Pardot offers powerful marketing automation tools and is used by many of today’s most sophisticated sales and marketing organizations. Now, with the RJMetrics connector for Pardot, marketing teams can see their sales and marketing data like they never have before.
Attendees will learn:
Shaun: Hey, everyone. Thanks so much for joining us on our webinar, How to Analyze your Marketing Funnel using Pardot and RJMetrics. My name is Shaun McAvinney, and I'm a Sales Engineer here at RJMetrics. A big part of my job is helping perspective clients evaluate the technical challenges of data consolidation and management but I'll get into that a little bit more later.
Before we jump into the material, I just have a few housekeeping notes I'd like to cover real quick. First, we are going to have a Q&A session at the end of today's presentation. If you have any questions at any point, please feel free to tweet them using #measuringmarketingdata or submit them via the chat window below. We're also recording this show today and we'll have that out to everyone on Monday, if not before.
As you all saw when you signed up for this event, there will be one lucky person winning a dozen cupcakes after today's event. If you have already tweeted about the webinar using the #measuringmarketingdata, then you're already entered win but please do keep tweeting. We choose a winner random and your chances are increased by more tweets, so keep on tweeting throughout today's event. I'm here today with my colleague, Tristan Handy. Tristan, do you want to introduce yourself?
Tristan: Sure. Thanks so much, Shaun. My name is Tristan Handy and I run marketing at RJMetrics . The reason that I'm on the webinar today is not because I want to market to you. It's because I am a user of the Pardot Connector and all of the data that we are going to share with you today is very similar to marketing data that we use internally. So, I'm excited to kind of presenting as a user today.
Shaun: Great. Thanks, Tristan. So, for a super-fast interview, for all those attending, we are a hosted business intelligence and analysis platform for online business. We help companies from all verticals to make sense of their data. I'm actually going to give you quite a bit more details surrounding the platform line up, so, let's get to the reason why you're here.
Pretty much today, we're going to focus on three things: the valuable gems hiding in your Pardot data, the three ways you can actually get to that valuable data and then Tristan's going to show you how to build the ultimate marketing dashboard in RJMetrics. So, let's start off with that first point and talk about what data is hiding in Pardot.
So, essentially, Pardot has a lot of great reporting built into the software. One of the main reasons our marketing team, Tristan and the rest of them, love Pardot is that it automatically stores UTM tags. This isn't standard with marketing automation platform, but it removes a lot of headaches when you want to measure ad campaigns. Pardot also makes it easy to keep tabs on overall landing page performance. With a quick glance, it's easy to spot pages in need of some CRO attention. And then, finally, Pardot also holds a wealth of data about email performances. Again, this is one top line view that can provide decent insight into your optimization efforts.
The best thing about Pardot is that all these reports are standard. Once you get Pardot set up, you get these reports with zero additional customization and we love this. But while Pardot can provide you with some analytics, it is not a tool built for analysis. So, you have this database of information, but you're unable to analyze the data to answer deeper questions, like how is my lead volume trending over time or how is my lead volume trending by channel? What is my lead quality by channel? Or how is my market performing against our goals? And then pretty importantly, how are unsubscribes trending over time?
Those are the types of questions that are not easy to answer, but to answer them, you need two things like the ability to combine Pardot data with other data sources, like Salesforce data, CSV files for goals or even customer support data, and the ability to analyze the raw data that's in Pardot via another interface. So, let's talk about how to collect the data that will help you answer those types of questions.
There's actually three options. You can export the raw data from Pardot and analyze it in spreadsheets. If you're exporting additional data sets from Salesforce as well, then you can start identifying the marketing channels that bring in customers with a highest life time value and back [inaudible 00:04:31]. This is a fine approach when you're just getting started and your marketing team is pretty small but, eventually, you're going to hit a wall where the time it takes to do this very manual analysis isn't worth the time for what you're getting out of it. And, at that point, you're going to need a solution that can automate all these analytical processes.
