Jumpstart Your Momentum

The Growth Drivers of Best-in-Class Ecommerce Companies

RJMetrics teams up with Bounce Exchange to give you research, best practices, and actionable advice that you can implement today.

  • Take a high level dive through the data of a landmark ecommerce study
  • Discuss what your primary growth goals should be for 2016
  • Give you an honest look at the state of the ecommerce industry

Transcript

Peter: And now coming to you live from Hyatt Top, the throbbing heart of western civilization. That's right, folks, it's time for another exciting edition of The Bounce Ex Files. Hi, hello, and welcome everyone. The voice you're hearing right now, that's me. My name is Peter Starr Northrop.

I'm the webinar producer and host here at Bounce Exchange, bringing you "Jumpstart your Momentum: The Growth Drivers of Best in Class Ecommerce Companies," a really awesome data and actionable advice driven webinar today, co-branded between Bounce Exchange and the really cool, better-than-cool folks over at RJMetrics in Philadelphia.

We're really excited to have Ryan Urban, CEO and co-founder of Bounce Exchange, and Robert Moore, CEO of RJMetrics. They've got some really great CEO-on-CEO action for you guys today, a really great conversation to come, and I'm super excited to have you all here. Just out of my mind excited.

But first, I want to get out of your way. Just a couple of housekeeping things just to make sure we're all on point here. First of all, yes, folks, this webinar is currently being recorded. Don't worry about that. You'll be given access to the recording within 24 to 36 hours of this broadcast, depending on how well GoToWebinar processes it.

It's kind of crab shoot. Technology is weird and wonderful, and sometimes does not go our way, but we will get this to you as soon as possible. However, if you want that recording ASAP, say, tomorrow morning, feel free to email me, peter@bounceexchange.com, and I can get you an advanced copy of it, just with the audio, just so you know.

All right, folks, cool. With that, also the slides will be made available to you via email as well, and that will be today as well. If you need those slides early, we can get them to you immediately. It's going to be a great series of slides. It's one of those things you're going to want to review a couple of times in Q1 as you start planning your ecommerce strategy.

Other than that, if you have any questions, comments, concerns, anything at all, that's what we're here for. We're here to help. We're here to give you great advice. Folks, you've got two CEOs worth of brilliance here at your disposal.

Ryan: Brilliance.

Peter: What?

Ryan: Brilliance. Yeah, brilliance.

Peter: That's Ryan Urban, CEO and co-founder of Bounce Exchange. Really excited to help you as best as we can, so literally ask us anything. I'll be fielding questions within the comments box on the left or right side of your screen. So any questions you have, feel free to hit me up with them, and I will send them to our amazing co-panelists as well.

With that folks, just so you know, let's go ahead and just get introductions out of the way as well. So once again, this is "Jumpstart Your Momentum: The Growths Driver of Best in Class Ecommerce Companies." First and foremost, we're here with Ryan Urban, CEO and co-founder of Bounce Exchange.

Ryan: Hello, Peter.

Peter: Hello, Ryan. How are you doing today?

Ryan: Good, good.

Peter: Awesome. I'm going to slow down, now that I'm introducing you. Ryan Urban [inaudible 00:02:30].

Ryan: We got to slow down before we Jumpstart.

Peter: I'll take that pun. Kind of funny. Ryan Urban is the conversion guru, basically invented behavioral marketing and behavioral automation, has been in the business for a long time.

Ryan: I've been to the Internet as well.

Peter: Also the Internet. But key for you guys, as well as his amazing success is the customer acquisitions director rep, Bonobos. Director? Director, yeah.

Ryan: Something like that.

Peter: Something like that. But at the same time, bringing us just, oh my god, oh my god, celebrity status. Awesome, awesome perspective. We have Robert Moore, the CEO of RJMetrics, the guy from Philadelphia, and as a Philadelphia native, I am just desperately excited to have him here today. That's why I'm talking so fast. So Robert, how are you doing today?

Robert: I am doing great. It's good to be here with you, Peter and Ryan. You guys are an entertaining bunch. I'm sitting back here just enjoying the show.

Peter: Awesome. Well, let's make you part of the show now. Just real quick, one more thing to get out of your way here, folks. Just so you know, we have a quick poll. Enough about us, I'd like to learn about you guys as well, so just let me know where do you lie in industry, in your roles, in your organizations?

So, guys, are you a founder? Are you a business owner? Do you find yourself being more of an analytics junkie? Are you an agency hero? Are you a marketer? Or are you something else? People from our previous webinar might remember that something else nope was a weird typo that crept into our GoToWebinar stats, so that has been fixed. Congratulations to us.

Folks, thank you so much for voting so quickly. Just want to know, at the same time, if you're something else, let us know what that is in the comments. Like, how many hats are you wearing? Precisely what is it that you're doing at your organization? Also, out of quick curiosity, let us know where you are. We got a really weird and diverse number of locations visiting our webinars. We have everybody...

Ryan: Beng [SP], beng, beng, beng.

Peter: Beng, beng, beng, beng. We have all of the amazing folks here in New York. We have some great folks out in the west coast, and we even have literally two attendees almost every webinar, burning the midnight oil in Australia.

Ryan: Awesome.

Peter: So thanks to our Australian fans.

Ryan: And just so you know, we ask the same poll question every time, and we get very different answers, and we do it just the webinar in real-time to make it more relevant to the audience and focus on those things. So that's why we ask. There's going to be some other questions that we're going to ask as well involving CPA objectives and LTV and some other cool stuff.

Peter: And we'll get to those momentarily.

Ryan: So stay on.

Peter: Yeah, exactly. So thanks so much for voting so quickly, folks. I'm going to go ahead and close this poll. I'm going to share the results with you all just so everyone sees who's here. Marketer has crushed it. It's over. It's all marketers. I don't even want to know who anyone else. We have a great number of founders as well, and something else, a really strong contrast, is we got 18%. So, sorry to see not as many analytics junkies here. Guys, get on it. Now you know how I feel whenever I ask when content marketers are here and I'm literally the only one.

Ryan: Now, this is perfect for marketers. A lot of our presentation is going to be actionable things for them.

Peter: Awesome. Going to go and hide those results then, and now we're going to get to the actual meat of this presentation, folks. So, again, this is "Jumpstart Your Momentum."

Ryan: We have the meat!

Peter: We have the meat. Let's go ahead and just figure out just what you're going to learn today. First of all, folks, I want to make sure you know that you're going to learn the three things you can't afford to get wrong, even on day one, in your ecommerce strategy. We want to make sure you can understand how to identify breakout success in the first six months of your business.

We're going to try to give you a means by which to focus your data, because it's not enough just to get metrics and look at Google Analytics; you have to go deeper and you have to understand what's happening. You have to be able to predict which product lines are going to be successful from that moment to really boost that LTV, that retention, and to make sure you're fitting the right products to the right markets.

And finally, kind of alluded to that earlier, we're going to find the three drivers that fuel growth at top companies, and much more. So I'm going to go ahead and get out of the way. I want to make sure you guys know where this data came from. So just a 15-second commercial on RJMetrics. Bob, prepare to take it away.

Robert: Sure, I am really excited to be here and to share just quickly what RJMetrics is, and in fact, a lot of the data we're going to talk about today was derived from all the things that RJMetrics is able to learn about the ecommerce ecosystem, thanks to the hundreds and hundreds of companies who used the tool. What RJMetrics does at its core is, it takes all of your data about your customers throughout their entire life cycle.

