Yes, it's time for Fashion Analytics. Read on..
Fashion industry is driven by trends and social media serves as an interesting barometer of what's in and what's hot. When analytics comes into this picture, it opens up a plethora of opportunities to do confident experiments with calculated predictions in the fashion industry.
Yes, it's time for Fashion Analytics. Read on..
Data is everywhere. All around us! But, are we utilizing it to the great potential it offers us?
According to an estimated figure, we produce around "2.5 quintillion bytes of data". It is indeed overwhelming but it also makes it imperative for us to explore the possibilities of how it can help our public sector and our businesses as well. The possibilities are endless. Read on here for a few of them.
Google analytics is an amazing platform which can really help pump up your business growth but you need to know the tricks of the trade.
For an instance, are you leveraging the mobile traffic insights that Google Analytics can provide you?
Check out my 5 bullet points to help your business go zoom!
Fabulous people - Of course. Fabulous people, each with a broad skillset that enhances team growth, coordination and understanding.
With the Analytics industry growing rapidly, some believe that a certain level of technical expertise is required for analytics teams– this may be true in some cases, depending on the needs of the business. However, if an intelligent individual can learn and familiarise themselves with industry knowledge, then there is no dire need for ‘data scientists’ and technical experts.
After reading around and observing a few analytics teams within organisations, we’ve come up with a range of skills that we believe are essential to making a team work together in harmony, and defeat all those analytical monsters.
‘Building a Digital Analytics Organisation’ by Judah Philips, has a great section regarding analytics teams. We’ve summed up our thoughts and Philips’ in this fancy looking graph, to show you how everything links together, like one big family.
Identifying Trends >>
As analytics is focused around data that shows how organisations are performing, it is vital that the individuals are able to focus on bringing the business value. This can be through reducing business costs or generating profitable revenue for example. With correctly interpreted data and facts that are discovered, suitable strategic approaches can be taken for the company.
Finally (well, sort of), being able to communicate findings to other teams and the business, in understandable terms, is crucial. If the analytics team is able to gather, measure and report data efficiently, but not able to relay their findings on to other departments, then there would be a delay in productivity.
Of course, there is more than just skills and expertise that makes an analytics team fabulous. Ranging from leadership, technology and team collaboration, various components lead a team to flourish.
These elements make up some of the most vital on the spectrum - the opinion, understanding and skills of the analytics team are very important. Its superpowers cannot be underestimated.
Google Tag Manager (GTM) is like Mario– Super cool and not just for the IT crowd.
This tool was designed to ensure that marketers would be able to launch their website quicker than before. Marketers are big fans, as are the techies – so are we. GTM enables marketers to manage tags directly, with no need to go through the tech team. This frees up time for other tasks and business needs (and an extra cup of tea) for both parties.
With a wonderful web interface, and the ability to be integrated with many non-Google measurements, Google's’ forever-innovative approach continues to impress. If you are using Google Analytics and haven’t yet updated to Universal, GTM can help with this. Instead of modifying each page of your website, adjustments can be made through GTM instead - Relief…!
By removing the implementation of various other tags, here’s how GTM lightens your load. All on its own, it will manage tags on your home page, product pages and thank you pages:
It seems that Google has developed a tag manager that has cut everybody’s work in half. And we’re not complaining.
Prepare for a long and informative list of various codes that need to be implemented and updated for all those fabulous features of Universal Analytics. We’ve given you the previous Classic Google Analytics codes as well, to give you a better understanding of how things have changed. Here we go.
To send events, your code will require the following change;
Small changes. Big differences.
There are a few steps required to implement the measurement of ecommerce activity.
1) After generating the tracker object and sending any initial pageviews, your updated code will be able to load up the latest ecommerce plug-in.
Remember to create your tracker object, then call the require command. The ecommerce function comes after this.
2) Now include the addTransaction command, for addition of transaction data.
3) In the same way, enter the addItem command to include data on each item within the transaction.
4) Finally, send the data on a journey off to Universal Analytics, by adding in the send command and ecommerce function.
This is how Ecommerce compares on the traditional Google Analytics:
Known as the cousin of Custom Variables in Classic Analytics, these are similar but are in fact altered through the Admin page, instead of in the code itself.
So let’s set a custom dimension to be the following-
Name: Customer Type
You would set these values in the Admin panel, which will immediately create a numerical index for the custom dimension.
Now, let’s use the set command to assign the Customer Type of a user and the dimension[index] field. Here, index matches the dimension index on the Admin page.
Fun fact - When upgrading to custom dimensions, you will be able to access previous custom variable data in the reports.
Comparing Customisation on Classic Analytics:
Another few small changes, for a nudge up the Universal Analytics ladder.
Cross Domain Tracking:
This tracking no longer corresponds between Universal and Classical Analytics as cross domain tracking has changed in Universal. The source and destination must both be functioning using the same code, so that visitors’ exchanges can be traced through a single session.
The basic analytics.js parent codes must be modified in the following way on all pages, in order for user tracking between top level domains and subdomains to take place:
The following code will track users between domains:
To keep the code identical across all domains, insert all domains in the list.
By using the domains you specify, the Auto link plug-in will instinctively allow cross domain tracking. It will tag any page links that direct to those particular domains.
Here is the previous ga.js cross domain tracking code :
To limit tracking to a certain subdirectory or path, your code must be updated to the following:
This is how your old ga.js code looked
When the create command is called, set the sampleRate field in order to allow client-side sampling
Previous ga.js where _setAccount carries out a similar action to the above create command
In some cases where certain larger websites have not entirely retagged with the updated analytics.js code, your cookie may require a transformation from a Universal Analytics cookie to a Class Analytics one.
