This is the final L3 Analytics blog post as we complete a merger with AEP Convert to become LeapThree. It became apparent last year that while L3 Analytics was achieving a lot of success, it wasn’t growing as fast as I would like. I made a phone call to an old friend Michael Feiner @ AEP Convert and LeapThree is the result.
The merger makes so much sense as, beyond sharing the management workload, the synergies were obvious. While here at L3 Analytics we have mostly been using Google Analytics, AEP Convert has been primarily focusing on Adobe Analytics. While most of our projects have been around tool set-up and analysis, AEP Convert have put more time into testing, conversion rate optimisation & personalisation. A bigger combined team and client base really helps as well.
We have joined our expertise to create one great agency dedicated to helping companies use data to make smarter decisions and, as a result, to achieve greater commercial success.
The new LeapThree website will be live (fingers crossed) by the end of this week. In my mind, once that is public, it all becomes official. It has been a long process on the paperwork side while continuing with client work but nearly all the boxes have been ticked now. The LeapThree team is going to bring back the content sharing so you can expect a lot more blog posts in the future.
Should you expect any other changes with LeapThree? In a way, not really. Delivering value to our clients, being experts at Analytics, having a focus on making Analytics usable throughout an organisation – none of this will change. The range of services that are offered, well it is the combination of services from the two agencies. But there are going to be some changes. From our years of experiences, we know the most common problems faced by companies. We developed solutions and approaches to solve these problems and standardised our delivery methods to cut time and cost to our clients. In the interests of transparency, LeapThree will be publishing our pricelist for these standard projects. All work is customised to client needs but you can see in advance what the starting point is.
LeapThree will be offering public training courses, initially for users of Google Analytics and Google Tag Manager. These are going to differ from the norm as well, no beginner Google Analytics and advanced Google Analytics courses here. Instead they will be functional, based on what is directly applicable to your job. Courses will include GA for Marketers & GA for Product Managers, GA for Retailers & GA for Publishers.
The Digital Analytics meetups will continue and the plan for 2017 will be released soon. Given our experience running the premier Digital Analytics conferences in MeasureCamp and the DA Hub, you can expect some more announcements on events in the coming months.
All these details and more will be available on the new LeapThree website shortly. 2017 has already started well on the new client front and we have a plan in place to continue and increase this sales growth . There is some sadness as L3 Analytics was my first company but it is time to move onwards and upwards for future success.
Goodbye to L3 Analytics is really hello to LeapThree. Bring on the future I say…
Your first question should be extra to what? It’s always great to get five Google Analytics tips, tricks & hacks, its even better to get more of them so why are these extra? Well I am speaking at the ObservePoint Analytics Summit this Thurs 17th Nov and there I am giving the first 25 Google Analytics tips, tricks and hacks. These are extra to that list.
Quick little bit of background. My original plan was to give 10 tips, tricks & hacks in 30 min. But then I decided that wasn’t a big enough challenge. I wanted to take it further, aim for one per minute and get up to 30. It appears I was too ambitious, during recording it became apparent that in order to focus on quality, I needed to cut back. So these are the five that didn’t make the final cut.
Oh yeah, taping, another piece of explanation required. The Analytics Summit is an entirely online conference, all the sessions have been pre-recorded. Not sure about everyone else but I was pretty good… Even better news, the entire conference is free to attend. Register via Analytics Summit and listen to not just myself but other analytics experts from around the world. Did I mention it was free???
The five tips include two related to Enhanced Ecommerce, a use case for two vital pieces of customer information to capture, a general tracking tip to make information more useful and a plea to invest time to understand Google Analytics definitions so you can understand the data you are looking at.
I have been privileged over the past three years to chair the London eMetrics Summit, gearing up now for #4. Besides chairing the actual conference, a big part of my responsibility is to select the speakers and content for the conference. Having attended numerous conferences with weak content and scattered with sales pitches, it is a responsibility I take quite seriously.
The next eMetrics is being held in London on the 12th & 13th Oct. We have the first few speakers already confirmed with Stephane Hamel returning from last year and Matthew Tod making his first appearance in a number of years. Plus of course Jim Sterne sharing his wisdom with us all. There is a Speaker Application form on the website but I thought I would try putting it out there a bit more publicly to see who else is interested.
Speaking at eMetrics
I have brainstormed a list of topics that I believe would work well at eMetrics with these listed below. Please have a read through and leave a comment if you think you would be right for one of these topics or if you want to suggest someone we should be talking to.
