Jon Ivanco shares advice on running and growing direct-to-consumer ecommerce businesses, with a focus on customer data, popup strategy, and connecting tools through automation. Jon also says they are returning to brand consulting.
Jon Ivanco is most useful for practical DTC ecommerce advice on customer data, popups, and tool automation.
Best for
A DTC brand wanting signup data and willing to change popup and welcome discount flows.
Not for
A brand seeking sponsorship, fixed deliverables, large reach, or published rate card.
Key takeaways
A popup should collect customer data, not duplicate checkout details.
List quality matters more than opt-in rate.
Tools should be judged as data layers and APIs, not feature lists.
Check first: Confirm scope, price, capacity, and whether they have supporting data for performance claims.
Main ideas
A popup should collect customer data, not duplicate checkout details · List quality matters more than opt-in rate · Many ecommerce promises do not survive basic statistics
Jon Ivanco shares advice on running and growing direct-to-consumer ecommerce businesses.
The posts reviewed focus on customer data, popup strategy, and connecting ecommerce tools through automation; they also show them returning to brand consulting.
Follow them for practical examples of how signup flows, discounts, and customer data can support decisions beyond growing an email list.
Jon’s distinctive angle is to treat customer signups as a source of useful business data, not just a way to grow a list.
In a Shopify setup post, they explain how to restrict a welcome discount to people who have never purchased, then connect it to a popup that asks multiple questions and attaches responses to a first purchase.
A separate post argues that brands should judge signups by whether they lead to purchases, not by opt-in rate alone.
They extend this systems view to software: tools become more useful when their data and integrations improve one another.
The posts also show a broader operator perspective, including using product reviews, presales, and slow-stock sales to learn about a business.
These examples make the work useful to readers weighing data collection and ecommerce operations.
Brands evaluating the performance claims should ask for supporting data.
Jon writes about running direct-to-consumer ecommerce brands and argues that customer data should be collected at signup and used to improve audience targeting, messaging, and repeat purchase.
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They are skeptical of opt-in rate and list size as targets, view software as a data layer judged by its API and integrations, and describe an automation-led model for running an eight-figure brand with a very small team. They also say they are returning to consulting.
A popup should collect customer data, not duplicate checkout details
Jon argues that a standard welcome popup usually asks for an email or phone number the store already collects at checkout, giving away margin without teaching the brand anything new.
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Instead, the popup should work as a survey, capturing several data points about who is buying and how likely they are to buy again. They see this as especially important for stores spending on paid traffic, because the data can improve messaging and audience quality.
In one post, Jon tells Shopify stores spending more than $200 a month on Meta ads to install Formtoro and ask multiple logic-mapped questions during signup.
The setup: change the welcome discount audience to “never purchased,” switch the popup to Formtoro, and reroute people who drop off mid-form to a thank-you step with the code auto-applied.
That limits use of the code to popup signups or opted-in abandoned-checkout users.
They say 92% of people will give five data points when asked correctly, and that 75–80% of a list can be deadweight without data to identify likely repeat buyers.
Jon argues that opt-in rate and list size are misleading targets.
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A large list of people who are not in the market to buy is close to worthless, while high-intent signups who convert make up a smaller group than casual browsers. The alternative is to collect data that identifies likely purchasers and use that insight in top-of-funnel ads to attract a better-quality audience.
In a dialogue post, brands ask for a higher popup subscription rate.
Jon replies that they should drive less traffic and reduce paid-media dependence if they want higher intent, or drive more traffic if they want a bigger list.
But high-intent signups and people actively looking to purchase are inversely related.
They also say that after 45 days, offering the product free to email subscribers produced less than 1% take-up, and those people did not return.
Many ecommerce promises do not survive basic statistics
Jon argues that many marketers lack a working grasp of statistics and systems, allowing agencies and vendors to promise impossible outcomes.
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Their reasoning centers on diminishing returns at scale, a limited pool of in-market buyers at any moment, and competition in ad auctions. Some published case-study numbers, they say, are impossible without a reporting error.
Jon says ad returns diminish as spend scales because the audience is limited at any given time and advertisers compete in an auction.
They have also seen statistics over the years that are impossible without a reporting error, yet still get published as case studies.
Short-term wins, they argue, get more attention than sustainable long-term gains.
