Social commerce spend data suggests marketers may be targeting the wrong buyer
ScrollSignal data suggests men report higher social commerce spend and top-end baskets. What it signals about measurement and targeting choices.
ScrollSignal shared new consumer research that challenges a common assumption in social commerce: men reported higher spend than women over the last six months, especially at the top end of the range. The details were outlined in the company’s official research post.
The deeper shift is not about “who shops on social.” It is about the difference between what platforms can easily measure (engagement) and what brands actually need to optimize (revenue).
Table of contents
Jump to each section:
- What ScrollSignal measured, and what it found
- The strategic tension: engagement signals vs purchase outcomes
- Why audience assumptions can distort category and creative strategy
- What marketers should know about social commerce segmentation
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What ScrollSignal measured, and what it found
ScrollSignal’s dataset covers 1,000 active US social media shoppers ages 18 to 64, fielded in April 2026 and stratified to the US Census on age, gender, and region.
Two topline spend findings stand out:
- 46% of men said they spent $200 or more on social commerce in the last six months, compared with 36% of women.
- At the $1,000 spend level, 6% of men reported that spend versus 2% of women.
A second finding complicates the usual “Gen Z drives social commerce” narrative. In this dataset, the highest spenders were adults 35 to 44, not Gen Z.
One concise way to think about it: social commerce can look young and female in dashboards, while revenue concentration can skew older and male in receipts.
The strategic tension: engagement signals vs purchase outcomes
The common assumption is that the most visible audience is the most valuable audience.
The contrasting reality in this research is that “presence” and “spend” can point in different directions. ScrollSignal argues that widely cited gender conclusions often rely on discovery-era behaviors: following brands, saving products, and general engagement. Those are valuable signals, but they are not the same thing as money spent.
Strategic implication: if a team optimizes social commerce primarily on engagement-heavy proxies, it risks building a growth story around the people who browse, while under-investing in the people who convert at higher basket values.
Memorable observation: engagement is a leading indicator only if your model connects it to purchase, not if it replaces purchase.
Why audience assumptions can distort category and creative strategy
ScrollSignal notes that category penetration in its data is broad, with no category below one in four shoppers. That matters because many social commerce programs still default to a “fashion-and-beauty shaped” playbook, in the firm’s phrasing.
Here is the uncomfortable strategic angle: channels do not just find demand, they train teams on what to notice. If a program is built to appeal to a single assumed audience, it will keep getting reinforcing feedback from that audience, and it will keep missing where high-value demand is accumulating.
Memorable observation: the biggest segmentation errors are often self-inflicted, created by who your creative was built to attract in the first place.
This also reframes creative testing. If your testing pool is dominated by high-engagement users, your “winning” creative may be the best at generating platform-native actions, not the best at generating larger baskets.
What marketers should know about social commerce segmentation
If this dataset is directionally right, social commerce strategy needs a tighter link between measurement and merchandising, not a broader pile of engagement metrics.
- Treat “who engages” and “who spends” as separate segmentsThe research suggests women are more present in social commerce behaviors (browsing, saving), while men may convert less often but at higher values when they do. That is a segmentation design problem, not a gender debate.
- Rebuild your KPI stack around revenue concentrationWhen 6% vs 2% shows up at the $1,000 level, the practical question is how your reporting highlights top-end buyers. Averages can hide concentration. Platform dashboards can hide it even more.
- Pressure-test category and creative defaultsIf your social commerce plan implicitly assumes fashion and beauty dynamics, it may be over-fitting to the most legible audience on-platform. ScrollSignal’s note that penetration is broad suggests the “default category” assumption can be a strategic blind spot.
- Stop assuming Gen Z is the main revenue carrierThe 35 to 44 finding is a reminder that “most talked about” is not “most valuable.” If you are allocating budget, creative, and influencer partnerships around a youth-only narrative, your revenue model may be mismatched to your actual buyer mix.
A useful way to frame the broader implication is this: social commerce is maturing from a discovery channel into a revenue channel, and revenue channels force accountability to outcomes, not signals.
The more interesting question is not whether one demographic “wins” social shopping. It is whether your organization is structured to see who pays, not just who taps.
In the next phase of social commerce, advantage will come from measurement discipline: connecting behavior to spend, and then letting those insights reshape targeting, creative, and category priorities.
