The more you understand your marketing analytics, the less time your team spends guessing about what works. Most new companies start with the basics, like looking at Google Analytics dashboards, but that only works for so long. You’ll really need the more advanced insights that come from measuring things like customer lifetime value, customer acquisition costs, and marketing ROI.
Join us for a crash-course in quantitative marketing, from understanding where you fall in the Marketing Analytics Maturity Model to the metrics and analyses you need to enable smarter marketing decisions.
Shaun: Hey everyone, thanks so much for joining us for our event today on marketing analytics. Before we jump into the material, I have a few housekeeping notes I'd like to cover. First, we're going to have a Q and A session at the end of today's presentation, if you have any questions at any point please feel free to submit them via the chat window. We're also recording the show today and we're making that available to everyone within 24 hours of the event. You'll receive that in a follow-up email. In a quick introduction, I'm Shaun McAvinney and I'm a Senior Sales Engineer here at RJ Metrics. What that means is that I spend most of my day talking to e-commerce marketers, SaaS company founders, Analysts, CEOs and COOs about their data infrastructure. My role in today's event will be to talk to you about the tech you need to run a modern marketing team.
Maddie: Good afternoon everyone, my name is Madeleine FitzGerald and I'm an account manager at RJ Metrics. So really I spend my days working with the individuals working with the individuals responsible for making decisions with data. So my role in today's event will be to talk to you about best practices when it comes to marketing analytics and also share some stories and examples of companies doing this really well as well as some areas where I see a lot of companies making some missteps.
Shaun: Great, thanks, Maddie. And just so you know what you're getting into, RJMetrics Cloud VI is an analytics platform for online businesses. Our platform connects to data sources you already use like Salesforce, MongoDB, Facebook Ads, other databases, Zendesk, the list goes on, and consolidates it all into a central data warehouse where you analyze it using our chart building interface. All the things you'll be learning on today's event are relevant for really anyone, RJMetrics customer or not. But if you're interested in learning specifically how RJMetrics Cloud VI can help you do some of the things we'll be covering today. We'll be showing a short five-minute demo of the product at the end.
Outside of learning about RJMetrics, you'll leave this event with some practical tips that you can put to work today to get more value out of your data. We're going to focus on three core areas today, the four stages of the marketing analytics maturity model and how to tell where you fall, what you can learn from exploring advanced marketing analytics, and lastly, what technology you need to enable data-driven decisions. Along the way, we're going to be sharing examples and stories of how small, growing and at-scale companies are using marketing analytics to grow their business. With that, I'm going to hand it over to Maddie to get us started.
Maddie: Perfect, thanks so much Shaun. So let's get started by making sure we're all talking about the same thing when we say marketing analytics. So here's a great definition from our friends over at WordStream. Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on revenue. And today almost every single marketing tool that your team uses will come with some analytics capabilities built in. Marketing automation tools like HubSpot or Marketo, and Partap [SP], have some very handy pre-built tools. And if you use any email service providers like MailChimp or maybe Constant Contact, you'll get email analytics. Then you have tools like Moz, which will provide marketing analytics for SEOs and, of course, you're using Google Analytics to measure web activity. So there's actually a lot of data out there that you can use for marketing analytics.
And you can see from this chart that companies are using marketing analytics for activities across their marketing function. So this data is from a CMO survey last year. But the big one is still customer acquisition. And honestly, if you look, 36% is low, so using data to improve customer acquisition is usually the quickest win. So at first glance, it's kind of surprising that more companies aren't doing this. So in my day-to-day, I work with a lot of clients on their marketing analytics, and I frequently hear this embarrassment from marketers feeling like they're not on top of their data. So just in case you're feeling that way, you shouldn't at all. You're actually not behind the curve. Most companies are struggling to figure this stuff out because it's legitimately hard to do marketing analytics well. And the fact that you're here today shows that you're on that right path.
So I mentioned earlier that every marketing tool tends to come with some built-in analytics, you have your MailChimp reports, which are great for understanding who is and isn't interacting with their emails and what content performs the best with your audience. If you think, you also have the Shopify reports which can help you optimize your checkout process and across all of your marketing tools you have analytics that will help you improve a specific area of your marketing.
