An e-commerce funnel has two kinds of problems: the visible kind and the expensive kind. The visible kind shows up in analytics — page views, bounce rates, add-to-cart rates, checkout completion. The expensive kind is the gap between what those numbers tell you and why they are happening, and specifically which gap is costing you the most money right now. The Conversion Pressure Score exists to make the expensive problem tractable.
CPS combines drop-off rate, daily traffic volume, average basket value, and the gap between current conversion rate and the best observed conversion rate for that page type. The result is a dollar-denominated ranking of funnel problems. A page with a mediocre drop-off rate that sees five thousand visits a day and serves customers with a high basket value might rank higher than a page with a terrible drop-off rate that sees fifty visits a day. The arithmetic is simple; the discipline of doing it consistently and acting on the ranking is where most e-commerce teams fall short.
The recommendation engine layer connects product affinity data to real-time browsing behavior. Market basket analysis identifies which products are purchased together at rates that exceed chance, enabling bundle and cross-sell recommendations that are grounded in actual purchase patterns rather than category intuition. The distinction matters because the most powerful product affinities are often non-obvious — they cross category lines in ways that no merchandiser would have predicted but that appear clearly in transaction data.
New product adoption analytics tracks how quickly a recently launched SKU accumulates its first purchase cohort, how its repeat rate compares to category benchmarks, and which customer segments are adopting first. This creates an early warning system for product launches: a product that is attracting the wrong segment or failing to generate repeat purchase in its first thirty days can be course-corrected with targeted promotion or repositioning before the launch is declared a failure. The cumulative effect of these capabilities is a growth operation that continuously improves its conversion efficiency rather than scaling spend to compensate for a leaky funnel.