Custom Names for your Cookies
Both the custom cookie name in analytics.js and ga.js must have the same value. To do this, set the uaName field.
Custom Paths for your Cookies
Using the uaDomain and uaPath, update your ga.js trackers. This will allow you to use numerous analytics.js trackers that have different domains and/or cookie paths.
Custom Subdomains for your Cookies
If your site has Classic Analytics cookies that are set in ga.js using _setCookiePath, you’ll be able to chose which ones you want to transfer to analytics.js using legacyCookiePath – like so:
Enhanced Link Attribution:
This function enhances the precision of your In-Page Analytics report, by using link element IDs. This distinguishes between a number of links to the same URL that are all on the same page.
To implement this:
1) Enable enhanced link attribution (ELA), found in the Admin interface of your account
2) Alter the code on each page to load the ELA plug-in, as follows:
A sample of how the ga.js looked
Enabling Display Features:
Re-marketing, Demographics and Interest Reports are all good fun. The display features plug-in for Universal Analytics can be utilised to allow these types of Display Advertising Features in Classic Analytics to work.
This function used to make use of the dc.js tag. Now, with an update, these features work through a plug-in. Add a require call to your code to do this, and specify indicate the displayfeatures plugin.
So there you have it. A complete run down of the code comparisons between ga.js and analytics.js. Courtesy of Google. That’s a lot of code to get your head wrapped around, but once you’ve implemented it, you will be well on your way to happier and brighter analytical future.
Google Analytics current ga.js codes are upgradable to analytics.js of Universal Analytics. A big plus point is that Universal Analytics offers a more developer friendly way of gaining a highly effective insight into your business. We’re big fans, and we know you are too. Lets take a peek at these codes and spot some differences.
Now to your vertical, we have exhibit number 1 and 2:
These are the basic parent codes of both tools. Of course, the two codes work in various different ways, in particular to how tracking data is sent to Google servers. Classic Analytics uses two different functions to establish the web property ID of the page, and another to relay tracking data to Google servers - _setAccount and _trackPageview respectively.
Universal Analytics on the other hand, has other tricks up its sleeves. After the code is run, it instantaneously creates a tracker object for the particular web property concerned. In turn, this then registers a pageview of your site.
There is a lot of room for customisation in both codes. By using Google Tag Manager (GTM) there is no need to alter your code every time to achieve this. Who couldn’t love that?
When upgrading from Classic to Universal, it can be a tiny bit of a headache when a lot of page modifications, code alterations, amongst others, are required. GTM eases that headache – simple changes and tags can be adjusted without the requirement of developer knowledge. Additionally, has an inbuilt debug feature, which ensures that any errors with your tags will become known to you before your site is published. There are also options which able you to go back to previous saved versions of your site. Less headache we say.
In a following post we will cover more features of Universal and Classical Analytics, and the coding that they involve.
But for now, lets bask in the glory that Universal Analytics presents to us.
On combining Web Analytics and Conversion Optimisation, the ultra sophisticated digital marketing name we get is Conversion Analytics
"Web Analytics + Conversion Optimisation = Conversion Analytics"
There are many elements which we will be going to take a look at this piece and how analysing relationship between them can enhance conversion, hence the name of the topic is conversion analytics.
To start with, grasp the following as we will be talking about it for most of the rest.
Let’s look at the above step by step. Nothing can be analysed without traffic on page and traffic source analysis allow us to look at various trends and similarities from those coming from like or unlike traffic sources.
It is very important to understand the link between ‘on page’ analytics (click behaviour) and traffic source analysis to view the complete picture that how traffic coming from social media is behaving than the direct traffic. The underlying context may be different from sales perspective for one campaign than the how actually the traffic behaved after responding to that campaign advert. Traffic analysis also enable us to understand how different are the behaviours w.r.t. various sources and they never be same over different days of the week and even different times of day.
Next is the actual on page analysis which includes analysing visitor movement, their click paths, how they are interacting with various elements on the page including forms, buttons, links, images, media, etc. I believe here it is ‘may’ be important to know what are the key converting events on the page but it is more important to know what are the poor performing events along with the answer ‘how to improve them’.
Is there any specific form field which is worth considering again such as a tick box instead of asking a complete date of Birth if the purpose for asking the same is age verification? Is it worth mentioning the price difference between a purchase made by debit and credit card or something similar?
After analysing the initial user journey, it is time to focus on purchase funnel. By this user have already decided to make the purchase so it is very important that there will no obstructions & distractions in the journey going forward from initial pages (category, product, so on) to purchase funnel pages.
Important things to consider here are the conversion rate at each step of funnel and from product to first step of funnel. We can always improve the overall conversion of purchase funnel but we need some innovative optimisation every time to make the impact. What about using clickable ‘Call Us’ link so that mobile users don’t have to worry about making an effort to call. Shorter purchase funnel journeys are more successful as per the Sage Pay E-Business Benchmark Report 2013. So, think about reducing any extra pages/elements which may become an obstacle in the user journey.
After purchase funnel optimisation, one final step we have to think about carefully is to analyse & optimise multi-channel and multi-platform journeys. Now a days our customer is not restricted to only one or few platforms but have nearly countless platforms to reach your digital asset may be from smart TV or Game Console or the Mobile App they might downloaded few days. So, analysing the platform performance is another add-on in the journey of conversion analytics. Similarly, when it comes to channel, it becomes very important that what traffic (and hence conversion) should be attributed to which channel.
Are you ready for the new revolution in the world of analytics called ‘Conversion Analytics’?
Share your ideas!