But what if you want to understand how your email clicks are translating into revenue? This is where things are going to get a little bit tougher and this problem is exacerbated by the multiple tools you're using to run your business. So what you end up with is a lot of marketers forced to make platform specific decisions because their companies aren't investing in the tech needed to provide marketers with a unified view of that customer. So this brings me to the marketing analytics maturity model, which could really called the business analytics maturity model, but for the sake of this event, we are going to stay focused on marketing.
So what you have here is essentially four different stages, at the beginning you're going to have tactic driven marketing, this is the only option available to marketers who are using a variety of tools, and honestly who isn't? So you have your social team which will rely on Buffer and HootSuite Analytics, you have an email team maybe which will optimize for clicks, and your content team will keep on getting more page views and leads. The next stage here is campaign driven marketing, so at this point marketers are using UTM tags and with enough data exports and spreadsheets, they can start to answer questions like, what are my top performing marketing campaigns, or how much revenue was generated from that last email send.
So this visual here is showing campaign driven marketing as being only incrementally better than tactic driven. But I really don't want to downplay how important this knowledge is. So one of our VPs wrote this blog post about UTM tagging and it was over two years ago, and it's still one of the most trafficked posts. So, if you need help with this, that's okay because this stuff isn't super easy, I would encourage you to visit this blog post.
So the big jump happens when marketers go from campaign driven to integrated marketing. IBM did a study on this recently and close to 60% of marketers deemed this jump to an integrated cross-platform view of the customer to be a significant challenge. So the final stage that we have here is predictive, and we're really not going to spend a lot of time here because the predictive world is advanced. You're going to be getting into Big Data territory and doing this type of analysis and modeling really takes some data science skills.
But just as a side note here, our marketing team actually recently did some research to try and figure out how many companies actually have these skills. We looked for companies that have a single data scientist on staff, and honestly almost no one does. Only 6% of big companies, so meaning companies that have more than 10,000 employees have access to these types of skills, so that's really just to say that the predictive level is a ways off and most companies aren't even close to that.
I want to take just a few minutes to dig a little deeper into defining each of the different stages. And then I want to run through a poll to find out where everyone thinks their company is at. So here are a few things to look for, at the tactic driven stage you're relying on data that lives across a variety of tools, your key metrics will likely be some things like new leads, or cost per click, or new subscribers. The biggest sign that you're at this stage is if you have to log into multiple platforms to get the insights you need about marketing performance. Again, it's actually worth mentioning how common this stage is. So our VP of marketing spoke at an event recently which was actually packed with SaaS and eCommerce marketing VPs. Most of them were really stuck at this stage and struggling to even understand that it was a solvable problem. So it's a problem so prevalent that marketers have just become resigned to it because to get to stage two you need to be a real spreadsheet whiz and a lot of teams don't have that.
So that's the biggest challenge with the second stage, you're likely drowning in spreadsheets. If you need to run reports on your backend database, you'll actually be taking up dev time. So this is the challenge HootSuite was facing when they started with RJMetrics several years ago. Every time their marketing team needed a report, they had to wait for developers to run the SQL queries, so this means a lot of wasted time and also pulling devs away from their core job, building your product. The good news is that if your company is here you have tasted the power of integrated data, you haven't solved this problem at scale but you understand how powerful it is if you do. You're able to run some basic ROI reports and you have some insights on how much revenue your different channels are generating. But you still don't have the holistic view of your customer.
So to get to integrated you actually need some pretty impressive technical components. First off, you need a data warehouse where all of your disparate data sources live. And then on top of that, you need some kind of analytics tool. So if you have analysts that want to be able to manipulate the data, but your business users are likely just going to want to regularly update reports on key metrics, and the ability to do some simple analysis on their own.
So really in summary, here are some big signs to indicate where you fall on the maturity scale. With tactic driven, you're checking multiple sources, you may not even think that analytics are a huge problem for your business, or maybe you actually do, but you're the only one at your company that thinks so. We then have campaign driven, right? You have access to some insights but there are too many spreadsheets going around and your data tends to be stale. At integrated, it's easy for you to access current status of marketing performance and understand your customer experience. And of course with predictive, you'll know when you're there. You'll have access to some real analytical power at this stage. Shaun, so you work with the sales team and you talk to hundreds of online businesses every year. How do you think these stages break down?
