Benefits Of Customer Journey Mapping
How Marketers Perceive The Funnel
How Customers Perceive The Funnel
Customer Journey Mapping is a ‘user experience’ term, but it overlaps with the digital marketing concept of a conversion funnel. The term has value, I think, because it infers understanding the FULL customer journey, beyond the website.
Customer journey mapping involves understanding what your audience does BEFORE they reach your website, and AFTER.
Robust funnels should reflect the full customer journey, so that you can understand where drop-offs occur, where visitors abandon your path to purchase, and where you are leaking money.
That’s because conversion bottlenecks occur both before and after the visitor reaches your website.
To map the customer journey you need to build a funnel. Funnel visualisation is harder than it sounds.
But the upside is significant. If you know that there is a conversion issue at a particular step in the funnel, you can quantify the financial upside to address this issue (you can put a dollar value against it). You can quantify the impact to your bottom line.
Once you quantify and build your financial business case, you can prioritise your optimisation efforts. Focus on the most profitable areas first, in order to make the most amount of money in the shortest amount of time.
Before we look at the full customer journey, let’s zero in on the website funnel to isolate the onsite user experience and improve conversion rates.
Say your website conversion rate is <2% and you want to increase that. Where do you start?
Firstly, you should acknowledge the inconvenient truth: your website doesn’t have one funnel:
The typical path to purchase for a typical ecommerce website
Your website has multiple funnels:
The above diagram is a simplification.The point is, throughout your funnel, website visitors are abandoning the process. They may drop off at any stage.
The goal of funnel analysis is to identify where are you leaking visitors and to what degree.
Example #1: “on step 2 of the checkout process, we lost 80% of our iPhone visitors, compared to 50% of our Android visitors. Something is wrong with our iPhone experience on this page”.
Example #2: “the add-to-cart rate on the product page is 10% on desktop and 2% on mobile. We should focus on improving the mobile experience on this page.”
Different funnels track different KPI’s. For example, the primary metric for your newsletter funnel is email opt-ins:
A simple newsletter funnel
Every website is unique, but here are some typical funnel steps for an ecommerce store:
|Product List Views||17500||5%|
|Product Page Views||875||10%|
|Add to cart||88||90%|
|Checkout step 1||79||90%|
|Checkout step 2||71||90%|
A segment is a subset of your Analytics data. Segments let you isolate and analyse subsets of data so you can examine them.
In Google Analytics, you should segment your funnel analysis to look at the most popular subsets of users. This makes your funnel analysis more actionable. It’s likely that many conversion bottlenecks are segment-specific (i.e. they don’t apply to EVERYONE).
Segments can take into account any dimension in Google Analytics. Below I have listed the most actionable:
|User type i.e. new vs returning||Operating System e.g. iOS, Android||Windows desktop vs Mac desktop|
|Device i.e. mobile vs desktop vs tablet||Iphone Model e.g. iPhone 8, iPhone Xs Max||Browser e.g. Chrome, IE, Safari|
|Android model e.g. Samsung 8, Pixel 2||Browser versions e.g. Chrome 78, IE12,|
Your funnel will isolate these different segments at each stage of the user journey.
For funnel analysis, I highly recommend creating segments with a user scope as opposed to a session scope. Analysing users rather than sessions is better. It’s a more accurate reflection of reality (since one user can generate multiple sessions).
To build some of these segments in Google Analytics, here is a cheatsheet. This document is from my Checkout Optimisation Portal (free to access). There are many, many more segments that can be built.
Clearly, analysing EVERY available segment in your funnel analysis is unrealistic. Apply 80/20 and be practical. Which segments represent a significant proportion of traffic?
Google Analytics Funnels
Google Analytics has two powerful funnel visualisation features that you can use within the reporting user interface.
The first is the Goal Funnel report:
This Goal Funnel report tracks the checkout funnel
I have written about the limitations of Goal Funnels before. The two biggest issues are that they are session-based, and you can’t apply segments to them.
The second funnel visualisation report in Google Analytics is in the Enhanced Ecommerce reports. There are actually two funnels here. The first is more ‘macro’ because it shows ALL website sessions.
The Shopping Behaviour Funnel in Google Analytics.
The second is more micro and relates exclusively to the checkout:
The Checkout Behaviour Funnel in Google Analytics.
Both Enhanced Ecommerce funnels are very powerful. However, both are session-based and suffer from the limitation of all session-based reports (i.e. one user can generate multiple sessions which skews the numbers).
To optimally analyse a funnel requires that you visualise it outside of Google Analytics. There are two reasons for this:
1. Build funnels that count ‘users’
User-based funnels more closely match the real-world reality.
2. Ability to create as many funnels as you like
The Enhanced Ecommerce funnels are designed with a ‘set and forget’ mentality. You create one funnel once. This can be problematic when you want to change the funnel or compare different funnels.
The two funnels in enhanced ecommerce reports are the Shopping Behaviour Funnel and the Checkout Behaviour Funnel. But you can only have one of each. You can’t analysis multiple funnels. As we have discussed, your website has multiple paths to conversion.