You need some kind of big data - big kid data infrastructure. You need a data pipeline that can integrate with all of your existing data sources and move that data to a central warehouse for analysis. Then, you need a BI2 on top of that that will allow you to manage your data and do analysis. Or you can buy a complete BI analytics platform and that's what we do, that's what RJMetrics does.
Let me just clarify, that that first option is pretty terrible. Marketing analysts will stand hours looking around in spreadsheets every single week to do something that can be easily automated. So, here's some considerations as you think about the build data pipeline versus buy decision. On almost all fronts, buying is going to get you much further ahead. It's going to be cheaper over the long term. It won't drain your development resources to build and maintain. The risk of failure is much lower and, also, you'll have much higher analytical power because you're working with a team of people who spend all their time optimizing data management processes.
The one place where building it yourself can be an advantage is if you're looking for a high degree of technical control. For example, if you're a business like Etsy, managing data is a core part of your business model. So, it makes sense for them to have a massive engineering team dedicated to that channel. But if your core business is building and selling a great software products or scaling your e-commerce brand, then you don't need the technical control that Etsy needs and your engineering team really should be 100% focused on building your platform.
A good analogy for the build versus buy comparison that I like to bring up is the email service provider. You and your team are probably using Google apps for business or something similar and you probably are not running your own email servers or building a web mail client. That problem has already been solved and that's where BI and analysis products are at now, too.
So, once you have all of your data in one place, you're ready to build your ad dashboards. Tristan and the RJMetrics marketing team work with Pardot data every day to make their decisions, so I'm going to hand it over to him to let him talk about how you should set these up. But whether or not you're an RJMetrics user, these are basic things that you should be tracking to get more out of your Pardot data and, just before I hand it over to Tristan, I just want to make sure you keep sending in your questions on Twitter with the #measuringmarketingdata or in the chat window.
Tristan: Great. Thanks so much, Shaun. As Shaun said, the dashboards that I'm going to be showing you today are actually modeled off of our own internal usage of Pardot Connector. The data is test data that we're going to show. I didn't really when I open the kimono that far, but most of the dashboards are actually ones that we use to run our marketing organization. Some of them are actually charts that I've presented at board meetings before. So, this is really very real for me and I do want to say that I have stuff that I have stuff that I'm going to run through but very much listening for questions. I can go deeper on any of this or, if what I'm saying is super boring, please send that in too and we can skip along to the next thing. I want to be as interactive as possible.
So, in these dashboards, I just want it to be clear. There are going to be three different data sources that I'm going to show. Actually, four. Sorry. Salesforce, Pardot, Facebook and AdWords. For our marketing team, we actually pull in more data than that, including from production databases, other ad services and more, but this webinar is not nearly long enough to go into that level of detail. I do want you to get a sense though.
The primary benefit that our marketing team gets out of RJMetrics is that it allows us to pull in data from throughout the funnel, all into a single of dashboards. At my last two companies, the primary challenge we had around analytics was that our data was scattered all over the place and it took, seriously, a very long time to pull all that data together so that we could make use of it. I would produce reports maybe once a month, rather than getting real time data that I really needed to make decisions. So, RJ is doing all of this for our 14-person marketing team today.
Great. So, let's switch into looking at our dashboards and I just need to make sure that I am, hopefully, you guys are all now seeing my dashboard. Great. I'm going to walk through these dashboards from the top of the funnel to the bottom and, in the fake business we've got, this is data that we've invented for illustration purposes. This fake business is doing customer acquisition through a few different channels. We're advertising via Facebook and AdWords and we're doing a bunch of inbound marketing.
The first dashboard is a serious deep dive into Facebook performance and you're probably looking around at this dashboard right now. It is a serious amount of information. That's true and it's a little bit overwhelming at first but the wonderful thing about this dashboard is that it makes it incredibly easy to spot and diagnose performance problems.