Tools should be judged as data layers and APIs, not feature lists
Jon describes Formtoro as a data layer for automation, saying people cannot see past it as a popup tool.
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They argue that ecommerce tool buying is organized around isolated tasks—list growth, creatives, repeat buyers—while automation comes from tools making one another more useful. Once the platform interface is removed, the real comparison is between architectures and what the API allows a brand to build.
Jon writes that tools should improve functionality across other tools, not just serve one use case.
They call this a depth-of-data game that the industry does not yet understand.
In a later post, they argue that a tool’s quality will depend on its API rather than its in-platform features, and that brands will not need to interface with tools once they are set up.
An automation-led operating model for small DTC teams
Jon says they expect to automate most ecommerce brands within six months using tools, AI, and strategy.
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The goal is a brand scaled to roughly $8–12 million a year with one or two people, with product launches de-risked and sell-through and profitability improved. They also say physical products cannot be replaced by AI, while everything else can.
Jon describes self-learning systems and iterative improvement as the important shift, adding that operators will not interface with tools once they are set up. In the same post, they announce a return to consulting after a few years away, with only a few slots and DMs open.
Jon cites an estimate that 80–90% of ecommerce stores fail within 24 months and lists practices they did and would repeat.
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The focus is cash, proof, and repeat purchase rather than awareness: collect data from day one and build a list in exchange for a discount; gather reviews for social proof; pre-sell new drops to cover costs; flash-sell slow-moving products to the email list; and make products that start conversations or can carry a logo.
Jon says flash-selling slow-moving products to the email list has also provided useful product insight, and that the brand plans to pre-sell new product drops once its audience is large enough. They say getting these practices right makes a business operationally sound.
In-person sampling as a cheaper alternative to paid media
Jon suggests most DTC companies would be more effective giving their product away in person and talking to people.
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Getting people to use it on the spot in exchange for a video would cost less than paid media and produce footage for future ads. This echoes their point that paid traffic varies greatly in quality.
Jon writes that bonus points go to getting people to use the product on the spot in exchange for a video—a cheaper route than paid media that also generates usable footage. They separately note that in ecommerce, where so much traffic comes from paid media, traffic quality can vary considerably.
Explains what a popup is for and why brands should ask different questions at signup.
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Jon Ivanco argues that a standard Shopify popup collects email addresses or phone numbers a brand already gets at checkout, trading margin for data it already has.
The post says brands spending more than $200 a month on Meta ads should use signup to collect multiple data points, and claims 92% of people will provide five if asked correctly.
They also say 75-80% of a list can be deadweight without data to identify likely repeat buyers.
Their suggestions include moving collapsed popup teasers to buttons under the add-to-cart button on product pages and surveying before purchase rather than after.
For brands planning a campaign
For a DTC brand brief on first-party data, this post frames popups as a data system tied to ad spend, not just a list-growth tactic. Test a multi-question signup flow and measure data completeness, predicted repeat purchase, and ad-audience quality—not just opt-in rate.
Shows how to connect a discount, a segment, and a popup, so newcomers can see the setup in practice.
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Jon gives a concrete sequence: open Shopify, go to discounts, find the welcome discount, and update the audience to never purchased.
Next, create an optional segment for people who opted into marketing and never purchased.
Then switch the popup to Formtoro, ask multiple logic-mapped questions during signup, and reroute drop-offs to the thank-you step with the code automatically applied.
The stated result: only popup signups or abandoned-checkout users who opted in can use the code, so most first-time purchases arrive with data attached.
For brands planning a campaign
This is the most directly executable post in the set. Use it to build a popup-to-purchase data system, then measure how many first-time orders include usable signup data and whether the restricted code changes conversion.
Explains why brands should care who signs up, not just how many, and lays out the logic behind a quality-over-volume test.
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In a dialogue format, brands ask for a higher popup subscription rate.
Jon says they should drive less traffic and reduce paid-media dependence—or drive more traffic if they want a bigger list—but notes that high-intent signups and conversions are inversely related to people actively looking to purchase.
A large list of people not in the market to buy is worthless.
They argue that brands should collect data to identify likely purchasers and use it in top-of-funnel ads to attract a higher-quality audience.
The post also criticizes marketers’ weak grasp of logic and statistics, as well as published case studies that Jon says are impossible without reporting errors.