As an example, I was working with a major retailer that ran an A/B test on the checkout. In Google Analytics they could not easily compare the performance of Checkout A vs Checkout B. I had to build them custom funnels outside of Google Analytics to do this.
How do you create funnels outside of the Google Analytics reporting interface? The exact setup is highly customised, but if you want to get stuck in, I include how-to in the checkout optimisation portal.
Traffic and Funnels
So. Many. Funnels.
How do you combine traffic into your funnel analysis?
A prerequisite is being able to stitch together the user journey from the originating traffic source to the website.
The technical way to do this is by applying UTM parameters. You can append UTM parameters to the marketing URL’s in your ads or other referral sources that you control.
In this way, when a user clicks on one of these links and lands on your website, Google Analytics is able to attribute specific information about the traffic source.
You can, in fact, get very granular with the level of data that you include in UTM parameters.
|UTM Parameter||Recommended use case|
|Campaign Source||The referrer (e.g. google, newsletter)|
|Campaign Medium||Marketing medium (e.g. cpc, banner, email)|
|Campaign Name||Product, promo code, or slogan (e.g. spring_sale)|
|Campaign Term||Identify the paid keywords|
|Campaign Content||Use to differentiate ads|
Here’s an example of a URL with UTM parameters:
The output in Google Analytics is additional granularity in your reports (under ‘campaigns’). You can access campaign-related reports under Acquisition > Campaigns in Google Analytics.
Bear in mind that your traffic and funnel analysis should combine elements from your website analysis (see previous section). Namely, the common segments that represent popular subsets of your users.
When you incorporate traffic into your funnel analysis, it adds an additional nuance to your funnel analysis. That nuance is user expectation.
‘Maintaining the scent’, ‘conversion coupling’, ‘message continuity’, ‘message matching’ – whatever you call it, it’s crucial to ensure that the ad and the landing page message are consistent
Since you know where the website visitor came from, you can infer more about their mindset and expectation. This, in turn, might inform how you interpret your funnel analysis.
Example #1: users from paid search who type “buy [your product/service]” into Google are in a ‘purchase’ mindset.
Example #2: users from Facebook who click on an ad might be in an ‘information and exploration’ mindset.
Example #3: subscribers who click on your email are already familiar with your brand and might be in a ‘discount’ mindset.
In practice, this means your landing pages will be tailored to specific traffic sources. It may also impact the pages further down the funnel.
Digital Marketing Funnel
It should be obvious by now that digital marketing funnels don’t ONLY apply to the website. That’s a common misconception.
Focusing on a website funnel is growth-limiting. You should look beyond the website in order to understand the full user journey. Doing so means you can optimise in the most profitable funnel steps, rather than just the ones that you can easily see in a Google Analytics report.
Again, you’re interested in what your audience does BEFORE and AFTER they reach your website. Not just what they do ON the website.
The digital marketing funnel starts before the website visit and ends after it.
Consider AirBnB’s website. When I rent an apartment with AirBnB, it involves MULTIPLE visits to the site. In fact, it’s impossible to arrive on AirBnB’s website and make a purchase in a SINGLE session.
That’s because AirBnb requires you to:
a) Register an account
b) Provide proof of ID
c) Find the right accommodation and contact the host
d) Make the first payment
e) Make the second payment
The whole process is usually spread over days or weeks. So tracking of the user funnel should reflect this behaviour. AirBnB will also be ‘stitching together’ individual user data to understand repeat purchase behaviour and map that back to the funnel.
Finally, a word of caution about ‘traditional’ marketing funnels. Common examples include the AIDA model and the Hourglass model:
AIDA funnel model
Hourglass Funnel Model
Honestly, this is old school thinking from traditional marketing methods. These models have very limited value because they are MINDSET based. They make ASSUMPTIONS about the user (in other words, it’s guesswork).
A much better approach is to focus on BEHAVIOUR rather than guessing the mindset. Behaviour is data-driven (what did they click, where did they go, what did they buy, where did they drop-off, etc.!).
Ecommerce Conversion Funnel
Ecommerce conversion funnels should take into account your ‘money metrics’. A ‘standard’ digital funnel that only takes into account conversion rate, sales and transactions is not presenting the whole story. Those metrics do not have a monetary value assigned to them.
Your ecommerce funnel analysis should include:
✔ Average order value
These are front-end metrics. There are also back-end metrics to track:
✔ No. of repeat purchases
✔ Lifetime customer value
Ecommerce funnel analysis isn’t limited to conversions. You can optimise to the financial bottom line. You should optimise your business to profit by taking into account the money metrics.
Ultimately, the killer KPI of an ecommerce business is lifetime customer value (LCV). This is the most important metric for any ecommerce business to track. It should be your North Star, the macro KPI that ultimately informs all your optimisation efforts.
Leads + Sales
Average Order Value
No. of Purchases
Your funnel analysis should optimise to the lifetime customer value metric. You do this by tracking the ‘full funnel’ (beyond the website) and tracking the money metrics that contribute to calculating LCV.
If you like flying blindfolded, try optimising your business WITHOUT funnel analysis. If you like increasing profits, try funnel visualisation to learn where to make the most money in the shortest amount of time. You’ll be able to prioritise your efforts, and make the most profitable changes first.