Meeting Agenda: Wholesale Deep Dive - Discovery Follow-Up
Date: Tuesday, January 13, 2026
Time: 2:15 AM PT (45 min)
Attendees: Laura Putnam, Madison, Awaish Kumar, Uttam Kumaran, [Others TBD]
Context
This is a follow-up to our December 11th wholesale intro call. In that meeting we learned what problems exist and why they matter. Now we want to understand how you’re currently solving them so we can automate/optimize.
What’s changed since last call:
- ✅ Shopify data is now ingested in the data warehouse
- ✅ Wholesale CRM Google Sheets are ingested
- 🎯 Now we need to understand how to model this data correctly
Meeting Objectives
- Walk through the Google Sheets CRM - understand each sheet’s purpose and how they connect
- Validate proposed customer model - is dim_customer + segment + metrics enough, or do you need segment migration history?
- Understand segment classification logic - how do you actually assign customers to segments?
- Document current reporting workflows - step-by-step process for key reports
- Identify quick wins - what can we automate first with the data now in warehouse?
Key Questions from Last Call to Resolve
From December 11 transcript, these items need clarification:
| Pain Point | What We Learned | What We Still Need |
|---|---|---|
| Segment migration | 2,000 customers moved from Trusted Health → Specialty Retail | How do you track this historically? Do you need to see “where they were before”? |
| Match rate issues | Only 2,000 of 13,000 match cleanly between CRM and Shopify | What’s the matching logic? Email? Company name? |
| Date tag added | Shopify doesn’t track when customer became wholesaler | Is onboarding date from CRM sufficient? |
| Customer account history | Some accounts existed before becoming wholesalers (e.g., 2022 account → 2024 wholesaler) | How do you want to treat pre-wholesale orders? |
Demos/Walkthroughs Requested
1. Google Sheets CRM Deep Dive (15-20 min)
We need to understand:
-
Application Tracker Sheet
- Walk through the columns and what each means
- How does Jotform data flow in?
- What’s the onboarding status workflow? (pending → approved → active?)
-
Old Application Tracker
- How far back does this go?
- Same structure as current tracker?
- Is this still being updated or frozen?
-
How do you join CRM to Shopify today?
- Email matching?
- Manual lookup?
- What breaks the match? (email changes, company name changes)
2. Segment Classification Walkthrough (10 min)
Questions:
- When a new applicant comes in, how do you decide: Trusted Health vs Bulk Buyers vs Specialty Retail?
- Is it based on:
- Business type on application?
- Order volume/patterns after first order?
- Manual classification by Madison/Laura?
- For the 2,000 being migrated to Specialty Retail:
- What criteria defined them as “actually specialty retail”?
- Was this a one-time batch process or ongoing?
3. Monthly Reporting Walkthrough (10 min)
Madison mentioned pulling reports takes forever - walk us through:
- What’s your process to answer: “How has wholesale revenue by customer and by segment changed over time?”
- Show us the actual Shopify report export + pivot table workflow
- Where does it break? (product names weird, filters needed)
Data Model Validation
We’re proposing to build a dim_customer table that includes:
| Column | Source | Description |
|---|---|---|
| customer_id | Shopify | Unique identifier |
| Shopify | Primary matching key | |
| company_name | CRM/Shopify | Business name |
| segment | CRM | Trusted Health / Bulk Buyers / Specialty Retail |
| segment_assigned_date | CRM | When they were assigned to current segment |
| onboarding_date | CRM | When they became a wholesaler |
| first_wholesale_order_date | Shopify | First order with wholesale tag |
| total_orders | Shopify | All-time order count |
| total_revenue | Shopify | All-time revenue |
| aov | Calculated | Average order value |
| last_order_date | Shopify | Most recent order |
| days_since_last_order | Calculated | For churn analysis |
Key Question: Do you need segment migration history?
Option A: Current segment only (simpler)
- dim_customer shows current segment
- Historical analysis is “as of today’s segment”
- Can’t answer: “How did specialty retail customers behave when they were in Trusted Health?”