For brands planning a campaign
This post makes the case for measuring list quality, predicted purchase, and downstream conversion instead of opt-in rate. Define a quality metric and compare it by traffic source; don’t assume every funnel stage will grow at once.
Defines the product category and sets out the strategic thinking behind the more tactical popup posts.
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Jon says Formtoro has always been a data layer for automation, though people see it only as a popup tool.
They describe ecommerce tool buying as linear and task-oriented, with separate tools for list growth, creatives, repeat buyers, and other individual problems.
In this view, automation comes from how tools integrate and improve one another’s usefulness, not from a single feature.
They call it a depth-of-data game and say the industry does not understand it yet.
For brands planning a campaign
For a technology or services campaign, lead with integration and data architecture, not popup features. One useful angle is task-level buying versus systems-level buying, with Formtoro as an example of a tool whose value depends on how it connects to the rest of the stack.
Explains what Jon is selling now, following the product and data posts, and connects the automation thesis to a concrete consulting offer.
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Jon predicts that within six months they will be able to automate most ecommerce brands using tools, artificial intelligence, and strategy.
The goal is a brand scaled to $8-$12 million per year with one or two people, de-risked product launches, sell-through, and profitability.
They say the key shift is toward self-learning systems and iterative improvement, and that a tool’s quality will be determined by its API rather than its in-platform features.
They also say physical products cannot be replaced by AI, while everything else will be, and announce a return to consulting after a few years away, with only a few slots and DMs open.
For brands planning a campaign
Jon is offering consulting again, focused on automation architecture and limited capacity. A campaign presenting them as an expert should emphasize systems and APIs, not frame consulting as a software feature or imply unlimited availability.
Offers a concise set of practices and shows the range of Jon’s ecommerce advice beyond popups.
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Jon cites an estimate that 80-90% of ecommerce stores fail within 24 months, then lists what they did and would do again: collect data from day one and build a list in exchange for a discount; collect reviews for social proof; pre-sell new product drops to cover costs; flash-sell slow-moving products to the email list for product insight; and make products that start conversations and can carry a logo. They say these practices make a business operationally sound.
For brands planning a campaign
Use this as a broad operator starting point.
For a popup and first-party-data brief, the points about collecting data from day one and using flash sales for insight connect directly to the more detailed posts.
The full list can frame a campaign around operational discipline rather than a single tactic.
Popups as a customer-data system, not an email-capture device3
List quality over opt-in rate2
Skepticism about promised ecommerce results2
Tools as a data layer, judged by their API2
In-person sampling as an alternative to paid media2
An operating model for running a DTC brand with one or two people1
A survival checklist for early-stage stores1
Popups as a customer-data system, not an email-capture device
Jon Ivanco argues that standard welcome popups collect emails and phone numbers the store already receives at checkout, teaching the brand nothing new while giving away margin.
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Instead, the popup should survey customers to capture several data points about who is buying and how likely they are to buy again. They say that with this setup, most people will answer multiple questions rather than just one.
Ask several logic-mapped questions during signup. Route people who drop off mid-form to the thank-you step with the discount code applied automatically, so only people who signed up through the popup or opted in at abandoned checkout can use it.
For brands planning a campaign
A brand briefing Jon on first-party data should expect them to position the popup as the start of a measurement chain, not a list-building widget. Be ready to define which customer attributes to collect and how to use them in messaging and segmentation.
Jon Ivanco treats signup rate as a misleading headline number and argues that a large list of people who are not in the market to buy is close to worthless.
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High-intent people who sign up are a smaller group than casual browsers, so pursuing both a large list and a high signup rate works against itself. Instead, they advocate collecting data to identify customers more likely to purchase, then feeding that insight into top-of-funnel ads to attract a better-quality audience.
Jon's posts state that people who gave an email but never bought did not return, even when offered the product free after 45 days. That experience informs their view that non-buying subscribers have little value.
For brands planning a campaign
A brand should not brief Jon around hitting an opt-in-rate target. The measurable outcome is the share of new first-time buyers who arrive with usable data attached, and the downstream effect on repeat purchase and creative quality.
A recurring argument in the posts is that many marketers lack a working grasp of statistics and systems, allowing agencies and vendors to promise impossible outcomes.
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Jon points to diminishing returns in ad auctions, a limited pool of in-market buyers at any moment, and published case-study numbers they consider impossible without a reporting error.
