A selection of client engagements. Numbers are real. Names are protected unless the client has given permission to be named.
A DTC fashion brand came to us 12 weeks before launch with no positioning, no channel strategy, and no creative system. We built all three — alongside a full attribution infrastructure and launch measurement framework. By launch day, the brand had generated a waitlist of more than 4,300 prospective customers and achieved a 4.2× blended ROAS across paid acquisition campaigns.
A scale-stage e-commerce brand was making budget decisions on last-click attribution. We rebuilt their measurement model, identified which channels were actually driving incremental revenue — and reallocated budget accordingly.
A growing SaaS company relied on a patchwork of manual processes for research, reporting, content operations, and internal knowledge management. Teams spent hundreds of hours each month gathering information, assembling recurring reports, and executing repetitive workflows that slowed growth and diverted resources from higher-value work. We mapped the company's operational workflows and deployed a network of AI-powered agents to automate research collection, reporting, knowledge retrieval, and content production. Human review remained in place for strategic decisions and quality control, while repetitive execution was largely automated. Within 90 days, the company eliminated more than 850 hours of manual work per month, reduced operational overhead by 35%, and generated approximately $420K in annualized net savings after accounting for AI software, infrastructure, annual token consumption and operating costs.
A subscription brand with rising CPAs and stagnating organic growth needed a new acquisition architecture. We rebuilt creative strategy, restructured campaigns, and integrated SEO — resulting in a 52% CAC reduction at 2× volume.
A regional better-for-you snack brand had strong product quality but stagnant retail performance. The packaging blended into the shelf and buyers consistently viewed the brand as a lower-tier alternative despite strong consumer feedback.
We rebuilt the brand from the positioning outward — defining a sharper category narrative, developing a more distinctive visual identity, and redesigning packaging around shelf visibility and purchase intent. The new system was rolled out simultaneously across retail, DTC, and social channels.
Within 14 months, retail velocity increased 3.2× and distribution expanded from approximately 1,800 locations to more than 5,200 doors nationwide. Retail buyers regularly cited the updated brand presentation as a key factor behind expanded placement discussions.
A DTC health supplement brand was generating $23.5M in revenue and still running negative EBITDA. Their paid media team was optimizing for ROAS — which looked healthy at 3.1× — but the margin picture told a completely different story. We rebuilt their measurement model from the ground up around POAS (Profit on Ad Spend), layering in contribution margins at three levels: CM1 (revenue minus COGS), CM2 (CM1 minus variable fulfillment and returns), and CM3 (CM2 minus allocated paid media spend). This gave us real profitability visibility by product category and individual SKU for the first time.
Using that data, we restructured their Google Shopping and Meta catalog campaigns with custom feed attributes that mapped each product's CM3 value directly into bidding logic. High-margin SKUs got priority placement and elevated bids; loss-leading products were deprioritized or pulled from paid entirely. We also surfaced that one product category — contributing 34% of revenue — was running at negative CM3 on every paid-driven sale. We paused it in paid, shifted focus to higher-margin bundles, and rebuilt the creative system around those offers.
A home goods brand had plateaued at a 2.2× ROAS across Google Shopping after aggressively scaling spend. Efficiency had collapsed as they pushed volume. We restructured campaign architecture into three tiers by margin band, rebuilt product titles and custom labels to reflect seasonality and inventory velocity, and rebuilt bidding logic using target ROAS targets calibrated to each tier. Prospecting was separated from retargeting with independent budget pools. Within 90 days, blended ROAS moved to 4.6× at roughly the same spend level — with a 28% increase in revenue.
A DTC apparel brand was debating cutting Meta spend after last-click attribution showed minimal contribution. Before making the decision, we ran a geo-based conversion lift study — splitting markets into holdout and exposed groups for 6 weeks. The study revealed Meta was driving 22% incremental lift in purchases that last-click never captured. Rather than cutting, we reallocated toward top-of-funnel video and restructured the funnel. Revenue per dollar of Meta spend improved 31% over the following quarter.
A CPG brand spending $8M annually across paid search, Meta, CTV, and influencer had no reliable way to compare channel efficiency. Each platform reported its own attributed revenue, which summed to roughly 3× actual company revenue. We built a media mix model using 3 years of weekly sales data, media spend, pricing, and external variables (seasonality, competitor spend estimates). The model quantified the marginal ROI of each channel. CTV was undervalued; branded search was overcapitalized. We shifted $1.4M in budget accordingly over two quarters.
A subscription wellness brand had seen CPAs climb 60% over 18 months as iOS changes eroded signal quality. We rebuilt their acquisition stack: restructured Meta campaigns into broader ad sets with fewer audience constraints, rebuilt creative testing cadence to a weekly velocity, and implemented server-side conversion APIs to recover tracking fidelity. We also introduced a CAC-by-cohort framework so the team could evaluate acquisition efficiency against downstream LTV — not just first purchase cost. CAC dropped 48% over two quarters while monthly new subscriber volume held.
A mid-market professional services firm was generating leads from Google Search at $380 CPL — with a 6% close rate. The volume looked acceptable until we mapped it against closed revenue per lead source. The majority of closed deals came from a narrow slice of intent-specific keywords that represented less than 20% of spend. The rest was generating unqualified inquiries. We restructured campaigns around high-intent, job-title-specific search terms, rebuilt ad copy to qualify out poor-fit prospects explicitly, and added a two-step landing page flow with a revenue-qualifier question. CPL dropped to $190. Close rate moved to 12%. Pipeline value per dollar of ad spend roughly tripled.
