After the cache warming and partner feed changes (along with a few other API tweaks, primarily reduction of the number of requests made to gather up-sell and conversion marketing products) there were noticeable improvements in sell-through and user retention. These time charts show that overall cart creations increased for similar sales periods, and also that cart loss due to timeout had been reduced.

Click any column color label in the chart legend to toggle its visibility and re-draw the chart.×
Early deployments of the API focused on delivery to in-house mobile applications, we later supported Apple, RIM, Facebook and other partners that would increase user activity by 40% overall and cart creation activity by 15%. As one might expect, executives were most encouraged by the conversion rate. While it still was not in the targeted 30%+ range, the findings were trending in the right direction.
The second chart is derived some a server in a different time zone for the same day period (7AM West Coast/10AM East Coast) on a later date. And while the overall “shape” of the Cart Loss charts looks similar, once those are aggregated and reviewed against the cart transactions that were completed – the fuller picture emerges.

Through a deeper understanding of the legacy commerce systems the API team found optimizations that could be brought to bear which yielded sizable system and financial performance improvements. When these issues were originally brought to senior management, the initial response was to simply increase the horizontal scale of the server infrastructure. While in most companies that would not be the measure of first resort, the long-standing technology groups at the company had always brought this option to the table as a reflex. In that legacy context this is not a surprise, as some of the systems at that company date back to the late 1970s. Horizontal scaling was (and in this case – is) the only option for technology of that generation. In this case we found new and useful ways to “pierce the veil” and work more efficiently in and around the existing solution stack. From that point we continued to refine the API layer – and likewise – measured changes in system and user behavior in a way that provided new insight to IT and marketing executives.

keyboard_arrow_up^