Step-by-step guide to online merchandising

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.

1. Which categories drove most value over a particular time period?
2. Which categories may have driven more value if you had highlighted them more?
3. Having viewed a category, at which point did users drop off?

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.

How to increase conversion rates using online merchandising techniques

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.

1. Category name
2. Category Views: The number of visits to the site that included a visit to the particular category.
3. Product Views: Number of times that any product page was viewed within the same visit as the given category page was viewed.
4. Baskets: Number of times that any product was added to the basket within the same visit as the category was viewed.
5. Transactions: Number of transactions that took place in the same visit as the category was viewed.
6. Revenue: Amount of revenue from transactions placed in the same visit as the category was viewed.

The example above tells us, looking at Category 2

• 10,338 visits to the site included a view of the ‘Category 2’ category.
• In 4,201 of those visits, the visitor also viewed at least one product page.
• In 1,112 of those visits, the visitor also added at least one product to their basket.
• 598 of the visits that included a view of ‘Category 2’ also included a transaction.
• The revenue brought into the site from visits that had included a view of ‘Weekend Treats’ was £21,121,36.

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:

1. Category name
2. Category:Product: Answers the question ‘what percentage of these category viewers also viewed a product page?’
3. Category:Basket: What percentage of this category’s viewers added a product to their basket?
4. Category:Buy: What percentage of this category’s viewers actually made a purchase?
5. £ Per Category View: What was the average revenue we received from visitors who had viewed this category?

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.

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The step-by-step instructions

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

• Sort the Category:Transaction column to identify those categories where propensity to buy is highest, and lowest
• Sort the Category: Basket column to identify those categories where propensity to add to basket is higher, and lowest
• Sort £ Per Category View column to identify those categories that generate the most revenue

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.