8+ years growing brands on KPIs, now with AI
More Revenue Per SMS Send
We build and optimize Postscript programs that make SMS a top-three revenue channel for your Shopify store.
Google · Meta · TikTok Partner | 8+ years performance marketing | Postscript-native operators
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The Challenge
Running Postscript Is Easy. Extracting Real Revenue From It Is Not.
You set up the welcome series, the abandoned cart flow, maybe a browse abandonment sequence. You're sending campaigns twice a month. Postscript's dashboard shows attributed revenue that looks decent, and yet when you pull your blended MER, SMS doesn't move the needle the way you expected for a channel that's supposed to deliver 25–50x return on spend.
The gap between a Postscript account that's 'live' and one that's genuinely performing is enormous. Most Shopify brands sit in the middle: flows are running but not tuned, campaigns are going to the full list instead of tight segments, and the EPM on automations (which should be doing $3.52–$10.95 per message on abandoned cart alone) is dragging because the trigger logic or the copy hasn't been touched since launch.
Meanwhile, the channel is getting harder. List saturation is real: ecommerce revenue-per-send has flattened year-over-year for the first time in five years. The marginal subscriber acquired in 2025–2026 is materially less responsive than the one you acquired in 2022. That means the brands still growing SMS revenue are doing it through better segmentation, smarter automation architecture, and disciplined list health, not by sending more messages to more people.
And underneath all of it sits a compliance layer that has zero tolerance for mistakes. TCPA violations run $500–$1,500 per unsolicited message. State mini-TCPA laws stack on top: a single non-compliant text to a Florida consumer can trigger both federal TCPA and Florida FTSA claims, doubling exposure to $1,000–$3,000 per message. One unregistered 10DLC sending through a major carrier and you risk being blocked for 30 days or more, unable to reach at least a third of your list. The operators winning on SMS aren't just better marketers; they're running tighter programs.
The Opportunity
A Well-Run Postscript Program Should Contribute 10–20% of Total Store Revenue.
That's not a stretch goal; it's the benchmark for brands that have actually built the program correctly. At the 75th percentile, Postscript brands are generating $4.54 in revenue per message across all sends. Elite programs push past that. The difference between a program at $0.80 RPM and one at $3.50 RPM, on the same list size, is often just architecture: the right flows firing at the right moments with the right segmentation.
The biggest lever most brands haven't fully pulled is automation depth. Abandoned cart flows on Postscript average a 9.1% conversion rate and $8.11 EPM across all industries. Browse abandonment (when a tracked subscriber views a product page and doesn't buy) fires at the exact moment of highest intent. Back-in-stock and win-back flows recover revenue that would otherwise disappear silently. These aren't theoretical; they're the flows that separate a $0.41 RPM program from a $4.00+ one.
Postscript's AI suite has also changed the ceiling. Infinity Testing uses generative AI to create and validate hundreds of message variants simultaneously against holdout groups: Postscript reports an average 38% lift in EPM for campaigns and 20% revenue lift for automations running Infinity Testing. Shopper, their always-on AI shopping assistant, added roughly a 10% revenue lift on top of original flow attributed revenue during BFCM 2025. These tools exist in your platform today. Most brands aren't using them systematically.
The opportunity is a Postscript program that is genuinely architected: flows built to the right trigger depth, segments pulling from Shopify's 45+ native data points, list growth compounding through checkout opt-in and CashBack incentives, and AI testing running continuously so every campaign learns. That program contributes real, measurable revenue that shows up in your MER, not just in Postscript's attribution window.
What Most Get Wrong
What Most Shopify Brands Get Wrong on Postscript
Treating campaigns and automations as equals
Campaigns drive $0.11–$0.55 per message. Automations drive $3.52–$10.95 on abandoned cart alone. Brands that invest the same attention in both (or worse, spend more time on campaigns) are optimizing the wrong half of the program. The revenue is in the flows. If your automation architecture hasn't been audited in six months, you're leaving the highest-EPM messages on the table.
Sending to the full list instead of tight segments
Postscript gives you 45+ segment filters pulling from real Shopify data: LTV tiers, SKU purchased, days since last order, subscription status, checkout abandonment, Shopify tags. Brands that batch-and-blast to the full list compress their RPM, spike their opt-out rate, and burn subscribers who would have converted if messaged relevantly. DTC opt-out benchmarks run 0.34–0.84% per send depending on category: undifferentiated sends push you toward the top of that range. Unlike email, an SMS opt-out is permanent. You cannot legally re-engage that subscriber without a fresh explicit opt-in.
