Cohort 1 Roster Planning
Status: Forming. Two confirmed; others in conversation. Target size: 5-6 people.
How product data responsibilities divide at Balsam
Shared up front so anyone reading this page has the same map the cohort is working from. Mary’s framing from the Apr 17 conversation:
Product Development (PD) — “Can we build this product, and what exactly is it?” Designs and engineers the product (features, specs, materials). Works with vendors on feasibility, samples, costing, and revisions. Owns product data creation — specs, dimensions, compliance, cost inputs. Iterates through versions until the product is viable and approved. Line lists from PD feed the PIF. On the PD side: trend, sourcing, new-vendor sourcing, initial photo samples.
Merchandising — “Should we sell this product, where, and at what price?” Defines assortment strategy (what products, mix of NPI vs carryover). Sets pricing targets, margins, and positioning. Builds line lists and retailer assortments. Responds to customer/retailer feedback and assortment performance, adjusting offerings. Ultimately owns final assortment decisions and buy direction. Also: business analysis and reporting, partnering with ecomm on site strategies, marketing and commercial planning on go-to-market, pricing team on pricing strategy, attending wall walks and proofing catalogs.
PD and Merch together carry the PIF — 200–300 columns across 20+ files, 65–75 vendors. That operational burden is theirs, not Kate’s or Mary’s.
Merch Ops. The people who actually put product data into the PIM day to day.
Kate and Mary bridge this. Kate bridges product/tech and business, leading cross-functional initiatives across technology, operations, finance, merchandising, and analytics. Mary owns ContentServ end-to-end (taxonomy, attribute architecture, product data workflows, platform documentation) and shapes how product data is modeled, governed, and connected across systems.
Shared territory. The PIF itself is shared between PD and Merch. Vendor relationships are shared. Pricing negotiation is split by scope — PD negotiates at item level for NPIs, Merch at total level. Catalog pagination is shared — PD kicks off and mostly owns, Merch stays in the loop.
Confirmed
Kate Hollywood — Program Manager
Department: Program Management
Kate bridges product/tech and business. She leads cross-functional initiatives across technology, operations, finance, merchandising, and analytics — driving enterprise system adoption (NetSuite OCM, Sigma), enabling major digital launches (BHCA website), and now improving the end-to-end product-development-to-launch process. She mapped 19 steps with 18 bottlenecks for DTC and is doing the same for GE and indirect channels. One of the most sophisticated non-engineering AI users at Balsam — uses ChatGPT, Claude, Draw.io, and Mermaid to map workflows and analyze data architecture.
Mary Corrick — Product Manager / PIM Owner
Department: Program Management / Product Data & Systems
Mary owns ContentServ end-to-end — from taxonomy and attribute architecture to product data workflows and platform documentation. She’s involved in geo launches (Spain/Italy), works alongside Kate daily, and shares the operational burden of the current system. Her depth in the PIM goes beyond administration; she’s actively shaping how product data is modeled, governed, and connected across systems.
In conversation
People who are in the conversation about joining the cohort. Their role and how it touches product data — not a final commitment from either side yet.
Clint Borrill — Director of SEO (Marketing)
Clint’s work is downstream of PIM — product copy automation, slug and meta data at the parent and family level, and how Balsam shows up in AI-mediated search (Google, ChatGPT, Perplexity). Clean, structured product data at source makes that work faster and more accurate. Clint is already using Claude Projects and Python in his day-to-day.
Swati Ayyar — Merchandise Operations Manager
Swati works in Merchandise Operations at the PIF → PIM boundary. She’s one of the people who keys product data into the PIM day to day, which puts her closest to the source-of-truth operation the cohort is upstream of.
Joe Balczo — Tools Admin Senior Strategist (CS Systems)
Joe works in CS Systems. He’s building a customer-facing AI chatbot that uses PIM data exports to answer product questions. That makes him a direct downstream consumer of PIM data — useful perspective on what clean product data makes possible for customer-facing AI work.
Priscilla Spicer — Product Development & Sourcing Manager
Priscilla’s on the PD side of the line — PD creates the product data at source, and the line lists from PD feed the PIF. She’s worked on both merchandising and product development, which is a perspective almost nobody at Balsam has. Cleaner, more structured data earlier on matters for vendor validation, costing accuracy, and getting tariffs, duty rates, and loaded costs into a real retail view sooner.
Seats still open
Two more seats would round out the full product-data cascade. If any of these describes your work, reach out — we’re still working on candidates.
Inventory Planning — the person who consumes PIF data to create POs
This is a downstream seat. The person in this role sees the cascade when PIF data is wrong — vendors reject POs, quantities don’t match, reference SKUs can’t be matched cleanly. They experience PIF data quality as a daily QA problem.
What you’d bring: The downstream consumer view. What breaks. What “data cleanliness” actually means in practice when you’re trying to place a purchase order.
Tree Merchandising — a director-level voice covering D2C, indirect, and GE
This is the merch-finalization seat. Trees are Balsam’s largest category, and a director-level tree merch perspective covers all three channel workflows (D2C, indirect, GE) simultaneously. Nobody else has that breadth on the merch side.
What you’d bring: How the PIF handover actually happens. Where finalizing gets painful. What the cross-channel differences look like from inside the assortment decisions.
How the cohort works
- Core owners commit ~50% during the cycle (4-6 weeks). They drive the work.
- Participants commit ~20% — roughly one day a week. They bring specific knowledge, test what gets built, and help extend the pattern.
- Adjacent ring members follow along at ~5% — office hours, showcases, and staying informed for future cohorts.
The cohort is not extra work on top of your job. If your day-to-day involves product data, this is your existing work done differently for a few weeks.
See Roles & Time for the full picture.
Get involved
If any of this sounds like your work, reach out:
- Message Alex or Mike on Slack to talk about it.
- Send us your GitHub username — the whole CoE lives in a repo and cohort members can cut issues, propose changes, and contribute directly.
Even if the first cohort isn’t the right fit for your schedule, the adjacent ring is a real thing — follow the work, attend showcases, and book office hours. The second and third cohorts will pull from the people closest to the work.