8+ years growing brands on KPIs, now with AI
More Revenue from Your Magento Store
We run demand-gen for Adobe Commerce merchants who already have the build and need the growth.
Google Ads · Meta · TikTok · 8+ years of performance marketing · now with AI
Get your free Growth Gap Analysis
Senior strategist review · response within one business day
Prefer to reach out directly?
The Challenge
Adobe Commerce is the most powerful commerce platform built. It is also one of the hardest to grow on.
You chose Adobe Commerce because your catalog is complex: attribute sets with dozens of custom fields, configurable products with hundreds of child SKUs, B2B shared catalogs layered on top of your B2C pricing, and a deployment pipeline that means even a feed fix requires a build cycle. That complexity is your competitive moat. It is also why most marketing agencies quietly underserve you.
Generic agencies treat your Google Shopping feed like a Shopify Simple Product export. They do not understand that your EAV model stores every product attribute as a separate row across joined value tables, that a stale `catalog_product_price` index means your PPC landing pages are showing the wrong price to a buyer who is ready to convert, or that your shared catalog reindex can take hours and silently leave your B2B pricing out of sync with what your ads promised.
The result is predictable: wasted ad spend on traffic that bounces at the price discrepancy, Shopping feeds with incomplete GTINs and MPNs because nobody mapped your EAV attributes to Google's required fields, and a blended MER that looks acceptable until you run contribution margin by category and find that your highest-volume SKUs are your worst performers.
If you are running Luma, your Core Web Vitals are almost certainly failing Google's LCP threshold, and every point of Quality Score you lose to page speed is a CPM premium you are paying on every impression. If you have migrated to Hyvä, you have the foundation for real organic and paid performance, but only if the demand-gen layer is built to match.
You do not need a platform rebuild. You need a marketing partner who understands what is already running underneath and can drive revenue on top of it.
The Opportunity
Adobe Commerce merchants who get demand-gen right compound advantages that simpler platforms cannot replicate.
Adobe Commerce facilitates an estimated $173 billion in annual GMV, and the merchants capturing disproportionate share are not the ones with the most sophisticated builds. They are the ones who have closed the gap between platform capability and marketing execution.
Your B2B AOV is roughly 2.5x your B2C AOV, and your repeat purchase rate runs about 50% higher. That math means a single percentage-point improvement in quote-to-order conversion on your negotiable quotes workflow is worth more than most Shopify merchants' entire monthly revenue. But that conversion only happens if your demand-gen is reaching the right company accounts with the right catalog visibility, and most Adobe Commerce operators leave that entirely to organic traffic and a sales team.
On the B2C side, your attribute-rich catalog is a Google Shopping asset that most competitors cannot match. A properly mapped feed (GTINs, MPNs, color, size, material, all pulled cleanly from your EAV attribute configuration) means your Performance Max campaigns have the signal quality to find buyers your competitors' thin feeds miss entirely.
Hyvä-migrated storefronts are scoring 95 on mobile PageSpeed. That is a direct Quality Score lever on paid and a crawl-budget lever on organic. Merchants who have made that migration and then built the demand-gen layer on top of it are compounding both advantages simultaneously.
The growth is there. The bottleneck is almost never the platform. It is the marketing execution layer sitting on top of it.
What Most Get Wrong
What Adobe Commerce merchants (and the agencies they hire) consistently get wrong
Treating the Shopping feed like a simple product export
A 50K-SKU Adobe Commerce catalog with configurable products and hundreds of child SKUs requires deliberate feed architecture: suppression logic for out-of-stock children, deduplication across variants, GTIN and MPN mapping from your EAV attribute set. Merchants who push a raw feed get low match rates, suppressed impressions, and Performance Max campaigns that spend budget on the wrong SKUs. The fix is not a bigger budget; it is feed engineering.
Running indexers on Update on Save with a large catalog
Every admin save that triggers a reindex on a large catalog (a price rule change, a new promotion, a category reassignment) blocks database performance and can cause deadlocks when multiple users are active simultaneously. The downstream marketing consequence: stale `catalog_product_price` data means your PPC landing pages show prices that do not match your ads, driving bounce and wasting every dollar of spend that drove the click.
Ignoring Core Web Vitals on Luma storefronts
Luma loads 230 requests and roughly 3MB of uncompressed assets per page. Google's ranking algorithm has been penalizing that for years. Every point of Quality Score lost to page speed is a CPM premium on paid search and a ranking suppression on organic. Merchants running Luma without a performance remediation plan are paying a tax on every impression they buy and every keyword they rank for.
