Cohort 1 — Product Data
upcomingFocus: Product data pipeline
People
Cohort size caps at 6. We're in conversation with 8 right now — this is a shortlist, not a seat list. We'll converge on a subset as calls happen.
Kate Hollywood Program Management ★ Confirmed
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. 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 Program Management ★ Confirmed
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.
Clint Borrill Marketing In conversation
Clint runs SEO and owns the content + feed spine: PIF → PIM → MuleSoft → Feedonomics → channel syndication (site, Amazon, Target, Meta, Google, ChatGPT, Perplexity). He's re-architecting canonical strategy from parent-level to child-level SKU pages so agent-era search can match granular attributes, and is already building Claude skills for product-description generation. Wants per-platform source-of-truth fields in PIM, a centralized Balsam product-image library AI tools can consume on-brand, and to bring Amazon copy in-house from the marketing agency. Brought up Priscilla, Mary, and Swati as the collaborators who'd make this land.
Swati Ayyar Merchandise Operations In conversation
Swati sits at the PIF → PIM boundary — she's one of the people keying product data into the PIM day to day, which touches every piece of product data at some point. Her name has come up three times independently in the conversation about this cohort, which is the strongest signal any candidate has had.
Joe Balczo Customer Service In conversation
Joe is integrating PIM data into a customer-facing AI chatbot, which has made him the accidental upstream-QA layer — catching inconsistencies like the Vermont White Spruce naming mess and the "nine-foot tree with 250 lights" cases. "If we could get the data cleaner and more structured, we would have an easier time making this information available for AI to use."
Priscilla Spicer Product Development In conversation
Priscilla leads PD, Sourcing & Design — the team at the source of product data. Trade-show trend capture → mood boards → ideation → line lists → vendor specs, 250–300 SKUs/yr across décor and greenery. Already 2 hrs/week into AI experimentation — ChatGPT image-gen for spec sheets, translation for non-native-English vendor comms — and actively trying to pull her team along. Her articulated wish-list: brand-grounded image generation, AI-assisted trend synthesis from trade-show photo dumps, and a shared cross-functional calendar so PD stops getting September asks in September.
Christine Chow Inventory Planning In conversation
Christine runs Inventory Planning — her team creates POs from PIF data, which puts them on the receiving end of product-data quality. When PIF data is wrong, vendors reject POs, quantities don't match, reference SKUs can't be cleanly matched. The downstream consumer view on what clean product data actually unlocks.
Aledgia McGriger Merchandising In conversation
Aledgia is Director of Tree Merchandising — trees are Balsam's largest category, and she covers D2C, indirect, and GE at the same time. That's the broadest view of PIF finalization across all three channel workflows, on the merch side of the handoff.
This cohort sits inside the Product Data & Lifecycle workflow. See the workflow map for where it fits in the broader picture.
The workflow
Balsam’s product data workflow is how new SKUs, attributes, images, and localized content move from merchandising decisions into the systems that sell products — the website, retailer catalogs, localization pipelines, and planning tools.
Today this workflow runs on Excel spreadsheets (PIFs, product information files, with 200–300 columns), tribal knowledge held by a handful of people, manual transformations for each retailer that carries Balsam products, and a sequence of hand-offs that are mostly people filling gaps between systems that don’t talk to each other.
Kate Hollywood mapped the workflow during the strategy sprint — 19 steps, 18 bottlenecks for DTC, now expanding to GE and indirect. Kate bridges product/tech and business as Program Manager. Mary Corrick is the PIM Owner — she owns ContentServ end-to-end and shapes how product data is modeled, governed, and connected across systems. The day-to-day PIF operation (200–300 columns across 20+ files, 65–75 vendors) sits with Product Development and Merchandising.
Who feels it
Producers of product data:
- Product Development creates product data at source — specs, dimensions, compliance, cost inputs — feeding the PIF through line lists.
- Merchandising finalizes the PIF and handles handover, responsible for final accuracy and cutover into downstream systems.
- Merch Ops keys product data into the PIM day to day.
- International teams need localized versions and hit delays.
- Creative ops needs matching assets and struggles when they don’t arrive on time.
Consumers:
- eCommerce needs clean, complete data to list products.
- Retailer operations (Bob McDonald’s group) manually translates Balsam’s product data into 4–5 retailer-specific formats per retailer. Target is 10 retailers by 2027.
- Marketing needs product context to personalize and target.
- Planning needs SKU attributes to forecast.
Customer:
- A shopper in a new market sees a partial catalog at launch.
- A retail partner waits for usable data.
- A holiday shopper sees inconsistent content across channels.
Why it matters
Three reasons this is the first cohort:
- No PLM in 2026. Balsam decided not to move forward with PLM implementation this year. The existing workflow now carries weight it wasn’t designed for.
- Retailer onboarding scales linearly today. Every new retailer means manual format translation. Ten retailers at that cost is a constraint on indirect sales growth.
- Upstream impact. Product data feeds content production, international launches, marketing, planning. Fixing the source helps four downstream workflows more than fixing any one of them.
Why upstream first
Claire named this explicitly: don’t solve downstream pain (retailer-specific asks) in a way that just moves inaccurate data faster. Moving bad product data into ten retailer formats faster does not help. Fixing the source does.
This cohort’s work is upstream — the PIF, the master data, the producer-to-consumer hand-off. Retailer-specific work is downstream of this and not in scope for Cohort 1.
What a better outcome looks like
By end of the cycle:
- A clear, shared map of the full product data workflow that people across functions can point at and align on.
- One or more manual hand-offs removed or automated, visible in Kate and Mary’s day.
- Skills (AI prompts, configured tools) that producers use to generate cleaner data at source.
- A playbook for how new SKU attribute proposals get evaluated and added.
- A handoff package for a follow-on engineering project (likely PIF versioning, or PIF-to-retailer translation) with requirements and evidence.
By end of the founding period (October 2026):
- The retailer onboarding process is not gated on manual data translation.
- International market launches don’t wait weeks for localized data.
- eCommerce sees clean, complete product data without heroic effort.
- Kate and Mary’s workflow looks different enough that they are the ones teaching the next cohort what changed.
Why now
The April 29–May 1 Monterey Bay onsite is the launch moment. Kate and Mary are in Balsam’s strong AI users already. PLM-free 2026 is the trigger. Claire has confirmed Product Data is where she wants to start.
Scope
In scope:
- Mapping the full product data lifecycle, producers and consumers.
- Understanding the PIF structure and its constraints.
- Prototypes that improve producer-side data quality.
- Prototypes that improve hand-offs between producers and consumers.
- Skills and playbooks for day-to-day use.
Out of scope:
- Retailer-specific format translation (downstream).
- PLM selection or implementation.
- Broader merchandising strategy.
- International localization pipeline rewrite (could be a follow-on cohort).
Open questions to resolve by kickoff
- Final confirmation of the five participant seats — candidates from Product Development, Merchandising (tree), Merch Ops, Inventory Planning, and CS Systems are being reached out to this week. See Roster Planning.
- What data access does the cohort need and who grants it?
- If retailer onboarding enters later cohort work, who sponsors that from the sales side?