We Analyzed 8.8M Shopify Sessions: Layout Alone Moves Conversion 13%

Across 8.85 million social-proof section impressions from 1.92 million shoppers on 45 Shopify stores, the median store's best-performing layout converted 13.4% better than its worst layout of the same content: same products, same reviews, same prices. For the top quarter of stores, the gap exceeded 23%. Shoppers who pressed play on a UGC video converted 1.37× more often than shoppers who saw the same section and didn't.

This is original first-party data from the Eevy network, collected between June 2025 and June 2026. Every number below comes from our own event pipeline: not from a survey, not from a vendor whitepaper, and not recycled from someone else's blog post. The full methodology is at the bottom.

The dataset at a glance

| Metric | Value | | --- | --- | | Social-proof section impressions analyzed | 8,846,026 | | Unique shoppers | 1,918,087 | | Completed checkouts in window | 615,100 | | Shopify stores | 45 | | Time window | June 2025 – June 2026 |

The stores in the network run Eevy's evolutionary testing on their review, UGC video, FAQ, and gallery sections. That gives this dataset a property almost no other conversion dataset has: the same content rendered in different layouts to randomized visitor groups, at scale, on production stores. It lets us isolate a question the industry mostly hand-waves: how much does presentation alone matter?

How much does layout alone move conversion?

The median gap between a store's best and worst layout variation of identical content is 13.4%. The interquartile range runs from 8.7% to 23.3%, meaning a quarter of stores leave more than 23% conversion uplift on the table if they happen to be showing the worst layout instead of the best.

| Best-vs-worst layout CVR gap | Value | | --- | --- | | Median store | +13.4% | | 25th percentile | +8.7% | | 75th percentile | +23.3% |

To be precise about what this measures: for each qualifying store, visitors were randomly and persistently assigned to one of at least three concurrently running layout variations of the store's social-proof sections. Each variation was seen by at least 2,000 unique shoppers. We computed visitor-level conversion (completed checkout) per variation, then the relative gap between the best and worst variation within the same store. Because assignment is randomized, this gap is attributable to presentation, not to traffic mix, season, product, or price.

Two implications worth sitting with:

1. Layout is not a rounding error. A 13% conversion difference is larger than the effect most stores get from a site redesign, and it comes from rearranging content they already have.
2. Nobody knows in advance which layout wins. Across our network, the winning layout differs by store and drifts over time, which is the argument for testing continuously instead of configuring a widget once and never touching it again. That is, transparently, the problem Eevy exists to solve, but the number is real whether or not you solve it with us.

What does UGC video engagement do to conversion?

Among stores with meaningful video traffic, shoppers who played at least one UGC video converted 1.37× as often as shoppers who saw the video section but never pressed play (median across qualifying stores; 75,156 video-section viewers).

| UGC video engagement | Value | | --- | --- | | Median play rate (viewers who press play) | 5.4% | | Conversion multiple, players vs non-players | 1.37× | | Watch sessions reaching 75% of the video | 18.8% |

One honest caveat that most published "video lifts conversion by X%" statistics omit: this comparison is correlational, not causal. Shoppers who choose to play a video are more engaged to begin with. The randomized layout result above is causal; this one describes behavior. Both matter, but they answer different questions, and any vendor quoting an engagement multiple as if it were a lift figure is overclaiming. (For aggregated third-party statistics on UGC video, see our UGC video conversion statistics roundup.)

The watch-depth number is the operational one: roughly one in five watch sessions reaches 75% of the video. Short, front-loaded videos earn their place on a product page; long ones mostly don't get finished.

What should a Shopify merchant do with this?

If you do nothing else: stop treating social-proof layout as a set-and-forget configuration. The 13.4% median gap exists within stores that all have decent content; the variance comes from presentation choices (order, density, format mix, placement) that almost no one tests because testing them manually is tedious.

Concretely:

1. Benchmark your conversion rate first. Our Shopify conversion-rate benchmarks break down medians by industry so you know your baseline.
2. Test layouts, not just content. Whether you use Eevy's evolutionary testing or run manual A/B tests, the data says presentation is worth a double-digit swing.
3. Put video early and keep it short. A 5.4% play rate with a 1.37× conversion multiple means video earns its slot, but with under 19% of sessions reaching 75% depth, the first ten seconds carry the weight.

Methodology

• Window: June 1, 2025 – June 12, 2026. Network: 45 production Shopify stores running Eevy.
• Identity: first-party anonymous visitor IDs set by Eevy's storefront tracker. No personal data is used; all figures are aggregates. Conversions are completed checkouts joined on the same visitor ID.
• Layout gap: stores qualified with ≥3 concurrently running layout variations, each seen by ≥2,000 unique shoppers (7 stores qualified). Visitors are assigned to variations randomly with sticky bucketing (a returning visitor keeps their variation). Gap = (best variation CVR − worst variation CVR) ÷ worst variation CVR, computed within store; we report the median and IQR across stores.
• Video engagement: stores qualified with ≥5,000 video-section viewers and ≥500 players (3 stores qualified). The conversion multiple is the median across stores of (player CVR ÷ non-player CVR) among visitors who saw a video section. This comparison is observational; self-selection applies and is stated above.
• Watch depth: share of distinct (visitor, video) watch sessions reporting ≥75% watch progress; 6,190 sessions.
• Thresholds were chosen before results were read. Stores below threshold are excluded rather than pooled, to avoid small-sample noise dressing itself up as signal.

Cite this study

You are welcome to cite any figure on this page. Please attribute it to the Eevy Network Data Study, 2026 and link to this URL so readers can check the methodology. If you want a cut of the data we haven't published (by industry, device, or section type), email [email protected]; if the sample supports it, we'll run it.