Using last-click attribution when B2B quote cycles span weeks
Adobe Commerce has no native multi-touch attribution. A B2B buyer who starts with a Google Search ad, downloads a spec sheet, requests a negotiable quote, and converts 18 days later will show as a direct or organic conversion in last-click models. Merchants optimizing paid channels against last-click data are systematically underfunding the touchpoints that actually initiate the pipeline.
Hiring agencies that have never touched an EAV schema
Agencies without Adobe Commerce depth treat your catalog like a flat product table. They cannot map your `is_filterable` and `use_in_layered_navigation` attribute settings to SEO faceted-navigation strategy. They cannot diagnose why your shared catalog reindex is blowing out your Product Price indexer runtime. They run generic campaigns against a platform they do not understand and report on impressions and clicks while your MER quietly deteriorates.
Why Now
The window for Adobe Commerce merchants to pull ahead is open right now, and it closes with the next peak season.
Most of your direct competitors on Adobe Commerce are in the same position you are: a powerful, well-built platform with demand-gen that has not kept pace with the platform's capability. The gap between what Adobe Commerce can do and what most merchants are actually extracting from it in paid and organic performance is wider right now than it has been at any point in the platform's history.
Two things are converging. First, AI-assisted feed management, creative testing, and bidding optimization have made it possible for a disciplined operator to run the kind of catalog-level campaign architecture that used to require a six-person in-house team. Second, Google's Performance Max has fundamentally changed how Shopping inventory is allocated: merchants with clean, attribute-complete feeds and strong landing-page quality scores are capturing share that used to be distributed more evenly.
The merchants who move before the next Q4 ramp (who get their feeds mapped correctly, their indexer health stabilized, their attribution model rebuilt around actual B2B purchase cycles, and their paid creative tested at volume) will enter peak season with a structural advantage that compounds. The ones who wait will be bidding against that advantage at higher CPMs.
This is not a permanent window. The operators who act on it in the next 90 days will own it.
The Mechanism
Where AI creates real, measurable edge for Adobe Commerce merchants
Real productivity, not AI theater. Here's where it actually moves a number for adobe commerce magento brands.
Feed Management and Performance Max
What AI does: AI-assisted feed auditing maps your EAV attribute configuration to Google's required and recommended fields (GTINs, MPNs, color, size, material, product type taxonomy) across catalogs with 10K to 500K+ SKUs, flags suppressed items and attribute gaps, and monitors feed freshness against your indexer schedule so stale price data never reaches a live campaign.
The result: Higher impression share on Shopping, lower suppression rates, and Performance Max campaigns with the signal quality to find high-intent buyers rather than spending budget on broad, low-converting traffic.
Why it matters here: Adobe Commerce's EAV model is the most flexible catalog architecture in ecommerce, and the hardest to translate cleanly into a Google feed. Most merchants are running with 60–70% attribute completeness and do not know it. Every missing GTIN is a match-rate penalty. Every stale price from an indexer lag is a bounce.
Paid Search and Shopping
What AI does: AI monitors campaign performance at the SKU and category level, shifting budget toward the attribute combinations and product types with the highest contribution margin (not just the highest revenue) and away from SKUs where ROAS looks acceptable but margin is negative after returns and fulfillment costs.
The result: Blended MER improves because spend follows margin, not volume. B2B search campaigns are structured around RFQ and quote-initiation intent, not just transactional keywords, so the pipeline fills earlier in the buying cycle.
Why it matters here: Adobe Commerce merchants selling B2B on the same instance as B2C have fundamentally different purchase cycles running simultaneously. A campaign architecture that does not distinguish between a B2C buyer searching for a single unit and a procurement manager researching a 500-unit order is leaving the highest-AOV conversions to chance.
Creative Testing
What AI does: AI generates and systematically tests ad creative angles: product-attribute-led (material, construction, certifications), use-case-led, and social-proof-led, at a volume that a human creative team cannot sustain. For B2B personas, creative testing includes LinkedIn and Google Display messaging around shared catalog access, net-30 terms, and quick-order-by-SKU workflows.
The result: Finding the message that converts in weeks rather than months, with a continuous feedback loop that feeds winning angles back into landing page copy and product description optimization.
Why it matters here: Adobe Commerce merchants with complex, attribute-rich catalogs have more creative surface area than almost any other ecommerce operator, and most of them are running two or three static ad variants. The merchants who test at volume find the attribute combinations and use cases that actually drive purchase intent.