Trusting Postscript's native attribution as the source of truth
Postscript's 7-day click-through attribution window is the platform standard and it inflates SMS's contribution versus a MER-based view. Brands that report to finance using Postscript's dashboard numbers are often overstating the channel's true incrementality. The result: budget decisions made on bad data, and a reckoning when blended MER doesn't improve despite strong Postscript numbers. Google Analytics or a third-party attribution layer is the correction.
Underbuilding list growth, then over-sending to a stagnant list
A checkout opt-in checkbox increases SMS list growth 30–50% compared to pop-ups alone. CashBack incentives report a 31% increase in list growth rate and 20–40% revenue lift on welcome series. Brands that don't build these acquisition layers end up with a slowly shrinking, aging list, then compensate by increasing send frequency, which accelerates opt-outs and further compresses RPM. The fix is upstream: compound the list, then protect it with subscriber-level frequency caps (4–6 messages per 30 days), which reduce monthly opt-out by ~28% versus campaign-level caps at the same volume.
Treating compliance as a platform responsibility instead of an operator responsibility
Postscript handles quiet hours, opt-out processing, and built-in TCPA guardrails, but consent capture at the opt-in form level is yours. Importing an email list and texting it is a TCPA violation at $500–$1,500 per message. Sending before 10DLC registration is complete risks carrier blocking across all three major US carriers for 30 days or more. State mini-TCPA laws stack on top of federal exposure. The operators who treat compliance as a platform problem are the ones who end up with a blocked sending number and a legal bill.
Why Now
The Window to Build a Durable SMS Program Is Narrowing: Here Is Why 2025–2026 Is the Inflection Point.
Revenue-per-send has flattened industry-wide for the first time in five years. The marginal subscriber added to a mature DTC list in 2025 converts at a lower rate than the one added in 2022. That trend doesn't reverse; it accelerates as SMS adoption matures and inboxes fill. The brands that win the next three years on SMS are the ones building structural advantages now, not chasing the same batch-and-blast playbook on a shrinking-return channel.
Postscript's AI suite is the structural advantage available today that most brands haven't operationalized. Infinity Testing is live in the platform and delivering a reported 38% EPM lift on campaigns and 20% revenue lift on automations, but it requires someone to set up the holdout groups, interpret the results, and feed learnings back into flow architecture. Shopper, the conversational AI assistant, added ~10% revenue on top of original flow attribution during BFCM 2025. RCS support is opening new creative surface area. These tools don't run themselves.
The competitive gap right now is between brands that have a Postscript account and brands that have a Postscript program. The former is most of the market. The latter is a small group running Infinity Testing systematically, building subscriber-level frequency caps, pulling Shopify's 45+ segment filters into every send decision, and using CashBack to compound list growth without margin erosion. That group is pulling away. The window to join it (before the channel matures further and the structural advantages become table stakes) is open now, not in eighteen months.
The Mechanism
Where AI Creates a Real Edge Inside Your Postscript Program
Real productivity, not AI theater. Here's where it actually moves a number for postscript sms brands.
Creative & Messaging (Infinity Testing)
What AI does: Postscript's Infinity Testing uses generative AI to produce and validate hundreds of on-brand message variants simultaneously, tested against holdout groups rather than simple A/B splits. We set up the Brand Center training layer so every variant sounds like your brand, then run Infinity Testing as a continuous process, not a one-time experiment.
The result: Postscript reports an average 38% lift in EPM for campaigns and 20% revenue lift for automations running Infinity Testing. For a program sending 500,000 messages per month at $1.50 RPM, a 38% campaign EPM lift is material revenue, not a rounding error.
Why it matters here: Most Shopify brands test one or two message variants per campaign manually. Infinity Testing compresses what would take months of manual iteration into a continuous, self-improving loop. On a channel where the marginal subscriber is less responsive than two years ago, squeezing more EPM out of every send is the primary growth lever available.
Conversational Commerce (Shopper AI)
What AI does: We configure Postscript's Shopper AI assistant: the always-on inbound SMS agent that answers product questions, makes personalized recommendations, and guides subscribers toward purchase 24/7. Setup includes training on your product catalog, brand voice guidelines in Brand Center, and integration with your Shopify product data so recommendations are inventory-aware.
The result: Shopper-enabled conversational flows averaged a ~10% revenue add on top of original flow attributed revenue during BFCM 2025. That increment compounds on top of every automation already running: it's additive, not a replacement.
Why it matters here: Inbound SMS conversations from high-intent subscribers (someone who just got an abandoned cart text and replied with a question) convert at rates that dwarf passive click-through. Shopper captures that intent at the moment it exists, without requiring a live agent. For Shopify brands with complex product lines (multiple SKUs, sizing, bundles), the recommendation layer reduces friction that kills conversions.