For brands planning a campaign
Brands evaluating Jon as a consultant should expect to defend the assumptions behind any forecast. Plain-language reasoning about audience size, auction dynamics, and measurement error is part of the working relationship, not a challenge to it.
Jon Ivanco describes Formtoro as a data layer for automation.
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Buyers struggle to see past its role as a popup tool because ecommerce software selection is organized around isolated tasks.
Automation, they argue, comes from tools improving each other's usefulness, not from any single platform's feature list.
Once the platform interface is set aside, the real comparison is between architectures and what the API allows a brand to build.
For brands planning a campaign
For a brand choosing software alongside advice, Jon's framing favors tools that expose their data and can connect to other systems over tools that win on in-platform features. The brand also needs someone who can design the integration, not just operate the interface.
An operating model for running a DTC brand with one or two people
Jon Ivanco says they expect to automate most ecommerce brands within six months using a combination of tools, AI, and strategy, with a model built to scale to roughly $8–12 million a year with one or two people.
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The stated goals are to de-risk product launches and improve sell-through and profitability, with tools running as self-learning systems that the operator does not have to interface with once set up. They also state that physical products cannot be replaced by AI while everything else can.
For brands planning a campaign
A brand weighing this approach should treat it as an operations and systems engagement rather than a content or media one. Ask which parts of the current stack would be replaced, which would be kept for their API, and how success would be measured on launches, sell-through, and margin.
In one post, Jon Ivanco lists five practices for avoiding the high failure rate among ecommerce stores: collect data from day one and build the list in exchange for a discount; gather reviews for every product until it has a minimum amount of social proof; pre-sell new drops to cover costs upfront once the audience is large enough; flash-sale slow-moving products to the email list, which they say has also produced useful product insight; and make products that start conversations or can carry a logo.
Read more
For brands planning a campaign
These five items form a checklist brands can compare against their own plans, showing which levers Jon would pull first. Brands with an awareness-led rather than operations-led brief should note the focus on cash, proof, and repeat purchase.
In-person sampling as an alternative to paid media
Jon Ivanco suggests most DTC companies would be more effective giving their product away in person and talking to people.
Read more
Having people use it on the spot in exchange for a video, they argue, would be cheaper than paid media while producing footage for paid media. They also note that traffic bought through paid channels varies greatly in quality, which is why they emphasize which audience an ad reaches.
Ask Jon for examples of brands where they have used this in-person approach and how they measured the results.
For brands planning a campaign
A brand considering this idea should treat it as a testable alternative channel with built-in creative output. Measure the quality of the people reached, not the volume of impressions bought.
Jon Ivanco's posts point to one commercial product relationship: Formtoro, a popup and customer-data tool they tell Shopify merchants to install and describe as something “we build.”
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They also write as an ecommerce brand operator and say they are returning to brand consulting, but name neither business. Shopify and Meta appear as platforms their advice runs on, not as disclosed partners, and are listed as mentions.
Formtoro
Disclosed partnership
Posts tell Shopify store owners who spend more than $200 a month testing Meta ads to stop and install Formtoro, arguing that its multi-question popup forms collect data a standard email popup does not.
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A separate post calls Formtoro “a data layer for automation” and says “when we build tools we're not building for one use case,” placing Jon on the product-building side. Ask whether they are an owner, employee or partner.
Ivanco’s posts make one running argument about how a direct-to-consumer brand should collect and use customer data; the popup material is the fastest way in.
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Start with the post telling Shopify stores that spend more than $200 a month on Meta ads to stop and rethink their signup form.
Then read the step-by-step welcome-discount setup: update the audience to "never purchased," ask several logic-mapped questions, and reroute drop-offs to a thank-you step with the code auto-applied.
Next, read the post that reframes the popup as a data layer for automation rather than a list-building widget.
From there, follow two threads.
One is measurement: a dialogue post on why opt-in rate and list size pull in opposite directions, and a post arguing that many published ecommerce results do not survive basic statistics.
The other is operating model: a five-item survival checklist for early-stage stores, an in-person sampling idea pitched as a cheaper alternative to paid media, and the post describing an automation-led brand run at roughly $8–12 million a year by one or two people.
That post is also where they say they are returning to consulting.
Read together, the posts show a consistent preference for collecting data before spending more, and for judging tools by what their data and APIs let a brand build.
