 1-10k 1% 34%
Mid 10k-500k 51% 63%
Bottom 500k-1M 48% 3%

The opportunity in ecommerce

This represents a big opportunity for vendors (like RJMetrics) serving the ecommerce market. Any company that can help merchants move from the bottom to the middle tier of the market will make a very significant impact on their top line. The middle of the market is where traffic volumes start to really bring in dollars, and getting to that scale is an imperative for any ecommerce company focused on growth.


Pinterest, founded in 2010, quickly became the visual social bookmarking service of the web. And, like the very best social platforms do, it invented a verb: pinning. Facebook users “like”, Twitter users “tweet”, and Pinterest users “pin”.

Pinning says “I want this.” It’s aspirational. People pin products they’d love to own, recipes they want to cook, and projects they want to tackle. Emma Stone pins about what she wishes her empty, 1-bedroom apartment looked like. “Pinterest Stress” is being blamed for pressuring moms to be increasingly creative in everything from food to parties. The platform has been so successful at getting users excited about tackling creative projects that it has spawned a sub-genre where users share their hilarious attempts at Pinterest perfection.

Pinning is aspirational, which means that data on pins is data on people’s aspirations. In this article, we’re going to explore the dataset of 50,000 random pinners and their pins. We’ll be using this data to understand user engagement, the aspirations of pinners, and what this means for the future of Pinterest.

Make sure to read all the way to the end—the final section on user engagement blew us away. Did you know that 84% of female pinners are still active in their fourth year? That’s some serious user retention.

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If you’re a content marketer, you’ve probably noticed that content marketing is growing. If it seems like everyone is doing it, that’s probably because pretty much everyone is.

The Crowded Content Landscape

crowded content landscape

Here’s what Google Trends tells us about content marketing. You can see that people have been talking about content marketing for several years, but we start seeing a definite uptick toward the end of 2012 and through 2013.

Which makes sense, since 91% of B2B marketers use content marketing today. In addition to the number of marketers using content marketing, there’s also been an increase in the kinds of content, blogs, videos, white papers, ebooks, social media messaging…but it still isn’t enough. 58% of marketers plan to increase the amount of content they’re producing over the next 12 months.


That’s a whole lot of content, and a really crowded space. With so much competition, and knowing your intended audience has a limited amount of time for research, you need more than just great content to attract people’s attention. You have to actively work to get your content in front of them.

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If all you’re doing is reading headlines, you may have made the assumption that Facebook is over. Major brands are leaving, the millennials are leaving, and half the people you know are talking about quitting. While they get readers, these headlines don’t quite tell the whole story.

Facebook has become deeply embedded in the social fabric of millions of lives. At the same time, it has been building a powerful advertising platform that thrives in real-time. These combined forces are turning Facebook into one of the hottest new advertising opportunities available.

The “Everyone is leaving Facebook” myth

Facebook remains the social media powerhouse. Google defined search, Kleenex defined tissues, and Facebook defined social media. A massive 71% of adults use Facebook, three times more than the next social media runner up, LinkedIn.

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The stress of the holiday season is behind you and it’s time to put your feet up and take a breather, right? Hardly. Successful ecommerce companies know that the next holiday season isn’t far away. Testing Adwords campaigns, preparing website updates, planning email campaigns, and procuring products takes an enormous amount of time. So grab your numbers from 2013 and get ready for a data-driven 2014.

Really? Do I have to?

Could you have been more prepared in 2013? We thought so. So get out that data. You probably have a whole lot of it and it comes with the added benefit that it was within a short time period – no Google algorithm changes or economic conditions to muddy your analysis.

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You may still be polishing off the last of the champagne and eggnog, but we’re already one week into the new year. It’s time to wake up from the food coma and get serious about growing your business in 2014.

Problem is, there are a million things you can do to grow your online business. You can write more content, better content, improve SEO, hire more employees, start an Instagram promotion, boost social sharing, do a publicity stunt, use PPC advertising, guest blogging, or get better at email marketing. There are so many things you can do, but what should you do? Where should you focus your energies in 2014 for maximum impact on the bottom line?

Fix Where You’re the Worst

Growing a business doesn’t happen from any single activity, it happens when you’re continually getting better and optimizing your business to deliver the best possible experience. Doing this requires shifting your thinking away from one-off tactics, and instead focusing on lifting conversion rates throughout the lifecycle.

The places where you will have the biggest impact are the areas in which you are currently the worst. You can’t just be good at turning strangers into visitors, you have to turn those visitors into users or customers, then turn those customers into repeat buyers. If you’re underperforming in one of these areas, your whole business will suffer.

How to Identify Where You are Underperforming


The acquisition stage is focused on one thing – get more eyeballs. Traffic is the lifeblood of any ecommerce store. You need eyeballs, and lots of them, to keep an ecommerce store growing.

