6 Dimensions To Building Robust Digital Marketing Insights
7th of Mar 2019
Last year I wrote about 5 Critical Digital Marketing Metrics You’re Probably Still Missing. Part 2 is about understanding some of the nuances of how data is tracked, and creating a more robust foundation for digital marketing analytics in your organisation.
1. Figure out how you’re going to get your hands on the data
Mindmeister or Lucidcharts are great tools for helping you to map out the customer’s DATA JOURNEY. That is, what are all the different systems that the customer’s data is collected, at what points and what data?
You’ll definitely need the help of a digital analyst for this. Someone who can deep dive into all your current systems and understand how they’re currently set up. They’ll need access to all systems from Google Analytics to Google Search Console to your tag management tool.
They’ll also need to meet with your various suppliers who may externally manage your CMS, CRM, Call centre platform and more to understand the ins and outs of how it works. Every company’s analytics tech stack is totally different so this process is critical to defining how your customer’s data flows throughout it all.
The output should be a process flow diagram that visualises the current state of affairs, as well as documentation on what connections and integrations need to be in place in order to achieve your ultimate tracking objective! This is what we call a “Measurement Plan”.
2. Get your head around how behaviour on your website is actually tracked
For a long time cookies have been helping advertisers to track visitors on their website over time. But there are some serious shortcomings with the cookie.
“The what?” I hear you ask. Ok so you know how when your computer hates you and stops working. You drag it off to your resident IT guru (who may also moonlight as your 10 year old son) and they tell you to “clear your cookies”. They go into your browser, click a few settings and then suddenly whatever was bothering you is working BUT you also find, annoyingly, that you have to re-log into absolutely everything again.
Well, that’s because every time you visit a website, if it wants to take an action to help identify you (for instance like auto logging you in, but in our case, it’s about associating YOU with all your past behaviour for analytics purposes), it uploads a tiny text file to your browser with some data on it.
That way, the next time you come back, the website can recognise you. But when you clear your browser’s cookies you’re effectively a “new” visitor to the website. It’s like you never existed. But let’s take this a step further. It’s not just the website you visit that can upload what are called “first party cookies” onto your device.
The website may have third party analytics scripts installed – i.e. scripts owned and data collected by other companies. And on mobile browsers (especially Safari on iPhones) those “third party cookies” may not persist to the next link a user clicks, or they may be time limited, making it impossible to track the user journey through.
Cookies also aren’t necessarily supported across other types of devices that a consumer might use throughout the day, so companies need to find other ways to try and tie behaviour BETWEEN devices back to a single user.
That’s where Advertising ID’s come into the picture. Device ID and Advertising ID are sometimes considered interchangeable but they are quite different.
When you hear “Mobile ID, Advertising ID or Device ID” based marketing, it’s not referring to your mobile hardware ID. You can’t switch access to that off or reset it, and so both Google and Apple disabled third party access to those IDs back in 2012 and 2013. In essence, those ID’s cannot be used to target you.
But there are software based advertising IDs which a mobile app can tap into, and this is something that third parties can use because you can control and reset them on your phone (although most people generally wouldn’t).
How advertising networks match IDs to users are different and the risk of duplicate data can occur with some methodologies.
The IAB report on mobile identity is an excellent resource to deep dive a bit further into this topic so you can understand the implications. Unless you want to go deep into the rabbit hole of analytics, that’s probably as much as you need to know for now. Google uses what’s called a “Deterministic” method of matching a visitor to their identity.
So for instance an email, a phone number, some credit card details which can further help in determining that the user is the same across multiple devices. If you’re using Google Marketing Platform or Google Analytics, you’ll see this type of matching in action when generating a cross-device report or when doing cross-device remarketing. It ultimately means your marketing dollars go further.
This is less of a problem when you’re spending $10k a month, but if you’re spending $1m a month you can imagine the deep cost of wastage and why it would be worth investing in the technology costs to deal with it. While this is still possible in Google Display Network (GDN), if you’re separately purchasing ads outside of that network, then your cookie isn’t shared across and ad spend wastage still occurs.
Unfortunately, this idea of a “single cookie” or ability to identify a single user across multiple platforms, still doesn’t fix the “Peppa Pig effect” we talked about in our last article. Kids can still joyfully use their parents devices to confound marketers everywhere!
3. Use that knowledge to determine the best analytics stack to manage advertising and track activity
For most ad spend budgets under $50k per month, simply using Google Display Network (GDN), Google Search and various other platforms to manage ads separately is the way to go. It’s not perfect, but it’s what’s on offer right now.
Think of it like this, if you’re managing things in separate platforms, then the targeting doesn’t know and it might show your ad to the same user across different networks – and you would have no idea it’s the same user you’re targeting. Google Analytics may show you after the fact in Cross Device reporting, but you can’t proactively manage your targeting to reduce duplication or wastage.