Analytics & Attribution
What AI does: We build a parallel attribution layer outside Postscript's native 7-day click-through window: pulling Google Analytics 4 data alongside Postscript's reporting to produce an MER-adjusted view of SMS's true contribution. We also set up Postscript's reporting against your Shopify revenue data to catch the attribution inflation that makes programs look better than they are.
The result: Brands that reconcile Postscript attribution against GA4 and MER typically find their true SMS contribution is 15–30% lower than the platform reports, which sounds like bad news but is actually the foundation for every real optimization decision that follows. You can't improve what you're measuring incorrectly.
Why it matters here: On Postscript specifically, the 7-day click-through window is the default and it's generous. A subscriber who clicks an abandoned cart text, bounces, and converts three days later on a Google search gets attributed to SMS. MER doesn't lie that way. Operators running Klaviyo for email alongside Postscript for SMS face two separate attribution models that need manual reconciliation: we build that reconciliation into a single weekly reporting view.
Segmentation & Flow Architecture
What AI does: We audit and rebuild your automation architecture using Postscript's 45+ Shopify-native segment filters (LTV tiers, SKU and collection purchased, days since last order, subscription status, checkout abandonment stage, Shopify tags) to ensure every flow fires to the right subscriber at the right moment. We also implement subscriber-level frequency caps (4–6 messages per 30-day window) rather than campaign-level caps, and configure browse abandonment triggers using the Postscript site-tracking pixel against your identified subscriber base.
The result: Subscriber-level frequency caps reduce monthly opt-out by ~28% on average versus campaign-level caps at the same total send volume. Properly segmented abandoned cart flows on Postscript average a 9.1% conversion rate and $8.11 EPM: most brands running unsegmented flows are well below both benchmarks.
Why it matters here: Postscript's Shopify-native data sync is its core moat over generic SMS platforms. Pulling from real Shopify order events (not a generic ecommerce abstraction) means your segments can distinguish a subscriber who bought your hero SKU twice in 90 days from one who bought once six months ago on a discount code. Those are completely different messages. Brands that don't exploit this depth are paying for a platform advantage they're not using.
List Growth & Acquisition
What AI does: We audit and rebuild your opt-in acquisition stack: checkout opt-in checkbox configuration (the highest-volume single lever for list growth), CashBack incentive setup using Postscript's Fondue integration to replace margin-eroding discount codes, keyword and Text-to-Join configuration for off-platform acquisition (packaging, social, events), and pop-up sequencing. Every opt-in surface is reviewed for TCPA consent language and 10DLC compliance before traffic is driven to it.
The result: Checkout opt-in alone increases list growth 30–50% compared to pop-ups as the primary acquisition mechanism. Postscript's CashBack integration reports a 31% increase in SMS list growth rate and 20–40% revenue lift on welcome series, because post-purchase cash back redeemable as store credit or prepaid Visa compounds LTV without training subscribers to expect a discount code every time they opt in.
Why it matters here: On a channel where revenue-per-send is flattening, list growth quality and compound rate are the only sustainable path to growing absolute SMS revenue. A list that grows 30–50% faster through checkout opt-in, with subscribers who entered through a CashBack incentive rather than a 15%-off popup, is a structurally different asset than one built on discount-code acquisition: higher retention, higher LTV, lower opt-out rate in the first 30 days.
Ready to see what this looks like for your postscript sms brands business?
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The Strategy
How a High-Performance Postscript Program Is Actually Built
The strategy for a Postscript program on Shopify follows a specific sequence. You do not start with campaigns. You start with the foundation: 10DLC registration confirmed, consent capture language audited at every opt-in surface, attribution layer built outside Postscript's native window so you have a number you can trust before you start optimizing toward it.
From there, the automation stack is the priority, not campaigns. Welcome Series, Abandoned Cart, Browse Abandonment (pixel-tracked, firing only to identified subscribers), Back in Stock, Post-Purchase, Win-back. Each flow is built with Shopify-native segmentation: LTV tier, purchase history, subscription status, days since last order. Subscriber-level frequency caps are set at the flow level, not the campaign level, before a single campaign send goes out. This is the architecture that produces $3.52–$10.95 EPM on abandoned cart, not the default Postscript setup.
List growth runs in parallel. Checkout opt-in is configured first: it's the highest-volume acquisition surface and it's underutilized by most brands. CashBack incentives replace discount codes in the welcome series. Keywords and Text-to-Join are set up for off-platform acquisition. The goal is a list that grows faster and retains better: 97–99% subscriber retention in the first 30 days is the elite benchmark, and it's achievable when acquisition is built correctly.
