Improving your use of Product categorisation, Marketing and Custom data will let you get more out of Google Analytics
Google Analytics has changed considerably in the last three years and in this new update to the Seven Steps e-book we have covered off all the major upgrades and improvements.
While there are many, probably the most significant is the upgrade to the new Universal Analytics standard and the introduction of enhanced e-commerce tracking.
For non e-commerce sites this is obviously of less significance but for online retailers there are some significant improvements.
The advent of enhanced e-commerce brings a far greater emphasis on key areas of site performance and marketing, in addition however it has also bolstered its tracking and reporting around product performance. That means whereas before GA was primarily the domain of e-commerce and marketing professionals it is now moving into the realm of the merchandising teams. It’s moving from being just a web / digital insights tool to being a business insights tool, of course that will depend to a greater or lesser degree on the business model of the organisation using it.
In this post I want to draw out just three areas of improvement and try to illustrate how they can be useful. They focus on:
- Product categorisation
- Custom data
1. Categorising products using enhanced e-commerce tracking
Using the standard e-commerce tracking protocol products can only be assigned to a single category but when using enhanced e-commerce tracking each product can exist in a hierarchy of five different but complementary categories.
Take, for example, a pair of Nike Free running shoes on a sportswear retail site. With enhanced e-commerce tracking these can be assigned to five complimentary categories such as…
- Product class (e.g. footwear)
- Gender (e.g. Female)
- Sport (e.g. running)
- Brand (e.g. Nike)
- Type of product (e.g. Shoe)
The two genders are, by definition, mutually exclusive and so cannot occupy the same enhanced e-commerce category so here there is no issue, but using standard e-commerce tracking it would not be possible to add the women’s running shoes to the footwear Product Class category and the Gender category since these are not mutually exclusive and standard GA e-commerce tracking allows only one category definition to be applied to a single product.
However, with enhanced e-commerce tracking it would be possible to assign the shoes under Footwear > Female > Running > Nike > Shoe categories.
That would then allow the analyst to compare revenue by gender and by product class without the concern that some product may be missing from one or the other category. This makes for much faster analysis.
Enhanced e-commerce tracking now enables the analyst to understand exactly which voucher codes resulted in a sale.
Previously this might have been done with event tracking and indeed that method can still be used for this job but, for example, by using enhanced e-commerce tracking it becomes possible to understand exactly which coupon codes were used to buy which products.
Exactly this can be achieved by using a secondary dimension to segment ‘Order Coupon’ with the Product Performance report, the same approach can be applied with the ‘Product Coupon’ dimension and Internal Promotion dimensions.
The benefit of this approach is that it will provide product level detail in a way that would have been harder if not impossible with the standard e-commerce tracking module. By contrast event tracking will tell half the story but it will be much harder to break it down to the product level and the insight far less reliable.
Indeed, there is theoretically no reason why this method can’t be used in other ways, for example, instead of populating the voucher code users could be asked to submit offline referral codes from above the line media activity such as radio or TV. This could then be used to attribute sales directly to ATL media channels as well as the digital channels that the user will ultimately have to use to access the site. It’s a bit of a hack but illustrates the point.
3. Customising your data.
Another key but under utilised aspect of GA has always been the Custom Variables. These have now been superseded in Universal Analytics by Custom Metrics and Custom Dimensions, a much more powerful combination.
Aside from being able to use these to populate external data sources into GA they can also be used for more prosaic but no less insightful purposes.
For example, a nice way to use Custom Metrics is to record both sale price and RRP as two separate custom metrics. These can then be used to build out a custom report which will reveal which products / product categories sell well at a discounted price and the total revenue forfeited to the sale.
Above are some fairly simple but no less useful example but the other key point to highlight is that it’s OK to be creative with enhanced e-commerce tracking and try re-tasking some of the reports for slightly different purposes to that for which they were originally intended.