When considering speakers for eMetrics we aim for experts in the field. It is a formal presentation, generally 35 to 40 minutes in length allowing some time for Q&A at the end. The standard of attendees is very high so the quality of the talks needs to be equally high. The talks should be practical in nature where ever possible, ideal are stories and case studies being shared by practitioners.
How to get value from a DMP
How/when to use Machine Learning
Analytics for start-ups
Analytics for small traffic websites
Analytics in very large organisations
Defining your analytics set-up
Choosing your analytics tool
My Analytics Toolkit
Performance Management (site speed)
Why you might want to build your own analytics tool
The Website Optimisation Process
Tracking customers – why & how
Media Mix Modelling
Data Driven attribution
The statistics of Customer Lifetime Value
Integration of analytics with marketing tools
Defining user personas within analytics tools
Best practices in data visualisation
This is not the definite list, we are happy to listen to any ideas and proposed topics (except for sales pitches). So take a look, have a think and get in touch, either via this blog or the speaker submission forms.
I should mention that speakers get a free ticket to the entire eMetrics Summit…
Come and Learn
Of course, if this has got you interested as an amazing opportunity to learn more (which it really is), tickets are now available. In face, there is currently a Super Early Bird special available with prices starting from only £795 for the two days of the conference – click through to register and get your ticket now.
The founder of L3 Analytics, Peter O’Neill, has received a DAA Awards for Excellence nomination.
The DAA Awards for Excellence, which are run by the Digital Analytics Association (DAA), aim to recognise the world’s most influential digital analytics practitioners.
Peter O’Neill has been shortlisted in the Most Influential Individual Industry Contributor category for creating MeasureCamp and MeasureBowling, and sits alongside Nicolas Malo, fellow Co-Founder of MeasureBowling. Peter’s nomination which was submitted anonymously reads:
This guy did what no one did. Take care of Europe and the rest of the world (except USA). He brought Analytics to our door through the creation of MeasureCamp and then, in combination with Nicolas Malo, through MeasureBowling. These guys changed the professional life of thousands of people with FREE events. They just ROCK.
MeasureCamp is a free-to-attend unconference, different to any other web analytics conference held around the world. The schedule is created on the day and speakers are fellow attendees. Everyone is encouraged to discuss and participate in sessions, even to lead sessions themselves. The whole point is that attendees focus on what they find most interesting and useful. There have now been 13 MeasureCamps across six cities with at least four more MeasureCamp cities joining this year.
The DAA, which announced the shortlist on the 19th of January 2016, is now calling on all members to place their votes for the person who has had the biggest impact on the analytics community. The first stage of voting is open until the 1st February 2016 and votes can be placed via the DAA website. Finalists will then be announced on the 8th February 2016 and the winners will be decided by an esteemed panel of judges, with the results being announced at the Awards for Excellence Gala in San Francisco on the 4th April 2016.
Peter O’Neill comments, “I am humbled to have a received a nomination for the DAA Awards for Excellence and would like to thank the person who put me forward. My passion for digital analytics started about a decade ago and my focus over the past few years has been on finding new ways to bring the analytics community together to share knowledge. This has led to the creation of events including MeasureCamp and MeasureBowling. I would like to extend my congratulations to all other shortlisted nominees, it shows the exciting growth in our industry with so many people doing great work.”
A key metric for most websites is their conversion rate. It is a measure of how well their website is performing but, as it is an average, it can mask serious issues. People who already know you and your website (as they are existing customers) are much more likely to purchase during a single visit than people just discovering your website for the first time. But the data you use is for both groups combined.
L3 Analytics has developed an approach that identifies these two visitor segments (potentially with more granularity), allowing you to understand the behaviour of each. This is recorded in a custom dimension (which could be an Adobe eVar or similar in other tools) that records the type of visitor. Based on experiences with our clients, this can be used to expose some interesting behaviour. A website conversion rate might be 3.5% but only 0.4% for prospects, giving a very different picture on performance and your internal priorities.
How this Works
It is actually more difficult than it first seems to capture a Visitor Type. The most important lesson is that the Visitor Type MUST be set at the start of the session. This allows us to calculate the conversion rates for prospects vs customers. Otherwise only customers could place an order (as prospects become customers after doing so). But, on their next visit to the website, we need to know they are then a customer.
