Andriy Boychuk writes about email and retention marketing for direct-to-consumer brands, with a focus on building customer relationships through owned channels and adapting to AI-driven shopping.
Andriy Boychuk is most useful for practical email and retention advice on owned channels as shopping changes.
Best for
A DTC ecommerce or Shopify agency campaign about owned-channel retention and agentic commerce.
Not for
A marketplace-first growth agency or Amazon seller event teaching out-discounting and Amazon as primary relationship.
Key takeaways
When an AI agent places an order, there is no click trail to attribute.
For marketplace sale events, use subscriber-only sale, free gift, early access, and email or SMS capture.
Google AI Mode back-in-stock alerts contrast with a brand capturing email and sending its own alert.
Check first: Before contacting, confirm recent post-level reach, engagement, sponsored results, audience survey data, and past session evidence.
Main ideas
The customer relationship, not the sale, is what a brand owns · Don’t out-discount a platform sale; run counter-programming you control · Platform features keep taking over the moments retention flows were built to own
Audience
8,123 LinkedIn followers · 3 posts, dated 4, 6 and 7 October 2026 Posts in sample
Andriy Boychuk writes about email and retention marketing for direct-to-consumer brands.
In the three posts here, they connect changes in online shopping to a practical question: how can a brand keep a direct relationship with customers when platforms shape discovery or checkout?
Their examples range from AI-assisted purchases and Google back-in-stock alerts to subscriber offers during Amazon sale events.
These posts show the focus of their recent work.
Boychuk turns platform changes into concrete retention decisions.
When discussing AI-assisted checkout, they ask what happens to attribution when an order has no click trail, then argue that a direct customer relationship matters more than a last-click model.
On back-in-stock alerts, they contrast Google choosing where a shopper goes with a brand capturing the shopper's email and sending its own alert.
For marketplace sale events, they offer alternatives to matching discounts: a subscriber-only sale, a free gift, early access, and stronger email or SMS capture.
Together, the examples make their work useful for readers looking for practical ways to protect customer relationships as shopping channels change.
They favor owned channels; ask how that approach accounts for tradeoffs, which these posts do not test.
Andriy Boychuk’s posts return to one argument: the customer relationship is the asset, and a sale through a marketplace is rented revenue.
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From that premise, they argue that DTC brands should not try to out-discount Amazon or other platforms, but should offer deals available only to their own email and SMS audiences and capture shoppers into a list at high-intent moments.
They treat each new platform feature — Google’s back-in-stock alerts, AI checkout — as a claim on a moment a retention flow was supposed to own.
When an AI agent places the order, the old attribution fight disappears, leaving one durable signal: whether the customer already trusted the brand.
The customer relationship, not the sale, is what a brand owns
Boychuk’s recurring premise is that a purchase through Amazon or another marketplace gives the brand the order, but the platform gets the customer, the data and the repeat purchase — what they call rented revenue.
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The asset worth building is a direct relationship the brand can reach on its own: an email list, a phone number, a subscriber.
From that premise, the tactics follow.
If the list is the asset, capture and list growth matter more than any single transaction, and a one-time marketplace order is worth less than a new owned contact.
In the Prime Big Deal Days post, Boychuk contrasts an order placed on an Amazon listing, where the buyer remains Amazon’s customer, with a shopper who joins the brand’s own email or SMS list. The claim is blunt: a discount hunter who joins your list is worth more than one who buys once on Amazon.
The same owned-versus-rented line runs through all three posts: first marketplace sales, then AI-assisted checkout. In both cases, Boychuk says the brands that win will be those with an email list, a phone number and a customer who chose them.
Don’t out-discount a platform sale; run counter-programming you control
When Amazon, Walmart and Target run overlapping sales, Boychuk argues that competing on price is the losing move for a DTC brand: it cannot match the discounts and should not try.
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Instead, brands should offer something a marketplace cannot copy: exclusivity for their own subscribers.
The surge of shoppers in a buying mood is a chance to convert them into owned contacts, not a cue for a price war.
The post pairs these offers with signup capture, urging brands to show every visitor in research mode their best email and SMS signup.
Amazon’s fall sale runs 48 hours across more than 35 categories, while Walmart and Target run competing sales. Boychuk proposes a list-only sale for email and SMS subscribers, a free gift with purchase instead of 20% off, and early access to a holiday drop for subscribers only — plus signup capture for shoppers who arrive just to research.
Platform features keep taking over the moments retention flows were built to own
Boychuk reads each new platform capability as a claim on a high-intent moment.