So that's out of your shopping cart, your back-end database, third party systems like Zendesk, all your advertising network providers, and gets it all under one hood into a central data warehouse that we manage. And then you get this brilliant front-end interface where you can go in and explore the data, ask data-driven questions without needing to write SQL, without needing to do data modeling, and get really actionable insights out of your data. So we are a great tool for ecommerce companies and a great tool for marketers. So I'm really happy to be meeting with the crowd that we are today.

Peter: Awesome. And we're just going to go ahead and jump into what's new, Bob. You're taking this as well, correct?

Robert: Excellent, yeah. So some of the more exciting things that we've come out with in the past year here at RJ are these benchmark reports that we've been able to release. So as I mentioned, we get access to all kinds of interesting insights about ecommerce companies and the ecommerce industry, and a lot of the charts you'll see in the webinar today were pulled directly from these. So you can come to our website at rjmetrics.com, and it really gives a lot of interesting views into the way really successful ecommerce companies are building their businesses, and the characteristics of the customers that really allow them to go all the way.

So, really to kick things off here, there are a couple of macro topics that I just want to touch on because they are so fundamentally exciting, and I think a lot of times we all get lost in the weeds thinking about how to run our businesses, and we lose sight of the amazing tail winds that all of us have working in the ecommerce industry.

So there is something unique about the ecommerce industry that is really, really remarkable, which is that it's the only trillion-dollar plus industry. It's actually expected to be a $2.5 trillion industry by 2018, that is still growing at more than 10% per year.

That is a really, really remarkable stat as an industry, both the size and the growth rate stands [inaudible 00:08:34]. And really what we saw in our data is, of course, that some companies, much like many of the ones being run by people listening to this webinar now, are actually growing at a much higher rate than others, which steers us in a little bit to some of the data story here. So this is from a very large sample set of companies that we've been able to analyze, and what we did was, we segmented the performance of ecommerce companies by quartile.

So the top 25% of performers are quartile 1. The next 25% are quartile 2, and so on. And what you see immediately in this chart, that jumps right out at you, is just how much better the great companies are. Already in month two of being in business, if you could zoom in really closely there, they're starting to pull ahead of the pack, and it really compounds over time.

So, the thing that is the clear takeaway here is, it's not just about necessarily being better than your neighbor; it's about being at the top of the pack because the relative improvements over time are just exponentially different. So what we're going to dig in today is, how are these companies doing this? What is it about that blue line that is really allowing them to separate from the pack, and how can you apply that to your own business?

Ryan: And also, we're going to have another quick poll question related to this slide. We want to see what stage business people on the line are in, so we can adjust our responses towards that. So if people are under $10 million, if people are in that 10 to $50 million stage, or over that. So we're going to go ask that right now and [inaudible 00:10:00].

Peter: [inaudible 00:10:00].

Ryan: [inaudible 00:10:01 to 00:10:03].

Peter: Just a real quick interruption. Thanks so much for voting so quickly, folks. Just going to give you one more second for everyone to vote. Thanks so much. I'm going to go ahead and...

Ryan: We had a lot of marketers. We also had almost 20% founders on the line, too.

Peter: That's amazing.

Ryan: So that's another reason why we're asking this question as well.

Peter: All right, with two-thirds of the audience voted, I'm going to go ahead and close this poll. Thanks so much for voting so quickly, folks. You want me to share those results, or we're going to keep that close to the chest?

Ryan: We can share it. That's good.

Peter: Just want everyone to know where we are in the stage.

Robert: Awesome.

Ryan: We have actually a lot of people in the early stage and a whole bunch of people at the top. It's over 50. Ten to 50 is a really broad range, so it's interesting. This is really relevant because most of the people here are under the 10 million and want to get this information. We'll also make sure that we talk about the people who are 50 million, that are obviously part of a larger, mature companies, or just really fast growing company. But we'll address some points for them as well.

Peter: Awesome.

Robert: I think one of the things that also we see a lot with our companies we work with that are in that over 50 million category, is that they're constantly innovating and incubating new product lines, new categories, new go-to-market strategies. A lot of the things that you will see here around building up a startup ecommerce business in the first few years of its life, they're equally applicable to trying to launch a new line, a new sub-brand, a new other category, and we very commonly see these tactics used by much larger companies that are incubating innovation in their own businesses. So, hopefully it's applicable across the bar.

So let's hop over to the top three growth drivers. So we talked about what these categories of companies are. We have these top quartile performers; they're just blowing everybody out of the park. But the question is, what is actually working so well for them? And what we found is that there are really three key growth drivers that are going on inside of these companies.

It's product market fit, it's efficient acquisition, and it's customer retention. I want to start with the first item on this list. What you'll see in the data I'm about to show you is, that the accelerated growth of those top performers is so much faster than other companies that we've got to assume something else is happening here, other than just efficient acquisition and good retention.

Smart acquisition and good retention will get you far, but first, you've really got to have a product that the market wants, and a large market for that product. So, Ryan is going to talk a little bit more about best in class acquisition and retention strategies, but for the purposes of describing product market fit, new customers, repeat purchases are metrics for identifying if your store is on the path toward good product market fit. So when we analyzed the data, we found four indicators of product market fit that show up very early on in a company's life cycle, and I'm going to step through those with you.

So, indicator number one: revenue. No big surprise here. This is the same chart I showed a moment ago, but really just out of the gate, just to talk about some of the actual numbers here, top performing companies are just growing much faster, and that's just in month 36.

If you were to zoom in, even just up until that six month mark, in month six, the top performing companies have already generated over $2 million in revenue, while the others are just averaging above about a half a million. So this continues to go on over time. By the time you're at the end of year two, these companies are in a position where it accumulated over $26 million in revenue and are still about four times the size of the next largest ones. So they've kind of run away from the pack at that point.

So what's going into this new revenue indicator? If we go into indicator number two, we can actually see new customers coming in the door. So this graph is a little bumpier, but the same trend really applies, where you can start to see that the absolute volume of brand new people who have never made a purchase before, coming in to buy for the first time, is driving the growth in a really meaningful way.

That's just not happening at the other companies at the same skill. By the end of year two, top performers have about two and a half times the number of new customers coming in than the group in the second quartile.

So we're going to talk a lot today about retention and engagement and getting repeat purchases, but amongst doing all those things well, they're also driving in a steady stream of new customers [inaudible 00:14:04].

Ryan: And I want to talk about this, too, for a second, and this relates to product market fit. Now, the companies doing so well, the argument can be made like, "Oh, they're very well-funded." Well, you know what? At the early stage of the business or under that revenue number, under a couple million, they're not overly capitalized.

They have product market fit because they have a very high viral coefficient, so when they look at the number of new customers coming in compared to the ones that they're actively driving with paid marketing, with Facebook ads, with non-brand Adwords ads, the overall majority of customers are coming in through organic sources just from offline word-of-mouth.

That's where they're coming from, and that's such a super important indicator of a really successful company and that you have product market fit, that people really love what you have and they're telling other people about that.

If you don't see that super high viral coefficient, if you're not seeing this explosive new customer growth or there's not a lot of non-paid new customers coming in, you haven't hit that yet and you got to work on that before exploding on the paid area.

Robert: It's discussed as market pull versus market push. When you're looking at that first six months, first 12 months, especially in the way the venture markets have been treating ecommerce in the last 12 to 18 months, believe me that none of these companies were spending millions and millions of dollars to get that acquisition. The market was pulling at them and really had the desire due to strong market fit.

