We built Snowplow to enable businesses to execute the widest range of analytics on their web event data. One area of analysis we are particularly excited about is catalog analytics for retailers. Today, we’ve published the first recipes in the catalog analytics section of the Snowplow Analytics Cookbook. These cover how to measure and compare the performance of different product pages on an ecommerce site, using plots like the one below:


In this blog post, we will briefly outline:

For very many online businesses, a “catalog” is a central part of the user-proposition. For a retailer, for example, a catalog is the collection of products they are selling. For a media site, a catalog is the collection of content items (be they articles or videos) offered. For an affiliate site, a catalog is the collection of links or offers available. For a vertical search site, a catalog is the list of indexed entries presented to the user.

Understanding how well different items in that catalog “perform” is key to enabling these businesses to:

  1. Source better catalog items - e.g. by buying more effectively if they are a retailer, or designing new products if they are a manufacturer, or producing better content if they are a media company
  2. Present catalog items more effectively - e.g. by surfacing more popular items, using search and recommendation to enable users to dive more deeply into the catalog, or personalise the items shown based on user or item data
  3. Optimize the existing catalog items - e.g. by tweaking product prices, adjusting content copy and so on

Today, we published a set of recipes to enable businesses to compare the performance of product pages. The published analysis is particularly relevant to online retailers - it makes it easy for them to identify:

  1. Which products are good candidates for increased marketing spend: namely, highly converting pages with low traffic levels
  2. Which product pages are underperforming: perhaps because the products on them are not competitively priced, or because the content or images are weak
  3. Which products are “star performers”: attracting large volumes of traffic and converting those users effectively
  4. Which products are “dogs”: products which do not attract traffic, and do not convert the traffic they do get

You can check out the recipes here.

These recipes are just the start in what we hope will develop into a long series of recipes for catalog analytics. Some of the other recipes that we plan to add include:

  1. Analysing how well individual content pieces drive engagement-on-site
  2. Analysing how much different catalog items contribute to driving traffic to a site
  3. Analysing how well different catalog items contribute to basket growth through up-sell and increased time-on-site
  4. Personalising the items displayed to users based on user data and item data

If there are other examples of catalog analyses you would like us to include - drop us a line! We’re always interested to explore new and innovative ways of using Snowplow data to drive business value…