Analytics and Attribution
What AI does: AI-assisted attribution modeling stitches GA4 event data, server-side GTM, and the Adobe Commerce order grid to reconstruct the actual path from first paid touch to converted order, including B2B quote-to-order cycles that span days or weeks and involve multiple company account users. Identifies where last-click models are systematically undercrediting paid channels and overcrediting direct.
The result: Paid channel budgets are allocated against actual pipeline contribution, not last-click proxies. B2B campaigns that initiate RFQ workflows get credited for the revenue they generate even when the order closes weeks later.
Why it matters here: Adobe Commerce has no native multi-touch attribution. B2B negotiable quote cycles break every last-click model ever built. Merchants optimizing against bad attribution data are making systematically wrong budget decisions, and the error compounds every month.
Conversion Optimization
What AI does: AI audits landing pages against Core Web Vitals benchmarks (LCP, INP, CLS), identifies the specific Luma render-blocking assets or Hyvä configuration gaps degrading Quality Score and conversion rate, and tests page variants (including B2B-specific landing pages surfacing shared catalog access, quick-order-by-SKU, and quote-request CTAs) against control.
The result: Higher Quality Scores reduce CPMs on paid. Faster LCP improves organic rankings. B2B landing pages that speak to procurement workflows convert company account registrations at higher rates than generic product pages.
Why it matters here: Page speed is a direct revenue lever on Adobe Commerce. Amazon's own data shows a 1% sales drop per additional 100ms of time to interaction. Luma storefronts routinely fail LCP thresholds. Every dollar spent driving traffic to a slow page is partially wasted before the buyer even sees the offer.
Ready to see what this looks like for your adobe commerce magento brands business?
No obligation. A senior strategist will show you exactly where the wins are.
The Strategy
The marketing strategy that actually works for Adobe Commerce merchants
Adobe Commerce marketing strategy starts with the platform, not the channel. Before a dollar of paid spend is optimized, three things have to be true: your indexers are healthy and running on Update by Schedule so your prices are never stale, your feed is attribute-complete with GTINs and MPNs mapped from your EAV configuration, and your attribution model can distinguish a B2C transaction from a B2B quote-to-order conversion. Everything else is built on top of that foundation.
For B2C demand-gen, Google Performance Max and Shopping are the primary paid channels, but only with a feed that is engineered for your catalog architecture. Configurable products with hundreds of child SKUs need explicit suppression and deduplication logic. Attribute completeness needs to be monitored continuously, not audited once at launch. Performance Max campaigns need to be structured around contribution margin at the category level, not blended ROAS across your entire catalog, because your margin profile varies dramatically by product type.
Paid search complements Shopping with campaigns built around high-intent transactional and comparison queries, with landing pages tuned to the specific attribute combinations driving those searches. If you are running Hyvä, your Quality Scores will reflect it. If you are still on Luma, page-speed remediation is part of the paid strategy, not a separate project.
For B2B demand-gen, the strategy is fundamentally different. Buyers researching a 500-unit order are not converting on a first click. They are reading spec sheets, requesting samples, initiating RFQ workflows, and coming back multiple times before a negotiable quote is even opened. The campaign architecture needs to reach procurement managers and category buyers at the research stage (Google Search for high-intent B2B queries, LinkedIn for account-based targeting by company size and industry) and nurture them through the quote cycle with retargeting that reflects where they are in the process.
SEO is a compounding asset on Adobe Commerce that most merchants underinvest in. Your attribute-rich catalog is a structured data opportunity (Product schema, Offer schema, AggregateRating schema) that your default Luma theme is almost certainly not outputting. Hyvä ships some schema; most merchants need it extended significantly. Faceted navigation on category pages, if not managed with canonical tags and `is_filterable` discipline, creates crawl budget waste at scale. These are fixable, high-leverage problems.
Email and owned channels close the loop on both B2C and B2B. B2C: post-purchase flows, browse abandonment, and category-level replenishment sequences. B2B: quote follow-up automation, requisition list reorder reminders, and company account onboarding sequences. Adobe Commerce's native email capabilities are limited; the strategy pairs Klaviyo or a comparable platform with your order and quote data.
The one number that governs this
Every channel is measured against blended MER (total revenue ÷ total ad spend) and contribution margin at the category level, not top-line ROAS that masks margin-negative SKU performance. B2B campaigns are evaluated against quote-initiation rate and quote-to-order conversion, not last-click revenue.