The exception is if the visitor logs in (having registered in a previous session) during the course of the session. After logging in, we know definitely what their Visitor Type was at the start of the session and the value should be updated to reflect this. It does mean this value needs to be passed through from internal systems.
Finally, we want to know if people are customers whether they are logged in or not. As a Visitor Type is not personally identifiable, we feel comfortable recording this after a visitor logs out.
The solution to all this is to use two cookies. One cookie is a short term cookie (equivalent to session-based) recording the value as at the start of the session (or after login). The other is a cookie with an expiration date in the distant future that records the actual Visitor Type as at that point in time. The short term cookie is the one recorded in your analytics tool.
How this Works in Practice
Creating the Cookies
The logic for defining the cookies for the simple use case where there are two Visitor Types only – prospects and customers – is as follows:
On every page load
If short cookie exists
Read current value
Write current value back out with 30 min expiration date
If short cookie doesn’t exist
If logged in
Identify visitor type (“prospect” or “customer”)
Set short cookie to visitor type with expiration of 30 min
Set long cookie to visitor type with expiration of 2 years
If not logged in
If long cookie exists
Set short cookie to value of long cookie with expiration of 30 min
Set short cookie to “prospect” with expiration of 30 min
Set long cookie to “prospect” with expiration of 2 years
On Login (not on registration)
Identify visitor type (“prospect” or “customer”)
Set short cookie to visitor type with expiration of 30 min
Set long cookie to visitor type with expiration of 2 years
If long cookie set to “prospect”
Set long cookie to “customer” with expiration of 2 years
Note the cookie needs to be set as “path global” so it can be read across the entire website. They should be set as first party cookies.
Capturing Visitor Type using Google Tag Manager
The following logic can be used with any Tag Management System, but the examples provided are for GTM. The first step is to create a variable for each of the two cookies. These are very straightforward, simply grabbing the value from the two first party cookies.
The final step is to use this latest GTM variable within your GA tags to populate a Custom Dimension. Don’t forget to define this Custom Dimension within your Google Analytics configuration as well (or other tools). As the value for this custom dimension can change from session to session, the scope should be set at session level.
The Caveats for Visitor Type data
The first caveat is that this data will not be, and could never be, 100% accurate. For visitors who are not logged in, it is just not possible to know definitely whether they are an existing customer or a prospect.
The accuracy will improve over time as customers return and can be identified as such even if not logged in. However, this does not work if they delete their cookie or use a different device.
These is also a skew towards customers for purchases. To explain that, imagine two unidentified visitors entering the website, although both are actually customers. As such, they are identified as “prospect”. They both do some research while not logged in. One then exits the website while the other decides to purchase the products they found. During the checkout process, this visitor logs in and is identified as a “customer” with their behaviour during the entire session recorded as for a customer.
The results will show a 0% conversion rate for prospects and a 100% conversion rate for customers. Reality is a 50% conversion rate for customers. Again, nothing can be done here except being aware of this skew in the data.
Extending the List of Visitor Types
This really depends on your business and how you will use the information. For some businesses, there is a stage in between prospect and customer of “registered” (visitors who are in your email database but have not yet purchased). You could split prospects by New & Returning. Or better, by some sorts of segment like New, Aware, Research, Interested based on session scoring. For customers, there are so many segments or personas that could be applied, taking this information directly from the back end database e.g. loyal customers, discount shoppers, inactive customers, family shopper, etc.
Using the Visitor Type Information
With the tracking in place, it is then just a matter of analysing the data. If using Google Analytics, you can create a segment for each Visitor Type (so you can apply to all reports), create a Custom Report with Visitor Type as the first dimension (comparing performance side by side for selected metrics) or apply as a secondary dimension to many reports (for ad hoc analysis).
As noted at the start of this blog post, the obvious metric to look at is Conversion Rate. Beyond that, all the engagement metrics should demonstrate clear differences in behaviour. There are likely differences in the traffic sources used by prospects and customers (discovering website vs already being aware of it), the entry points into the website and the content being viewed.
An interesting one is looking at marketing costs and seeing just how much is spent on existing customers. This will allow a more accurate Cost of Acquisition to be calculated, taking into account only new customers, and only the marketing spend for prospects.