However, if you have the capacity to spend up to $15k per month in technology costs (before the cost of ad spend management and ad spend), so i.e. you may be spending in the vicinity of $50k-$100k+ per month in display advertising, then you’re at a point where it is worth considering a more complex solution. I won’t say “enterprise” because there are many enterprise companies who aren’t spending enough online for a solution like this to make sense, while on the flip side there are some 3 person e-commerce teams who would qualify.
What does the extra investment actually get you? Google Analytics 360 and Doubleclick (which were rebranded in 2018 as “Google Marketing Platform”) provide an extra level of accuracy by virtue of its access to a broader swathe of direct buying ad publisher partners and the ability to share cookies and Mobile ID’s between them for both proactive targeting and reporting.
The other big benefit is you can buy ads directly from partners within the ad network. In many cases this gives you access to better “ad inventory” – i.e. more premium space on their website dedicated to advertising. This means your ad could be featured above the fold rather than right at the bottom of the page.
If you’re asking yourself any of these questions, it’s worth considering a more complex option like this.
But the above notes are quite paid digital centric, and the analytics tech stack for both tracking and display marketing analytics is about more than just paid digital. It needs to consider:
- Organic search
4. Follow the "Rule Of Looking Backward"
The technical requirements for all of this can sometimes feel quite daunting, but there are often quick wins to be had before you get too complex. Even large enterprises should consider starting small and agile with an analytics tech stack.
Someone once told me that the best way to renovate a house and find the right tradies was to start from the end. I’ll call it the “Rule of Looking Backward” or “Backward Integration”. The idea is that you might start with the painter. Find the perfect painter and then get them to recommend their preferred plasterer, get the plasterer to recommend a builder and a builder to recommend an architect.
As I’m sure you know, I’m not an IT architect – but I am a natural systems thinker, and these types of techniques are useful for decision making regardless of your role. I have often applied the “Rule of Looking Backward” when building up the tech stack we use for our clients.
I’ll usually start by looking at any constraints we may have in terms of a system of output, or what the ideal output needs to look like and then work backward from that point to determine what apps integrate, using those as the core options to investigate and assess.
We were in a sales pitch recently where the company had invested a large amount of money in Adobe Marketing, but they were then wanting to make the most of Google Analytics 360 for all their marketing analytics. Immediately we could see they were going to be at a disadvantage in terms of integration points. Why use Adobe earlier in the marketing analytics tech stack funnel if the end point is Google?
There isn’t really a right or wrong answer here, but there is certainly a way of setting up marketing analytics that reduces the risk of inaccurate or incomplete data.
5. Understand discrepancies between using website analytics verses metrics from higher up the sales funnel
You can get a lot more info on your marketing campaigns in Facebook with a direct connection to Facebook Marketing analytics, but the numbers often don’t add up to what you may see in Google Analytics. Often this is because the Lookback Window (or cookie length) is different for these upstream data points.
Huh? What do you mean?? A Lookback Window is really just the period of time that an analytics system is programmed to “look back” and see whether a visitor happened to click on a particular campaign on the way to buying something from you or taking another action once they got to your website.
In an ideal world, aligning look back windows across all digital advertising channels can be useful because then what is being seen by the people managing those channels is more likely to match what gets reported in Google Analytics.
Now exactly what that window should be very often depends on what you sell. If you have a long sales and decision making cycle, make your Lookback Window longer. If you don’t, make it shorter. For example, if you’re selling houses – a 90+ day lookback window is ideal. If you’re selling groceries, it might only be 7 days!
6. Take a multi-dimensional view of marketing analytics
This is easier to explain with an example. Perhaps 20 days ago, Johnny clicked a Facebook ad that the marketing production team lovingly created.
Now in Facebook Business Manager analytics we see that Facebook was tagged as responsible for the sale. The Paid Digital team is cheering, with high fives all around. But Jane, who looks after the company’s Google Analytics reports sees Johnny clicked an organic search result 15 days ago, right before he made the purchase.
The SEO team is all cheers and high fives. So which team should really be celebrating?
The logical answer feels like it is “the SEO team”, but that’s flawed reasoning. If someone became aware of the company via Facebook and that was the reason they searched later on, then both channels deserve some credit. If we don’t acknowledge Facebook’s role in the sale, then we start thinking that our brand awareness marketing doesn’t matter and we try to focus only on the things that drive the final click.
So ultimately how you get insight and make decisions is very dependent on your attribution model, i.e. how you choose to attribute the value of a sale to the things a customer has seen or done in the lead up to buying from you.
I’m just covering the tip of the iceberg here, but I am sure you can get a sense of how deep this rabbit hole goes. For a bit more info on differences in conversion tracking systems even just within Google platforms alone, see here. And more information on default attribution models here.
What’s great about analytics is very often there is more than one single answer, it’s about viewing the data from different angles and then drawing insight from that.
Elon Musk is touted as an individual who has an uncanny ability to hold two truths in his mind simultaneously. Ultimately all this means is being able to see one thing from multiple angles, not a single dimensional view. That’s how great marketers think about the sales funnel. It’s not just about the end point, it is also about understanding all the varied moments in between.
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