Marklo: AI Campaign Planning Built for Shopify Brands

A promotion that never runs can be a useful result for Marklo. In the company's Starfish Project customer account, the planning software warned that a product had too little inventory to support a proposed campaign. The reported intervention places Marklo inside the merchant's choice about what deserves attention, before anyone spends money distributing the message.

Founded in 2025 by Jessica Herrin, the Bay Area company builds AI-assisted campaign planning for direct-to-consumer brands using Shopify. It joins commerce information and marketing history in a calendar and prepares campaign work for people to review. Herrin leads Marklo as founder and CEO, with former Shopify and Google engineer Scott Royston identified on its current team page as founding principal engineer.

Marklo opened its public beta alongside the $5M Seed funding announcement covered by DevCuration on October 14, 2026. Forerunner led the financing, with Freestyle Capital participating. The company's expanding product and customer work makes it a useful example of how AI can enter a brand's planning routine, including the decisions about which campaigns to leave off the calendar.

About Marklo and the operator behind it

Herrin comes to the software business with experience building consumer companies, including Stella & Dot. Marklo's team page connects that operating background to its mission: helping marketing teams coordinate commercial information and campaign work. Royston's engineering experience sits alongside it, giving the founding team exposure to both running brands and building the infrastructure they use.

The company describes a culture of ownership, close customer involvement, fast iteration and careful product work. Those are Marklo's stated principles, rather than an independent assessment of working conditions. Their relevance is practical: software built around recurring marketing decisions has to absorb what a merchant learns while using it.

A brand can have plenty of data while still depending on a particular employee to assemble the useful explanation. Marklo's product tries to place more of that history in a workspace the team can revisit. The resulting business relationship extends beyond receiving a generated brief; the merchant needs to recognize its own catalog, commercial priorities and campaign habits in the system.

That is also where the company's leadership story connects to its product promise. An experienced operator can help define what belongs in the planning conversation, while engineers have to make it available consistently. The company still has to carry that understanding across customers whose products and internal working habits will differ.

The workspace around a campaign decision

Marklo organizes its product around a calendar, a campaign canvas, analytics and collaboration. Its Shopify listing describes connections to services including Klaviyo, Attentive, Meta, Google Ads and TikTok. The proposed workflow combines earlier campaigns with commerce information, suggests what to run next, and prepares assets and tasks for human review.

The calendar matters because a promotion exists in time as well as in a channel. Product availability, a previous launch and an email campaign can all affect how a team interprets the result it is studying. Gathering that context creates a starting point for the next decision, with the surrounding work visible to the people expected to execute it.

Marklo's comparison with Klaviyo makes the intended relationship especially clear. The vendor presents Marklo as cross-channel planning and Klaviyo as email and SMS execution, explicitly describing ways a merchant can use both. That is positioning supplied by Marklo, but it helps explain why the company seeks a role alongside a brand's existing systems.

The buyer's evaluation therefore includes more than the quality of an individual generated message. A team needs to understand where a recommendation came from, which assumptions are current, and how an approval becomes usable work. Those questions are an interpretation of the product's stated mechanics, with the answers to be established in each merchant's use.

Customer accounts give the coordination problem shape

Marklo's Pashion case study describes marketing, ecommerce and merchandising working around a catalog of more than 1,000 SKUs. Each department had its own exports to reconcile before making decisions. The company reports bringing those teams into a shared view of products, performance and campaign plans.

That example explains the organizational audience for the software. Marketing needs a product to feature, merchandising needs to understand which items require attention, and ecommerce needs a view of what is converting. A shared workspace is valuable when the decision can move between those responsibilities without every participant rebuilding the account from a separate spreadsheet.

The Starfish Project case supplies a smaller example of the same relationship. Marklo says it connected product performance, inventory and campaign history, including the warning that a proposed promotion lacked sufficient stock. The case illustrates a decision supported by context; it does not provide an independently measured financial result from that warning.

A separate Latico Leathers account reports a month of marketing planned in 1 day and 9 campaigns generating $60K+ in revenue. These are company-published customer outcomes, without a controlled estimate of incremental lift. Across the examples, the useful pattern is the work merchants describe: gathering the business picture, choosing an action and preparing the campaign around it.

Hiring shows the product relationship Marklo is building

Marklo's current careers page lists a CTO and a Head of Customer Success & Onboarding, both hybrid, full-time positions in Burlingame, California. The CTO description combines product and engineering ownership with direct customer discovery. A job listing for that role should not be read as an appointment or used to assign a current CTO title to another employee.

The customer-success role joins frontline support with the design of onboarding, a customer journey map and a customer scorecard. Its stated measures include activation, weekly active use, free-to-paid conversion and issue resolution. These are the measures Marklo wants the role to build around, rather than disclosed company achievements.

For operators watching the company, that pairing is consequential. The engineering leader will have to turn customer experience into product choices, while the customer-success leader helps merchants reach a useful first outcome and return to the system. The roles put adoption work beside product development as Marklo expands its beta.

Those customer relationships also involve sensitive commercial information. Marklo's privacy page says customers choose and can revoke integrations, and that their personal data is not used to train or fine-tune AI models. Merchants evaluating the platform can bring those stated commitments into the same conversation as workflow fit, while the next calendar fills with the campaigns their teams decide to run.