In case you missed it, it seems that the online megastars in the apparel and fast fashion world aren’t wasting time buying up the high street.

Update: It looks like ASOS decided to shut down TOPSHOP, TOPMAN and Miss Selfridge websites whilst I was writing this post; which means the tips/musings are unlikely to be suitable for the ASOS group going forward. However, you or someone you know may still find this thinking and info helpful!

Debenhams was purchased by Boohoo earlier in January (albeit sunsetting all of Debenhams’ retail footprint in the process) and ASOS have just announced that they’ve signed a deal to buy Miss Selfridge, TOPSHOP and TOPMAN too.

Literally, besides Poundland, is there anything left to go back to on the high street post pandemic!?

Intro aside, let’s jump in to the meat of this post; specifically the new and huge datasets that ASOS have literally acquired overnight.

First and foremost, I believe ASOS should keep these brand identities as they are – separate

Yup, I think merging Miss Selfridges, TOPSHOP and TOPMAN in to a single super brand would be a mistake for many reasons that sit outside the scope of this post.

However when it comes to the web analytics data, the fun really starts when you do the exact opposite.

Think about it for a moment; how insanely powerful and insightful would it be to see this newly formed fashion portfolio all reside in the same Google Analytics property?

Just to be clear, I’m not suggesting that ASOS just do away with the respective GA properties for the fashion brands they’ve just acquired – not for a moment.

However, what I am suggesting is to create a new, standalone property that merges them all beautifully

If you at this point you think I’ve completely lost the plot, just bear with me whilst I explain…

How powerful would it be to see how people shop across all four brands in one report?

Imagine being able to see how categories, search queries, product performance, AoV (the list goes on) compares across all four brands in a single view and/or compare them all side by side.

To use an example – all four brands are likely to use different tech stacks each with their own benefits and drawbacks.

Using four different product recommendations platforms? Which technology and tech vendor is performing best and why?

Make a quick comparison all within the same GA view and quickly identify which are performing best. It could then be an option to roll out the best performing tech out to your other three outfits too.

If each website have different image dimensions and filesizes on the product detail page (PDP)? Does having a smaller image (filesize) impact conversion and if so by how much?

You could rollout some new improvements to one brand, treating it as a guinea pig and then see how it compares to the baseline of the other brands before considering it elsewhere.

Stepping back a moment, combining all four brands in to a single property also levels the playing field and keeps everything as fair as possible. By eliminating config differences, such as channel grouping overrides, or filters discrepancies to name a few, ASOS can ensure comparisons between them are as ‘sterile’ and as fair as possible.

Just imagine the depth and vibrancy of all that data under one roof

If you see a spike in demand for a specific item / type of product on say TOPSHOP, you can quickly see how that demand is translating across the other brands really quickly and react accordingly.

To use another example, male orientated trends could take shape sooner and more aggressively on TOPMAN that it does on ASOS. Of course, one brand is 100% male centric and the other, ASOS, is unisex.

By comparing category performance across all four brands in a single report, trends are easier to spot than looking at brands in their respective silos.

Additionally, with this intel and viewing it all within a single view, the data could be used to help their other brands adapt and pivot their male categories faster, potentially giving them a competitive advantage too.

Are customers jumping between your brands before buying? See it all happen within a single GA session

Did a customer start their session on ASOS but jumped to and completed a transaction for a similar item on TOPSHOP?

This is where cross-domain tracking could really pay dividends for ASOS’ new hot property. By having the same tracking code across all the sites, It can begin to track users, sessions, transactions and other key metrics across them all and consolidate tracking and behaviours in to a single session and/or user.

I’ve made the whole art of stitching sessions to a single user across three seperate websites sound super-simple. Well, it isn’t! However that doesn’t mean it isn’t possible for ASOS to achieve over time.

A potential solution could be to leverage a SSO (single-sign on) across all three sites, which would make it easier to track multiple sessions from the same user(s) over time. This is because each authenticated session would be generated by the same user ID.

To use a potential real-world example here – if a customer abandoned a purchase on ASOS, went to browse TOPMAN and then completed a purchase there instead, it could help the business to understand how customers perceive each of the brands and help to either remove or reinforce those distinctions.

Maybe there was free delivery on their TOPMAN purchase at the time (albeit the same item was more expensive overall) and that’s why they purchased there instead.

Ok, so realistically (or at least in my mind) the number of customers jumping between websites before making a purchase isn’t going to be huge – but even a small pool of data could really help to build a picture and understand visitor movement patterns across these cornerstone brands before making their purchases; that’s hugely valuable!

One word of warning before you pull the trigger on this, ASOS…

Before committing to something like this, you’ll going to need a way to differentiate each of the sites that are being captured in the same property.

Here’s how to do it…

Out of the box, Google Analytics doesn’t pass the hostname to reports. Sure, it’s captured (aptly named the hostname field) but when you’re looking at page reports etc, you won’t see the hostname reported.

This causes an issue – if you’ve three home pages (represented as a ‘/’ in GA) how you will know which website that corresponds to? Well, you won’t without a lot of swearing and additional headache.

The good news is, there’s a really simple fix; create a custom advanced filter. Although it may sound scary, it really is surprisingly simple to set up.

Remember – Making this change won’t update historic data. It will only update the URL data from that point forward. So in this case, it’s important to get this filter up and running before deploying the tracking.

Lastly, you’ll also need to be sure that store affiliation is set and is behaving correctly so it’s easy to see which store bagged the sale.

Oh wait, I’ve another word of warning…

I can’t make this clear enough… Don’t remove or edit existing GA properties and views that are already in place!

What I’m talking about in this post is complimentary to the pillar/core properties and views that are already firing on their respective websites!

There, I’ve said it loud so those at the back can hear too..

Lastly, there’s no WAY that these four websites will sit within the hit limits of the free version of Google Analytics, which means it would require a Google Analytics 360 subscription.

But what’s another six figure sum when you’ve just spent £330m? Pocket change…