Both work in retention marketing from an operator’s perspective and see signup as the moment when a brand either gains useful customer information or wastes paid traffic.
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Schieffer’s OLIPOP study counts 182 emails and maps how a brand splits offers from non-offers. Their popup post walks through a five-step structure and warns that a bad popup wastes money on traffic the brand has already bought—the same premise behind Ivanco’s popup-to-purchase argument.
Zach Schieffer reasons mostly from audits of other brands’ programs and offers to review a reader’s setup.
Jon Ivanco writes as someone running a brand and building its tooling, so their advice reflects what they have wired up themselves.
Where they push for collecting multiple mapped data points during signup, Schieffer’s popup advice focuses on sequence and friction: teaser, one useful question, email before SMS, immediate reward.
Both are brand owners who write about popups and email programs from the other side of the dashboard.
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Both argue that channel-level metrics such as popup conversion rate can look good while hurting the business. Gallagher’s account of moving the popup to the home page only, running no extra discount, and watching bounce rate push Meta CPMs up reaches the same conclusion as Ivanco: judge the signup form by the quality and usefulness of what it produces, not by how many addresses it captures.
Conor Gallagher frames email as a stabilizer that offsets declining Meta efficiency across the whole system.
The change of mind is the story: five years selling email as extra revenue, then running a brand.
Ivanco’s frame is narrower and more structural: the popup as a survey, and the tooling behind it as a data layer judged by its API.
Gallagher focuses on bounce rate and CPMs, while Ivanco focuses on collecting mapped data points at signup.
Buyers considering Ivanco’s approach should ask how those data points are mapped and used.
Both write about using AI and tool connections to remove repetitive ecommerce marketing work and run it without someone clicking through platforms.
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Corto S. describes turning Claude Code into a Klaviyo execution engine and pairing a model with a CRM process to produce a month of campaigns and flows in one session. Ivanco describes self-learning systems that operators no longer interact with once set up, and brands scaled to eight figures by one or two people.
Corto S. works within one platform and one job—Klaviyo and CRM production—and packages the setup as a guide, focusing on prompts, processes, and outputs.
Ivanco takes a broader view, arguing about architecture: what the data layer collects, what the API allows a brand to build, and how tools make one another more useful.
They also run a physical-product brand, a topic Corto S.’s posts do not cover.
The posts target people who run or market DTC ecommerce brands, covering ad spend, popups, list quality, Shopify discount logic, and tool and automation choices.
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Their tone assumes readers manage a store, an agency, or a marketing budget.
Posting dates run from 29 July 2025 to 7 October 2026, with posts clustering in September and October 2026.
The topics and style suggest an audience of practitioners who buy or manage ecommerce tooling, but do not verify who follows Ivanco.
The only measured audience figure is 10,387 LinkedIn followers, observed 8 October 2026.
That indicates scale on one channel, not whether followers own brands, work at agencies, or match a specific buyer group; it cannot support a promise of reach to a defined buyer group.
For a DTC data-collection and popup campaign, ecommerce operators are a plausible audience based on the posts.
Before committing, ask for a recent LinkedIn analytics report showing impressions, unique viewers, and any platform-reported follower demographics (geography, age, gender).
For occupation and seniority, ask whether they have audience survey results, newsletter subscriber data, or customer and client lists that describe who they reach professionally.
Also request engagement on popup and first-party-data posts over the last six to twelve months, rather than lifetime totals, to compare like-for-like content before choosing a format.
LinkedIn followers
10,387
LinkedIn profile (jivanco), creator account record · 2026-10-08
Jon Ivanco’s LinkedIn posts do not describe a sponsorship programme, media kit, rate card, or dedicated partnership inquiry page.
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The commercial route they do state is a return to brand consulting, announced in a September 2026 post: a deliberately small number of slots, subject to fit, with direct messages as the entry point.
Most other commercial content points to Formtoro, the popup and data-collection tool they describe building and using, and how a brand would need to operate it.
Ask directly whether sponsorships are available and what the pricing and booking process are.
Brand consulting engagement
Buyer requirement
Jon Ivanco describes returning to consulting after a few years away, with only a few slots and fit as requirements.
Brands should expect a scoped, limited engagement rather than an open-ended retainer, and be ready to explain their offer and why it’s a good fit.
The post names direct messages as the route in; ask directly about fees, deliverables, and application requirements.