Shaun: Yeah, so this is a pretty crude estimate, but here is what I see. We talk to a lot of people at the tactic driven stage who are only really beginning to understand a problem and then another big chunk who are trying to figure out how to make the jump to integrated. So to back up to what you were saying earlier Maddie, most companies aren't knocking it out of the park on marketing analytics, if you're trying to figure out how this works, you're actually, are a little bit already ahead of the curve.
Maddie: Great, thanks, Shaun, so now I'd like to get a sense of where you all are at, we have a small group today, so you'll probably see these numbers jump around a lot, but let's take a look.
Shaun: So if you guys could put in your answers for which group you think that you fall into, Tactic Driven, Campaign Driven, Integrated or Predictive, that'd be great, then we'll share the answers after the poll. And one thing that I noticed is that, and Maddie, you can probably attest to this as well, what we found is that sometimes it doesn't even matter the size of the company, which group you fall into, it's more the maturity of the marketer or the person asking the questions. Something that I see a lot with second time founders is that if they've had a very successful previous company and then they go to start the next company, a lot of times they could be two guys in an attic but they're already looking into integrated, they're already at an integrated stage where they're pulling all their data together and answering questions effectively. So, let's go ahead and take a look at the results.
Maddie: So, that's the state of things today, we can see that it looks like 15% of you believe you fall into that tactic driven stage, we have 60% in that campaign driven stage and then another 20% in the integrated stage. So we're going to spend the rest of our time today covering two different things, the first is what you have to look forward to when you make the jump to integrated marketing. So this is going to be especially valuable if you're the sole person or one of the few people at your company advocating for this. Because it is a big change and honestly, it's not that easy, it costs money and it takes time, and a lot of people are happy to just keep relying on Google Analytics and some other Shopify reports. So hopefully, this will give you some ammo to take to your boss or maybe your coworkers and get them excited about doing this work with you.
The second thing I want to focus on is the technical capabilities you'll need to integrate your data. We won't get too in the weeds here, but we wanted to give you enough to go to your dev team and have a conversation with them about what this would potentially take. So there are several approaches you can take here and Shaun will walk, excuse me, Shaun will walk you through those different options in a little bit. So let's go, here's what you can do with integrated data, one of the quickest wins that we see on a day-to-day is revenue analytics. One of the most frequent complaints I hear from clients is that every department has their own interpretation of revenue, is it pre-tax, is it counting returns, so when you want to do simple analysis like figuring out where your revenue is coming from, you end up having a lot of disagreements.
So we actually have a quote here from Bebel, that captures this perfectly. "There are a lot of places where you can go to get data on your ad spend, cost per click, and revenue, but with integrated data, you have an authoritative source of truth for your organization.You have a shared set of metrics that ensure everyone has the same view of the customer."
Shaun: Yeah, and another analysis you'll want on your marketing analytics dashboard is "leads by source". This is probably particularly relevant for SaaS companies, but you'll want to keep tabs on where your customers are coming from regardless.
Maddie: Customer lifetime value is such an important metric for any business, arguably the most important. It's the measure of how much a customer spent with you over time, and without integrated data, it's actually incredibly difficult to get your hands on this kind of data. You'll be bothering your engineers a lot for SQL queries. So in this particular analysis, the marketer wants to understand the overall impact of their Google AdWords optimizations. They're constantly tweaking their campaigns and testing, but they want to know if customers being acquired are actually better.
So in the hypothetical scenario you see on the screen here, there's two different things I want to point out. First, customers acquired in the June cohort were more valuable right out of the gate, but then they tapered off over time. And then we see that customers acquired in more recent cohorts are reaching CLV of over $1000 after their first year with the business.
So, I've been working with Chuvvies [SP] for over a year at this point, and they've fallen in love with cohort analysis and customer lifetime value. They actually call themselves the Cohort Kings. And so these guys are not full-time analysts, they just have integrated data that allows them to get the insights they need on their customers. So I've mentioned marketing ROI, so let's spend a little bit of time here. This is an area of marketing analytics that every marketer dreams of but it's tough to actually do right. So what you're looking at is fake data but it's similar to the dashboards our clients build.
What you have here is performance by campaign in the top section, and this is integrated data, this is what we're talking about. You have the name of the campaign with data from your ad platform, impression, click-spend then paired with the results, customers acquired, new order revenue, even 90-day lifetime value, and the ROI. So even better, this report can combine your campaigns across platforms. So say you're running the same campaign on maybe AdWords and Facebook, you can compare them apples to apples, or you also have the option to roll them up into a single view, and then compare your cross-platform campaigns. So that's going to be multi-channel marketing, and it's all thanks to the integrated data.