A home renovation company was running Google Local Services Ads and a small Search budget with no dedicated landing pages — all clicks went to the homepage. We built service-specific landing pages with social proof and direct call booking, restructured their Local Services profile with review response strategy, and added a retargeting layer via Google Display. Monthly qualified leads went from 14 to 83 over four months. The owner went from doing all intake calls himself to hiring a part-time coordinator.
A B2B SaaS platform (project management, SMB-focused) had strong top-of-funnel from content and SEO, but a 14-day trial that converted at 8% — well below category benchmarks. We ran a full funnel audit: session recordings, activation event analysis, and a cohort study comparing users who converted vs. those who churned from trial. The data showed that users who completed a specific setup workflow in the first 48 hours converted at 38% — but only 22% of trial users ever reached it. We rebuilt the onboarding sequence to prioritize that workflow, added in-app nudges at stall points, and reduced friction in the first-run experience. Trial-to-paid moved to 21% inside one quarter without any changes to pricing or plan structure.
A B2B SaaS company was 5 months from a raise with a deck built on assumptions. Their CAC/LTV model used averages that masked wide variance by channel and ICP segment. We rebuilt unit economics by cohort — separating inbound vs. outbound CAC, segmenting LTV by company size and industry vertical, and rebuilding the GTM narrative around the highest-leverage ICP. The revised model showed a 3.4× LTV:CAC on one specific segment that the prior model had buried inside a blended average. Investors interrogated that number directly. They closed $9M.
A SaaS productivity tool was growing top-line but not bottom-line — monthly churn at 4.2% meant the company was effectively leaking its growth. We built a churn prediction model using 18 months of usage data to identify leading indicators of cancellation. The top signals were inactivity for 12+ days in month 2 and failure to integrate with one of three key tools. We designed automated intervention sequences targeting users showing those signals — a combination of in-app prompts, email, and optional 15-minute calls with a success rep. Monthly churn dropped to 1.8% over six months, adding roughly $280K in retained ARR annually.
A fintech startup had built a strong product but almost no search presence. Acquisition depended heavily on paid channels, compressing margins as the company scaled. We conducted a full SEO and AEO audit, identifying six underserved search themes where competitors had thin or outdated coverage.
We rebuilt the company's search acquisition infrastructure from the ground up — combining technical SEO improvements, content cluster development, structured data implementation, entity optimization, and answer-focused content architecture designed for both traditional search engines and AI-powered discovery platforms. The result was a system built to capture demand wherever prospects searched, whether through Google, AI assistants, or answer engines.
Within 12 months, monthly search-driven sessions grew from 12,400 to more than 94,000. Search now drives 34% of all new account signups and has become one of the company's largest acquisition channels.
A DTC subscription brand had no structured referral program despite high NPS (72). We designed and launched a tiered referral mechanic tied to loyalty milestones, built the trigger-based email and SMS sequences to activate it at the right moments in the customer lifecycle (post-second-purchase and post-positive-review), and A/B tested incentive structures across three cohorts. Within 6 months, referral-driven orders represented 22% of new customer revenue — at a CAC roughly 60% below paid channels — and the program was cash-flow positive from month two.
A CPG brand was sending one weekly promotional email to their full list and generating about $18K/month from the channel. No segmentation, no flows, no testing. We rebuilt their Klaviyo architecture from scratch: 9 automated flows (welcome, abandonment, post-purchase, win-back, VIP), list segmentation by purchase behavior and engagement tier, and a weekly send cadence broken into three segments with personalized offers. Six months in, email was generating $56K/month — 3.1× — from the same list size, with unsubscribe rates below industry average.
A DTC skincare brand was running 4–6 ads at a time, most of which were polished brand videos that tested flat. Their Meta account showed creative fatigue within 10–14 days of every launch. We rebuilt their creative operation from a brand-first model to a testing-first model: raw-cut UGC, native-feeling static cards, and problem-statement hooks — all built to qualify specific audience segments through the ad itself rather than via targeting parameters. We established a weekly production cadence (8–12 new creative assets per week), a structured testing framework with clear kill metrics, and a creative brief system that mapped audience pain points to hook angles. Within one quarter, the creative win rate — defined as ads that beat their control — moved from 1-in-9 to 1-in-3. Average thumb-stop rate improved from 22% to 41%. ROAS on prospecting campaigns improved 38%.
A women's fashion brand wanted to scale UGC but kept getting generic content that didn't convert. We built a structured brief system that gave creators specific hook scripts, product moment guidance, and clear do/don't frameworks — while leaving room for authentic delivery. We sourced and managed a rotating roster of 22 micro-creators. Output: 40+ new assets per month. Cost per asset came in at $110 on average. The top 3 UGC performers across one quarter outperformed the brand's studio content on CTR by an average of 2.4× and drove 31% lower CPA than non-UGC creative.
A challenger food brand had a product with strong word-of-mouth but almost no owned social presence. Rather than run paid, we built an influencer-led organic growth engine: identified 35 micro-influencers in adjacent lifestyle niches (home cooking, meal prep, fitness nutrition), structured gifting and partnership agreements around content rights, and created a content calendar that coordinated organic posting across all accounts to create perceived momentum. The brand's own TikTok account grew from 1,200 to 68,000 followers over 10 months. Top-performing posts from partners drove 3–4 day surges in DTC site traffic, and the brand saw a measurable uplift in branded search during each content cycle.
Every engagement starts with an honest diagnostic. We don't propose solutions until we understand the actual constraint — which is rarely the obvious one.
We build infrastructure, not campaigns. Everything we create is designed to compound — getting better, cheaper, and more effective over time.
We set clear success metrics before we start, measure against them rigorously, and report results without spin — including when things don't go as planned.
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