Popup-to-data system built on Formtoro
Buyer requirement
The recommended setup uses Formtoro as the popup and survey layer, with logic-mapped questions at signup, a fallback route to the thank-you step, and an auto-applied welcome discount limited to customers who have never purchased.
A brand planning this work needs a Shopify store, a Formtoro account, and someone who can configure segments, redirects, and discount audiences—not just a standard email-capture popup.
Ivanco frames the tool as a data layer for automation rather than a single-purpose popup, so buyers should plan for integration work beyond the initial install.
A DTC ecommerce brand that wants to collect useful customer data at signup and is willing to change how its popup and welcome discount work is a strong fit for a scoped advisory engagement with Jon Ivanco.
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Four things in the posts support that fit.
First, the work is operational, not theoretical: one post walks through the Shopify sequence of opening discounts, setting the welcome code audience to never purchased, switching the popup to Formtoro, adding logic-mapped signup questions, and routing drop-offs to the thank-you step with the code applied automatically.
That restricts the code to people who signed up or opted in.
Second, the rationale is specific: a standard popup collects an email or phone number the store already receives at checkout, trading margin for data it already has, while a multi-question form collects attributes that identify likely repeat buyers.
Third, the measurement philosophy fits the need: opt-in rate is a misleading headline number.
The useful target is the share of first-time buyers who arrive with usable data, followed by how that data improves targeting and repeat purchase.
Fourth, they have stated they are returning to brand consulting after a few years away, with a deliberately small number of slots and fit as a stated requirement.
Two limits shape the engagement.
The route in is a direct message; no published fee or deliverable list appears in the posts, so agree on scope and price directly.
Capacity is explicitly limited, so the brand should bring one clearly defined problem rather than an open-ended programme.
Treat this as a systems and operations engagement, not a content or media buy.
When to choose a specialist
A brand looking to buy reach through a sponsorship or paid placement—with fixed deliverables, a large audience and pricing from a published rate card—is a poor fit.
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The posts describe no sponsorship programme, media kit or booking page; the only commercial route Jon Ivanco states is a small number of consulting slots entered by direct message.
The posts do not support quoting a sponsorship rate, guaranteeing impressions or promising a defined content output.
Two other needs are also a poor match.
A brand that wants to keep opt-in rate as its headline KPI, or install a popup and leave it alone, is working against the stated position: a large list of people not in the market to buy is close to worthless, and a popup tool’s value depends on how it connects to the rest of the stack and what its API allows, not on in-platform features.
A brand that cannot assign anyone to configure segments, redirects and discount audiences should not expect a plug-and-play install.
The posts support short-form text on one channel, not needs that depend on video, visual assets or long-form editorial output.
The account has 10,387 LinkedIn followers, but no verified demographics; ask for audience composition before buying against a defined buyer group.
A worked campaign example
A worked angle for building a popup-to-purchase data system and measuring list quality instead of opt-in rate.
The build.
Create or edit the Shopify welcome discount and set its audience to never purchased; optionally add a second segment for opted into marketing and never purchased.
Replace the standard email-capture popup with a multi-question, logic-mapped signup form that asks for the attributes the brand needs to assess a customer—for example, how the person found the product, what they intend to use it for, and how soon they plan to buy.
Route anyone who drops off mid-form to the thank-you step with the discount code applied automatically, so the code only reaches people who signed up through the popup or opted in at abandoned checkout.
On mobile, test moving the collapsed popup teaser to a button under the add-to-cart control on product pages, closer to purchase intent, instead of blocking screen space.
Ask questions before purchase rather than after, on the stated logic that pre-purchase questions collect far more data.
The measurement.
Make the primary metric the share of first-time orders that arrive with usable signup data attached, not the opt-in rate.
Add a quality metric—how well the collected attributes predict repeat purchase—and compare it by traffic source to see whether top-of-funnel ads bring in people likely to buy.
Track repeat purchase and the effect on creative and messaging as secondary outcomes.
Because high-intent signups and conversions are inversely related to the pool of people actively in the market, avoid targets that assume opt-in rate, list size and conversion will all rise at once.
The handoff.
Feed the collected attributes back into top-of-funnel ad targeting to attract a better-quality audience, and use flash sales to the list as a product-insight input as well as a revenue one.
Before the build, define the attributes the brand wants and how each will inform segmentation and messaging, then agree on scope and terms directly; no published fee or deliverable list exists.