The good news here is you don't have to be a fortune 500 company to do this level of marketing analytics, and Shaun's actually going to talk about that in just a minute, so I'll quickly finish up here. You can also build reports that service your top and underperforming campaigns. The important thing here is that because your data is integrated, it's very easy to build automatically updating reports on top of that data set. So you build these reports once and you actually enjoy them forever. You don't have to come back here and say, "Hey, we just launched a new campaign." Your campaign tab will automatically be pushed into your data warehouse and show up here.
So our client Hmall has gotten really good at this, and use dashboards like this to constantly optimize their campaigns and the results truly speak for themselves. So I talked around this just a bit but didn't say it specifically. There are really two ways to get value out of integrated data. For one, you can go deeper on platform specific data than you can go on a single platform. The second way is to combine disparate data sets. The marketing ROI dashboard I just showed you is an example of the latter. You're combining multiple ad platforms and joining the customer ID from the ad platform and your shopping cart platform to have a single view of a campaign or even an individual customer. This email marketing analysis is an example of the former.
All of this data exists in your ESP, but it's impossible to get to with the standard reports these platforms deliver. So having your data integrated in a single warehouse allows you to look at your data in completely new ways. Here you're looking at the performance of a reactivation email [inaudible 00:19:15]. If you really want to get fancy here, you could add a filter on a chart, like this and explore how the reactivation campaign is performing by customer value segment. So if your reactivation campaign is offering deep discounts, it might be getting low-value customers back, but not doing anything for your highest value customers to get them in the door. So this really is only a small taste of some of the things that you can do with integrated data, but I hope it was enough to get you excited and see how powerful this is. So at this point, I want to hand this back to Shaun, and he's going to talk through how to actually make this a reality.
Shaun: Yeah, thanks, Maddie, so really you have three primary options when it comes to integrated data, the first is spreadsheets, and as Maddie already covered this will give you, I think I went too far there. This will give you, get you set on campaign optimization but it won't scale you all the way to integrated. And then the next option is to build a data stack on your own, so what I'm showing you here is a little bit of an oversimplification, but again my goal here is to give you a good enough understanding to have a conversation with your engineers.
A data stack is made up of roughly these three components. At the foundation you need a way to integrate with your existing data sources, this could be building custom APIs, writing scripts etc., it really depends on what the data source is. Once you have a way to extract the data, then you need to load it into a data warehouse. And then once it's in a data warehouse you need a way to interact with the data, probably with a business intelligence platform, or another visualization tool. And I'm not going to spend much more time on this, but if you really want to go deeper you can check out this post on our blog, and our webinar team here will be tweeting that out from @rjmetrics if you want to grab the link there. It's become really popular for engineers to write about how they're building their data stacks. So we did a meta-analysis of these posts to look at the tech that they used for each part of the stack. This is a must read if you want a crash course in understanding integrated data.
And then obviously, the third option is to buy a complete platform that will do all these things for you. RJ Metrics Cloud VI is one tool that does this, but the tool that's right for you is going to depend on where you are as a business. Let's talk just a little bit about how to evaluate this. First, let me add this, if you go the build option, you don't need to go 100% build. Building an analytics platform is an incredible amount of work, but tools like Looker and Mode can be layered on top of integrated data so you can start getting value out of that data immediately. Writing a bunch of APIs to integrate your data at the bottom of the stack is also a phenomenal feat of engineering, just read that blog post to get a sense of some of the challenges. But tools like RJMetrics Pipeline are making that part of the stack so much easier.
So there's this fourth option which is a build-by-blend, where you buy the various components of your stack and then kind of piece them together. And this is becoming a really popular option, but I'm kind of getting ahead of myself, let's look at these one by one. So you have the of 100% build, and here you get the maximum flexibility, but it comes with maximum engineering time, you're going to need at least two full-time engineers to pull this off, it's the right choice if you're actually building data products, like Spotify, Uber, or Jawbone. For those companies data is the product, so this level of investment can really make a lot of sense.
