How to Measure Facebook ROI Using RJMetrics

eMarketer estimates that in 2015 Facebook will generate $15.5 billion in ad revenue, claiming 65% of all social ad spend. Nearly every business today, from SaaS to ecommerce to your local bakery is running ads on Facebook.

In this video, Shaun McAvinney, Sales Engineer at RJMetrics is joined by Jennifer Limb, Data Analysis Team Lead at RJMetrics, to show you how to measure your Facebook ROI.

You'll Learn:

  • The metrics you should be tracking to quickly make invest or kill decisions across your campaigns
  • How to optimize marketing campaigns to get more of your best customers (the kind that are worth 18x more than the average customer)
  • How to build the ultimate marketing ROI dashboard in RJMetrics


Shaun McAvinney: Hey, everyone. Thanks so much for joining us on the How to Measure Facebook ROI Using RJMetrics .My name is Shaun McAvinney. I'm a Sales Engineer her at RJMetrics and a big part of my job is helping prospective 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. First, we will be having a Q and A session at the end of the presentation. If you have any questions at any point, please feel free to tweet them using #FacebookROI or submit them using the chat window. We're also recording the show today and we'll have that out to everyone on Monday, if not before. As you all saw when you signed up for the event, there will be one lucky person winning a dozen cupcakes after today's event. You can see Robert won last time and I was real jealous of him. So, you can tweet at FacebookROI as many times as you want to tweet there. We pick a winner at random, so your chances are increased by the more tweets. So, keep on tweeting throughout today's event. I'm here with my colleague Jenn. Jenn, do you want to introduce yourself?

Jennifer Limb: Sure. Thanks, Shaun. I'm the Data Analysis Team Lead here at RJMetrics, and a big part of my job is helping clients optimize their advertising spend across all of their channels. That's what I'm going to be talking to you guys about in just a bit.

Shaun McAvinney: Great, thanks Jenn. Now for a super-fast overview of what RJMetrics is for all those attending. We're a hosted analytics platform for online businesses. We help companies from all verticals make sense of their data. I'm going to give you a lot more detail about that surrounding platform later. Let's get started with the reason that you're here. Today, we're going to focus on a few things. The things we're going to focus on are the metrics you should be tracking to quickly make, invest or kill decisions across your campaigns, how to optimize marketing campaigns to get more out of your best customers, the kind of customers that our analysis have proven can be worth more than 18 times more than your average customer.

Jenn is going to show you how to build the ultimate marketing ROI dashboard in RJMetrics. We'll be sharing a case study with you of a client that's gotten great results from conducting the type of analysis we're going to cover. So, let's start with that first point and talk about the metrics you should be tracking. So, if you're on this event today, you're probably already advertising on Facebook. The Facebook Ads Manager is great at managing your campaigns. It provides all the data you need around reach, span, impressions, clicks, and the list goes on. All of this is valuable information, but it won't really help you answer the deeper questions, like, "What campaigns are bringing in customers with the highest lifetime value?" or "How is Facebook as a whole compared to other channels?" or "What are my best and worst performing campaigns by ROI?"

Really, the only way to answer these types of questions is to be able to match up Facebook ad data with other data sources. If you want to measure the lifetime value of a customer, you need to be able to pair it up with transactional data that maybe lives in Shopfiy or Magento, if you're an e-commerce company, or in Braintree or Stripe if you're a SaaS company. Either way, Facebook doesn't have data on lifetime number of payments, so it can't answer that question for you. Obviously, you can't rely on this interface to tell you how campaigns in Twitter are doing. That's fine, if you're only running a few campaigns or just advertising on Twitter but if your marketing is currently successful, you're going to want to do more of it. At some point, keeping all the data straight is going to get cumbersome.

Though I'm sure most of you are advertising on more than one digital ad channel. We just put up a quick poll on the webinar screen that will allow you to provide feedback on which digital ad channels you're currently using. We'll share that on the next slide. So, if you guys can start voting now. Jenn, you see a lot of ad campaigns go through and you do a lot of dashboards and help clients build dashboards. What are some of the top ad campaigns that you've seen or any thoughts around this?

Jennifer Limb: Sure. The most popular ones that we really see are Google AdWords and Facebook, for sure. Those are ones that we see with a big majority of our clients actually. We do occasionally do see Twitter, sometimes see Instagram and I have seen Bing on occasion.

Shaun McAvinney: Great. Thanks, Jenn. So I'm still seeing some results come in, going to give some people a little bit more time. By the way, just plugging it again, if you guys really want those cupcakes or have any questions for us, tweet us at #FacebookROI or in the chat window. We're going to be covering a lot of questions at the end. Okay. Great. Let's show the results here. It looks like, no surprise, Facebook and AdWords are, by and large, the clear winners here. We have some Twitter going on. Instagram and Pinterest are much lower. So, that's all very interesting. Thanks so much for doing that.