Check this:

  • Time in business: If you’re a new store then this area will undoubtedly be a top priority, and it should be.
  • Traffic growth: If you’re not experiencing double digit traffic growth month-over-month then you have an acquisition problem. Keep in mind, if you’re looking at 2013 data to check the health of your acquisition strategy, be sure to exclude 2013 holiday data – it will muddle your averages.
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Recommendations for improvement:

  • Test your strategies: Every ecommerce marketer should be testing acquisition strategies weekly. If you’re struggling in this area, you’ll be well served by ramping that up to daily.
  • Don’t put all your eggs in the PPC basket: PPC is often a quick win for ecommerce, but it’s not cheap. Be sure not to ignore the slower-momentum channels like SEO and content marketing. These activities won’t have the immediate impact of PPC, but over time, the payoff is big. Particularly in the post-holiday slow down there’s additional opportunity to gain attention as plenty of ecommerce stores will be slowing their content production during this time.
  • Targeted landing pages: One very common mistake ecommerce marketers make is having ads that don’t point to targeted landing pages. This acquisition error is one that tends to have a negative impact on conversion rates. Make sure the customers’ experience stays consistent from ad to site.


Once you’ve got the eyeballs, it’s time to turn them into paying customers. Small, targeted tweaks to the conversion process can have a big impact on overall growth. If you use data wisely at this point you can get big results with much lower effort.

Check this:

  • Macro conversion rates: One big warning sign that you definitely do have a problem is if less than 3% of your visitors are turning into customers.
  • Micro conversion rates: Leading up to the big conversions there are often smaller conversions that happen along the way such as subscribing to your blog or newsletter, adding items to a wish list, or interacting with you on social media. These numbers don’t immediately translate to revenue growth, but steady improvement over time indicates a healthy ecommerce business.

Recommendations for improvement:

  • A/B test your face off: Make tools like Optimizely your best friend. Get smarter about how, where, and when you test.
  • Test visitor flows: Tools like Google Analytics will provide insight on your visitor flow so you can spot the areas where visitors are dropping off.
  • Revive abandoned carts: Abandoned shopping carts are a huge opportunity for online stores to boost conversion rates. Use your data to find the best time and approach for sending these follow-up emails to soon-to-be customers.


If you have a steady stream of new customers coming in, it’s time to look at how you keep them coming back.

Check this:

  • Repeat purchase rates: Repeat purchase rates vary dramatically depending on your business. If you’re selling computers, this number is likely very low, if you’re selling used books, it should be quite a bit higher. Benchmark your repeat purchase rates and use this as a barometer of your future success.
  • Churn rate: Churn is a vital health metric of your business. Even if customer acquisition is wonderful, high churn will destroy the success of your business.
  • Customer lifetime value: Your customers are not all created equal. If retention is low among customers with a high customer lifetime value, that’s a far bigger problem than if retention is low among your discount-chasing customers.

Recommendations for improvement:

  • Repeat purchase analysis: Use your data to find out where shoppers are dropping off. Is it between order 1 and order 2? What were the characteristics of the people who bought a second time and those who didn’t?
  • Churn analysis: Things like website difficulties or shipping delays can cause surges in churn. If you find that this was the case for you then go back to these customers and offer an explanation, a sincere apology, and an invitation to give your business another chance.
  • Test email marketing: Email marketing is a staple in the ecommerce marketing toolbox. You can drive repeat purchase rates by testing things like sending follow-up emails recommending items frequently purchased together, subject lines, and content.


If you’re younger than a year, don’t worry too much about reactivation. You will lose customers, but at this point it’s more important to focus on acquisition and conversion. Once you’re more established, it’s time to start looking at how to get those churned customers back.

Check this:

  • Win back rate: Your win back rate is a measure of customers who came back and remained customers. If this number is too low it can indicate a problem with your reactivation strategy.
  • Customer lifetime value: If you’ve lost a number of high lifetime value customers than this is the #1 place to start getting reactivation results.

Recommendations for improvement:

  • Win back analysis: There’s a tendency to approach reactivation with the discount hammer. This can lead to customers coming back once for the discount, only to never purchase again…again. Understanding why customers returned and turned into repeat customers will help you design a reactivation strategy with the offers and messaging to create repeat customers.
  • Retargeting: Most people think of retargeting as an acquisition strategy, but you have your customers’ cookies, go ahead and test using this for reactivation.

Test and Measure

Once you know your weak spot, it’s time for the fun stuff – testing new tactics and measuring the results. Remember: you don’t have to have all the answers up front. Just make sure you’re always trying new things and learning what works!

Shameless Plug

Want a marketing strategy grounded in data and aimed at fixing your business where it needs it most? Get in touch. We’ve got the tools and the people that can help you conduct the types of analysis described in this post.

Image courtesy of KROMKRATHOG /