And then you have on the other side, you have the 100% buy, and that's the other side of the extreme, this will give you a good amount of flexibility, depending on the tools you use, but it comes with the lowest engineering investment. For these types of solutions, you'll need some API keys from your engineers, but that's pretty much about it. This is a great choice if you're interested in skipping the campaign driven stage and jumping straight into integrated marketing. Small teams can get a platform like this up and running, and you don't need an analyst to help get value out of your data.
And then you have that build-buy option. And that offers high flexibility with low engineering investment. This option can come with a sizable price tag so it's usually not right for small businesses, but if you start with a 100% buy and grow as a business, this is likely the stack you're going to grow into. So those are your options, and if you're unsure which choice is right for you just send me an email, my team spends all of our day helping companies navigate this decision. Right now I'm going to open up RJMetrics Cloud VI and show you how the 100% buy solution works, and what you can actually do with some of the marketing analytics that Maddie showed you. And after that, we're going to open it up for Q and A.
Great, so you all should be seeing my screen now, and this is RJMetrics Cloud VI, this is our dashboarding and reporting tool full stack business intelligence platform. So we've already done the initial work of replicating data from all the various systems and putting it into an RJMetrics data warehouse, and this dashboarding and reporting tool lives on top of that data warehouse.
One thing that I really like to drive home on these demos is that everything is customizable and editable and again to reiterate, you don't have to be an analyst or a data scientist to do those customizations and editing. So obviously we can drag and drop these reports around the screen, you can make things bigger or smaller, just customize the look and feel of the dashboard, you can also share these dashboards amongst your organization. So you can see right now I'm sharing this with Dalton and Noah. These are two of our SCRs here at RJMetrix, and they actually have editing rights, so when they log in they can make changes to this dashboard, and we're actually collaborating together. And then I'm sharing this with a couple of other people here at RJMetrics, and I've only given them viewing rights, maybe I don't want them to either dig any deeper or I just want to either report up or down on the dashboard to them. It's also a great option for investors.
And you can also edit any of these reports on the dashboard and create new ones from scratch. So the first step to building a new report is to add a metric, and these metrics are defined during your initial onboarding and you can edit them on an ongoing basis. A way that I like to describe them, I can describe them in two ways, one would be a broad business definition for how you're going to measure your business, so something like revenue or average order value or customer lifetime value, those are all examples of metrics, these are all things that should be standardized in your company. And then on a more technical note, you can think of them as skeleton SQL queries, where we have a table in the data warehouse and we're performing an operation on a column in that table with some additional filters and WHERE clauses in there and then we plot it out across the time stamp.
So it's like you have this skeleton SQL query and then you use the report builder to flesh it out a little bit if I can go along with that analogy too far. So let's actually tighten up this time range here, to show revenue from last quarter and show it by week. And I'm just going to put on my, let's say I'm a marketer, I'm just going to put on my marketer hat here and say ok so I've had $155,000 in revenue from the last quarter in that time period November 22. And what I really want to see is first time vs repeat revenue, I'm going to go ahead and add a filter here and just select users order number and set this to 1. And now we're looking at all first time revenue so $15,000 in first-time revenue, and I'm just going to duplicate this and change it to greater than 1 and now we're looking at first time revenue vs repeat revenue, pretty quickly.
So we can see pretty obviously, we had $15,000 in first-time revenue compared to $169,000 in repeat revenue. That's a huge difference in revenue, and what we probably want to ask the second question would be where did we acquire all this repeat revenue from, what was the original acquisition source for these users that spend a ton of money over the long run. So to do that I'm just going to hide the first time revenue, and group out this repeat revenue by the users original campaign source.
And so what we're looking at here is all of the repeat revenue broken out by the original acquisition source for the users that made those purchases. And you can see it's pretty obvious there's this peachish swath flowing through here and this is celebrity testimonial shirts and what this is showing is that this campaign, celebrity testimonial shirts brings in users or buyers that become increasingly high value, lifetime value customers, they spend the most, 48% of the repeat revenue in that period can be attributed to users that we acquired through that campaign.
This is just a brief example of how you can go from something very broad just all-time revenue to answering a very specific question about your data, and then being able to make a decision on that, the decision might be, let's invest more money in celebrity testimonial shirts, let's try to get more users like that, let's model our next campaigns after that one, things like that. So that's pretty much what I wanted to go over in the demo and I think we're about ready to open it up for questions.
