Periscopix presentation

“You have been commissioned to analyse the performance of the online grocery shopping section of asda.com using Google
Analytics.
Besides the most basic out-of-the-box features please explain which features you think will be most useful to implement and
why. Please use examples of any coding that is required to implement these features.
Secondly, having completed the installation and gathered some data please describe how you would begin to analyse the
data and understand the performance of the site. Where would you focus initially and which features would you use to help
you.
You may wish to comment on any observations you made about the site whilst browsing.”
The Proposition
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Ecommerce Tracking
For me, ecommerce tracking would be the top priority feature to implement.
asda.com’s ultimate goal is to make a profit.
Enable ecommerce tracking in GA.
Implement the ga.js ecommerce tracking in either the shopping cart pages or through the ecommerce software.
Successful implementation of this tracking code allows us to view ecommerce data in GA.
Features To Implement
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Ecommerce Tracking
Collate a sufficient amount of data.
Monitor daily, with an in-depth review of the data weekly.
Produce monthly and quarterly reports.
KPIs: Revenue, ROI, and, if we have the margin data available to us, Profit and Loss.
Segment this data.
The first two data points are available in GA, while Profit and Loss would have to be calculated outside of GA.
Secondary metrics: Conversion Rate, Average Order Value, Time to Purchase, and number of Transactions.
Please note these are my KPIs for ecommerce tracking solely, and not for the site as a whole.
Data Analysis
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<html>
<head>
<title>Receipt for your groceries purchase from Asda</title>
<script type=”text/javascript”>
var _gaq = _gaq || [];
_gaq.push([‘_setAccount’, ‘UA-XXXXX-X’]);
_gaq.push([‘_trackPageview’]);
_gaq.push([‘_addTrans’,
‘1234’, // transaction ID - required
‘Asda’, // affiliation or store name
‘11.99’, // total - required
‘1.29’, // tax
‘5’, // shipping
‘Southampton’, // city
‘Hampshire’, // county
‘United Kingdom’ // country
]);
// add item might be called for every item in the shopping cart
// where your ecommerce engine loops through each item in the cart and
// prints out _addItem for each
_gaq.push([‘_addItem’,
‘1234’, // transaction ID - required
‘DD44’, // stock keeping unit(SKU)/code - required
‘Steak’, // product name
‘Fillet’, // category or variation
‘11.99’, // unit price - required
‘1’ // quantity - required
]);
_gaq.push([‘_trackTrans’]); //submits transaction to the Analytics servers
(function() {
var ga = document.createElement(‘script’); ga.type = ‘text/javascript’; ga.async = true;
ga.src = (‘https:’ == document.location.protocol ? ‘https://ssl’ : ‘http://www’) + ‘.google-analytics.com/ga.js’;
var s = document.getElementsByTagName(‘script’)[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</head>
<body>
Thank you for your order. You will receive an email containing all your order details.
</body>
</html>
Ecommerce Tracking Code
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In Page Analytics
The next feature I would use is In Page Analytics.
In Page Analytics allows me to make a visual assessment of how users are interacting with the pages on the website.
Determine whether or not the page layout is optimised for what I want users to accomplish on the page.
An add on to In Page Analytics that I would choose to implement on asda.com would be enhanced link attribution.
Enables me to view separate information for multiple links on a page that all have the same destination.
This tagging can be added into either analytics.js (left hand code) or ga.js (right hand code).
I would use ga.js as this is the code I have used for the ecommerce tracking.
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ga(‘create’, ‘UA-XXXX-X’);
ga(‘require’, ‘linkid’, ‘linkid.js’);
ga(‘send’, ‘pageview’);
Add the tag to the code, after you create a tracking object.
var _gaq = _gaq || [];
var pluginUrl =
‘//www.google-analytics.co.uk/plugins/ga/inpage_linkid.js’;
_gaq.push([‘_require’, ‘inpage_linkid’, pluginUrl]);
_gaq.push([‘_setAccount’, ‘UA-XXXXXX-Y’]);
_gaq.push([‘_trackPageview’]);
Add the tag to the tracking code, before _trackPageview.

In Page Analytics
After I implement the enhanced tracking code, I would then enable enhanced link attribution in GA using the following:
- Click Admin at the top of Analytics page.
- Navigate to the property for which I want to use enhanced link attribution and click the Property Settings tab.
- In the In-Page Analytics section, select the Use enhanced link attribution option.
- Click Apply.
In Page Analytics allows me to see where users are interacting on the page and which links are driving conversions & traffic,
For example, I could test moving a special offer higher up the page to a position where the link garners higher clicks, or if the
special offer isn’t earning more clicks in the prominent position, I may consider liasing with the client to remove offers of this
type if I find a suitable level of supporting data.
If I have three different links pointing to the same destination, however one or two of those links are outperforming the oth-
ers, I may consider suggesting the removal of the underperforming links in order to streamline navigation and save “link juice”
if it is being passed between pages.
Data Analysis
6

Set Up Goal For Checkout Completion
The next step I would take would be to set up a goal for checkout completion.
Use a succes page (i.e. www.asda.com/checkout/complete) as the destination goal.
This enables us to analyse the checkout completion process, and the efficiency of our checkout.
In order to do this I would however have to enable a Funnel Visualisation.
I have illustrated the process with screenshots on the following slide.
I would refrain from using Event Tracking instead of a success page.
Once this goal has been set up and running for an adequate period of time, I can then look at the Funnel Visualisation under
the Conversions > Goals menu in GA.
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Data Analysis
Set Up Goal For Checkout Completion
Analyse and see at where I am losing users in the checkout process.
Optimise the pages in the checkout.
Once these changes are made I can again collate new data, analyse the outcomes, and repeat as necessary.
In my personal experience, customers normally abandon the checkout process at the first step (shopping cart page) or the
final step (payment information page).