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Google’s AI Mode monitoring alerts, opened to everyone on September 28, let shoppers ask to be notified when a product is back in stock or drops in price.
Google watches sites, forums, social posts and its Shopping Graph to find it.
The key point: Google does not say which site the alert points to, so the click can go to a competitor.
A brand with a back-in-stock flow captures the shopper’s email when the item is out of stock and sends a message when it returns.
Without that flow, Boychuk argues, the brand hands its highest-intent moment to an algorithm that owes it nothing.
Boychuk contrasts a Klaviyo back-in-stock flow — email captured when the item sells out, message sent as soon as inventory returns — with a shopper who waits three weeks for Google’s alert and then clicks wherever Google sends them. The post leaves the net effect open, asking whether the Google alert threatens the list or brings free demand the brand would never have captured anyway.
The pattern repeats: Google’s alert contests the restock moment, while AI checkout contests the purchase itself. Boychuk applies the same diagnosis to both: an outside system now owns a step the brand’s retention program was supposed to own.
When an agent does the buying, trust replaces attribution as the signal
Boychuk argues that the long debate over whether email or ads drove a sale becomes moot when an AI agent completes checkout.
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There is no browsing session, clicked email or pixel, so there is no click trail for Klaviyo, Meta and the CFO to dispute.
What remains is a question the brand can still answer: did the customer already trust it enough to buy directly?
For Boychuk, first-party relationships become both the measurement and the advantage.
They also note a limit to the shift: Amazon blocked Meta’s Muse, so agentic checkout varies across platforms rather than universally replacing current buying paths.
Boychuk cites Shopify’s Tobi Lütke announcing that Meta’s Muse AI can check out directly with Shop Pay, while Amazon blocks Muse outright. Their illustration of the old fight: Klaviyo says the email drove the sale, Meta says the ad did, and the CFO believes neither of you.
Answers a newcomer's first question about AI shopping: if an AI agent buys for the customer, what happens to email, ads, and attribution?
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It clearly introduces Boychuk's argument for owned channels.
Boychuk starts with Shopify's Tobi Lütke announcement that Meta's Muse AI can check out directly with Shop Pay, while Amazon blocks Muse.
The order can arrive without a browsing session, clicked email, or pixel.
They argue this ends the old attribution fight—Klaviyo credits email, Meta credits the ad, and the CFO trusts neither—because there is no click trail left to dispute.
In this telling, the brands that win have a direct relationship: an email list, a phone number, and a customer who already chose them.
For brands planning a campaign
Use this as a talk or training opener for a DTC or Shopify agency audience. It frames retention as an infrastructure question—list capture, first-party data, and trust—not a channel tactic, and gives the agency a reason to audit owned-channel readiness before agentic checkout changes the measurement picture.
Shows a specific threat to a standard retention flow and explains why the out-of-stock moment is worth owning.
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Newcomers can see how Boychuk connects platform changes to email and SMS practice.
Boychuk describes Google opening AI Mode monitoring alerts to everyone on September 28.
Shoppers can ask for alerts when a product is back in stock or drops in price, while Google monitors sites, forums, social posts, and its Shopping Graph.
Google does not say which site an alert points to.
Boychuk contrasts this with a Klaviyo back-in-stock flow that captures the shopper's email when an item is out of stock and sends an email when it returns.
Without that flow, they argue, the brand outsources its highest-intent moment to an algorithm.
For brands planning a campaign
A training module can map high-intent capture points and test back-in-stock email and SMS flows against platform alerts. For an agency pitching retention work, it shows why owned flows matter even when discovery happens elsewhere.
Offers a practical playbook for a common DTC problem: what to do when a marketplace event pulls shoppers to Amazon.
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It is the most tactical of the three posts.
Boychuk notes Amazon's fall sale runs for 48 hours across 35+ categories, while Walmart and Target run competing sales.
The trap: buying on an Amazon listing gives the brand the order but Amazon the customer, data, and repeat purchase—rented revenue.
They recommend against out-discounting Amazon.
Instead, the examples are a list-only sale for email and SMS subscribers, a free gift with purchase instead of 20% off, and early access to a holiday drop for subscribers.
They also argue for capture: every visitor during research mode should see the best email and SMS signup offer, because a discount hunter who joins the list is worth more than one who buys once on Amazon.