And we'll see this, too. We're going to hop over to indicator three, number of orders. So it's an important nuance here. The number of people coming in the door is obviously correlated to the number of orders, but it's not guaranteed to be tightly linked. All of these fit into each other in a way that actually adds up and multiplies against one another, and we see this in the number of orders as well. So in month 10, top performers are processing over 10,000 orders, and 10,000 orders isn't even a line that gets crossed by any of these other quartiles.

So by the time you're less than a year in to your existence as one of these top quartile performing companies, you are doing more orders in a month than the other three quartiles will be doing at their three-year mark. So this runaway growth, again, it couldn't be more clear indicator of product market fit, and [inaudible 00:16:14].

Ryan: And I always found there's a magic number. It's interesting you say 10,000 a month, because I always felt the magic number is, once you can quickly get to 30 orders a day, you're off to the races. That's it. So when you're product market fit and you grow up to that, it's...you could even see early in this data when they're out like 10 or 15 orders a day. When I'm saying 10 or 15 orders a day, I'm not saying that's your best day.

That's your average. So I would mean like...say, maybe an example where I'm saying 30 is this magical number, and according to this chart, it might even be like 20, 25-ish, 25 a day. So when your Mondays through Thursdays are looking like 30-plus orders and maybe your weekends are about 20 orders or so, that's when you've really hit it and that's when you can explode on a lot of things, and that's when money will flow in and you can really invest a lot more in customer acquisition. So there's that how quickly can you get to that averaging 25, 30 orders a day? That's a really important number to hit when you're off to the races.

Robert: Awesome. Totally agree. And one of the things that we can do to roll all of these things up is, look at this fourth indicator, which is CLV or customer lifetime value. This is the mother of all indicators. It is the thing that every single thing that we've talked about here so far today and everything that we will talk about can be distilled down into customer lifetime value in one way or another.

This is the reflection of basically what your customer is worth, and to conduct the calculus in order to determine that, you've got to know the new customers that are coming in the door, the repeat purchase tendencies, depending on your calculation, how much you paid to acquire them, and what the acquisition source was. You can really see here that there's a very clear breakdown, from a customer lifetime value perspective, as well as these other metrics we've been talking about, where this first quartile really, really stands out.

Although the difference might not appear to be as stark on CLV, from a quartile perspective, as opposed to those other metrics, when you combine what these customers are worth over the long term because they keep coming back without needing to necessarily be resold, with this dimension, it's just all that much more valuable. Not only are these people performing better in terms of who they're selling to and how many of them they're selling to, the people they're selling to are worth more implicitly as well. It is really amazing how these things all seem to dovetail with one another.

Ryan: And the good news is, for the companies in the line that are the big, mature companies over 50 million, you have a lot of this data, and we're going to tell you how to interpret it, I see a lot of big companies making very bad decisions on aggregate data. Because when you're large, if marketing budgets sometimes need to get reduced or you're looking for your CPA objective to drop, and that puts the marketer in a very difficult position when you're looking at aggregate data.

So we're going to talk about some channels and some product lines where you want to really overshoot your CPA objective, the back ends to the right one, and why you need to argue with sometimes the board or the CMO or the CEO of why a different CPA objective makes a lot more sense for the business according to the data.

And early companies, we'll talk about ways you can extrapolate. You may not have 365 days or a year's worth of LTV data. So there's ways that you can do some extrapolation and some Nielsen fun stuff to make educated decisions on which chance to double down on and which losers to cut.

Peter: And that's going to bring us to...

Ryan: What does it mean?

Peter: What does it mean? And I'll tell you. [inaudible 00:19:53] what it doesn't mean. It's about analyzing and essentially...

Ryan: Thank you, Beiber. We appreciate it.

Peter: Let's get into growth driver number two, though, guys.

Ryan: We shouldn't put Beiber in there.

Peter: Oh well.

Ryan: Growth driver number two: vision acquisition!

Peter: Here we go. With that, we're going to move into the second growth driver. This is some exciting stuff. We already talked about this in the earlier slide, but we see that these performers acquire customers much faster. You can see this difference between all these different quartiles even more clearly when you look at the sheer number of customers, which is this chart that we can put in front of you here.

So I'm going to ask Ryan. Maybe you can hop in and talk a little bit about let's [inaudible 00:20:34]. Let's talk about actually acquisition strategies. We've seen what these companies look like. That's exciting. The question on everybody's mind is, how do we do it? And that's entirely up to you, Urban. Like, how do we do this? This is your entire game right here.

Ryan: All right, so what's really funny and ironic, so my whole life, I was a customer acquisition guy from the '90s. You've heard me speak, "I was around before Adwords. I remember when it came out." I did. I remember when every channel came out, and I got to spend a lot of money, lose a lot of money making a ton of mistakes. My job, up until Bounce Exchange, was pretty much being brought into companies, sometimes private equity or VC-backed, and I put together a custom acquisition modeling like, "Okay, we have all this money. How do we get all these new customers?"

Here's the thing I learned, especially for an early stage business: none of the full momentum or the paid growth, that's not really it. So efficient customer acquisition is all about this offline word-of-mouth, the viral coefficient. That's the important thing, and when that amount is really high, then you can pour money into acquisition because it will work out for the long-term. With Bounce Exchange, we basically took everything we learned in the ecommerce world. We have much different customer acquisition models.

We don't do the traditional, "Oh, let's exhibit every trade show, pour a lot of money into Adwords, have a million sales reps, have all this social media stuff." We don't do any of those things because I learned that the offline word-of-mouth effect is the most important thing that's driving your business.

When you look at the highest growth companies, no one's talking about Amazon's social media strategy, or even like, say in New York, you use a thing like Seamless or Grubhub. Their brilliant marketing is because you had a very good experience with it, and you stake their reputation and you told someone else about it.

So your business, early on, you need to get your viral coefficient, otherwise known as K factor, to the number of one. That means, without spending any money on paid marketing, your business is going to keep growing without anything. That's the optimal time to pour fuel on the fire and spend money on that. So there's a lot of different formulas online where you can look at viral coefficient and K factor. You want it to be 1.0. That's efficient customer acquisition.

So, how do you do that? Well, that's going to be whether your product you're carrying sucks or not. Is this a really good quality product? If you're the founder on the line, you're very jaded. You think everything is awesome, but reality is, from the mature companies on the line, they know you've come out with a lot of products and services, and a lot of them are flops. A lot of them don't hit expectations. You just realized that after. But at the beginning, everyone's really energized and excited about it. "We're so mobilized. This thing's going to be great."

So a lot of times, your optimism for the product manager or the merchandiser or whoever made the decision to carry this product line or this service, whatever it is, you have to put their optimism aside. You want to be optimistic, but you have to look at the data. Look at the repeat rates. Are you getting all these organic transactions?

One of the mistakes we're going to talk about is, is sending out leading surveys to your customers. You can make a survey get any result you want based on the way you ask questions, so I don't really like those surveys. And return rate's an okay indicator, but reality is, the best indicator on efficient customer acquisition is, are your customers repeating at an efficient rate?

And this is something that RJMetric's going to go into. What are really good repeat rates for people? So if you have a new [inaudible 00:24:10] or product line or something new came out, you're going to isolate that cohort and you can...one of the ways, obviously, is using RJMetrics. When I was up at Bonobos, I literally used RJMetrics. I got to put it in, and I used it to make decisions, not only on what channels, but also really on people, what product people were buying?

