Every ecommerce business wants the secrets to achieve more sales. This one is a simple method – all you need are TWO numbers for each product. Every retailer features products to maximise their sales and makes tweaks to products that are underperforming. This workshop will talk through how to use these two metrics to identify the right products for both actions to increase your sales.
The Digital Analytics weekly workshop on the 18th June 2021 was on the topic of the Ecommerce Merchandise report. This is a very practical report that can be used daily to identify opportunities to increase sales at a product level, based on a simple internal benchmark.
It is used to identify products which need an action taken to improve their conversion rate and sales, whether by increasing stock, redoing the product images or something else. The report can also be used to identify products that are in a sweet spot (based on the same internal benchmark) and should be promoted more to take advantage of the sales opportunity.
Making the change for any single product will only have a small impact on your sales. Doing this every day will have a big impact on your sales.
The recording of this workshop is below. Within it, you can listen to me:
- talk through the principles behind the Ecommerce Merchandise report
- give examples of how to use the report
- describe the tracking, in any web analytics tool, that is required to create the report
- provide some tips & workarounds to access data if tracking is not ideal
- provide a template you can use to get started with your own Ecommerce Merchandise report
Beyond the video, there are various resources being shared in this post. My answers to audience questions are provided below the video. And there is a button at the bottom of the screen to download a PDF of the deck. Here are links to
- the original blog post on the Ecommerce Merchandise report blog post
- a Google Sheets template of the Merchandise report with a second tab containing dummy date
Audience Questions
The whole focus here is on people’s actions when they view a Product Page. Do they take the ultimate success action of adding the product to basket or not. All things being equivalent, the % who take that action should be the same for all products. Any products where people’s behaviour is not “normal” is interesting and offers an opportunity to improve sales.
The bubble chart/scatter plot maps all products against the two axis of Product Views (sessions) and Product Page Success Rate. Products with high views & low success rate need actions taken to improve the % of people who add that product to basket. Products with low views & high success rate need to be featured more to drive more views, the data says people like these products.
Products can be grouped in multiple ways with the obvious is by category, sub category and brand. But there are potential other categories such as product age, price level, if full price or discounted, etc.
Use whatever tool you prefer for extracting information, they all work. I include examples of Supermetrics, Next Analytics, Google Sheets GA addon, PowerBI, Data Studio, Tableau or just use the API directly. This one is up to you.
But to ensure you are capturing the right information in Google Analytics, review my recommended set-up for any Ecommerce website.
This can always be done but the need to do it depends on if the bouncing traffic is similar across all product pages. The focus is on people’s behaviour, does the data accurately reflect this data. If it is bots, their behaviour is not normal and so should not be included. People who enter on product pages from Google Shopping results and then bounce have not added to basket for a reason – this is the behaviour we want to understand for the insights it can provide.
