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The Ultimate 30-Minute Guide to Marketing Analytics
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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.
Janessa LantzFollow
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The Ultimate 30-Minute Guide to Marketing Analytics
- The event will begin at 1 PM (EST). The recording will be emailed to all attendees afterwards.
- What you’re going to learn 2 3 The 4 stages of marketing analytics maturity and where you fall What you can learn from advanced marketing analytics The technology you need to enable data-driven marketing decisions 1
- The State of Marketing Analytics
- What is marketing analytics?
- How are companies using marketing analytics?
- Why marketing analytics is so hard
- It’s a big problem
- The Marketing Analytics Maturity Model
- Tactic-Driven Marketing Campaign-Driven Marketing Integrated Marketing Predictive The Marketing Analytics Maturity Model
- Tactic-Driven Marketing Campaign-Driven Marketing Integrated Marketing Predictive The Marketing Analytics Maturity Model
- The Marketing Analytics Maturity Model Tactic-Driven Marketing Campaign-Driven Marketing Integrated Marketing Predictive
- The Marketing Analytics Maturity Model 58% of marketers say this is a “significant challenge” Tactic-Driven Marketing Campaign-Driven Marketing Integrated Marketing Predictive
- The Marketing Analytics Maturity Model 58% of marketers say this is a “significant challenge” Tactic-Driven Marketing Campaign-Driven Marketing Integrated Marketing Predictive
- Tactic-Driven Campaign-Driven Integrated Predictive ● Google Analytics dashboards ● ESP dashboards ● shopping cart dashboards ● ad dashboards ● Spreadsheets w/ multiple data sources integrated ● Regular campaign performance reports ● Data warehouse (single source of truth) ● auto-updating dashboards with key metrics ● Predictive models ● Team of data scientists/analysts ● New leads ● Cost-per-click ● New subscribers ● Customer acquisition cost ● Revenue by campaign ● Cost per lead ● Marketing ROI ● CAC to LTV ● Gross margin ● Goal-vs-actual Checking multiple sources for insights Drowning in spreadsheets, need dev time to SQL queries Developing people with data skills (to analyze and ask good questions) Need high technical skills (data scientists, senior analysts) Where are you? Metrics Key Challenges Analytics Tools
- Where are you? Tactic-Driven Campaign-Driven Integrated Predictive ● Google Analytics dashboards ● ESP dashboards ● shopping cart dashboards ● ad dashboards ● Spreadsheets w/ multiple data sources integrated ● Regular campaign performance reports ● Data warehouse (single source of truth) ● auto-updating dashboards with key metrics ● Predictive models ● Team of data scientists/analysts ● New leads ● Cost-per-click ● New subscribers ● Customer acquisition cost ● Revenue by campaign ● Cost per lead ● Marketing ROI ● CAC to LTV ● Gross margin ● Goal-vs-actual Checking multiple sources for insights Drowning in spreadsheets, need dev time to SQL queries Developing people with data skills (to analyze and ask good questions) Need high technical skills (data scientists, senior analysts) Metrics Key Challenges Analytics Tools
- Where are you? Tactic-Driven Campaign-Driven Integrated Predictive ● Google Analytics dashboards ● ESP dashboards ● shopping cart dashboards ● ad dashboards ● Spreadsheets w/ multiple data sources integrated ● Regular campaign performance reports ● Data warehouse (single source of truth) ● auto-updating dashboards with key metrics ● Predictive models ● Team of data scientists/analysts ● New leads ● Cost-per-click ● New subscribers ● Customer acquisition cost ● Revenue by campaign ● Cost per lead ● Marketing ROI ● CAC to LTV ● Gross margin ● Goal-vs-actual ● Custom metrics Checking multiple sources for insights Drowning in spreadsheets, need dev time to SQL queries Developing people with data skills (to analyze and ask good questions) Need high technical skills (data scientists, senior analysts) Metrics Key Challenges Analytics Tools
- Tactic driven: you’re checking multiple sources for answers (ad platforms, ESP, sales in shopping cart, etc.) Campaign-driven: too many spreadsheets, data isn’t real-time enough Integrated: it’s easy to assess current status and progress towards goals Predictive: you have a high degree of confidence what future customer behaviors and company performance will look like How to identify what stage you’re in
- Current state of the world Where Companies Fall in The Analytics Maturity Model
- Tactic-driven Campaign-driven Integrated Predictive Poll
- The value of moving to Integrated Our goals today 2 1 How to get there
- What You Can Learn from Integrated Marketing Analytics
- Revenue analytics
- New leads by source
- Customer lifetime value
- Customer lifetime value