And I found such huge differences in LTV, in repeat rates, depending on what product line people bought. That changed the onsite merchandising strategy. That changed the product strategy because you're planning up products and different new offerings six to 12 months, sometimes even more in advance, and you need to get the data on what are the winners and what's not. So a lot of the things I like to look at is, what are the repeat rates for those customers, even in a short 30 or 60 day period?

So those are critical things. The big indicator of health of product market fit, again, is, do you have...if you look at, say, your early stage here and you're at the point where you're getting maybe not a thousand transactions a month, but maybe you got a few hundred a month. So out of those few hundred, look at your marketing channels, and what can you reasonably attribute to marketing channels?

Attribution's a big thing here, so your re-targeting company might want to take credit for the whole thing. But reasonably, what was paid marketing, and what was organic? So you want your organic to be a much bigger version, much bigger part of that puzzle.

Maybe it's something [inaudible 00:25:36], for anyone [inaudible 00:25:37] maybe we'll share some viral coefficient equations. There's a few formulas to it. I have my own I used to use that's more based on recent customers. But that's really the key. The founders on the line, all these people, you have to have really good products, and anything that's not a great product, that's product lines that have lower repeat rates than normal, you got to cut them. You got to de-merchandise them.

Don't send out emails about them. Don't put them in your homepage. Don't feature them high in categories. Focus on the products and services that you're offering that people love, that people stake their reputation on, that people will tell everyone like, "Hey, this is it." So the product quality is key for efficient customer acquisition.

Peter: And how do you determine product quality, though? Is it based on your own personal taste, or is there any data you can actually use?

Ryan: Yes. It's not based on personal taste. Personal taste is the worst thing to do, because that's what I said. Anyone who is the product manager or the designer or whoever it is, the person who made the decision to bring that product in, they think it's great, and they're putting their best foot behind it. So that person almost can't be part of the evaluation process because they may sway in one direction. That's why I want to talk about repeat rates and some other things as we move on with this presentation.

Peter: Awesome. And that's going to bring us to growth driver number three. Speaking of which, as we move on, we're going to talk now about retentions. I'm going to bring it back to you, Bob, if you can nail it to the wall.

Robert: Absolutely. So we are going to get into some really fun data at this point. We are going to start talking about these charts, which tell us something about these best in class companies. Which, as I said, they're getting customers back at much better rates than the rest of the pack. So let me explain these charts a little bit. On the left here, you are seeing the new versus repeat revenue for the top quartile of performers.

So the left half is the top quartile. On the right half, you're seeing everybody else combined. Now, one thing to note about these lines on each chart is that these are percentages on the Y axis, which means that one line plus the other line always adds up to 100%. So what you'll end up seeing is that these two lines have a tendency to converge on one another. But while they may seem somewhat similar between the two charts, it's actually a really, really meaningful difference that I want to highlight here between the top performers and the rest of the pack.

So here's what I mean. Naturally when you're a new business, you're selling to your very first customers. They're, by definition, new customers. So on both charts, those blue dots start up near 100%. But even in the very first month, you can see this on the very first dot, whereas in the lower performers, basically 100% of revenue is coming from new customers. You're seeing that dot start down even lower than 90% in the first month for the top performers. What that means is that they are already generating 10% or so of their revenue from people who have come back and bought again in the very first month in which they were a customer, which is really remarkable.

So the pace at which you start to recognize that you have repeat purchasing tendencies amongst your customer base, it is not something that you need to wait 36 months for. It's not something you need to wait six months for. You're going to start seeing that behavior very early on, and these top companies absolutely do.

But the other thing that gets really exciting is when you actually look at the opposite end of this charts. So it's really to me somewhat mathematically beautiful that on the right chart, everything just perfectly converges right to 50%. So by the time you get to that month 16, month 17 mark there, you can see that there's two lines that are almost literally sitting on top of one another, with occasional divergences from one another.

So on that right chart, it's like the natural course of the world that happens in the average company is that about half your revenue's coming from people who are coming back. About half of your revenue is coming from people who are new customers. That's the circle of life; it's the way things go. In these top performing companies, the lines cross, and what you can see by the end there, at month 36, is actually that the revenue coming from repeat customers is more than the revenue that is coming from first time customers.

And what's really crazy about that is, if you remember back to a few slides ago when I was talking about new purchasers coming in the door, there's no way this is happening because they're not attracting more new customers. We know already that this group of people attracts more new customers than any other quartile [inaudible 00:29:58].

So not only are they bringing in way, way more new customers than anybody else, those new customers actually represent a smaller percentage of revenue. They end up snowballing the repeat purchase performance of these repeat customers over time. It ends up creating this really, really amazing dynamic where you got this higher CLV, you're processing more orders, and you're one of the best companies in the world. So Ryan is going to talk a little bit about some of the companies that are doing really, really well in this regard of boosting retention.

Ryan: You know what? I love this slide. I've never seen this data ever shown before. And for everybody, it took me a while to like, "Oh, I'm missing all these things." So on the bottom axis, you're looking at a three-year period. This is what I see all the time, too. It's so funny. On the right side, it looks like a Star Wars movie, like [inaudible 00:30:49]. And it's about companies that don't have that true product market fit. There are companies that could be doing 50 million or 100 million or a few hundred million of revenue that have this 50-50 split of new and returning.

It's not that healthy. It's really not. The ratio of the really good companies, this is exactly what I see, too. So it changes. And I've seen companies when they're at the six-year point, which is double [inaudible 00:31:17], where 65, 70% of their business is repeat. I've seen companies that are, say, $20, $30 million companies on the downslope where it's only 20% repeat, and the business ultimately dies. It just loses all its hydrogen fuel.

You can see this data is really tough to figure out if you're a business that's under two years old, but it has to ultimately change. A lot of the ways you can do some extrapolation are just...you have to isolate cohorts. So, say, right now, what month are we in? We're January of...

Peter: 2016.

Ryan: 2016, cool! So if you isolate cohorts from, say, January 2015 or January 2014 and look at their repeat rates, those are the things you want to do. So isolate months of transactions, of transactors, and look at all the new people, the new customers that come from January 2015. And then look at their repeat rates. So those are going to be signs of health. Or, in the future, once you get three, four, five, six years out, what's the dynamics or your business going to look like?

And unless you really get to that point where you're on the trend of two-thirds plus your customers being repeat, which means they like what you're doing, and email, which is really cheap, is driving a lot of repeat purchases, your business is not ultimately going to scale. It's going to have some trouble. It's going to stop. It's going to plateau to a certain point, and there really won't be a plateau buster for it. It would just be a business that stagnates and doesn't go anywhere, which is what happens to 90% of ecommerce businesses that get past a few million dollars. It's really, really hard. So let's go to next slide and let's talk about...

Peter: The actual processes behind it, which you mentioned is key but really hard. This is where we talk about how we can focus our data and how to get the right ideas starting off. We're talking about three years in advance. We have a lot of young companies here. How do I start driving towards these really awesome retention rates when I only have like two to three months' worth of data?

Ryan: Well, the next slide is going to go right into the one KPI. It's more than one KPI, but it's going to be about the customer lifetime value. Let's go right to that. So on the RJMetrics side, let's talk about the data I was talking about where you're really analyzing cohorts by month. What's your opinion on that, Robert?

Robert: Really, the customer lifetime value stuff is super exciting. Looking at this stuff from a cohort basis ends up really being the key to success for a whole bunch of people. So the exciting thing about all these cohort topics is that you just end up being able to get a perspective on how the customer life cycle translates into value for your business.