Okay. So, let's talk about how to actually collect the data that will help you answer those types of questions we were just talking about. There are essentially three options. You can continue on the Excel path, exporting CSVs and mashing together data in Excel. It often works fine for early stage companies that are only running a few campaigns. If you're looking for some indicators that it's time to upgrade from Excel; if you're advertising on more than one channel or if you're running multiple ad campaigns, both of these mean that you're spending a good amount of time analyzing the data to optimize performance. That means that you could get to the point where you're spending all of your time doing that or you have to hire a full time person to do that. When you're at that stage, it's probably time to move off of Excel. Now, once you've outgrown Excel, you need a way to get all the data into one place without doing all those manual exports. At this point, you need a data pipeline, right.

You can build this yourself. Years ago, that was really the only option you had. Or you can buy a new pipeline. That's what RJMetris does. That's what we do every day. Now, I just want to clarify that the first option with spreadsheets is actually pretty terrible. Marketing analysts will spend hours mucking around in spreadsheets every single week, to do something that can be really easily automated. Here are some considerations to think about when you're considering the build versus buy data pipeline decision. On almost all fronts, buying is going to get you farther ahead, faster. 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 you'll also have much higher analytical power because you're working with someone, a team of people, who spends all their time optimizing data management processes. The one place where building 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. It makes sense for them to have a massive engineering team dedicated to this challenge, but if your core business is building and selling a great software product, or scaling your e-commerce brand, then you don't need the technical control that Etsy needs and your engineering team should be 100% focused on building your platform.

A good [inaudible 00:07:32] I like to bring up for the build versus buy comparison is the email service provider. You're probably using Google Apps or something similar, but you probably aren't running your own email servers and building a webmail client. That problem has already been solved and that's where data pipeline products are at now, too. Once you have all of your data in one place, you're ready to build your ad dashboards. Jenn is the ROI dashboard master here at RJMetrics, so, I'm going to hand it over to her and let her talk about how to set these things up. But whether you're a RJMetrics user or not, these are things that you should be tracking to get more out of your Facebook ad spend.

Jennifer Limb: Thanks, Shaun. I'm just going to throw up a-- Just a second, I'm trying to figure out how to use this app. So, we put together a quick video with a dashboard to show you how this will work. I'm just going to walk through the relevant charts on an example client. This company specializes in men's casual clothing. So, first, let's get a feel for a few high level numbers. Overall, we spend $231,000 on Facebook ads over all time. Then, there are 6,356 people who ultimately converted. Another way of saying that is they made a purchase. If we sum up the revenue generated from those purchases, it looks like these campaigns generated a total of $171,000 in revenue. So, we made back about 74% of our initial spend or, in other words, we lost 26% of our ad spend. This is also known as return on investment, aka ROI. Overall, this isn't great, right. We didn't even break even. The Facebook ads aren't looking so hot right now, but what we need to consider is that the traditional ROI formula isn't exactly a great measure because it only takes into account revenue from the first transaction. It doesn't take into account any other revenue that the customer accumulates during their lifetime. You want your ROI calculations to include the lifetime value of customers and, ultimately, the goal is to incorporate more of the history of customers acquired through each campaign. So, instead of using the first transaction revenue, let's use the total revenue generated in the first 90 days of the customer's lifetime instead.

After converting via Facebook ad, customers generated $325,000 in revenue in their first 90 days, which is much higher than the $231,000 spent on Facebook ads, for a positive ROI of 41%. This company had four primary Facebook campaigns. First is a July 4th promotion, where they offered specific deals associated with the July 4th holiday. There is the lifestyle promotion, where they promote their brand's lifestyle and drive traffic to the homepage. There is a shorts evergreen promotion, where they sell men's shorts. There is a trunks evergreen promotion, where they sell swim trunks.

Next, I have a set of charts that looks at the numbers of impressions, clicks, and conversions by campaign, just some basic overall campaign performance measures. This fourth row of charts here includes ad spend, how much is spent on each campaign, what this works out to in terms of CPM, which is dollars spent per thousand impressions, cost per clicks, which is abbreviated CPC; and cost per acquisition, which is CPA. The next row of charts here shows you the steps in the funnel. So, there's click through rate, abbreviated as CTR, which is clicks per impression; and conversion rate, CR, which is conversions per click. This last chart on the right over here looks at the proportion of 90 day spending that is accounted for in the customer's very first purchase.

The easiest way to talk through this is with an example. For customers who were acquired via the 4th of July campaign, the first purchase makes up about 80% of revenue that will occur in the customers first 90 day period. It essentially means that the customers are kind of unlikely to return. Compare that to customers acquired via the shorts evergreen campaign. The first purchase only makes up about 30% of the revenue that will occur in the customer's first 90 days. It means that they're probably coming back and probably a couple more times.

We're finally going to finish off this dashboard with two really awesome charts that we'll make together. First off is going to be total revenue generated in the first 90 days of a customer's lifetime by Facebook campaign, and arranged by date of the customer's conversion. To do this, I simply need to create a new chart, add the 90 days CLV metric, change the time frame to by day, and add a group by for Facebook campaign. Awesome. Let's give the chart a name. Facebook 90 day CLV by day. So, then, the piece de resistance, ROI by campaign. We need two metrics to create this chart. The first is ad spend and the second is 90 day CLV. Now, we need to add a formula. Divide CLV, which is B by spend A and subtract 1, and now, percent effective. We're going to hide the other metrics. Change the time frame to by day, and group by campaign. Now, you can see the ROI by campaign over time. Let's name this one Facebook ROI by Campaign. With these types of measures, we can better understand how people in each campaign are performing, and which campaign should be cut or deserve additional budget. Let's take a look at [inaudible 00:13:36] campaign. I'm actually going to navigate back to the dashboard here.