Let's take a look at a couple of things that are going on in this data and what is it this dashboard points out to us. So, obviously, we've got some top level metrics, how much have we spent and it looks like this is going from the beginning of this year up through July 30th. So, we've spent 170k. We've got about 6300 new prospects out of that and that's an average cost for prospect of around $27. Great. Okay. None of those seem crazy right now but let's look at what else this dashboard is telling us.
This one campaign, which is a July 4th promotion, obviously got a ton more prospects in the door right around the holiday and that makes total sense. You can see, at the same time, that if we zoom in on the other campaigns, performance is down across the board around the holiday which, again, you would expect that to be the case. Anyone who's done PPC for a little while, you recognize that volume and click-through rate and all of that generally are down around the holidays.
What else can you see here, when you're just looking at new prospects? This green campaign, overtime, it definitely did better and that was great but then, in the recent months, it has tailed off and it's kind of back to even below the performance that it started out the year at. So, we really need to figure out what's going on there and that's when this really helps. You can see that spend has tailed off. That's not necessarily a problem. Click-through rate is pretty consistent. What you really notice when you look through this is that the landing page conversion rate for the green campaign started at around 2.5% and it got up to around 5% on a pretty consistent basis but since May, it tailed off. That's what causing us to not to get as many prospects in via that campaign.
And so, right away, you can see something actionable come out of this. We need to look at the landing pages that are associated with this green campaign and we need to do some conversion rate optimization on them. We also can look at whether the targeting has changed and whether we're getting people who are less likely to converge.
What's actually really interesting too or what's important to know here is that while all this charts are seamlessly woven together, it's all part of the same dashboard. They're actually been pulled from two completely different data sources and all the data on spend, impressions and clicks comes from Facebook. So, all of this at the top is coming from Facebook and we pull that in automatically but all the data about prospects and landing page conversion rate is coming from Pardot. So, I want to talk a little bit about how that's happening. That's what RJ is so good at.
Behind the scenes of these dashboards, we're pulling in the data from different sources and making it available for you to analyze. And once that data is synced, you're actually go and you build a metric on top of that. So, I'm going to show you really briefly how to create a metric. And a metric just corresponds to a KPI that you would use in your business.
So, the basic one that we use for our marketing team is new prospects. Let's just track how many prospects we're getting in for a given day. And we want to use the table that we are sucking this data into and, once we select the table, we're going to say, "Okay, this is called New Prospects and we want to count the new prospects that are coming in and we want to select the date that the prospect was created." So, that's the created at field and literally, that's all we have to do. We now have a metric and remember what this is called, For Webinar, New Prospects.
I am going to come back to our dashboard here and I am going to go to my sandbox, which is where I play around with when I create new stuff. So, we just created this metric. I'm going to need to just do a quick refresh on that page so it makes sure to show up. Shaun's kicking me to make sure that I remember to do that and it will take one second to do a reload.
When this does load up, you can see that our new metric will be in. Yep, there's our metric and we can immediately see that, okay, we've got 102,000 prospects and you can see that, since July 2014, that's been consistently trending up and our data only goes through June of 2015. So, let's play around with this a little bit. We have a new prospects metric and we are going to change up the report.
So, we're going to select a date, hit apply and, right off the bat, this makes a little bit more sense because that's the period that we have data through. So, great. Now that trend makes a ton more sense. We can also change the reporting period. We can say, "Okay, let's look at this by day." And this is going to give us a lot more granularity and you can see that hiding behind those monthly numbers, is actually a lot of variability. And this is important. A lot of times, when you are looking at analytics that Pardot provides, you're seeing very high level numbers and it's hiding the detail behind that. And that's a theme that you're going see over and over again. You need to be able to drill in to see the detail behind the numbers that Pardot gives you.
