Tracking transaction throughput with Adobe SiteCatalyst.
Headquartered in Florida, USA, the client is the largest governing body for sports events. The client has a content-rich website where the visitors can interact, post, and connect to its social platforms. Recently, it decided to add “checkout” on its website to allow the fans to buy sports merchandise, memorabilia, etc. To track this, the client had deployed basic Adobe Analytics tracking code which generated reports on behavioral metrics but not on e-commerce metrics.
The client was unable to track the Product, Revenue, and Purchase variables with the current implementation. Because of this, there was no clarity on the conversion rate and fall out during the checkout process. Also, the client wanted to know the kind of errors the visitors were facing while filling the checkout form.
The client chose Nabler for this assignment. Nabler has vast experience in Adobe Analytics and SiteCatalyst. We have certified and trained experts in the field who dive deep to resolve analytics related challenges faced by our clients.
Nabler helped a US-based apparel brand optimize its investment on Adobe SiteCatalyst with a well-investigated re-implementation strategy and user training through our unique Digital Analytics Evangelization Program. This helped all the client’s teams get better and more relevant results and insights from SiteCatalyst, thereby increasing the tool usage and website effectiveness.
We divided our approach into three phases:
1. Requirement gathering and solution design
- After gathering the necessary requirements from the client to implement the solution, our analysts identified the opportunities to track the e-commerce metrics on the checkout flow and created a solution design document.
- We mapped the business KPIs to Adobe Analytics variables. Here are the few variables that were used to track E-commerce metrics: Product variable (s.products) along with success events (prodViews, s.purchase, scCheckout), and eVar to capture the form error along with success event.
- After the deployment of the code in the Staging area, we conducted an Automated Tag Validation using our crawler application to ensure the quality and integrity of the tags.
- Once the client validated our results, we published our code to Production and performed another round of Tags and Reports validation.
After the launch of tags on Production, we monitored the reports for three weeks to identify opportunities for optimization. With the help of our newly launched reports, the client was able to:
The approach taken by the predictive analytics team was:
- Track the e-commerce variables such as conversion rate for the new products that were launched.
- Understand the behavior of a user and identify which section had a high fall-out rate during the checkout process.
- Analyze the form errors and the other errors users were facing while filling the checkout form.
- Compare different products and determine which product had a better conversion rate.