So whether you're picking a cohort of folks that are from a particular month in time or have some particular characteristic in common, you get to track these out and actually watch them evolve as your business grows and as your customer set grows, and learn about basically the evolution of value.

That initially starts out with you recouping your investment costs and how you acquire them, and then turns into what are your unit economics on this individual customer? What's the profitability profile that you might have? And when you get to a point where you can really drive this sole KPI around customer lifetime value, you end up in a really good spot.

Ryan: Speaking about spot, Robert, I'm going to put you on the spot now. I didn't tell Robert this. So I want you to talk about your own business right now. Let's talk about RJMetrics and how did you use RJMetrics in your own business to determine which customers are the right ones? Which are going to have the highest LTV for you, and how you focus your business direction? We're doing a webinar right now. We're focused on ecommerce companies. Obviously there's a lot of companies. Going after B2B companies would have been a completely different direction for your business, though really important.

At Bounce Exchange, we look at what's our LTV based on size of client, based off of industry vertical, based on what country they're in, based on how much money they raised, how much they're spending on marketing. We found there's a lot of stuff. There's a lot of businesses that we avoid, and there's some that we explode all of our efforts on. Let's talk about RJMetrics and how you've used your own data to make those decisions.

Robert: So we eat our own dog food, big time. We live inside of RJMetrics stat boards. One important nuance for us that I think is pretty relevant here, and while we don't talk about it too much in these slides, I think is really driven by the core fundamentals that we're showing in this presentation is, for us, we're a venture-backed company. We raised about $23 million in venture capital across three rounds of funding. What that means is that we have the high class problem of needing to put money to work to grow our business.

So in the early days, when we were bootstrapping the business for the first couple of years, it was a very, very clear-cut economic question every day: how much money was coming in the bank, and how much of that were we comfortable spending, trying to grow, and trying to bring in new business? And that was what dictated our growth rate.

When we raised capital, that flipped a little bit because we had more dollars available at our disposal, but we also had this [inaudible 00:36:37] to grow at a faster click. One of the most dangerous...

Ryan: I see once companies, once they even get to the point of the business where you get a lot of money in capital, you had to make a lot of right decisions before then, and most companies are bootstrapped, [inaudible 00:36:51] a small amount, friends and family, whatever it is. Then once you have the capital, I see companies make a lot of bad decisions and they spend money foolishly. So talk about the early days, and how did you make decisions? Which customer segments to go after and which ones to drop off?

Robert: For us, it really boiled down to looking at the behavior of these users inside of our tool and what leading indicators might show us that they'd be likely to be engaged and stick around for a long time. So it can be very, very tempting to just bring in a lot of low quality customers that the ecommerce equivalent is they make one purchase and they never come back. The SaaS or B2B equivalent is that they're going to subscribe for a month or two and then they're going to turn off and disappear.

Really, it is so important to identify the characteristics that are associated with customers that stick around and are engaged, and are going to come back and be valuable long-term customers. And for us, that really boils down to just how deeply are they investing in getting to know how to use RJMetrics. We have this thing called a golden motion analysis that we run during our trials, which is taking a look at all kinds of different things that people can do during their free trial with RJMetrics.

So you can log in a bunch of times. You can invite your colleagues to sign in as well. You can view charts. You can enlarge charts. You can edit charts. You can create charts from scratch. You can connect new data sources, etc., etc., etc.

We have this laundry list of things. What we do on a regular basis is run an analysis of what of these actions actually are leading indicators around whether or not people will convert into paying customers after the trial, and also whether or not people will stick around in the long run. It's really interesting how these things break down and how effective it is for us to learn what works well.

What we found time and time again is that it's building charts, by the way. If people end up logging into the tool and just looking at dashboards, they don't get the value that they would if they're creating new analyses and actually doing ad hoc analysis on the fly, which requires learning how to build charts. Which is pretty easy but ultimately is an investment that people need to put energy into knowing how to use the software well.

So once they've done that, people not only convert at a rate that is more than three times as much as those who don't, but they stick around longer as well. It's extremely intuitive. People who know how to use the tool, they're invested in it, they get more value out of it. But you could make an argument, without seeing the data, that logins could indicate the same thing, or connecting more data could indicate the same thing. But nothing is like editing charts.

Ryan: And in some cases...

Robert: And we've seen some of our...

Ryan: And in some cases, and maybe in your case of the business, was too early stage. They weren't logging in as much. Maybe they didn't have as much data; or maybe very mature businesses, they had too many things going on. Was there certain segments that were just doing really well, and then that changed how you adjusted your whole marketing approach?

Robert: Yeah, and it's as much also folks who are marketers but are in the product marketing side of the world. For us, marketing matters in a big way. The reason we do webinars like these is, it's about thought leadership, and what we want to do is engage people that are interested in making investments in their own professional development and in learning new tools and learning new skills.

That's precisely the profile of people that end up being really effective RJMetrics users, because they log in and they're ravenously consuming all the great things that the tool can do. That leads us to getting more engaged customers, and that's the reason that we do stuff like this.

It also is impacting the way that we deliver the product to people. So the guided tours, the trainings, the first few things that we step people through doing when they log into the product for the first time, that is dictated by our data on what things will lead to people being most likely to get engaged and stick around longer. So it's a really holistic retention strategy based on the data that we can collect around people's interaction with our product and how that relates to conversion and retention, and that's the way we think every business ought to be doing it.

Ryan: And I would say, right now, if...say you're an ecommerce company online. Obviously we're applying some of the stuff to B2B companies, which is harder because we don't...some B2B companies in early stage, we don't have a lot of solutions. It's not like RJMetrics had five products, and like, let's see which different piece of software was working better for us. So you have to really, really break down these cohorts by vertical and by company size.

If you're an ecommerce company, you probably have a few different product lines, a primary product line. So figure out a way how can you divide that into, say, something manageable, five to 10, and then sometimes you can break it down by demographic or by age. Then start looking and then isolate a time, maybe a year ago. So you look at the new customers coming in from that cohort, and look at the repeat rates of all those things you broke out.

So I love to look at it by product line and category, and that's when you can see what the winners are. So there might be some categories that just don't get a lot of sales. The repeat rates are through the roof. And you know what adjustments you can make to that? One, you can merchandise it a lot more. You can get a lot more real estate on your website. You promote on email more, and that also changes your business. It changes the products you carry.

It changes your product roadmap or, if you're a fashion company, what you design, if you're a product company, the new stuff you're carrying. So that's what you really got to look at. I love looking at it by their average order of value on the first transaction, too, because there's a lot of things that you can do to influence that. [inaudible 00:42:14] we'll see some of the data here.

People who start off with a higher AOV, they correlate so much higher lifetime values, but if they're buying a product line that's crappy, they're not. And you can't get your reputation back. Reputation is everything for your business. So the actual advice here is, is really break down your business into a few different product lines, and then go back in time a little bit and do an analysis. Look at the repeat rates of new customers by those different product lines. Maybe you can break that into some things, like male and female, or by different ages, whatever it might be. So take a look at that.

Peter: Awesome. I want to make sure that we can get to as much of the content as we can in the time that we've got here. Really, in the next couple of slides, we're just ultimately talking about how, as you look at all these different quartiles, all the different dimensions that are inputs to customer lifetime value, are delivering us into a spot where we've got differentiated performance.