So, one of the other things, you can also filter this. That's also the one thing I wanted to show you is a group by. We've got a field in here called qualified and qualified is actually something that we do internally. We qualify every lead that comes in the door. We work with the sales team to do that and that's a field that's in Salesforce and, for those of you who are familiar with Pardot and a custom field set up, fields between Pardot and Salesforce sync over. So, this is data that's actually coming over from Salesforce and it's from a sales rep qualifying this lead. So, what we see here is that we are definitely getting more unqualified leads, those are the blue line, then qualified leads, which is the orange line. But the orange line is pretty solid and it's growing over time. So, this is just a quick example of what you can do with a chart builder.
Shaun: Hey Tristan, I think this is actually a good time to ask you one of the questions, since we're in interface now. One of the questions was, can you compare different metrics from Pardot together and, if so, how does that work in RJMetrics ?
Tristan: Yeah, you can totally do that. So, over on the left here, you see the add metric? This is totally off-script, so I don't have anything prepared to show you but here is what I want to do. I want to simplify this report back to where we were going. So, let's look at new prospects and, now, I'm just going to add opportunity. So, how many opportunities? Let's actually should look at closed one opportunity. So, how many customers did we get on the door? And right there you go, you can see both of those lines on the same chart and they actually have two different axes. You can see the orange axis over here on the right is the closed one opportunities. So, they grow kind of together but they have a completely different scale. Obviously, there's way more prospects than there are opportunities. Hopefully, that answers your question. Feel free to follow up and I'll be happy to go deeper on that.
So, here's what I'm going to do. I am going to get out of that report and I'm going to then kind of step through some of the other dashboards that we use internally and this is going to be a lot of information. I'm going to kind of fly through it but, again, happy to answer questions on any of this. Again, going down the funnel, the next thing that I want to look at is lead quality. We were just talking about the lead quality field and how we do that internally and we actually looked at these same charts. So, qualified leads has gone up over time, which is great. The question is, why has that happened? We want to know what's driving this lead quality because, if things are working, we want to keep doing more of that.
So, right below this, you can see number of qualified leads can be decomposed into how many total leads did we get and what percentage of those were qualified? And that is what we see on this chart here. And you can see that we began to focus on lead quality at the end of 2014 and into early 2015 and, at that point, lead quality rose from around 20% consistently up to mid-30% and it's been pretty consistent since then. So, 35% is a pretty decent lead quality rate and, at the same time, we've grown our total lead volume and we've grown that from about 3,000 to about 15,000 a month. So, strong gains there too but, when you combine those two things together, you can see that we've grown our qualified leads by almost 10x. So, those two things trended together really kind of provide the answer there.
And because we track our UTM campaigns from all of our different marketing channels, we can actually look at lead quality by channel. Again, this is something that our marketing team works hard to make sure that we UTM tag all of our links that we put out there so that we can track this data on the back end. If you're not UTM-ing your links, you're not going to be able to get this data. It doesn't just happen by magic. So, you can see here, and this really does mirror our experience, AdWords sends over super qualified leads. Our inbound marketing, which is the other here, sends over pretty qualified leads and our other ad channels, they do okay but they're not quite as high.
So, if you want to figure out why lead quality has gone up, it's because, and if you look at this chart over here on the right, we've moved our inbound marketing from around 20% up to 35% and we introduced AdWords, which is making a huge impact. Cool.
So, that's our lead quality dashboard and let's take a look at funnel performance next. So, obviously, what you really want to measure when you're looking at your marketing and sales performances, you want to measure performance all the way through the funnel. All the way from the original touch, all the way to qualified lead, to opportunity, to customer and, because we have all of the different data sources here, we can measure that entire funnel.
So, in the top left chart, you can see that trended over time with the blue line being prospects, is always going to be the biggest, and then there's a fall off when you get to qualified leads. We already talked about this. The qualified leads have gone up, which is great. Then, you see another big drop off till you get to opportunities. So, the green line here is opportunities and then a certain percentage of opportunities convert into customers.
