Product discovery has moved to video and live formats, and shoppers increasingly decide inside the content rather than on a product page. Most brands access that behaviour through third-party social platforms, where the audience, the first-party data, and a share of the margin accrue to the platform rather than the brand. This paper examines the alternative: running the same formats on a brand's own domain, and measures the commercial effect against the brand's own baseline.
This is a directional field analysis rather than a controlled experiment. Findings reflect self-selection among engaged shoppers, seasonality, and differences between brands, and should be read as indicative of magnitude rather than as a precise causal estimate. Figures are aggregated across brands running Terrific; individual identities are withheld. See methodology, page 6.
For a decade, the standard e-commerce page has been a grid of static images and a search box. That design assumes an intentional shopper who arrives knowing what they want. A growing share of demand no longer behaves that way. Shoppers discover through short-form video, they build trust by watching a product in use, and they act while attention is high.
Brands have largely met that behaviour on third-party social and video platforms. The economics of doing so are well understood: reach is rented, attribution is partial, first-party data is limited, and a portion of the margin is intermediated. The audience a brand builds there is, in effect, leased.
An alternative has become viable. The same formats that perform on social platforms, shoppable video, live shopping, and an always-on shoppable feed, can now run natively on a brand's own site, with checkout on the brand's domain and data returning to the brand's own systems. The question this analysis addresses is straightforward and commercial: when a shopper engages with those formats on a brand's own site, what happens to conversion and to order value, relative to a shopper who does not.
Does bringing social-style engagement onto owned properties change purchasing behaviour enough to matter, and by how much?
For each brand, we computed two conversion rates over the same period and on the same domain: the rate among visitors who engaged with a Terrific surface, and the rate among those who did not. The ratio between the two is the conversion lift; a value of one would indicate no effect.
The median brand converted engaged shoppers at 4.2 times its own baseline. The distribution matters as much as the median: the effect clustered between three and eight times rather than resting on a single exceptional result, and only a small minority of low-traffic brands fell below parity. The pattern is consistent with a behavioural rather than a statistical explanation. A shopper who has watched a product in motion, seen it used, and can transact without leaving the content arrives at checkout with most of their purchase questions already answered.
The conversion gap between engaged and non-engaged shoppers is large, consistent, and observed across the great majority of brands analysed.
A higher conversion rate would be a hollow result if engaged shoppers simply bought cheaper items. They did not. Comparing the average order value of engaged and non-engaged buyers on the same brand, engaged shoppers spent approximately 17% more per order, and the large majority of brands gained on order value alongside conversion.
Several mechanisms plausibly reinforce one another. Video and live formats convey the full look, the complementary item, and the use case in a way a static grid cannot, which supports larger baskets. Interactive elements such as polls, live chat, and host guidance resolve the hesitation that typically ends a session. And the momentum of an engaged, in-the-moment shopper carries through to a more complete purchase. The result is a shopper who both converts more readily and buys more when they do.
The magnitude of the effect argues for treating owned social commerce as infrastructure rather than as a campaign. Three implications follow.
A practical way to establish that number is a bounded test: enable a single surface, measure engaged versus non-engaged conversion and order value against your existing baseline over roughly 30 days, and decide from your own data.
Observation window: a single 30-day period. Population: brands actively operating a Terrific surface during the period, restricted to those with sufficient exposed traffic to be treated as statistically reliable. Conversion lift is defined as buyers among exposed visitors divided by buyers among non-exposed visitors on the same domain; order-value lift compares the mean order value of exposed and non-exposed buyers per brand. The analysis is observational: engaged shoppers self-select, and category and seasonality vary by brand, so figures indicate magnitude rather than a precise causal estimate. Very low-traffic brands were excluded from headline figures. All results are aggregated across brands and individual identities are withheld. Terrific operates on a privacy-by-design basis; experiences run on the brand's own domain and first-party data returns to the brand's systems.
A single surface, a 30-day read, measured against your current baseline. To scope a test, visit terrificlive.com.