So I'm just opening up our questions now, and so one question is, we've briefly touched on, this is to me, you briefly touched on the spreadsheet option for analysis, but why specifically is this not a viable option? That's a great question by the way and happy to answer that. One thing that I often see is that when I talk to new clients is that they are in that spreadsheet option right now, they're mixing things up, they're getting into Excel and they're doing all sorts of pivot tables but what isn't happening is they don't have that consistent version of the truth, they don't have a single definition of various things and even in that revenue metric that I showed, that seems like a pretty straightforward calculation, we're just summing up the final order total column in an orders table. But what often happens is that, if you're asking two different departments to report up to you, if you're the CEO you'll have two wildly different answers to the same question. And if it's in Excel often the case is, they don't even know why the numbers are different because it's all locked up in these calculations.
So having a single source of truth and having the same yardstick to measure the business by is a big benefit to repeatedly measure your business and improve growth, so that's a great question. Another question here, out of all the possibilities we mentioned, which analysis do we see most in the customers that we work with? I think Maddie's probably the one that's best suited to answer this, so I am going to let her handle it.
Maddie: Sure, yeah, I'd be happy to, thanks for that Shaun. So I guess what I'd say here is this is kind of a complicated answer, but really I see most people probably calculating revenue metrics, because that seems to be the most low hanging fruit. So it's immediately valuable and fairly easy to set up, once you've determined the source you're going to look at. I would say that once companies get more comfortable, they really, really are interested in looking at CLV because it's become so valuable for their business to understand what customers are spending. So once companies really have that chart or that analysis set up, they can check it against the changes and tweaks that they've made to maybe their campaigns or their spending, to really get into this metric. So it makes it pretty clear a lot of the questions that companies have about their business, that they probably couldn't really answer before without this integrated data.
Shaun: Great, thanks, Maddie. And so we have another question here, it says, you spoke about the build option requiring, at least, two engineers at the best case scenario, what's the most labor intensive scenario we've ever come across. So, we actually create a table in our data strategy guide that pretty much maps that out for you, I think, maybe the marketing team can link to that. Basically based on the assumptions in that strategy guide, really at a minimum, set up cost is going to be around $10,000 and the lowest recurring costs are roughly around $100,000, that would be annually, and really that goes up from there, right? So at the highest, we saw setups upwards of $50,000 and recurring costs are really really high, like around $500,000 or $600,000.
And then we also had some calculations, we were assuming companies kind of like mid-market, so for Enterprise companies that cost is like 2 to 10 times higher, so as your company scales and you need much more data that cost to maintain, even the upfront costs to just get it up and running can be extremely high, thanks for the question, hopefully, that answered it. And we only have a couple other questions left, another question here, can we go into more detail about the email performance charts we showed earlier, Maddie, do you want to cover that one?
Maddie: Yeah, why don't I take that Shaun. So I think as I said earlier, this is actually an example of deep analysis of data from one specific platform, right? And so that's possible once you start integrating data. And what we see here is if we look back on the slide, or if I guess I remember correctly, on the left-hand side we showed I think sends, opens and clicks for a reactivation campaign and then I think across from that we had the changes in those opens and click-through rates over time, so then I guess the important thing, I would say, to remember here or realize is that you can't just get this type of view from the reports provided by your email provider, right? So these are where the real insights come from, you can pinpoint exactly when open rates may be increased, or how it relates to the click-through rate, or maybe even how the number of sends might affect it. So really the point is pretty much any question you may have, has an answer that's the result of this perfect chart. So really what I see is the best marketing teams are looking at their data like this and it's how they're making the right changes for them moving forward, that's what I'd say.
Shaun: Okay, great. Thanks, Maddie. So I think that's going to wrap it up for our Q and A section unless anybody has any last minute questions, but in the meantime, we have a question for you. If you'd like further information about RJMetrics Cloud VI, or if you'd like more information about RJMetrics Pipeline, just let us know, if you're not sure which approach is right for you right now but you're really interested in this topic and you'd like to talk more about it and kind of learn where you fit, reach out also, and we're going to leave a poll up at the end here for you to just respond in there and say which one you're interested in and how we can best help you. So that about wraps it up for us today, I just wanted to briefly say thank you to everybody for joining us today, also thank you to our marketing team here who did a great job setting this up and getting us going with the audio and everything. And also for Maddie, this is Shaun McAvinney, this has been RJMetrics webinar, thanks so much, take care.