So, for the July 4 promotion in blue in all these charts, the cost per click on this campaign is actually [inaudible 00:13:58]. It's not great, right, because we're competing for a small number of clicks during a time when there's a ton of retailers that are trying to reach people. These buyers, because they bought based on the promotion, don't really return that frequently. The next one we're going to look at is lifestyle, which is in orange. It's pretty cheap in terms of cost per click, and they have really high click through rates, as you can see over here. While buyers really do buy expensive stuff, you can sort of see that here, they buy expensive stuff but they don't come back. This is kind of like a very typical strategy for a lifestyle campaign. You cast a really wide, but cheap net and try to find people who aren't really comparison shopping.

Next, we're going to look at shorts evergreen, which is in green on all these charts. They don't really have the best metrics in any field. The thing to focus on here is that shoppers who come through this campaign are a lot more loyal. This makes sense because shorts aren't as much as a one-time purchase as bathing suits are, or coming through a Fourth of July campaign. Finally, we're going to look at trunks, which is in red. You'll see it's kind of a low spend campaign, though this increases with the start of summer season. It's a pretty consistent campaign, so if you look at the number of impressions, the number of clicks, it's pretty consistent throughout the year but there are more conversions as summer approaches, which really reflects the seasonality. Generally, it's pretty middle of the road in terms of click through rate. There's a steady conversion rate that increases with the season, and a relatively low cost per acquisition. If you look at the ROI, it's really not that great. So, people really start preparing for the summer in late May or early June when ROI really starts ramping up.

I talked a bit about the importance of measuring ROIs using customer lifetime value and, before I hand this back to Shaun, I just want to give you one additional plug for that. We've done extensive research on the buying habits of e-commerce companies and what we've found is that the top 1% of customers in an e-commerce store are worth 18 times more than your average customer. That's just incredible. But what this really means is that, if you're measuring ROI by using the first purchase, you will completely miss the campaigns that are bringing in these super valuable customers. With that word of warning, I'm going to hand it back over to Shaun.

Shaun McAvinney: Thanks, Jenn. That was perfect. Let me go back to the presentation here. Okay. Great. So, before we get started with the case study and before we open up the event for questions, sorry, I wanted to close out with a case study from one of our clients, Hmall. By the way, keep sending in your questions, Facebook ROI or continue sending them in via the chat window there. Hmall is an online marketplace located in Morocco and their team had some really great success advertising on Facebook. The metrics their marketing manager, Yusef, is most concerned with is cost per order, revenue per click, and average lifetime value. He uses RJMetrics to manage Facebook campaigns in really two ways. The first strategy is just data optimization of campaigns. Like you saw in Jenn's demo, if you look at Facebook performance overall, it might show negative performance even though you have campaigns that are ROI positive. This is actually exactly what Yusef was seeing. Poor performance from Facebook as a channel was actually due to just one or two campaigns that were performing very poorly. So, he would use RJMetrics to find these under-performing campaigns, make a few tweaks, wait a day or two, and then go back in RJMetrics to check the performance. The auto-updating reports meant that he wasn't spending hours or days in Excel to get the latest status updates on a campaign. The second strategy he uses is re-marketing to existing customers. This is a really great tactic. To any RJMetrics client on this event today, start doing this.

What Yusef does is build lists in RJMetrics of one time buyers from Facebook and then he imports these lists to Facebook and targets those one time buyers again, attempting to bring them back as second time purchasers. This highly targeted approach generated repeat purchases with a very low cost per order. It also has a second benefit. We know from online research on customer behavior that, if a customer purchases once, there's a 32% chance of him purchasing a second time. But once they purchase twice, the probability of the next purchase actually jumps to 53%. So, Yusef can expect that over half the customers he paid a small sum of money to get back a second time, will go on to purchase a third time with no additional spend on his part. With the combination of these two strategies, Yusef was able to increase revenue by a whopping 80%, while increasing marketing spend by only 40%. That's pretty good ROI. At this point, we're going to open it up to the Q and A section. We've had a ton of great questions come in, so let's just start at the top.

Jennifer Limb: If you run campaigns only through a specific portion of the year, you pretty much will naturally already have a cohort analysis set up for you. So, to some degree, definitely.

Shaun McAvinney: One other question we had was, "What is a creative thing that we've seen customer do combining Facebook data with other data sources?" Jenn probably has something to input here as well. For my point of view, I think most of the, it's not super creative, but I think most of the things that our clients like to see is just the ability to bring that data all together in one place. So, every place you guys are advertising. We showed that poll earlier. Everybody is advertising in multiple data sources. So, just the ability to bring all the data together in one place makes it so that, that is actually a creative way to look at your business as a whole, holistically. So, that's one way we do it. Jenn, do you have anything for that too?