The funny thing here is that this seems like a high conversion rate. We wouldn't expect quite so many of those opportunities to become customers. So, what we want to do is dig into the conversion rates here. We've got four funnel stages, so that means there's three conversion rates between them. And we already know that the lead quality has gone up. It looks like we're at about low 20's percent of MQLs converting into opportunities, but then, we've seen this crazy high opportunity win rate come through, which I'm a little skeptical of. So, let's figure out what exactly is going on there.
At a high level, charts like this are ones that you'll typically see and one of the reasons I love using RJ for this type of analysis, it's very simple math. You divide 615 divided by 754 and you get your win right there but actually what's going on beneath the surface is that there are cohort behaviors going on. So, for those of you who operate sales funnels, you know that the close time for an opportunity is usually more than a couple days. It looks like, in our business, most opportunities are closing by month three or month four and that's what this cohort analysis is really showing you.
And so we can see that, in month one, almost no opportunities close. But by month two, that's gone up. Month three, it's gone up by a lot and things start to level out by month four. This allows you to actually see, okay, what's going on here is not that some magically high conversion rate is going on. It is that we are getting faster at converting these opportunities and so the customers are kind of stacking up on top of each other. We're still pretty good at converting them. Some cohorts look like they're topping out at around 50% conversion rate, which is great, but we shouldn't anticipate that this 80% conversion rate is real. It's actually more like 50%, which is still great.
So, what I wanted to mention there is that we've done some modeling internally and everybody always wants to be able to predict their sales funnel. Once we applied cohort analysis to our sales funnel, we found out that we were much, much more able to predict the results of any given month or any given quarter and we're running a little late on time. I'm just going to step through one quick dashboard. It might be my favorite one.
This is stuff that it was always super-perplexing for me why Pardot didn't provide you any of this type of insight but, ever since getting our email marketing dashboard, I check it on a regular basis. So, three simple charts here. The first chart is the total number of opted in prospects you have. So, if you do any inbound marketing, you spend a ton of focus on growing your opt-in list and you can see that our business has grown it steadily from around 2,500 to around 70,000, which is great.
What's actually going on here though is that, on any given month, you're adding subscribers and then you're losing subscribers, because unsubscribes are a natural part of email marketing that's going to happen. What you want to make sure is that you're always adding more than you're losing. So, in the bottom right, you can see every month, we're decomposing the total number into how many did we add and how many did we lose. Again, you can see that the orange bar is always less than the blue bar, which is great. You just want to keep an eye on that ratio.
One of the other things that we've done some analysis on is how many emails can we send out in a given month before we spike our unsubscribe rate, because, obviously, you'd love to be in front of your prospects but you also don't want to get in front of them so much that you annoy them and they unsubscribe. So, this chart actually mirrors fairly closely the effects that we found. We, for a long time, had been trying to send out around two emails a month. Then, one month, we had more things to say than normal and so we ended up sending four emails that month. We had a product release and we had a new big piece of content that was coming out, so we ended up sending up more emails. We saw our unsubscribe rate spiked that month and now, it wasn't double. We sent twice the emails but we didn't get twice the unsubscribes. So, maybe it's worth it but it is very useful to notice that these behaviors move together and it makes total sense. If you're getting in front of somebody more frequently, you're giving them more opportunities to opt out from hearing from you.
I kind of flew through all that but this is the some of the stuff that we use the RJMetrics product connector very heavily on our team. I'm just going to pass it back to Shaun and I'm going to figure out how to get back to our slide deck.
Shaun: Great. Thanks, Tristan. So, we have a lot of great questions that came in and by the way, I want to say thank you to Tristan again. I learned a lot on that and I hope everybody else did as well. Talking about like taking the fire hose, right? So, we have a bunch of great questions here. I'm going to grow through and it's probably going to be me mostly interviewing Tristan at this point. So, let's see here. One of the questions is, will these types of analysis that you showed on the webinar today work for other marketing automation products, let's say HubSpot or something else?