So, have I convinced you of the value of this piece of information? I find it provides the clearest and most valuable segmentation into performance, especially when it comes to driving actions. Of course, while full details have been provided on how to set up Visitor Type tracking, we would be happy to help out any companies who would like our assistance in doing so – please contact us on firstname.lastname@example.org or +44 (0)20 8004 0835.
At the end of last year I attended the iLive Conference in Riga where I talked about how to use analytics to improve a business’ bottom line.
Overall, the iLive conference was a great experience. I was really impressed with the quality of presentations and the level of digital expertise shared across a wide range of digital channels, including SEO, analytics, social media, community building and brand development.
iLive Conference 2015 Presentation: Impacting Business Performance with Analytics
The issue for businesses today is not that they don’t have the technology for analytics, it is they don’t know how to harness it. When the potential rewards are as big as they are, this is not acceptable. As shared at the iLive Conference, there are a range of techniques and approaches to make the benefits of analytics accessible and achievable for businesses of all sizes and in any sector.
Over the coming weeks I plan to share the tips discussed at the event in the form of a blog series. The aim here is to help you translate your overwhelming mess of data from within your analytics tool into digestible, actionable insights which will in turn help your business’ digital performance.
Topics covered in this blog series will include:
Analytics Set Up Tips
Analysis & Insights Tips
Company & Process Tips
Interested in Learning More?
Please let me know if you have any questions or would like samples/templates of any of the reports described within the presentation. Of course, if this does inspire you to start using analytics to impact the performance of your business and you need some expert help, please get in touch on 07843 617347 or via email@example.com.
At the end of last month, the team at L3 Analytics attended eMetrics, an annual summit which facilitates learning and collaboration in the realm of digital marketing. As most of our clients are interested in improving this side of their business, we managed to dip into as many presentations as possible to get a broad overview of new tools, techniques and thoughts around marketing and analytics. Read on for two of our analysts describing their experiences.
Counting, Tagging, Transforming
On October 28 & 29, L3 Analytics attended eMetrics Summit, a conference which covers the impact of data and technology and dives into the latest tools and strategies. The first day started with Jim Stern opening the conference with a presentation stating that Einstein was wrong and everything can be counted. A presentation with useful insights about marketing data taxonomy and a Shakespeare poem (!) were his arguments to support his statement. I agree with the idea that everything can be counted and in analytics everything is able to be counted. However, let’s leave Shakespeare’s idea of love to be unconditional and uncounted.
Following that, a coffee break took place to keep us energised for the next talk. Simo Ahava took the lead, informing us how to transform tag management from a project into a process. It is well known that many organisations struggle to deploy the tagging system into their organisation properly. Simo explained to us how we can think of the implementation as a process. That way, we avoid messing up with data collection, and we get valuable and useful insights. L3 Analytics follows a strong implementation process, communicating with all people involved in that. We are available to explain and discuss every aspect of the process during the implementation project.
The day continued with Dave Rhee, talking about cultural changes in a complex organisation. His presentation was fun, interesting and insightful. The presentation provided information of how analysts can change from being data geeks to being cool Jedis. He shared with us his ideas of how to transform ourselves into wise analysts, and a set of skills that we need to evolve our career quickly and efficiently. Having in mind his instructions and advice, I am looking forward to transforming myself into a wise Jedi! And I believe I will soon!
Finding Your Niche
After coming back from eMetrics I’ve found that one talk really stayed with me – ‘The State of the Analytics Nation’ by Stéphane Hamel. There were numerous things that I took home from this talk, but the one that stuck with me was ‘find your niche and specialise’. This leads me to think is it time for me, and others, to pick a speciality and to focus on this.
Personally, I’d consider myself to be a generalist in digital analytics. I have a broad selection of skills but wouldn’t describe myself as having a ‘specialist subject’. There are things I’m more interested in (user experience, understanding business problems and surveys) but I wouldn’t consider myself an expert in any one area.
With data science becoming more prevalent, as Digital Analysts we need to be able to provide more than just great analytics. We need to drive changes within our organisations or clients. This can be achieved by differentiating ourselves, which can be done through specialising in a specific area; below is what Stéphane listed:
Business Outcomes & Actions
For me, it is a case of finding out where in this list my interests lie and how to explore them further. One of the benefits of working within an agency is, by working with a wide range of clients, providing myself with exposure to many different aspects of analytics, which allows me to find my niche and to specialise. Doing this makes me feel like I’m in the best place to allow me to figure out where I want to specialise.