Choose this approach when the brand wants its popup and email programme rebuilt against patterns observed across many DTC brands, rather than a data-architecture project.
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The published work is benchmark-driven: it describes studying the popups of hundreds of DTC brands and reducing the strong ones to five steps — lead with a small persistent teaser tab instead of a screen takeover, ask one useful question that actually changes what the subscriber receives later, collect email before asking for SMS, request SMS consent only after the email is submitted, and deliver the reward immediately with a copyable code — plus mobile-first QA and post-launch monitoring of submit rate, because a theme update, targeting change or script conflict can quietly tank conversion even when the popup still looks fine.
A separate teardown of 182 OLIPOP emails maps offer cadence month by month and separates what email carries from what paid ads carry.
A brand that wants a concrete popup rebuild checklist and a comparable read on its own email calendar, measured against other brands, gets more here than from a first-party-data systems build.
The tradeoff is focus.
This work answers what a high-performing popup looks like across the market; it does not build the data layer.
It is not organised around restricting a welcome discount to never-purchased or opted-in buyers, or using signup answers to grade audience quality and feed top-of-funnel ad targeting, which is the centre of Jon Ivanco's approach.
The popup guidance draws on other brands' patterns rather than a system they run.
One post invites a direct message for a popup and email/SMS review; ask whether this is a published service and what it costs.
This is the better read when the brand already spends heavily on paid social and wants someone who has run the whole system as a brand owner, weighing the popup against site-wide conversion and Meta efficiency rather than treating it as a standalone capture tool.
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The published position is that email is not a separate revenue channel but an efficiency tool that keeps the marketing system from collapsing as Meta costs rise; running the popup only on the homepage avoids raising bounce rate; and no extra discount is needed because the popup can simply reflect the existing bundle offer. The reasoning that a popup can lift signups while lowering site conversion — which pushes CPMs up and compounds the problem — is a direct caution for any brand about to add a discount-gated popup.
The tradeoff is the opposite stance on discounts and data.
Jon Ivanco's system deliberately trades a discount for several signup data points and uses Shopify discount-audience rules so the code reaches only people who signed up or opted in and have never purchased.
This alternative removes the extra discount and narrows where the popup appears, arguing that bounce rate and ad costs matter more than raw list growth.
A brand aiming to collect richer first-party data at signup will find less to act on here than one aiming to protect conversion and ad efficiency.
The case comes from running one supplement brand rather than a portfolio of client engagements.
This is the better choice when the bottleneck is execution inside Klaviyo rather than deciding what data to collect.
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The published work describes turning Claude Code into a Klaviyo execution engine that analyses an account, finds opportunities and runs CRM automations, and pairing an AI model with a CRM automation to plan a monthly campaign calendar, build lifecycle flows, generate creative directions and write headlines, copy and calls to action. A brand that already has a workable signup and data setup but cannot produce campaigns, flows and creative fast enough is addressing a real constraint here.
The tradeoff is that this sits downstream of the capture problem.
These automations assume the account data already exists and is worth acting on; they do not cover popup design, which attributes to ask at signup, how to restrict a welcome code, or how to measure list quality against opt-in rate — the buyer task Jon Ivanco addresses.
Automating campaigns on top of a list with no useful attributes mostly accelerates existing programmes.
The posts promote automation guides, so the material shows the shape of the method, not a tested client outcome.
This profile is based on eight LinkedIn posts by Jon Ivanco.
They cover popup and first-party data collection, a Shopify welcome-discount and segment setup, list quality, Formtoro, ecommerce automation, and general DTC operating advice, and the posts are the source for every quoted detail, example, and claim on the page.
The posts do not consistently show publication dates, so the page describes recurring arguments and subjects rather than a timeline, and it does not claim how Jon Ivanco's views changed.
No long-form articles, videos, transcripts, interviews, or other channels appear on this page, and the page does not cover the wider posting history.
Similar-creator comparisons draw on two posts each from Zach Schieffer, Conor Gallagher, and Corto S.
The only audience figure used is the LinkedIn follower count shown on 8 October 2026; no demographic or occupation data appears here.
Two gaps remain open and would change what the page can say: Jon Ivanco's posts here do not mention signature books, shows, or earlier career material, and the posts here do not include a published sponsorship policy, consulting fee, or deliverable list.