For brands planning a campaign
This supports a workshop exercise in calendar counter-programming: choose a marketplace event, design an owned-channel offer that does not depend on matching Amazon's discount, and define the capture step. It also gives the agency a measurable retention KPI to pitch: list growth during competitor sale windows.
Owned-channel retention over rented marketplace revenue2
Counter-programming during marketplace sales events1
Agentic commerce and the end of click-based attribution1
Back-in-stock alerts as a contested retention moment1
Owned-channel retention over rented marketplace revenue
Andriy Boychuk frames Amazon and other marketplaces as rented revenue: the order arrives, but the marketplace keeps the customer, data, and repeat purchase.
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The alternative is a direct relationship through email, SMS, and a brand-owned list. In the Prime Big Deal Days post, the advice is to avoid trying to out-discount Amazon and instead run counter-programming on owned channels.
Counter-programming on owned channels: list-only sales, free gifts with purchase instead of discounts, early access for subscribers, and email/SMS capture for research-mode shoppers.
For brands planning a campaign
A DTC agency evaluating Andriy Boychuk for a talk or training should ask for a session on list growth and retention flows that reduce marketplace dependency, with concrete examples of owned-channel offers and capture tactics.
Counter-programming during marketplace sales events
During Amazon Prime Big Deal Days, with Walmart and Target running competing sales, Andriy Boychuk warns that DTC brands lose the customer if buying energy lands on an Amazon listing.
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The proposed response is not deeper discounting but a list-only sale for email and SMS subscribers, a free gift with purchase, or early access to a holiday drop.
For brands planning a campaign
A training brief can focus on building a sale-event playbook for owned channels: what to offer subscribers, how to capture visitors during research mode, and how to measure repeat purchases rather than a single marketplace order.
Agentic commerce and the end of click-based attribution
Andriy Boychuk describes a shift where Meta's Muse AI can check out directly with Shop Pay, leaving no browsing session, clicked email, or pixel.
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Amazon blocked Muse. They argue that attribution fights between email, ads, and finance become less useful when no click trail exists, and that the remaining signal is whether the customer already trusts the brand.
Amazon blocked Muse, so the post presents agentic checkout as an uneven shift, not a universal replacement for current commerce paths.
For brands planning a campaign
For an agency brief, this supports a session on preparing retention programs for agentic checkout: first-party data, direct customer relationships, and trust signals that survive when an AI agent places the order.
Back-in-stock alerts as a contested retention moment
Andriy Boychuk points to Google AI Mode monitoring alerts, opened to everyone on September 28, which let shoppers ask to be told when a product is back in stock or drops in price.
Read more
Google does not say which site the alert points to. A Klaviyo back-in-stock flow captures the customer's email when an item is out of stock and sends an email when it returns; without that flow, the brand outsources its highest-intent moment to an algorithm.
Capture the shopper's email when an item is out of stock, then send a back-in-stock email when inventory returns.
For brands planning a campaign
A retention training should include an audit of back-in-stock and price-drop flows, capture at the out-of-stock moment, and measurement of whether owned alerts still win the click.
The posts use a short breakdown format: one retention or ecommerce development, a clear risk or opportunity, a practical response, and a question inviting comments.
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Two posts ask readers to follow for daily retention breakdowns or one retention breakdown every weekday, and all three use hashtags such as #EmailMarketing, #DTC, and #RetentionMarketing.
For brands planning a campaign
This cadence and format can shape a LinkedIn collaboration or training series: short, repeatable breakdowns that end with a direct question and a practical next step.
Andriy Boychuk publishes short LinkedIn posts about ecommerce retention, each built around one platform development and what it means for a brand that wants to own its customer list rather than rent it from a marketplace.
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Start with the posts on Amazon's fall sale and Google's back-in-stock alerts, which apply the same argument to a discount event and a single high-intent moment, then read the post on Meta's Muse checkout with Shop Pay for the most forward-looking version.
The recurring point is that an order through Amazon, Google's alert, or an AI agent gives the brand the sale while someone else gets the customer, the data, and the repeat purchase.
The alternative they return to is an email list or phone number the brand can contact directly.
For adjacent perspectives on the same retention problems, see Bogdan Birtom's Klaviyo feature and migration work, Vira Sadlak's flow preparation and long-game email positioning, and Ashley Wright's channel-attribution and TikTok Shop expansion.
Both write for the same audience about email and SMS retention, lean on Klaviyo flows as the working unit, and treat the subscriber list as something to build over time rather than mine for one campaign.
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Sadlak argues that email is a relationship channel that builds predictable revenue rather than a sales channel that burns out a list.