Campaigns layer on top of a healthy automation foundation, not instead of one. Segmentation determines who gets each campaign send, not list size. Infinity Testing runs as a continuous process, not a one-time experiment. Shopper AI is configured for inbound conversational flows on the highest-intent automations. MMS is used deliberately, with cost-per-send modeled against expected EPM lift, because MMS runs 3–5x the cost of SMS at every tier.
Reporting is MER-adjusted from day one. Postscript's native attribution is tracked but not the governing number. The governing number is SMS's contribution to total store revenue as a percentage, measured against blended ROAS, the number that shows up in finance, not in the platform dashboard.
The one number that governs this
The governing KPI is Revenue Per Message (RPM/EPM) and SMS as a % of total store revenue, reconciled against blended ROAS and MER, not Postscript's 7-day click-through attributed revenue.
How We Help
What We Actually Do Inside Your Postscript Account
We take on a small number of Shopify brands at a time so every engagement gets senior attention. For Postscript programs, that means hands-on work inside your account, not a strategy deck, not a recommendations PDF. Here is the specific work we do, mapped to the program architecture above.
Compliance & Foundation Audit
Before touching a single flow, we audit your 10DLC registration status, consent capture language at every opt-in surface (checkout, pop-up, keyword, CashBack), quiet hours configuration by timezone, and opt-out handling. We identify any TCPA exposure (including state mini-TCPA stacking risk for Florida, Texas, and other high-liability states) and close it before it becomes a legal bill.
Attribution Layer Build
We set up a GA4-reconciled attribution view alongside Postscript's native reporting so you have an MER-adjusted picture of SMS's true contribution to store revenue. We identify where the 7-day click-through window is inflating numbers and build the corrected baseline your optimization decisions will run against.
Automation Architecture (Flows)
We audit or build from scratch your full automation stack: Welcome Series, Abandoned Cart, Browse Abandonment (pixel configuration included), Back in Stock, Post-Purchase, Win-back. Every flow is segmented using Shopify-native data points: LTV tier, SKU/collection purchased, days since last order, subscription status, Shopify tags. Subscriber-level frequency caps are set at 4–6 messages per 30-day window before any campaign volume goes out.
List Growth Stack
We configure checkout opt-in, CashBack incentive flows using the Postscript-Fondue integration, keyword and Text-to-Join setup for off-platform acquisition, and pop-up sequencing. Every opt-in surface is reviewed for consent language before traffic is driven to it. The goal is a list that grows 30–50% faster and retains at 97%+ in the first 30 days.
Infinity Testing & Creative Operations
We configure Brand Center with your voice, tone, and messaging guidelines, then run Infinity Testing as a continuous process across campaigns and automations, not a one-time setup. Holdout groups are built correctly so you're measuring true incrementality, not platform attribution. Every campaign send is segmented; batch-and-blast to the full list is not a tactic we run.
Shopper AI Configuration
We configure Postscript's Shopper AI on your highest-intent automation flows (abandoned cart and browse abandonment first) training it on your product catalog, sizing and fit logic, bundle options, and brand voice guidelines in Brand Center. Inbound conversational flows are built to capture the purchase intent that fires when a subscriber replies to a triggered message.
Ongoing Reporting & Optimization
Weekly reporting covers RPM by flow and campaign, opt-out rate per send versus category benchmarks (0.34–0.84%), 30-day subscriber retention rate, list growth rate, and SMS as a percentage of total store revenue against your blended ROAS target. We bring the optimization agenda to every check-in, not a status update.
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
187% YoY, $8+ ROAS on Meta, +79% web conversion
Clean Monday Meals
Challenge
Clean Monday Meals had paid media running but no unified view of what was actually driving revenue across channels: attribution was fragmented, email was underbuilt, and the program wasn't compounding the way a retention-led DTC brand should.
What we did
Sagum took over paid media on Meta, built out the email program, and brought Amazon into the channel mix, establishing clean attribution across all three so optimization decisions were based on real contribution, not platform-reported numbers.
Result
The program delivered 187% year-over-year growth, $8+ ROAS on Meta, and a 79% lift in web conversion. The same discipline (fixing attribution first, then scaling the channels that actually moved blended revenue) is exactly how we approach a Postscript SMS program: trust the numbers before you optimize toward them.
Find Out What Your Postscript Program Is Actually Worth
No obligation. We audit your current flow architecture, attribution setup, list health, and compliance posture, and tell you exactly where the revenue gap is and what it would take to close it. Built around your Shopify store, not a generic SMS template.
Sagum · January 2017 · St. George, Utah · 8+ years