Run controlled experiments on signal weights, widget designs, and bundle discounts. Let data — not guesswork — decide what converts best on your store.
Three experiment types cover every aspect of your recommendation engine — from the algorithm weights to the visual presentation to the pricing strategy.
Test different recommendation algorithm weights — boost co-purchase vs. semantic similarity vs. intent signals and measure which mix drives the most revenue.
Compare different widget layouts, positions, and styles. Test carousel vs. grid, sidebar vs. inline, minimal vs. detailed — see which presentation converts best.
Find the optimal bundle discount. Test 10% vs. 15% vs. 20% off and measure which discount maximizes total bundle revenue (not just conversion rate).
Experiments run until they reach statistical significance. No premature winners — SellerZoom tells you when results are reliable enough to act on.
Visitors are randomly and persistently assigned to control or variant groups. Session-based assignment ensures a consistent experience for each shopper.
When an experiment reaches significance, one click applies the winning variant to 100% of traffic. The old settings are saved for rollback if needed.
Select experiment type: signal weights, widget variant, or bundle discount. Configure the variant settings and traffic split percentage (default 50/50).
The experiment runs automatically, splitting traffic between control and variant. Track impressions, clicks, conversions, and revenue in real-time on the experiments dashboard.
When results reach statistical confidence, SellerZoom declares a winner. Apply the winning variant with one click — or keep running to gather more data.
Most stores install a recommendation engine and never optimize it. Default settings work, but they leave revenue on the table. A/B testing reveals how small changes in algorithm weights, widget design, or pricing strategy can compound into significant revenue improvements over time.
SellerZoom's experiment framework makes this easy by integrating testing directly into the recommendation engine. You don't need a separate experimentation platform — experiments are built into the same dashboard where you manage recommendations, bundles, and network settings.
Start with signal weights. The default blend of co-purchase, semantic similarity, intent, margin, conversion, and popularity signals works well out of the box, but every store's customer base is different. A luxury fashion store might benefit from heavier semantic similarity weights, while a grocery store might see better results with co-purchase patterns weighted higher.
Many merchants make the mistake of checking experiment results after a few days and declaring a winner based on early trends. Early results are unreliable — small sample sizes produce wildly misleading numbers. A variant might appear to have a 30% lift after 500 sessions, only to converge to a 2% lift after 5,000 sessions. SellerZoom waits for statistical significance before declaring winners, protecting you from false positives that could actually hurt revenue.
The platform calculates confidence intervals in real time and only declares a winner when results reach 95% confidence — meaning there's less than a 5% chance the observed difference is due to random chance. This discipline separates data-driven optimization from educated guessing.
The most successful ecommerce teams treat experimentation as a continuous process, not a one-time project. After completing one test, the winning configuration becomes the new control for the next test. Over months, these incremental improvements compound into dramatic performance gains. SellerZoom makes this easy by saving all experiment history, automatically applying winners, and letting you queue up the next experiment before the current one finishes.
The default 50/50 split gives you the fastest path to statistical significance because both groups receive equal traffic. However, if you're testing a risky change — like dramatically different signal weights or an untested widget layout — consider an 80/20 split. This protects 80% of your traffic with the proven control while still gathering enough variant data to detect meaningful differences. The trade-off is slower time to significance, typically 2–3x longer than a 50/50 split.
Run your first A/B experiment on your recommendation engine and let data decide what converts best.
Run Your First Experiment — Free