Boychuk makes the same foundational claim when calling marketplace sales rented revenue.
Sadlak's holiday episode covers price-drop and back-in-stock flows and Klaviyo content blocks—the same flow types Boychuk discusses when Google's alerts contest the restock moment.
Sadlak works closer to the seasonal operating calendar, with checklist-style preparation for BFCM and a steady emphasis on balancing promotional and educational content. Boychuk takes a more strategic, reactive view of platform news: they read each new marketplace, search, or AI-checkout capability as a claim on a moment a retention flow used to own, and focus on who owns the customer rather than how to structure the next send.
Birtom also works in Klaviyo-based retention for ecommerce brands and writes practical breakdowns for the same practitioner audience, covering subscriptions, transactional email, segmentation, deliverability, and platform releases.
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Their post on moving Recharge subscription flows into Klaviyo and unifying order, subscription, and engagement data in one system shares Boychuk's premise that consolidation behind a direct customer view makes retention work.
Birtom goes deeper into implementation and tooling, with feature-by-feature reads of Klaviyo livestreams, migrations between platforms, and open questions about what new features will cost past beta.
Boychuk stays at the store-owner strategy level, using platform news to argue about list ownership against marketplaces and AI checkout.
Ask for hands-on migration or configuration examples if those details matter to your decision.
Wright covers the same DTC ecommerce retention ground and asks the same skeptical question about platform claims.
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Their post on every tool taking credit for the same sale, and on one customer appearing as several customers across systems, closely echoes Boychuk's argument that attribution fights are losing their point. Both also track how discovery channels feed the rest of a brand's stack.
Wright concentrates on channel performance and attribution mechanics, with TikTok Shop as a growing revenue share and a sponsored platform launch as the hook for their analysis. Boychuk's focus is narrower and more defensive: what a brand still controls when a marketplace, a search alert, or an AI agent sits between the brand and the buyer, and how to capture the customer at the moment that handoff happens.
Andriy Boychuk’s LinkedIn presence reads as a working feed for DTC retention operators rather than a mass-audience channel.
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The profile had 8,123 followers on 8 October 2026.
The three posts here offer practical briefings for people who run email, SMS and lifecycle programs: how Prime Big Deal Days shifts buying activity to Amazon and what brands should do instead on channels they own; what Google’s AI Mode back-in-stock alerts mean for a Klaviyo-style restock flow; and how agentic checkout affects attribution and last-click models.
Buyers hiring for retention expertise can quickly see the subject fit.
The posts target brand-side retention and lifecycle marketers, as well as the agencies serving them.
Each closes with a question for commenters and an offer of daily breakdowns, signaling an effort to build a practitioner following.
The strongest fit for a campaign aimed at an ecommerce or Shopify agency is topical: owned-channel retention, email and SMS flows, and agentic commerce.
Before booking a talk or training, ask for recent post-level results on comparable topics and formats, including reach and engagement for the retention and agentic-commerce posts and results from sponsored placements at the same depth.
Ask whether audience survey results are available for job role, seniority and company type.
LinkedIn followers
8,123
Follower count recorded on the LinkedIn profile at the time of capture · 2026-10-08
Posts in sample
3 posts, dated 4, 6 and 7 October 2026
Publication dates attached to the three LinkedIn posts · 2026-10-07
Posting cadence in posts here
3 posts over 4 days (roughly daily)
Publication dates attached to the three LinkedIn posts · 2026-10-07
Andriy Boychuk’s posts offer retention-marketing breakdowns for DTC and ecommerce operators: a Prime Big Deal Days counter-programming post, a comparison of Google’s AI Mode back-in-stock alerts with a Klaviyo restock flow, and a post on agentic checkout through Shop Pay and Meta’s Muse AI.
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Each ends with an invitation to comment and follow for more breakdowns.
The posts don’t disclose a sponsor policy or sponsored placement, and they link to no official inquiry page.
Brands considering a talk, training, or sponsored series should ask directly about availability, pricing, and delivery.
A DTC ecommerce or Shopify agency wants to hire an email and retention marketing expert for a talk or training on owned-channel retention and agentic commerce.
Reasons
The posts match a campaign about agentic checkout. Boychuk explains agentic checkout through Meta's Muse AI and Shop Pay, notes Amazon blocked Muse, and argues that when an AI agent places the order, there is no browsing session, clicked email, or pixel left to attribute. The practical conclusion: email, SMS, first-party data, and a direct customer relationship become more important, not less.
