# Jon Ivanco

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.

- Profile: https://b2binfluencer.marketing/creators/jon-ivanco/
- LinkedIn: https://www.linkedin.com/in/jivanco
- LinkedIn followers: 10,387
- YouTube: https://www.youtube.com/channel/UCWCs8QSMmdvFhkhqIXMqCBQ
- Topics: ecommerce, paid-ads
- Based on 16 dated posts, latest 2026-10-09

## Summary

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.
- Check first: Confirm scope, price, capacity, and whether they have supporting data for performance claims.

### 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.

## Main ideas

- **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. 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.
- **List quality matters more than opt-in rate**: Jon argues that opt-in rate and list size are misleading targets. 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.
- **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. 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.
- **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. 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.
- **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. 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.
- **A survival checklist for early-stage stores**: Jon cites an estimate that 80–90% of ecommerce stores fail within 24 months and lists practices they did and would repeat. 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.
- **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. 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.

## Work to read

- [Why a popup should collect data, not duplicate emails](https://www.linkedin.com/posts/jivanco_if-your-on-shopify-and-you-spend-more-than-activity-7513245784377581569-6x-G)
- [A step-by-step Shopify welcome-discount and data-capture setup](https://www.linkedin.com/posts/jivanco_open-shopify-go-to-discounts-find-your-activity-7512157584062246914-IVJt)
- [Opt-in rate is the wrong target if the list never buys](https://www.linkedin.com/posts/jivanco_most-dtc-companies-would-be-more-effective-activity-7439815269558067201-wtAi)
- [Formtoro as a data layer for automation, not just a popup tool](https://www.linkedin.com/posts/jivanco_formtoro-has-always-been-a-data-layer-for-activity-7507826965165572096-5M5F)
- [The six-month automation thesis and return to consulting](https://www.linkedin.com/posts/jivanco_inside-of-6-months-i-believe-ill-be-able-activity-7506786750028144640-5P9i)
- [A compact operating list for avoiding early ecommerce failure](https://www.linkedin.com/posts/jivanco_its-estimated-that-80-90-of-ecommerce-stores-activity-7513736663156609024-Se2R)

## Brand relationships

- **Formtoro** (disclosed_partnership)
- **Shopify** (mention)
- **Meta** (mention)

## Partnerships

Jon Ivanco’s LinkedIn posts do not describe a sponsorship programme, media kit, rate card, or dedicated partnership inquiry page. 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.

## Alternatives

- Zach Schieffer: 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. 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.
- Conor Gallagher: 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. 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.
- Corto S.: This is the better choice when the bottleneck is execution inside Klaviyo rather than deciding what data to collect. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/