- “Questions like ‘how are those cohorts trending’ are things we couldn’t have answered before, and with RJ we certainly can. It’s almost table stakes at this point.” Kyle Hency, Founder, Chubbies Shorts
- Marketing ROI reports
- Email marketing performance
- How to Get to Integrated Marketing Analytics
- Your 3 options 1 Spreadsheets
- Your 3 options 1 Spreadsheets 2 Build a solution
- Elements of an analytics stack Data Integration Data Warehouse BI/Analytics
- #datastack
- Your 3 options 1 Spreadsheets 2 Build a solution 3 Buy a platform
- 100% Build Build + Buy 100% Buy Pros Maximum flexibility High flexibility Some flexibility Cons Maximum engineering investment Moderate engineering investment Low engineering investment What it is Build API integrations, warehouse, and analytics layer Purchase the various components that are the best fit for your business A complete data stack, easily configurable Who is this right for? Companies building data products (Spotify, Uber, Pinterest) Companies with 5+ analysts, data scientists Companies with <5 analysts, little engineering time Examples In-house RJMetrics Pipeline + Looker, Mode, Tableau RJMetrics CloudBI Choose your data stack
Editor's Notes
- Shaun: Hey everyone, thanks so much for joining us for our event today on marketing analytics. Before we jump into the material, I just have a few housekeeping notes I’d like to cover.
- Shaun: We’re going to focus on three core areas today: 1. The 4 stages of the marketing analytics maturity model and how to tell where you fall 2. What you can learn from exploring advanced marketing analytics 3. and lastly, what technology you need to enable data-driven decisions Along the way we’ll 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 this over to Maddie to get us started.
- Maddie: Thanks Shaun, let’s get started by making sure we’re all talking about the same thing when we say “marketing analytics”
- Maddie: 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 (ROI).” And today, almost every single marketing tool that your team uses will come with some analytics capabilities built in. Marketing automation tools like Hubspot, Marketo, and Pardot have some very handy pre-built tools. If you use an email service provider like Mailchimp or Constant Contact, you’ll get email analytics. Tools like Moz will provide marketing analytics for SEO, and of course, you’re using Google Analytics to measure web activity. So, there’s a lot of data out there that you can use for marketing analytics.
- Maddie: And you can see from this chart that companies are using marketing analytics for activities across their marketing function, this is data from a CMO survey last year, but the big one is still customer acquisition. And honestly, 36% is low. 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. 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 -- don’t! 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 the right path.
- Maddie: so, I mentioned earlier that every marketing tool today tends to come with some built in analytics. Your Mailchimp reports are great for understanding who is and isn’t interacting with your emails, and what content performs best with your audience. Your Shopify reports 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...ok...so now things get a little tougher.
- Maddie: 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 the customer.
- Maddie: Which brings me to the marketing analytics maturity model. Which we could really call the business analytics maturity model, but for the sake of this event, we’re going to stay focused on marketing.
- Maddie: what you have is essentially 4 stages. At the beginning you have tactic-driven marketing. This is the only option available to marketers who are using a variety of tools (and who isn’t?!) Your social team will rely on Buffer and Hootsuite analytics, your email team will optimize for clicks, your content team will keep on getting more pageviews and leads.
- The next stage here is campaign-driven marketing. At this point, marketers are using UTM tags and, with enough data exports, and spreadsheets, they can start to answer question like: what are my top performing marketing campaigns? or, how much revenue was generated from that last email send? This visual here is showing campaign-driven marketing as being only incrementally better than tactic-driven, but I don’t want to downplay how important this knowledge is.
- Maddie: One of our VPs wrote this blog post about utm-tagging over two years ago and it’s STILL one of our most trafficked posts. So, if you need help with this, that’s ok. This stuff isn’t super easy.
- Maddie: But the big jump happens when marketers go from campaign-driven to integrated marketing.
- Maddie: IBM did a study on this recently and close to 60% of marketers deem this jump to an integrated, cross-platform view of the customer to be a significant challenge.