Apart from specialisation, another major theme from eMetrics was that it is time to get out into the business. I’ve found that the analyst sometimes can stay in their ‘cave’ and just been known as the ‘numbers guy’ – this needs to change. Getting out into the business, and having a really good understanding of it, enables an analyst to move from being considered the ‘numbers guy’ into being considered an agent of change. This progression would allow you to have a greater visibility within the business, and to get help from other departments when needed. I know we can all have our frustrations with IT and Marketing, but we need to learn to embrace them and to work with them – we are all working towards the same goal of making the business more profitable.
So there it is, the thing I took away from eMetrics: find your passion, explore it and specialise. Whilst you’re doing this, get out into your business/clients to truly understand how they work. Make friends with IT and Marketing – you’ll always need their help, plus they normally have bigger budgets.
L3 Analytics has worked with multiple publishers over the past few years, learning a lot along the way. Clients include Hearst Magazines (Elle, Cosmopolitan, etc), Euromoney, News UK and SciDev. From these experiences, we have developed a fairly standard approach around analytics for publishers, that provides the information they need to understand and improve their performance. The solution is customised to exact needs but we find the core components are common to any content website (articles, blog posts, etc).
For my recent talk at eMetrics London, I presented the approach used by L3 Analytics and these core components. Starting with the information needs of publishers and their actions that can be informed by analytics, the presentation details the information to be captured and how it can be used. Practical examples are provided with experiences of how publishers have used analytics and learnings from these experiences.
So have a read through and let me know what you think. If this is the information your team needs but doesn’t have, get in touch.
By now everyone should be excited about the release of Calculated Metrics within Google Analytics. This has the potential to be yet another powerful tool for analysis, although it will only prove useful to companies who are investing the time and resources in a good GA set-up. As a bonus, it shuts down another line in the arguments between Google & Adobe Analytics.
Details on how to create Calculated Metrics can be found in some great blog posts, notably those by LunaMetrics and AnalyticsPros, including a solid list of suggestions to get you started. It is a staggered roll out of the new feature so don’t be alarmed if you don’t have access yet, it shouldn’t be far away from appearing in your GA Account.
But I was confused by these and other blog posts as they appeared to be missing the most obvious and powerful use of Calculated Metrics. Most businesses have some form of funnel at the core of their website. In nearly every GA set-up that L3 Analytics performs, we create a goal for each stage of this process. Our clients can then create a horizontal funnel, with this being an incredibly useful tool for analysing performance.
With Calculated Metrics, you can now create the completion rate between each stage of the funnel. It is as simple as Goal Y Completions / Goal X Completions. This set of calculated metrics can then be used with any session or user based dimension to see where visitors are dropping out of the process. We have been doing this within Excel for years and it is great to finally be able to do it directly within GA. It will speed up the analysis process immensely and offer more flexibility in which dimensions to drill into.
Step by Step Instructions
Step 1 – Create a Goal for each stage of the funnel
As mentioned, most websites have a funnel as the core component of their customer journey. It is obvious for any ecommerce website but also true for booking engines and lead generation websites. As a first step, identify each stage in the funnel, ensure it is being tracked and create a Goal based on the page name or event being fired.
The following set of goals reflects the funnel for a retail website (where the visitor is not taken directly to the basket after creating it). Note that an Ecommerce Session is one where a visitor is interested in a purchase.
Step 2 – Create the Calculated Metrics
The next step is to create a Calculated Metric for the completion rate between each stage of the process. This uses Goal Completions. So the calculations are:
The formatting type needs to be percent as per above. I discovered that as long as you are creating good Calculated Metric names, the external names will take care of themselves.
Step 3 – Use these Calculated Metrics within Custom Reports
All of these Calculated Metrics can then be used within a custom report. In this example, we will be creating a Funnel metric group. Start the sequence with “Sessions” and “Goal X Conversion Rate” to show total traffic and % of sessions that progress to stage 1 of the funnel. Then list the calculated metrics for completing each stage of the funnel process, finishing with the number of total conversions.
Multiple Metric Groups could be used in these custom reports, for traffic metrics, the funnel, ecommerce metrics, etc. However the powerful thing here is the range of dimensions to choose from. Common options would include:
If you are capturing Visitor Type (prospect vs customer) in a custom dimension and/or Page Type in a Content Group (use Landing Page group to get Entry Points), this all gets more amazing.
