Three sales.
All above benchmark.

A multi-channel specialty ecommerce retailer (online store + physical retail + B2B distribution) running periodic promotional sale events to drive online revenue. The business operates in a moderately-priced consumer-goods category with a target audience of hobbyists and small-business buyers.

I was the in-house marketing and operations lead, responsible for paid advertising (Google & Meta), search-engine optimization, social media, email marketing, and direct retail-floor operations — five distinct functions consolidated under a single role.

The work covered here spans three consecutive promotional sale events over the first half of 2026. The first was executed by a previously-retained external marketing agency. The next two were planned and executed in-house after the agency was let go.

Leadership flagged ad spend as a likely culprit in a perceived margin problem. The implicit question — was the business "overspending on ads"? — needed a rigorous, defensible answer. Rather than respond rhetorically, I treated it as an analytical question and worked the numbers across three comparable campaigns.

The challenge was twofold:

• Demonstrate, with platform-validated metrics, whether ad spend was actually producing returns consistent with industry benchmarks.
• Present the analysis in a way that would withstand pointed scrutiny — including counter-arguments around discounting, returns, AOV inflation, and channel-mix decisions.

Same product catalog, same channels (Google Ads + Meta Ads), comparable promotional structure. Different execution teams and different channel mixes — which made the comparison directly informative.

Industry-standard DTC ROAS benchmarks: 2× minimum (rough break-even after typical product cost), 3× considered "good performance," 4×+ considered strong. The first sale, run by the external agency, landed just above the minimum benchmark. Both subsequent in-house campaigns cleared "good" by significant margins.

All ROAS calculated as net sales (Shopify) divided by total ad spend across both platforms — the conservative blended measure.

For Sale 1, "all marketing" includes both ad spend and the prorated cost of the external agency retainer attributed to the sale window. For Sales 2 and 3, "all marketing" is just ad spend — the campaigns were executed in-house with no agency cost. This is the most direct apples-to-apples comparison of what each sale actually contributed.

A $3,809 swing in favor of the in-house execution from Sale 1 to Sale 2, despite Sale 2 running 50% longer.

Four operational decisions moved the numbers between Sale 1 and Sale 3. None of them were "spend more" — every one was about spending the same dollars more deliberately.

To pre-empt single-metric objections, I calculated nine independent measures spanning revenue, profitability, efficiency, and customer behavior. Every metric moved favorably from Sale 1 to Sale 2.

The three metrics that mattered most to neutralizing the "we discounted harder" objection: the discount rate actually fell from 13.8% to 11.6%; AOV held steady at $61 (no bundle-stuffing); and returns rate dropped from 6.88% to 0.07% — Sale 2's revenue stuck.

For the smaller Sale 3 flash event, I structured Meta differently than in Sale 2 — moving from a conversion campaign to an awareness campaign to match the smaller budget and shorter window. The result: a $103 spend reached more unique people than Sale 2's $1,678 spend, by virtue of optimizing for impressions rather than purchase events.

The right campaign type for the goal, at the right scale of budget.

Data sources

• Sales, gross profit, COGS, returns: Shopify Analytics product reports with cost-per-item populated.
• Traffic and conversion: Shopify Analytics online-store sessions reports, broken by referrer source.
• Google Ads: time-series and campaign exports.
• Meta Ads: Ads Manager performance dashboards.
• External agency cost: monthly retainer prorated to the relevant sale window.

How the analysis was structured to withstand scrutiny

• Used conservative blended ROAS (Shopify net sales / total ad spend) instead of each platform's higher self-reported attribution.
• Pre-emptively included counter-metrics (discount rate, returns rate, AOV stability, items per order) so single-axis objections could be answered before they were raised.
• Made the methodology explicit and citable, including industry-benchmark sources and definitional choices.
• Treated boundary issues (salary attribution, shipping ledger, payment-processing fees) transparently rather than excluding them silently.

The skill surface that this single project touches:

• Channel-mix optimization that moved the needle. Rebalancing from Meta-heavy to Google-heavy more than doubled ROAS on equivalent spend.
• Budget discipline. Concentrating ad spend on peak conversion days rather than spreading it evenly produced higher returns per dollar.
• Campaign-type sophistication. Matching campaign objective (awareness vs. conversion) to budget and goal produced ~9× more efficient impressions.
• Analytical rigor. Each campaign was measured against benchmarks, prior performance, and consistency checks — and the methodology was made transparent enough to defend under pressure.
• Cross-functional execution. Paid ads, email sequences, SEO, social, and retail operations were coordinated by a single in-house operator rather than separate specialists.