- The final stage here is predictive. We’re not going to spend a lot of time here because the predictive world is advanced, you’re getting into “big data” territory here and doing this type of analysis and modeling takes someone with data science skills. Just as an aside here, our marketing team 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, guess what? almost no one does.
- Maddie: Only 6% of big companies (meaning companies that have more than 10,000 employees) have access to these types of skills. So, that’s just to say that the predictive level is a ways off and most companies aren’t even close to that.
- Maddie: I want to take just a few minutes to dig a little deeper into defining each of these stages, and then I’m going to run a poll to find out where everyone thinks their company is at. So, here are some things to look for. At the tactics-driven stage you’re relying on data that lives across a variety of tools. Your key metrics will likely be things like new leads, cost-per-click, or new subscribers. The biggest sign that you’re at this stage is if you have to login to multiple platforms to get the insights you need about marketing performance. Again, it’s worth mentioning how common this stage is. Our VP of Marketing spoke at an event recently that was packed with saas and ecommerce marketing VPs, most of them were still stuck at this stage and struggling to even understand that it was a solvable problem. It’s a problem that’s so prevalent that marketers have just become resigned to it. Because to get to stage 2, you need a real spreadsheet whiz, and a lot of teams don’t have that. So, that’s the biggest challenge with this second stage, you’re likely drowning in spreadsheets. If you need to run reports on your backend database, you’ll be taking up dev time.
- Maddie: This was the challenge Hootsuite was facing when they started working with RJMetrics several years ago. Every time their marketing team needed a report, they had to wait for developers to run the SQL queries. This means a lot of wasted time, and also pulling devs away from their core job -- building your product.
- Maddie: 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, you have some insights on how much revenue your different channels are generating. But you still don’t have that holistic view of your customer.
- Maddie: To get to integrated, you actually need some pretty impressive technical components. 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. If you have analysts, they’ll want to be able to manipulate the data, but your business users are likely just going to want regularly updated reports on key metrics and the ability to do some simple analysis on their own.
- Maddie: So, to summarize, here are the big signs to indicate where you fall on this maturity scale. Tactic-driven: you’re checking multiple sources, you may not even think that analytics are a huge problem for your business...or maybe you do, but you’re the only one at your company that thinks so Campaign-driven: you have access to some insights, but there are too many spreadsheets going around and your data tends to be stale Integrated: It’s easy for you to access current status of marketing performance and understand your customer experience Predictive: you’ll know when you’re there :) You’ll have access to some real analytical power at this stage. Shaun, the sales team talks to hundreds of online businesses every year, how do you think these stages break down?
- Shaun: This is a pretty crude estimate, but here’s what I see. We talk to a lot of people at the tactic-driven stage who are only beginning to recognize a problem, and another big chunk who are trying to figure out how to make the jump to integrated. So, to back up 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 already ahead of the curve.
- Maddie: ok! So now, I’d like to get a sense for 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 (comment on the results as they come in and share them w/ the audience)
- Maddie: So, that’s the state of things today. We’re going to spend the rest of our time today covering two things: What you have to look forward to when you make the jump to integrated marketing. This will 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 takes time, and a lot of people are happy to just keep relying on Google Analytics and some Shopify reports. So, hopefully, this will give you some ammo to take to your boss or co-workers and get them excited about doing this work with you. The second thing we’re going to cover is the technical capabilities you’ll need to integrate your data. We won’t get too in the weeds here, but we want to give you enough to go to your dev team and have a conversation with them about what this would take. There are several approaches you can take here and Shaun will walk through those different options.
- Maddie: so, let’s go. Here’s what you can do with integrated data.
- Maddie: so, here’s one of the quickest wins -- 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.
- Maddie: Here’s a quote from Bevel that captures this perfectly. There are a lot of places where you can go to get data on your ad spend, cost-per-click, 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: 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.
- Maddie: Customer lifetime value is such an important metric for any business, arguably the most important. It’s a measure of how much a customer spends with you over time and without integrated data it’s incredibly difficult to get your hands on this kind of data -- you’ll be bothering your engineers a lot for SQL queries. In this particular analysis, the marketer wants to understand the overall impact of their google adwords optimizations. They’re constantly tweaking campaigns and testing, but they want to know if customers being acquired are actually better.