Set Up Goal For Registration Completion
Now I would set up another goal for user registration using a succes page.
The process is the same as on the previous two slides, however the Funnel Steps would show as the following instead:
1. /registration 2. /registration/enterdetails 3. /registration/success
Once this goal has been set up and running for an adequate period of time, I can then look at the Funnel Visualisation under
the Conversions > Goals menu in GA.
Given the registration process is shorter than the checkout process, I wouldn’t be as unhappy using Event Tracking instead of
a success page to track registrations.
The code for the final Registration Success button, in a friendly url, would be as follows:
<a href=”groceries.asda.com/asda-webstore/registration/success” onClick=”_gaq.push([‘_trackEvent’, ‘Registration’, ‘Groceries’,,,
false]);”>Register</a>
This is under the premise that we do not consider it a bounce when a visitor views one page, submits a contact form and
leaves without viewing any other pages.
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Once the event tracking script is in place, I now need to set up the event as a goal in Google Analytics by going through to
Admin panel to Goals, and clicking + New Goal, with the resulting 3 steps looking like the below.
Goal Implementation
11

Analyse where users are abandoning the registration process.
Optimise the pages in the registration process.
Once these changes are made I can again collate new data, analyse the outcomes, and repeat as necessary.
Registration is an important micro-conversion.
We can garner some valuable information from registration completions.
You have to register an account before you can purchase.
We would be losing potential customers in users who are unwilling or unable to complete the registration process.
Data Analysis
12

Checkout
This is the process that converts to Revenue and ROI, ultimately leading to Profit.
Therefore Checkout is the most important factor in my opinion.
I would believe that online customers have a shorter “attention span” and less time set aside than in-store customers.
As such the Checkout should operate as efficiently as possible to retain online web customers.
Landing pages
These are the first pages that users see when they visit the site.
If the design is poor, or they cannot accomplish their goals, then users will exit the site, thus lowering our Conversion Rate.
In Page Analytics allows us to see what & how users are attempting to accomplish their goals so we can optimise accordingly.
Areas of Importance
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Product pages & Category pages
Product pages as more important than Category pages.
A bad Category page will likely lead to Conversion Rate, ROI, Revenue, and Profit suffering. Similar goes for Products.
Ecommerce Tracking will allow us to see which are the most popular products being sold.
In Page Analytics will show where the most-interacted-with products are being displayed on the Category page. Do not au-
tomatically put the best selling product in the most prominent position, as there may be a less popular, but more expensive
product, that draws more Revenue and Profit.
In Page Analytics allows us to see how users most commonly interact with Product pages. It may be that Product pages with
3 or more images see better interactions on these images, and then a higher purchase rate than those with fewer images.
Registration
Registration is an important micro-conversion as it offers us the chance to collate more information about our customers.
We can then better target key demographics.
You have to register in order to complete checkout so if we are losing Registrations we are also losing potential Revenue.
Areas of Importance
14

Audience - segment through time on site, location, technology, traffic sources.
A site like asda.com is bound to have a large, diverse audience,
Using data segmentation we are able to categorise site users into targetable demographics.
By looking at metrics such as time on site, location, technology, and traffic sources we can ascertain a clearer image of users’
requirements on the site, and as such serve them better in order to increase Revenue and Profit.
How people interact on site dependant on traffic sources and technology.
Look at how people from different traffic sources interact on the site.
For example, a user landing directly onto a Category page from an AdWords ad might be the highest converting user on the
site, however if we were to send that user directly to a Product page instead, the Conversion Rate could increase further.
Organic could be more informational queries, while PPC is more transactional.
The same goes for technology/devices. A mobile user may spend less time on site, but convert just as well as desktop users,
as mobile users may not want to use up their data, or conversely it is much more comfortable to fully browse a site on desk-
top.
Data Points to Investigate
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Keyword Strategy
In my experience organic search has been the largest traffic source for all websites I have dealt with.
A highly focused keyword targeting strategy can vastly improve the performance of a website.
There could still be a large untapped revenue stream from other search terms.
Observations
URL structure - asda.com/asda-webstore/drinks instead of groceries.asda.com/asda-webstore/landing/home.shtm
l?cmpid=ahc-_-ghs-sna1-_-asdacom-dsk-_-hp#/cat/1215135760614
Forced registration - Forcing users to register an account can be offputting.
Potential revenue stream lost by forcing an account sing up.
The registration box is also the first thing you see on the home page rather than products.
A/B testing or In Page Analytics analysis could be conducted to optimise Conversion Rate.
Landing pages - The landing pages are extremely busy.
The groceries.asda.com home page for example has a ton of information, images and pricing on it.
Heat map testing or lab usability testing could be carried out to optimise this situation.
Data Points to Investigate
16

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