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Supermarkets spend a lot of time and resources researching the best way to lay out their stores. They know which of their product categories are the most successful, and position these categories at significant points in our shopping journey.
E-commerce websites can also benefit from increased sales and conversion rates using these online merchandising techniques.
The way in which you organize and promote your product categories can have a significant impact on how well categories perform in terms of product page views, add to baskets and ultimately, transactions.
In this post, I explain how you use data contained within your web analytics tool (in this case, Google Analytics) can be exploited to analyse the performance of your product categories and give a boost to your online merchandising.
After doing this analysis, you will be able to answer the following questions, and possibly some more I haven’t thought of.
Armed with this data, you’ll be able to promote those ‘overperforming’ categories, and re-position ‘underperformers’ that that aren’t doing as well. What’s more, these changes to your categories will require little or no website development.
Unlike a lot of website analysis and conversion rate optimisation, these improvements you can (usually) implement yourselves, and start enjoying the benefits of better category and product page performance straight away.
OK, so how do we do this…?
To quote Stephen Covey, let’s start with the end in mind. In other words, what data do we need to perform the analysis?
Below is an example of what we data we want to collect.
The example above tells us, looking at Category 2
Note: if a visitor looked at both the ‘Category 1’ and ‘Category 2’, and then spent £100, that money would be included in both lines. Therefore, the £ values are very useful as a guide of the effectiveness of each category, but should not be treated as genuine monetary values.
Once we have this data, we can turn the relationships between category, product and basket views as well as transactions and revenue into percentages, like this.
The above table includes the following columns:
Note: it is worth noting again that if a visitor viewed 2 categories, the value would be allotted to both rather than split between them.
In order to collect this data, there are a number of steps to follow
Step 1: Download all the unique pageviews of your product categories
Using the Top Content or Content Drilldown report within Google Analytics, you need to download all the unique page views of all categories.
To avoid downloading pageviews of pages that are not categories, you will need to filter the report before you download.
You will need to study the URL structure of the website you are analysing, and then use RegEx (regular expressions) to ensure only category pageviews are being displayed in the Content Drilldown report.
Step 2 : Use 2 x Advanced Segments and 1 x Custom Report to filter category pageviews
In order to segment category views by those category views that result in one or more product pages and how many views of the basket pages there have been, you will need to create two Advanced Segments.
Again, you will need to study your website’ URL structure to isolate product page and basket page views. The screenshots below are provided for example only, and your Page Value is likely to be different.
In order to capture the transactions and revenue for category views, you will need to create a custom report.
See below for details.
Again you will need to filter this custom report for only category views.
Step 3: Download data and manipulate in Excel
Having set up the new two Advanced Segments and one Custom Reports, you need to download the following data into your spreadsheet.
This is the same spreadsheet into which you have downloaded your unique pageviews by Category.
You should organize the segmented data into four worksheets in Excel, like this:-
Now you want to bring all this data into one dataset, to produce this:-
Using Excel’s VLOOKUP functions, populate the empty “Product views”,”Baskets” and “Transactions” columns into your Category Views worksheet.
Once you have populated these columns, it’s straightforward to calculate the relationship between Category views, Product views, Baskets and Transactions, to produce this.
To easily identify interesting variations in your data, by using colour, you can apply Conditional Formatting and Color Series in Excel).
Step 4: Analyze, generate insight and take action
With the data in place, you can sort the columns to produce the following views
Having done this, you can then compare these segments with the actual layout of your categories on your website, and decide how you are to going to re-position or promote certain categories.
Combine this approach with the knowledge you and your team have of your products, and see what improvements you can make to your merchandising.
If you’d like to learn more about online merchandising and CRO, the first step is to understand the CRO process as a whole. To help with that, we’ve created this useful guide to help you get started. Download today to learn more.
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