- Maddie: In this hypothetical scenario, you see two things. First, customers acquired in the June cohort were more valuable right out of the gate, but tapered off over time. Customers acquired in more recent cohorts are reaching CLV of over $1k after their first year with the business.
- Maddie: I’ve been working with the Chubbies 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 these guys are not full-time analysts. They just have integrated data that allows them to get the insights they need on their customers.
- Maddie: I’ve mentioned marketing ROI, so let’s spend some time here. This is an area of marketing analytics that every marketer dreams of, but it’s tough to do right. This 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. This is integrated data! You have the name of the campaign with data from your ad platform (impression, click, spend) then paired with results (customers acquired, new order revenue, even 90-day lifetime value, and ROI). Even better, this report can combine your campaigns across platforms. So, if you’re running the same campaign on Adwords and Facebook, you can compare them apples-to-apples, or roll them up into a single view and compare your cross-platform campaigns. Ok! That’s multi-channel marketing! And it’s all thanks to integrated data. And here’s the good news, you don’t have to be a Fortune 500 company to do this level of marketing analytics, and Shaun’s going to talk about that in just a minute, so I’ll finish up here. You can also build reports that surface your top and under-performing 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 enjoy them forever. You don’t have to come back here and say “Hey, we just launched a new campaign”. Your campaign tag will automatically be pushed into your data warehouse and show up here.
- Maddie: Our client, Hmall has gotten really good at this and use dashboard like this to constantly optimize their campaigns, and the results speak for themselves.
- Maddie: I’ve talked around this, 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 combining disparate data sets. The marketing ROI dashboard I just showed you was 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 so you 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 drip. If you want to get really fancy, you could add a filter on a chart like this and explore how the reactivation campaign is performing by customer value segment. If your reactivation campaign is offering deep discounts, it might be getting low-value customers back, but not doing anything to get your highest value customers back in the door.
- Maddie: This is only a small taste of some of the things you can do with integrated data, but I hope it was enough to get you excited about how powerful this is. At this point, I’m going to hand it back to Shaun to talk you through how to actually make this a reality.
- Shaun: Thanks, Maddie. Yeah, so you have three primary options when it comes to integrating data. The first is spreadsheets, and as Maddie already covered, this will get you to campaign optimization, but it won’t scale all the way to integrated.
- Shaun: The next option is to build a data stack on your own.
- Shaun: this is an over-simplification, 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’ll depend based on what the data source is. Once you have a way to extract data, then you need to load it into a data warehouse. Once it’s in the warehouse, you need a way to interact with the data, probably via a Business intelligence or other visualization tool.
- Shaun: I’m not going to spend much more time on this, but if you want to go deeper, check out this post on our blog. And our webinar team will be tweeting this 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 they used for each part of the stack. This is a must-read if you want a crash-course in understanding integrated data.
- Shaun: The third option is to buy a complete platform that will do all of these things for you. RJMetrics CloudBI 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. So, let’s talk just a bit about how to evaluate this.
- Shaun: 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 for some of the challenges), but tools like RJMetrics Pipeline are making this part of the stack so much easier. So, there’s kind of a 4th option which is a build-buy blend where you buy the various components of your stack and piece them together. This is becoming a really popular option, but I’m getting ahead of myself. Let’s look at these one-by-one. 100% build: here you get maximum flexibility, but it comes with maximum engineering time. You’ll need at least 2 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 these companies, data is the product so this level of investment can make a lot of sense. 100% buy: on the other side of this extreme you have 100% buy. This will give you a good amount of flexibility (depending on the tool you use), but also comes with the lowest engineering investment. For these types of solutions, you’ll need some API keys from your engineers..but that’s about it. This is a really good choice if you’re interested in skipping the campaign-driven stage and jumping straight to integrated marketing. Small teams can get a platform like this up and running and you don’t need an analyst to help you get value out of your data. The build and buy option: offers high flexibility with a low engineering investment. This option can come with a sizeable price tag so it’s usually not right for small businesses, but if you start with 100% buy and grow as a business, this is likely the stack you’ll grow into. So, those are your options. If you’re unsure what choice is right for you, send me an email, my team spends all of our time helping companies navigate this decision. Right now, I’m going to open up RJMetrics CloudBI and show you how a 100% buy solution works and how you can actually do some of the marketing analytics that Maddie showed you. After that, we’ll open it up for Q&A.
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