How We Help
Here is specifically what we would do for your Adobe Commerce business
We start where the revenue leaks are, not where the campaigns are. For most Adobe Commerce merchants, that means fixing the feed, the attribution, and the indexer health before we touch a single bid. Then we build the demand-gen layer on top of a foundation we can actually trust.
Feed Engineering and Performance Max
We audit your EAV attribute configuration against Google's required and recommended fields, build suppression and deduplication logic for your configurable products, and set up continuous feed monitoring tied to your indexer schedule, so your Shopping and Performance Max campaigns always have attribute-complete, price-accurate data.
Paid Search: B2C and B2B
We build separate campaign architectures for your B2C transactional buyers and your B2B procurement audience: different intent signals, different landing pages, different conversion events. B2B campaigns are structured around RFQ initiation and quote-to-order conversion, not last-click revenue.
Attribution Modeling and Analytics
We reconstruct your attribution model using GA4, server-side GTM, and your Adobe Commerce order grid, including B2B quote cycles that span weeks and involve multiple company account users. You stop optimizing against last-click proxies and start optimizing against actual pipeline contribution.
SEO: Schema, Crawl Budget, and Faceted Navigation
We build the Product, Offer, and AggregateRating schema your Luma or Hyvä theme is not outputting, implement canonical tag strategy for your faceted navigation to protect crawl budget at scale, and target the high-intent B2B and B2C queries your attribute-rich catalog is positioned to own.
Conversion Optimization and Page Speed
We audit your Core Web Vitals against LCP, INP, and CLS thresholds, identify the specific render-blocking assets or configuration gaps degrading your Quality Score and conversion rate, and test landing page variants, including B2B-specific pages surfacing quick-order-by-SKU, shared catalog access, and quote-request CTAs.
Email and Owned Channel Automation
We build B2C post-purchase, browse-abandonment, and replenishment flows in Klaviyo connected to your Adobe Commerce order data, and B2B quote follow-up, requisition list reorder, and company account onboarding sequences, closing the loop on demand-gen with owned-channel revenue that does not inflate your MER.
AI-Assisted Creative Testing
We generate and test ad creative at a volume that surfaces the attribute combinations, use cases, and B2B value propositions that actually drive conversion, feeding winning angles back into landing page copy and product description optimization on a continuous basis.
Who's Behind This
Who we are, and what makes us different
Sagum is a performance marketing agency founded in January 2017 in St. George, Utah. We've spent 8+ years growing real brands and being judged on KPIs, not vanity metrics.
We deliberately limit how many clients we take so each one gets senior attention. We treat your numbers like our own, we never run generic playbooks, and your strategy is built for your business, because shouldn't your brand's marketing be custom to your brand?
Sagum.ai is our AI arm: the same proven operators now build AI into the work wherever it creates real edge, not as theater, but as leverage applied with discipline.
- 8+ years growing brands on performance KPIs, not vanity metrics
- Limited client roster, with senior attention on every account
- An extension of your team; your success is tied to ours
- Custom strategy per brand, never a generic playbook
- AI built in where it moves a number; judgment over hype
“Sagum is a performance marketing agency that's spent 8+ years growing brands by treating their numbers like our own. We take on few clients, never run generic playbooks, and now build AI into the work wherever it creates real edge, not hype. Your strategy is built for your business, and our success is tied to yours.”
“After six years, Sagum is our most important partner: trusted, communicative, and caring about our business as if it's their own.”
Proof
Reversed 3 years of decline to 237% YoY
Bisaddle
Challenge
Bisaddle had spent three years watching year-over-year numbers decline despite having a strong product and an established catalog. Their site was slow, their conversion rate was deteriorating, and their paid channels were not making up the difference.
What we did
We rebuilt the demand-gen architecture from the foundation up: a site redesign that doubled page speed, email flows that grew to drive nearly half of total revenue, and paid campaigns restructured around the metrics that actually reflected business health.
Result
The business reversed three years of decline and reached 237% YoY growth. Site conversion rate lifted 122%. Email grew to 48% of total revenue. The same compounding logic applies to Adobe Commerce merchants: when the foundation is right, every demand-gen dollar works harder.
Your Adobe Commerce build is already done. Let us make it grow.
No obligation. We will audit your feed health, attribution model, and paid channel structure and tell you exactly where the revenue is leaking, built around your catalog, your B2B configuration, and your actual MER targets.
Sagum · January 2017 · St. George, Utah · 8+ years