Tristan: Yeah, that is an excellent question. If you've spent any time in any marketing automation tool, there are two main different pieces of data that you have. You have prospect data, which is the people, and then you have the prospect activity data, which is what the people are doing. Basically, any marketing automation platform has that and so you could do very similar analysis to what I've showed you with basically any marketing automation platform.
Shaun: Great. One other question, how does RJMetrics help super-small startup businesses, two guys in a garage or an attic, as it was in the case of RJMetrics? [inaudible 00:29:35] but they don't have large marketing stuff or budget.
Tristan: I wasn't around for the RJMetrics attic days but I was the first marketer at RJ and that was actually one of the reasons that I liked both using Pardot and using RJMetrics to analyze Pardot. Well, it saved me from doing so much work that I would have had to do otherwise and, because of that, I was able to focus on implementing new campaigns and I didn't have to go back and constantly report on the campaigns that I was already running. I honestly spent maybe an hour just looking through performance on any given day and the rest of my time was actually focused on actual implementing things.
Shaun: Great. Along the same lines, when in a startup's life cycle is probably the best time to invest in a marketing automation system like Pardot? Is it two person? Is it 10 people? Should you have a marketer?
Tristan: Gosh, that's a great question. Shaun's pointing at himself. I think he asked that question. The answer that HubSpot would want me to give you is that you should have a marketing automation system before you even think about starting a company. I would be a little bit more measured. I would say that you should really get serious about marketing automation at around the same time that you're getting serious about marketing.
So, if you've got two people and most of that is focused on building a product, I don't know the Pardot's what you really need at that point. Maybe you need MailChimp and just some simple email marketing but, as soon as you're ready to actually do a lot of testing and work on marketing and you're ready to deploy some budgets towards some ad campaigns, yeah, it's definitely time to both bring on your marketing automation platform as well as bring on analytics on top of that.
Shaun: Yeah, great. I was pretty early here at RJMetrics, also. We had marketing automation before I was here and I don't think we could live without it anymore. One last question. Besides the data from Facebook and AdWords and Pardot, what other data sources are most valuable to compare against your marketing data?
Tristan: There's so much data that you could throw here. One of the things that Pardot actually isn't amazing at is tracking onsite behavior, so all this data that you get typically from Google Analytics. So, we also do a lot of analysis on top of GA-type data. We also use Mixpanel to track how people are moving through our funnel and our product. We have our operational database connected, so that we can measure things like churn rate and up sells and see how that plays through.
Honestly, we can measure that all the way from the very first campaign that somebody was acquired via to track them as a customer and all of their renewals. So, really, the answer is what kind of data do you have? It's not what kind of data should you be analyzing, really. If you have data, you should be analyzing it because there are things it can tell you.
Shaun: Great answer. Okay, great. That kind of wraps up the questionnaire part of this. Finally, we have a winner for the cupcakes. It is Joshua Forstat [PH]. You can email marketing@rjmetrics or we'll probably reach out to you on Twitter as well.
Tristan: I don't know. Shaun, you don't seem nearly as excited as you should be about this. Joshua, you just won some cupcakes. They are delicious. I am so excited for you!
Shaun: I think it's literally bittersweet for me, because I always announce these and then I just have to bemoan the fact that I'm not going to get to eat them. I love cupcakes, so good on you, Josh, but I'm kind of envious.
Okay, great. Anyway, just thanks everybody for joining us today, a lot of great questions. I really appreciate Tristan for making time in his day to join us as well. Any more questions, please reach out to us @RJMetrics on Twitter, Rjmetrics.com and Twitter is jthandy?
Tristan: Yeah, my twitter is @jthandy. Please feel free to tweet me with any questions you have.
Shaun: And I am @McAvinney. For Tristan Handy, I'm Shaun McAvinney and this was a great webinar. Thanks for joining us.