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Last updated: June 2026

Adobe Commerce Conversion Optimization and Checkout Redesign

Structured A/B testing, full-funnel optimization, checkout friction removal, behavioral analytics for a global B2B Adobe Commerce storefront

Project Summary

Ran a structured conversion rate optimization (CRO) engagement for Killer Ink, a global B2B Adobe Commerce storefront; increased overall conversion rate by 31%, improved checkout completion by 22%, and lifted average order value by 18% through systematic A/B testing, UX optimization, and funnel redesign.
The engagement was a performance program, not a platform build. Killer Ink had a working Adobe Commerce storefront but no disciplined way to find and fix the points where trade buyers dropped off. Elogic Commerce ran a structured experimentation framework across the Adobe Commerce funnel, removed checkout friction, and used behavioral analytics to turn UX and B2B pricing decisions into tested outcomes rather than opinions.

Key Outcomes

31%

increase in overall conversion rate across the B2B storefront

22%

improvement in Adobe Commerce checkout completion through funnel and friction work

18%

lift in average order value via B2B pricing display and personalization experiments

Client

Killer Ink

Industry

B2B distribution, tattoo and body art supplies

Engagement type

CRO, A/B testing and funnel optimization

Platform

Adobe Commerce (B2B)

Analytics stack

GA4, Hotjar, A/B testing tooling

Scope

PLP, PDP, cart, and Adobe Commerce checkout

Primary outcome

31% conversion rate increase

About the Client

Killer Ink is a global B2B distributor in the tattoo and body art supplies sector, serving studios and trade buyers through an Adobe Commerce storefront. The catalog is large, buyers reorder recurring supplies frequently, and the storefront carries meaningful daily traffic across mobile and desktop. The business had invested in the platform but was leaving revenue on the table at the conversion layer, where small improvements compound across high B2B order volume.

Project Complexity

Live high-volume storefront

Testing had to run on a live Adobe Commerce storefront carrying meaningful daily B2B traffic, so experiments could not disrupt active ordering or put revenue at risk while changes were validated.

Undiagnosed funnel

Buyers were lost across the Adobe Commerce funnel from category page to product detail to checkout, but the drop-off points were not instrumented or understood.

B2B buyer behavior

Killer Ink’s trade buyers purchase in quantity and reorder on a cycle, so company-account pricing visibility, reorder flows, and quantity-based purchasing shape conversion in ways a DTC funnel does not. These behaviors had to be understood before they could be tested.

No experimentation discipline

Optimization was ad-hoc, with no controlled measurement, segmentation, or significance testing to build on. The program had to establish a measurement foundation before it could produce reliable results.

Conversion below traffic potential

The storefront converted below what its traffic should produce, and the gap was invisible in aggregate. The business saw a single conversion number without knowing where or why it leaked.

Checkout friction suppressing completion

The Adobe Commerce checkout carried avoidable friction that suppressed completion, a costly problem for a B2B operation where the buyers reaching checkout are high-intent and already qualified. The specific friction, whether in the number of checkout steps, account-pricing display, or address and purchase-order handling, had not been isolated or measured, so it could not be prioritized for removal.

B2B pricing and reorder untested

Company-account pricing visibility, reorder flows, and quantity-based purchasing had never been tested against the storefront’s actual buyer base, leaving known B2B conversion levers unused.

Optimization driven by intuition

Changes shipped on opinion rather than evidence, so the team could not tell which decisions moved conversion and which quietly hurt it.

Structured experimentation framework

Elogic Commerce replaced ad-hoc changes with a controlled A/B testing program on Adobe Commerce: hypothesis, variant, measurement, and decision. Tests were prioritized by expected impact and traffic, so effort went to the changes most likely to move revenue. Segmented experiments were analyzed by traffic source, device category, and returning-versus-new buyer cohorts, and results were validated for statistical significance before rollout. The test backlog spanned the B2B levers identified in the analysis, across product discovery, pricing presentation, reorder paths, and checkout, with each change run against a control so its effect on conversion could be isolated.

Adobe Commerce funnel analysis

The team instrumented and analyzed the full Adobe Commerce path from PLP to PDP to cart to checkout, isolating the highest-loss steps for Killer Ink’s trade buyers. This converted a single aggregate conversion number into a stage-by-stage view of where buyers dropped and which fixes would compound.

Adobe Commerce checkout optimization

Checkout was redesigned to strip avoidable friction, reduce steps, and clarify the path to completion within the Adobe Commerce B2B checkout. This work drove the 22% checkout completion improvement by protecting high-intent buyers at the most decisive stage.

Behavioral analytics and UX research

GA4 and Hotjar fed the program with session recordings, heatmaps, and funnel reporting, grounding every hypothesis in observed behavior rather than assumption. UX issues surfaced by the data, including differences between returning and new buyers, were turned into specific, testable changes.

B2B pricing display and personalization

Experiments targeted Adobe Commerce company-account pricing presentation and personalization, testing how tiered prices, quantities, and reorder cues were shown to trade buyers. These tests contributed the 18% average order value lift.

Device-level optimization

Mobile and desktop conversion were analyzed and optimized separately rather than as one blended figure, because trade-buyer behavior and friction differ by device. Gains were confirmed independently across both, so the headline result was not carried by one segment alone.

Conversion and revenue

31%

Overall conversion rate increased across the storefront

18%

Average order value lifted through B2B pricing display and personalization

Compounding revenue impact across high B2B order volume

Funnel and checkout

22%

Adobe Commerce checkout completion improved after friction removal

Drop-off reduced at identified high-loss stages across PLP, PDP, and checkout

Validated

conversion gains separately on mobile and desktop

Capability and durability

Structured experimentation framework left in place for Killer Ink to continue

Decisions grounded in GA4 and Hotjar data rather than intuition

Repeatable test-and-measure process replacing ad-hoc optimization

Adobe Commerce conversion rate optimization for high-volume B2B storefronts

Experimentation program design with A/B testing, segmentation, and statistical significance validation

Adobe Commerce funnel analysis across PLP, PDP, cart, and checkout

Adobe Commerce checkout optimization and friction removal

Behavioral analytics implementation with GA4 and Hotjar

Buyer-segment and cohort analysis across traffic source, device category, and returning-versus-new buyers

B2B pricing-presentation and personalization testing

Enterprise ecommerce funnel performance management

B2B and enterprise Adobe Commerce storefronts with meaningful traffic and order volume

businesses that have built the platform but are losing revenue at the conversion layer

teams shipping changes on intuition without a controlled way to measure impact

operations where checkout friction or funnel drop-off is suppressing high-intent trade buyers

When This Solution Is a Good Fit

This approach is ideal for companies that:

Operate a working Adobe Commerce storefront converting below its traffic potential

See funnel drop-off in aggregate but cannot diagnose it stage by stage

Need B2B pricing visibility, reorder flows, and quantity purchasing tested

Want a repeatable experimentation capability rather than a one-off redesign

Run enough Adobe Commerce traffic to reach statistical significance without long waits

This approach is not the right fit for simple DTC or brochure storefronts, pure visual redesigns with no measurement discipline, or early-stage stores without the traffic volume to run statistically valid experiments.