The Prime Big Deal Days post offers a tactical retention playbook for a marketplace sale event: do not try to out-discount Amazon; run a list-only sale for email and SMS subscribers; offer a free gift with purchase instead of a discount; give subscribers early access; and capture email or SMS from research-mode visitors. An agency can turn this concrete material into a training session.
The Google AI Mode back-in-stock post identifies a specific retention risk. Google alerts shoppers when an item is back in stock or drops in price, but does not say which site the alert points to. Boychuk contrasts that with a Klaviyo-style flow that captures the shopper's email when an item is out of stock and sends an email when it returns. It turns a platform change into a practical flow audit.
Andriy Boychuk has 8,123 LinkedIn followers as of 8 October 2026 and posts roughly daily. The posts target brand-side retention and lifecycle marketers and the agencies serving them—the right audience for a campaign aimed at that group.
The partnership section lists no sponsorship offer, rate card, media kit, or booking route. The editorial format is a useful signal for a talk or training: short retention breakdowns, a practical response, and a question inviting comments. Ask directly about terms.
Caveats
Andriy Boychuk's posts do not state engagement figures, follower demographics, or sponsored-delivery results. Before booking, ask for recent post-level reach and engagement on the retention and agentic-commerce posts, plus audience survey data on job role, seniority, and company type.
The three LinkedIn posts show topical fit and a teaching format, but not a track record delivering talks or training. Ask for a past session recording, outline, or client reference.
When to choose a specialist
Poor fit as written.
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Brief
A marketplace-first growth agency or Amazon seller conference wants a speaker to teach brands how to win Prime Day and marketplace sales by out-discounting competitors, scaling marketplace ad spend, and treating Amazon as the primary customer relationship.
Reasons
The posts argue against a campaign to win inside marketplaces. Boychuk describes marketplace sales as rented revenue: the brand gets the order, while Amazon gets the customer, the data, and the repeat purchase.
The Prime Big Deal Days post explicitly says brands cannot and should not try to out-discount Amazon. It recommends counter-programming on owned channels, not a deeper marketplace discount strategy.
The posts do not cover marketplace ad optimization, Amazon listing tactics, sponsored product campaigns, or discount-war strategy. Ask for other relevant work before booking a session on those subjects.
No published commercial format for sponsored marketplace content appears in Andriy Boychuk's posts. Ask directly for a rate card, media kit, or sponsorship offer.
Alternative
If the campaign shifts from winning inside Amazon to owning the customer during marketplace events, these posts are relevant. The agency could ask for a session on counter-programming Prime Day, capturing demand on owned channels, and defending the back-in-stock moment against platform alerts. For a marketplace-tactics campaign, choose a creator whose posts cover marketplace advertising and listing strategy.
A worked campaign example
Audience
Agency strategists, retention leads, and lifecycle marketers serving DTC and Shopify brands.
Close
End with a client-ready next-step brief: an owned-channel retention audit, one counter-programming test, one back-in-stock flow repair, and a simple measure of list growth and repeat purchase.
Format
A 60 to 90 minute workshop or a three-part training series. Each module uses one of Andriy Boychuk's posts as its spine, then runs a short exercise and gives the agency a client-ready output.
Module one
Exercise
Map one upcoming marketplace sale event. Identify where buying energy leaks to Amazon, Walmart, or Target. Design one counter-programming offer that does not match the marketplace discount. Define how to capture visitors who arrive in research mode.
Name
Owned-channel retention versus rented marketplace revenue
Output
A one-page owned-channel counter-programming plan for a client sale event.
Source post
Prime Big Deal Days counter-programming
Teaching point
During a marketplace sale event, buying energy can land on Amazon while the brand keeps only the order. The response is not a deeper discount: run a list-only sale, offer a free gift with purchase, give subscribers early access, and capture email or SMS from research-mode visitors.
Module three
Exercise
Map the first-party data and trust signals that survive an AI-agent checkout. Draft a retention readiness checklist covering list growth, phone numbers, consent, the direct customer relationship, and post-purchase experience. Discuss Amazon blocking Muse as an uneven shift, not a finished replacement for current commerce paths.
Name
Prepare for agentic commerce
Output
A retention readiness checklist and a 30-day test plan for one client.
Source post
Agentic checkout and attribution
Teaching point
When an AI agent buys for the customer, the old attribution argument among email, ads, and finance loses its click trail. The remaining signal is whether the customer already trusts the brand.