So, be it average order value or the number of orders that get placed in the first 365 days, across the board, there is a lot of difference that ends up coming into play. We haven't talked about average order value too much. If we got time, Ryan, maybe you can comment a little bit on just the way that average order value might be able to be affected by somebody that's in the position of many of our listeners.

Ryan: In general, I secretly advise a ton of ecommerce companies in the early stage, and also, I talk with a lot of late stage companies, too. The mistake, and hopefully a lot of people here have proprietary products, what I find in general is commodity products, which are things that are available everywhere else, including on Amazon, that's really challenging for a lifetime value.

If you look at those, if you break it down, if you carry both the proprietary products and commodity products, go look at what the LTV is on both those things, because when you're a marketer, you have a return on ad spin objective or you have a CPA objective. The CPA objective, there's no order from the C level, saying, "Well, for proprietary products, we have this CPA objective, and for commodity products, we have this."

But they're all wrong. So, commodity products should have much lower CPA objectives because those people are typically transactors. They're looking for a good price for something that's available elsewhere. They made a decision to buy from you, but it's very hard to retain that customer because they're not in it for your brand; they're in it for that particular product.

When someone chooses to buy a proprietary product, there's a different decision-making process. They weren't looking for something specific. They might have been looking for a pair of dressy skinny jeans, but they chose to buy it from your brand, and those typically have much higher LTV.

So that's a good indicator. The mistake I see companies make with their commodity products is it's a lot of times a race to the bottom. They're not charging a fair price. They're looking at what competitions are doing, and they assume theirs have to be the cheapest. I've seen it over and over again. If your product quality is good, you should charge a fair price for it, because what's that going to do is, you're going to invest a lot more money in R&D.

It's going to make your product quality even better and better over time. So I say charge a fair price. A big thing, too, is, when you're deciding what new product line to release, some mistakes I see people make in trying to boost LTV is like, "Hey, let's go ahead and [inaudible 00:45:36] or T-shirts for our website, and we'll make our $60 AOV up to $80."

What happens in every single case when you have items, say, normally priced at 50 to 60 bucks and you start adding ones in that are like 10 to 20 bucks, AOV decreases and you start getting lower quality customers. You start getting people buying these lower things that lead to lower lifetime value, too. This is not a supermarket. At a supermarket, you're forced to a checkout process, where you can add a candy bar or a drink. On the Internet, that doesn't happen. Upsells and attachments are super, super low, so it's very hard to do that in the Internet. So, on the Internet, you have to earn everything.

So I recommend the opposite. If you're looking at making a decision on a new product line or new things to carry, always choose the higher end ones and go towards proprietary. So that's a direction you should go in, and that's what boosts AOV. So don't carry the cheaper things. That's really, really good advice that you see almost every business owner and marketer and merchandiser make.

So always go for the higher value stuff. The other benefit of the higher value things is you get price framing and it makes your normal products seem like a better value, because you have a better anchor point to that. All right, done with that [inaudible 00:46:48].

Peter: With the time we have left, you guys want to talk about cohorts? I think this is really the meat that we got remaining here. If we can skip ahead a little bit.

Ryan: We can go over a little bit, too. Your next meeting is not as important as this, come on.

Peter: We'll come back if we have time, but let's talk about cohort analysis. Let's see what you can do with the CLV. So I'm at the cohort analysis slide now.

Robert: Outstanding. So cohort analysis is, hands down, without a doubt, my favorite analytical tool. And we hinted at it before, a lot of people don't really get introduced to it until they're at a company of some scale. I want to just provide a little bit of an introduction to what this chart is that you're looking at and why it is so extremely powerful.

So, this is a chart that is where each individual line of this chart is a group of people. Now, the thing that these people have in common is the month in which they made their first purchase. So you see that orange line that's at the top there, that's all the people who made their first purchase in August of 2013. The yellow line is all the people that made their first purchase in November of 2013, etc.

Now, ordinarily in a chart, when you've got something that is time-based on the X axis there, you think it's the calendar. The first data point is January. The second data point is February. That's not the case here in a cohort analysis. In a cohort analysis, that first data point is the first month of that cohort's performance.

The second data point is the second month of that cohort's performance. Which means that for every single line, it actually represents a different calendar month, but what is the same about them is the moment in the customer's life cycle that that event happened. So what we're really looking at here is a bunch of lines of, this is actually an average value per customer in a cohort of what they cumulatively spent over time at an ecommerce store.

So that orange line, for example, if the very first data point represents all the dollars that were spent on average by a person who made their first purchase in August 2013, the second data point of that line is August plus September of 2013. The third data point is August, September, October, etc. What this allows us to really visualize is, over time, number one, the steepness of the line indicates to us the relative impact of repeat purchases versus initial purchases from these customers.

So if you got a steeper cohort line going on, you can very clearly see that you've got more and more purchases happening at higher dollar values from that population, and over time, the long-term purchasing that happens from that cohort is actually more valuable cumulatively than the initial purchase, which is really exciting.

The other thing that you get to see is, you get to compare these cohorts with one another. In a previous life, I worked in venture capital. When you're making a venture capital investment, you do this thing called due diligence, which is where you do a deep dive into all the data of the company.

You try to find all the skeletons in the closet. Our number one favorite tool in due diligence for figuring out the health of a business and the predictable future of a ecommerce business's revenue, was this cohort analysis. Because what you can actually see is, not just our new customers purchasing as much in their first purchase, but you can look and see if the newer cohorts are performing as well as the historical cohorts.

Ryan: Exactly. [inaudible 00:50:03] example, the business had better quality products or was just more exciting back in 2013 and 2014. When there's a big mump [SP], like say the August stands out in a good way and some other months stand out in a bad way, sometimes if a month stands out in a bad way, it's because you had a lot of people coming in.

Maybe it was from a Groupon or Living Social deal or something you did, and those people were just [inaudible 00:50:25] lower quality. Or you're featured on the Today Show, and those people came and they bought [inaudible 00:50:30] low quality where you got picked up in the press a lot, and the people came at a lower quality. Or you got picked up in the Wall Street Journal, and the people that came in were of higher quality.

One thing I wanted to mention, some really good actionable tip that I want to mention in the last slide about how you influence their average order value, which influences LTV. So the introductory offer that you have for someone making a first purchase on your business, there's a great way to do a cohort analysis. So look at people who came in on, say if your products are like $80, and maybe your AOV is like 100. Maybe your goal is to move your AOV up to $150.

So when you look at offers, I would test different offers. So when people test offers, they're just looking at conversion rate. They don't look at LTV. LTV is really important because if the discount's too much, they might not respect your brand as much. But what I found is, I see people start off with a test. They're like, "Oh, we'll do 10% off the first purchase versus $10 off," and they're not being bold.

So I've seen companies with great product lines. They can do something like $50 off their first $125 purchase, and their repeat rates are through the roof, and the product quality is good. So I would test bolder introductory offers if you have proprietary products, and look at the analysis. Look at the LTV. So make a bold test. If you have something like 10% off, something dinky, that's not going to move the needle in customer acquisition, go do a bold test and see how it effects your LTV.

So if your product quality is good, people are going to come back for more. If you do a bigger discount, you can have a higher minimum threshold. So if you're doing $50 off, you can say off $150 purchase. What winds up happening is, people will purchase two of your item, and if they have two of your item, they're much more likely to repeat if it's good quality. If it's bad quality, they're less likely. So if the product quality is good, test different introductory discount thresholds. Don't be afraid to be bold, and then obviously look at the data really, really quick.