Below is what you get as an output: a simple breakdown by stage of the funnel for whatever dimension/s you have selected. As a custom report, you would be creating it so you can drill down through dimensions to make it even more useful.
Knowing that your Conversion Rate is lower for segment X vs segment Y is not that valuable. Knowing that two dimension values behave exactly the same except for one stage in the funnel pinpoints where you have to take action.
Additional Points, Notes and Caveats
It must be noted that this technique works on the assumption that visitors must progress through each stage of the funnel. We know that this is not the case, especially when, for the above example, visitors could be entering the website with a persistent basket or creating a cart without viewing a product page. It is the job of the analyst to take these factors into account with any recommendations they make.
Further note that this is all session based analysis, as it is using Goals. For many businesses, visitors will take multiple sessions to convert. This approach is still useful though, in terms of seeing how far through the funnel the visitors proceed each time.
The overall technique is similar to an approach suggested by LunaMetrics back in June 2010. Their suggestion was to create a series of two step GA funnels for each stage of the website funnel and use Goal Abandonments for reporting. It would produce a similar report, although I prefer completion rates. It also means each stage needs to be based on pages, whereas this approach means you can use goals created from events.
Funnels do not have to be complex journeys. If you have a Contact Form on your website, it is more useful to know the number of form completions than to know the % sessions in which the form was submitted. This requires a goal for View Contact Form, a goal for Submit Contact Form, and a Calculated Metric for the completion rate.
Finally, we are looking into other Calculated Metrics as well. There is a list for content websites to calculate: Read Rate, Share Rate, Entry Rate, Engagement Score, etc. Watch this space for more ideas in the future…
I discovered a few months back that you can use Custom Dimensions within Google Analytics filters. This was great as it allowed us to created segmented Views based on session/user level custom dimensions (created using GTM via a Data Layer) for segments like Subscriber Traffic, Free Traffic, Desktop website and Mobile website.
But I had an idea in the back of my mind and finally found an excuse to test it out a couple of weeks ago.
We have a client that we are working with to implement enhanced Google Analytics tracking. Like most organisations, there is a development cycle and the new Data Layer is scheduled to be worked on in a couple of months’ time (ideally it is worked on sooner but we have experienced far longer waits as well). So we were looking for quick wins in the meantime.
Reviewing page names, their core page is a product details page. And the URL includes an ID, a code for the previous page and even the index for the location of this product within the product list on that previous page. All great information but excessive for these page names (they were actually breaking the GA table limit).
Back to my idea. Could we use GA View Filters to create hit level custom dimensions? This client is still on classic GA so can’t create custom dimensions via the code. Only one way to find out…
The current page name uses the structure below. It is not the sort of URL I would construct for SEO purposes but happy to take it for analytics as it was about to become very useful. /product-<product id>/<source index>/<source description> e.g. /product-456827/5/hpg
The first step is to create the Custom Dimensions within the Google Analytics configuration. Just enter the name you want to use and leave the scope as Hit (as per the screenshot at the top of this post)
Then you will discover that these custom dimensions appear in the list of dimensions that can be used within the GA View Filters. They are right at the bottom of the list.
To populate these new custom dimensions, you need to create three View Filters. As always, use a good naming convention so it is easy to identify the purpose of the filter. The filter type we need is a Custom Advanced filter.
With this example, Field A needs to be the Request URI (which is the page name). I used a regular expression to identify the page naming convention that I need. A key point in this is to enclose any element that you wish GA to remember within brackets. All three key elements are wrapped in brackets for this example. ^/product-([0-9]+)/([0-9]+)/([a-z]+)
Then set the Output Field to the desired custom dimension and populate it with the element extracted from the page name. In this example, we have used $A1 e.g. the first element in brackets within Field A. The other two custom dimensions will need to use $A2 and $A3.
Checking the data a day later showed success. The custom dimensions are populated with the product ID, access method and access index values. Through another filter, we even renamed all the product pages to /product-page, bringing the number of unique page names within GA table limits without losing any data.
With custom reports or secondary dimensions, we can now review performance of these pages in more detail than previously possible. All without any need for dev involvement or even the use of Google Tag Manager (and I know you could do all this via GTM but this was easier still). Long term we will do this properly via the Data Layer but short term, a big win with a small amount of work.
So what about you, do you have any nuggets of information hidden within URLs/page names that could be extracted into Custom Dimensions?