Module two
Exercise
Audit a client’s back-in-stock and price-drop flow. Mark where email or SMS capture happens when an item is out of stock. Write a back-in-stock message that doesn’t depend on a platform alert. Choose two measures: capture rate when an item is out of stock and conversion from the owned alert.
Name
Defend the high-intent back-in-stock moment
Output
A back-in-stock flow audit and a draft owned-alert message.
Source post
Google AI Mode back-in-stock alerts
Teaching point
Google can alert a shopper when an item is back in stock or drops in price, but doesn’t say which site gets the click. A Klaviyo-style flow captures the shopper’s email when the item is out of stock and sends an alert when it’s available. Without that flow, the brand outsources its highest-intent moment to an algorithm.
Opening
Open with agentic checkout in ten minutes. Use the agentic commerce post: Meta’s Muse AI can check out directly with Shop Pay, Amazon blocked Muse, and the order can arrive with no browsing session, clicked email, or pixel. Ask the room what attribution means in that world and what signal remains. The post’s answer is a direct relationship: an email list, a phone number, and a customer who already chose the brand.
Title
Own the customer before the AI buys: a retention training for DTC and Shopify agencies
Why this works
The three posts supply the argument, examples, and exercises, and the format matches the practitioner audience described here. Andriy Boychuk has no published offer; ask directly about scope and rates.
Sadlak is the stronger alternative when the agency needs holiday and BFCM flow training rather than an argument for agentic commerce.
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The BFCM post covers reviewing every discount-code flow before the sale, aligning campaigns and flows, using Klaviyo universal content blocks during the rush, building price-drop and back-in-stock flows, and running post-BFCM retention promotions, with brand stories and examples.
The long-game post adds a lifecycle framework: balance promotional and educational content, respect customer lifecycle stages and nurture trust before asking for another purchase.
Choose Sadlak for a practical workshop on auditing and preparing email and SMS flows for a major sale, and moving clients from campaign spikes to relationship revenue.
The posts focus less on AI-agent checkout, attribution after agentic commerce or marketplace counter-programming.
They assume the agency wants to improve an existing email program rather than rethink who owns the customer when an AI buys.
One BFCM post promotes a podcast and names Andriy Boychuk as co-host, so treat it as a collaboration, not a solo methodology.
The posts do not state Sadlak's audience size, engagement or delivery record; ask for those figures before comparing reach.
Birtom is the better alternative when the agency needs platform-level Klaviyo implementation training, especially on subscriptions and migrations.
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The Recharge-to-Klaviyo post details what changes when subscription flows move into Klaviyo: cohesive branding across transactional and marketing email, a unified customer view, segmentation triggers such as billing reminders for loyal subscribers or anniversary upsells, transactional deliverability, A/B testing in renewal reminders and consolidated reporting.
The Klaviyo feature breakdown covers Custom Objects, channel preferences and engagement ranking, SMS automatic conversations, an omnichannel campaign builder, Customer Hub and analytics recommendations.
It reads the pattern as Klaviyo expanding into a B2C CRM.
Choose Birtom for a session on evaluating Klaviyo releases, migrating subscription flows and using customer data to segment retention messages.
The posts focus on platform and operations.
They do not make the owned-channel-versus-rented-revenue case for agentic commerce or cover marketplace counter-programming or TikTok Shop.
The feature breakdown is candid about beta and paid features, but provides no training outline or client results.
If the agency wants a strategic talk on why retention matters when AI agents place orders, Boychuk's posts are the more direct match.
Wright is the stronger fit when the agency prioritizes social commerce and cross-channel attribution over email retention mechanics.
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The Postscript post explains a one-customer-one-profile problem: one brand found that a customer had signed up under three emails and paid three times.
It describes attribution models with custom lookback windows so brands can see how credit moves, and shows how conversational context from SMS can carry into a back-in-stock message.
The TikTok Shop post describes a shift from evaluating TikTok Shop to scaling it across Meta ads, Amazon, DTC, retail and retailer conversations such as Walmart, Target, Boots or Superdrug.
Choose Wright for training on TikTok Shop growth, creator content as ad input, retail conversations, or choosing an email and SMS platform based on attribution and identity.
The Postscript post is a paid partnership and includes a commercial link, so separate the product pitch from the teaching points.
The posts do not cover email flow audits, BFCM preparation or the agentic-commerce attribution argument as Boychuk's posts do.
Use Wright for social-commerce-led attribution or platform-selection content; use Boychuk for owned-channel retention and agentic commerce.