Robert: [inaudible 00:52:36] first second, too, piling on that bold test proposition. One of the other side effects of doing bold test is that you tend to get larger disparities between the two groups of results. So if you're doing something that is a very aggressive discount, if you're doing something that maybe is a completely different presentation of the information as opposed to changing the color of the button that you're giving 5% off, from a statistical point of view, you're typically running one of these tee [SP] tests and you need to get a certain number of data points to have some confidence that you've actually got A that's better than B.

If the gap between the results in the two is larger, then you need a smaller sample in order to have statistical confidence that you're going to get an answer out that's conclusive. So if you're able to really test an assumption by drawing very stark lines between two different states of the world, then you might be able to answer question in days that would have otherwise taken weeks to get a statistically significant result out. So really big fan of the boldness in testing idea.

Peter: You have one more thing to add?

Ryan: And a lot of times, if you're running different analysis based off of the purchase they came in on, you want to see where there's separations. So in this case, you start seeing a lot of separation around month. There's just a big jump. This August 2013 one, I think this is just an outlier for some reason. But simply you're going to start to see some separation around month three and four, so you don't need to wait a year or two to do an analysis.

So look at the lifetime revenue. You can extrapolate. Look at what it is around three or four compared to other cohorts, and you can see the ones that are like...say in the case where a customer has the LTV for one cohort is $400. Another one is $200. Maybe over the course of the year, in month three, one might look like $200. One might look like 150.

So if you have a new channel, look at the ones that look like $200, and you can extrapolate that a very high likelihood those will reach the LTV at 400 over the course of the year. So you can make decisions. You could cheat a bit, and make quicker decisions based on looking at a few months of data instead of having to wait a year or two. So look at the behavior of your highest quality cohorts, and then that's where you're trying to emulate.

Robert: So the astute listener at this moment will look at this chart and they'll say, "Okay, that's all well and good, but what happens if you paid $500 in order to acquire any of these customers that are on there? Was it really worth it?" And that's when we get into this discussion of marketing ROI. So people who spend a thousand bucks in their lifetime with your business, they're not worth it if you got to spend $2,000 in order to acquire them.

There's just going to be differences in lifetime value by the source you acquire customers from. We often see very, very stark differences, in fact, by channel. There's a need to tie all this back to acquisition. This is just a sample chart. It's not actually from benchmark data. It's just a hypothetical scenario of what an analysis might look like. But it's not uncommon to see marketing ROIs from particular channels that vastly outperform ROIs from other channels.

So in this case here, we're seeing email marketing being significantly more effective on an ROI basis than, for example, Facebook ads or affiliate marketing. All of these relative ROIs are extremely important because not everyone of these channels is going to scale infinitely. You're going to have a cap on the number of potential [inaudible 00:56:06] where you can buy with certain keywords, the number of emails that you can send to the people that are on your list.

So you need to diversify and you need to be constantly testing, but the proportional spend that you have on each of these should be influenced heavily by this. So when our customers are using RJMetrics, it's very, very common that this is the thing that they're looking at on a very regular basis, because it's driving invest or kill decisions. This is actionable data that you're looking at to the question of where is it that we should be putting our money.

Ryan: And here's a tip. And I mentioned before, oftentimes, marketers are given the same return on ad spin objective or CPA objective for different channels. It shouldn't be the case. Say something like Google product listing ads, Google shopping PLA, those typically have lower LTV than something coming in from just a generic Adwords term or say a Facebook top of funnel ad or Facebook Lookalike Campaign.

So the Facebook Lookalike Campaign might have a much higher CPA objective than that PLA, and it should. Also look out for channels that...remember, we live in a cookie-based world, and a lot of channels are under-attributed. A lot of channels are over-attributed. Affiliate and re-targeting, way over-attributed. You have things like top of the funnel Facebook and adwords campaigns that are not your brand terms. Those are way under-attributed.

In some cases, you're getting two times the transactions than those [inaudible 00:57:27] are reporting. So you have to look at that as well. One thing that analysis, when you're looking at CPA objectives, when you're looking at LTV, it doesn't factor in your viral coefficient, as I mentioned before. For every 100 new customers you get, if that leads to an extra 50 customers, that should be factored in. That's a viral coefficient of slightly over 0.6.

If for every 100 customers you get leads to another 130 customers, your viral coefficient is closer to one, and that changes your CPA model as well. That's something that I see VCs don't even factor in. And certainly, I've never met clients that are really looking at that, but that's a smart way to do it. So look at what that K factor is, and then build that into your customer acquisition modeling for the highest quality cohorts. So in some cases it looks like you're overspending, but you're not. It'll all back into the right numbers at the end.

Robert: Good stuff. I think part of the reason why this is also important, is shown in this chart here. There's this [inaudible 00:58:29] called the Paredo [SP] Principle that some of you may have heard of. It's more commonly known as the 80-20 rule, and it's this idea that the top 20% of a population tend to influence 80% or more of the effects or the results. In the case of ecommerce here, it would be the argument that the top 20% of your customers are actually responsible for 80% or more of your revenue. We see this happen time and time again at ecommerce stores, even wildly successful ones.

So when you think about that characteristic, it really all ties back to the idea of everything that we've talked about here today, getting the most valuable customers in the door, how to identify the behavior of those most valuable customers. Because at the end of the day, we found that the top 10% of customers is worth six times as much as the industry average, and the top 1% is worth 18 times as much as the typical average other customer. So if you're not optimizing your marketing and your retention strategies to find and keep these customers, these ones that are at the top, then you're missing out on a really huge growth driver for your business.

Peter: Awesome. And with that, we're going to go ahead and break into our Q&A, folks. Before we do that, first of all, thanks again so much to Bob Moore and Ryan Urban for all this really awesome data and really awesome advice. Just a quick poll, though. Just want to see, after this webinar, what kind of information you're interested in, folks.

Just real fast, what would you like to receive more information on, folks? And feel free, you can click one or both of these. Let's see that poll. Awesome. So would you like to hear more about the amazing data that happens in [inaudible 01:00:02], or would you like to hear about how Bounce Exchange can give you this advice via a free demo of how we are automating behavior?

Ryan: Some pandering.

Peter: A tiny bit.

Ryan: [inaudible 01:00:13].

Robert: [inaudible 01:00:13].

Peter: The amazing, beautiful RJMetrics interface.

Robert: [inaudible 01:00:17].

Peter: You can check them both.

Ryan: And we're doing this with RJMetrics today because who we choose to do marketing partnerships with like this, it's not that we just do it with anybody. We're a big fan of the product. I've used them personally myself, so I would vouch for it. It offers a lot of value, especially to marketers, because I used to hate going to my Excel monkey in quotes. You may do this analysis that may take forever. Once it gets set up properly, which is not too hard, your data's flowing in. Yeah, I was able to dial these reports myself, which is that's just what I wanted.

So it was really good. And look, you saw on that ROI report, email is the best channel; we agree. That's why our software does a really good job at getting more emails from prospects and creating really smart email automation. So if you want a demo of us, and see how we do that, just dial us up. Just go to our website and go request a demo, and we'll talk to you there. Same thing with RJMetrics, too.

Peter: Nice. I'm going to go ahead and close these polls. And now I'm going to pander to myself real quick. Just so you know, if you like this one, be sure to check our webinar next week. [inaudible 01:01:24] our webinar is going to be a week from tomorrow. On February 4, we're talking about the Artist Telling Everywhere with Shopify.