Option B: Segment history table (more complex)
| customer_id | segment | effective_from | effective_to |
|-------------|---------|----------------|--------------|
| 123 | Trusted Health | 2022-01-15 | 2025-11-30 |
| 123 | Specialty Retail | 2025-12-01 | NULL |
- Can track segment migrations over time
- Can answer: “Show me customers who moved segments and compare before/after behavior”
Which is more valuable for your 5-year strategy work?
Specific Questions
CRM Data Structure
- In your CRM Google Sheet, what are the actual sheet names/tabs?
- What columns exist in the application tracker? (We have the sheet ingested - want to validate we’re interpreting correctly)
- Is there a “customer segment” column already, or is it derived from Shopify tags?
- How is “onboarding status” tracked? What are the possible values?
- Do you have a “date became wholesaler” or similar field?
Segment Logic
-
What makes someone “Bulk Buyers” vs “Trusted Health”?
- Is it order volume threshold?
- Business type (university vs chiropractor)?
- Manual classification?
-
For the Specialty Retail segment (launching January):
- New applicants go straight to Specialty Retail based on application answers?
- Or do they start in Trusted Health and get moved?
-
When you migrated 2,000 customers to Specialty Retail:
- What was the selection criteria?
- Was there documentation of who was moved and when?
- Is there any record in CRM/Shopify of the migration date?
Metrics & Reporting
-
When you say “revenue by segment” - is that:
- Revenue from orders placed while in that segment?
- Revenue from customers who are currently in that segment (regardless of when they ordered)?
-
For the five-year strategy, you mentioned needing:
- Revenue by customer and segment over time
- AOV by segment
- Growth rate by segment
- Net new accounts by segment
- Lapsed accounts by segment
Is this the priority list? Anything else critical?
-
Lapsed account definition:
- What’s the threshold? (3 months? 6 months? 12 months no order?)
- Does it differ by segment?
Investment Tracking
- For investment items (refrigerators, merch kits):
- How are these tracked in Shopify? Specific SKU?
- Can we identify them by product name/type?
- Finance removes these manually - is there a list of “investment” SKUs?
Customer Journey Milestones
- The welcome kit and third-purchase unlock:
- Are these tracked anywhere beyond Shopify order count?
- Do you have a sheet tracking which customers received which unlock?
Quick Win Validation
Based on last call, here are potential quick wins. Which would be most valuable first?
| Quick Win | Description | Your Priority (1-5) |
|---|---|---|
| Historical order pulls | Replace 36-hour download with instant queries | |
| Revenue by segment report | Auto-generate monthly revenue by segment | |
| Lapsed customer alert | Daily/weekly list of customers approaching churn threshold | |
| Segment health dashboard | AOV, order frequency, retention by segment | |
| Product mix report | Sparkling vs Sticks breakdown by segment | |
| Application-to-customer matching | Improve match rate beyond 2,000/13,000 |
Resources We May Request Access To
- Screen share of CRM Google Sheets structure
- Sample of the application tracker (sanitized if needed)
- List of Shopify wholesale tags currently in use
- Documentation of segment definitions (if exists)
- List of “investment” product SKUs (fridges, merch kits)
Notes
Space for notes during the meeting
CRM Sheet Structure
Segment Logic Rules
Reporting Workflow Details
Priority Quick Wins (per Laura/Madison)
Open Questions for Follow-Up
POST-MEETING SUMMARY
Fill after meeting
Key Decisions Made
- Segment history tracking: Option A / Option B?
- Match key for CRM ↔ Shopify: Email / Customer ID / Other?
- Lapsed threshold definition: X months
CRM Structure Documented
- Sheet 1: [Name] - [Purpose]
- Sheet 2: [Name] - [Purpose]
- …
Agreed Quick Wins (Priority Order)
Follow-Up Actions
- Brainforge: [Action] (Due: [Date])
- Madison: [Action] (Due: [Date])
- Laura: [Action] (Due: [Date])