Ryan: [inaudible 01:01:32] actually.

Peter: The enterprise [inaudible 01:01:34] Shopify is going to be super cool. We're going to talk about the future. We talked about the past and data a little bit here. We're getting a little high concept and show you literally how to sell everywhere, especially in Cityscape [SP]. So be sure to check that out. But an hour...

Ryan: [inaudible 01:01:47].

Peter: What? But an hour before that, perhaps you'd like some more RJMetrics. Dude, I've got you covered. An hour before that, 1:00 p.m., February 4, we've got the Ultimate 30-minute Guide to Marketing Analytics. If you'll explain this real quick, Bob.

Robert: Sure. We touched a lot on marketing ROI and cohort analysis today. If you want to dig in a little deeper, you want to see how that actually behaves when you're inside the RJMetrics product, you want to really get into the marketing analytics stack a bit more, Shawn and Maddy from our team are going to be covering marketing analytics maturity frameworks and the metrics and the tech that you need to actually make the stuff happen at your business. We'll dig right into that. So don't miss that one.

Peter: Definitely. So if you liked an hour of us, be sure to check out two hours of us back-to-back next week. It's going to be awesome. But now I'm going to break into our very fast QA. Guys, you had a very wrapped audience. You've explained everything so well, we just have a few questions. First one to Rob, real quick. I lost it. Where did it go? Oh no. So, your CLV metrics, were you looking at revenue, or were you looking at gross margin? That's a question from Al.

Robert: Great question. So you want to be looking at gross margin. The metrics that we're showing, the benchmark report, are revenue derived, which are, depending on your objectives, there's good reasons to look at it one way or another. If you're purely looking at customer lifetime value for the purposes of return on investment though and you're an ecommerce company, I would definitely advise having that gross margin statistic available, because that's something that is going to be a much more accurate representation when you're doing something like an ROI calculation. So, great question.

Ryan: I like using contribution margin dollars as well. I would also recommend factoring in the return rate on products, too. Some things that have 25% return rates don't show up in your Google Analytics data. So factor that in. Contribution margin dollars is the best way to build a good CPA model.

Peter: Nice. And just one more; I have a long one. I'm going to get through it real quick here again. This is for you, Bob. So the data that you showed, did that data show a distinctive difference between ecommerce and mcommerce customers? Are mobile consumers different in shopping behavior because maybe they spend more?

Robert: That's such a good question. People will start on mobile.

Ryan: Bob, what do you think about that?

Peter: And follow-up question to that just right there: does mobile create more impulse purchasing as well? And we can get into that momentarily.

Robert: So here's what we've seen, and this is not a [inaudible 01:04:08]. This is just me [inaudible 01:04:10] on having seen it at a bunch of companies. So mobile commerce is growing really rapidly - we know that - and increasingly is becoming a lot more mainstream. However, by and large, it is still very much the bleeding edge of where new commerce growth is coming from.

It's probably the most rapidly growing and evolving channel that has any kind of meaningful scale in ecommerce. One of the side effects of that is that there's a very high propensity for the people who are actually adopting mobile apps as a point of purchase for commerce or even access in commerce through the mobile web. They tend to be people who are already deeply engaged or have deep brand loyalty to whoever it is that they're buying from.

The likelihood of someone making a first purchase at a site from a mobile channel or through a mobile app is much lower, in our experience, than the likelihood of someone who's an existing customer than getting engaged through the mobile channel and making a repeat purchase or coming back. So this is one of those correlation versus causation things that you want to be really careful about. If I just showed you the data on paper, it would lead you to believe that mobile customers are hands down the most valuable, amazing customers in the world. You should do everything in your power to recruit everybody that you can on mobile.

However, I think there's a selection bias there which happens, because the people that are acting on mobile were already the most engaged. That's why they ended up engaging you on mobile, because it's so bleeding edge. So, hands down, the data says mobile's amazing, people on mobile are way more engaged, but I think it's a more complicated story than that.

Ryan: It's such a good point. I used to look at data...if you look at your tablet conversion rate, you'll see it's not that far off from desktop, but find it's really engaged people. When you try to acquire new customers on tablet, say, if you looked at Google Analytics and you looked at your conversion rates, you're like, "Oh, tablet's good. Let me start running some different campaigns to acquire customers on tablet." It is so hard. You're like, "What happened to the conversion rate? Why [inaudible 01:05:59] desktop now or [inaudible 01:06:01] desktop? The data said it was only going to be 20% less."

So that's really the point. Tactics you can do is, for paid driving to mobile, and even tablet, you might want to have an email gate on the page, and then you're going to put them into an autoresponder and get them back on desktop. It's really difficult to get that first [inaudible 01:06:21] on mobile. So getting the email upfront, really, really critical to that. It's unfortunate Adwords doesn't let you change tablet, but tablets are really difficult, too.

I tend to weight as much new customer acquisition spent to desktop as possible. I agree with what you said. Getting that first on mobile is really, really hard. A lot of times it's someone who's already engaged in desktop or a very loyal customer.

Peter: Awesome. And just one last question for Rob. I feel like this is a softball. I just want to make sure, though. So say somebody signed up for RJMetrics. Can your product look backward on data? Thank you. That's a question from Yan [SP].

Robert: Oh, absolutely, yeah. So one of the important distinctions with our data replication engine is that we're actually going back and pulling all of your historical data, not just from your databases but also from those third party providers. So one of the things you want to be careful about with data sources like Google Analytics and other event collection mechanisms is, you guys set it up, you get it installed, and then it starts collecting data, and your starting day is the day you installed it, and it's everything forward from there.

What RJ does is actually goes and retroactively pulls from APIs and from databases to get your complete history, gets that all loaded up into one central location, and then you really got what's the necessary data ultimately to do things like these cohort analyses in one spot, and it's all right there at your fingertips. Thanks for the softball. Good one.

Peter: No, of course, we try real hard. Our audience is [inaudible 01:07:50] and interested. And folks, thank you so much for sticking with us to the triumphant finish. That brings our Q&A to a close here. Thanks so much for sticking with us past the hour. Thanks so much again for the amazing data from RJMetrics.

Robert, Bob Moore, thank you so much for giving us all these really great insights. Folks, if you want more, you really need to check out all of the benchmark reports that RJMetrics has at their site. We're going to be sending you that in an email followup, but feel free to Google it as well. Really beautifully laid out, really awesomely explained. Just data from a really cool [inaudible 01:08:17] startup.

All right, folks, and as always, thanks so much to our CEO, Ryan Urban, for all of the actionable advice we gave on top of this [inaudible 01:08:26] today. A really, really cool webinar. Just really happy to have had this here with you all. Just so you know, folks, first of all, Robert Moore, any final thoughts before I completely close this thing out and run the credits?

Robert: Close those deals, everybody.

Peter: Good.

Robert: Good luck out there. It's going to be an exciting year for us all.

Peter: I'm really excited to see where ecommerce is going this year. Just so you know, folks, all of the data and all of the content here today came from the awesome RJMetrics team, from Janessa [SP] and Anna. Thank you so much to them. This webinar on the Bounce Exchage side was produced and worded by me, Peter Starr Northrop.

Our demand is generated by the dynamic duo, D Mos [SP] and Dave Quinn, and we are edited by [inaudible 01:09:02]. So folks, thank you so much for your time. And as always, we like to leave you with peace, love, and optimized conversions. Everyone have a wonderful day, morning, evening, or afternoon, wherever you are.