Brainforge Knowledge

2026-01-13_cx_team_discovery

10 min read

Meeting Agenda: CX Team Discovery Call

Date: Tuesday, January 13, 2026
Time: 3:30 PM EST (1 hour)
Attendees: Landon (LMNT CX Team), Uttam Kumaran, Awaish Kumar, Shivani Amar

Note: This is the first CX discovery session. Questions are designed to understand how the CX team collects, uses, and analyzes customer support and feedback data.

POST-MEETING SUMMARY

Fill this section out after the meeting concludes - see MEETING_WORKFLOW_GUIDE.md for instructions

Key Learnings

Current State:

  • Add major insights about how CX currently operates
  • Pain points discovered
  • Unexpected findings

Data Sources & Systems:

  • List of systems CX uses
  • How data flows between them
  • Data quality/trust issues

Quick Wins Identified:

  • High-impact opportunity (e.g., automate ticket volume reporting)
  • Automation opportunity
  • Reporting improvement

Answers to Key Questions

Reference question numbers from below and provide concise answers

Q1-6: CX Tools & Data Sources

  • Q1: Primary tools → A: Fill in after meeting
  • Q2: Gorgias as single source → A: Fill in after meeting
  • Q3: Feedback tools (reviews, surveys, NPS) → A: Fill in after meeting

Q7-12: Data Availability & Access

  • Q7: Data fields in Gorgias → A: Fill in after meeting
  • Q8: CSAT captured → A: Fill in after meeting

Q13-18: Current Usage & Reporting

  • Q13: How data is used today → A: Fill in after meeting
  • Q14: Existing reports → A: Fill in after meeting

Q19-24: Customer & Order-Level Analysis

  • Q19: Ticket-to-customer linkage → A: Fill in after meeting
  • Q20: Repeat issue tracking → A: Fill in after meeting

Q25-34: Metrics & Insights of Interest

  • Q25: Priority ticket metrics → A: Fill in after meeting
  • Q29: Priority agent metrics → A: Fill in after meeting

Q35-40: Future Analytics Expectations

  • Q35: Questions they can’t answer today → A: Fill in after meeting
  • Q36: Interest in predictive CX → A: Fill in after meeting

(Continue for other question groups as needed)

Decisions Made

  • Decision about tool/process
  • Alignment on definition

Follow-Up Needed

  • Landon: Action item (Due: Date)
  • Brainforge: Follow-up task (Due: Date)

Meeting Objectives

  1. Understand CX tools and data sources (Gorgias + any others)
  2. Assess data availability and access (what exists, where it lives)
  3. Document current usage and reporting workflows
  4. Explore customer and order-level analysis capabilities
  5. Identify priority metrics and insights for the CX team
  6. Capture future analytics and modeling expectations

Context from Prior Discovery

What We Know:

  • Gorgias: Confirmed as CX platform (from Jason IT meeting, Dec 2, 2025)
  • Source Medium Integration: Landon has a dedicated CX-specific Looker dashboard
  • Guru: Internal knowledge base/FAQ system (confirmed in wholesale meeting)
  • Gorgias Priority: Marked as P2 in data sources inventory

From Jason (IT) Meeting:

“Even though we don’t own the CX systems like Gorgeous and Guru… every time there’s a question that comes up, inevitably we get pulled in.”

From Source Medium Feedback:

Landon (CX) has a CX-specific Looker dashboard for “Gorgeous/email workflow data” - satisfaction level unknown.

Open Questions:

  • Ticket tagging structure and categories
  • CSAT collection methods and frequency
  • Customer-to-ticket linkage capabilities
  • Integration status with Shopify/ReCharge

Demos/Walkthroughs Requested

  • Gorgias Ticket Workflow

    • How tickets are created, categorized, and resolved
    • Tag/category structure used
    • Agent assignment process
    • Escalation workflow

  • Source Medium CX Dashboard

    • What metrics are currently visible
    • How often it’s used
    • Any limitations or gaps

  • Manual Reports or Spreadsheets

    • Any Google Sheets used for CX reporting
    • How leadership reports are compiled
    • Data that gets exported from Gorgias

  • Customer Feedback Collection

    • Post-purchase surveys or CSAT collection
    • Review management (if any)
    • NPS or satisfaction scoring

Questions to Ask

CX Tools & Data Sources

  1. What systems and tools does the CX team use on a daily basis?

    • Gorgias (confirmed) - any others?
    • Email, phone, chat, social channels?
    • Which is the primary channel for customer support?

  2. Is Gorgias the single source of truth for all customer support interactions?

    • Or are there tickets/conversations happening outside Gorgias?
    • What about social media DMs, community forums, or other channels?

  3. What tools are used to capture customer feedback beyond support tickets?

    • Reviews: Okendo, Yotpo, Judge.me, or others?
    • Surveys: Post-purchase surveys, NPS surveys, CSAT surveys?
    • Feedback forms: Website feedback, product feedback?

  4. How does post-purchase communication work?

    • Klaviyo for emails? Gorgias for support follow-ups?
    • Any automated survey triggers after order delivery?
    • How are review requests sent?

  5. Are there any tools for monitoring social or community feedback?

    • Brand mentions on social media?
    • Community forums or Facebook groups?
    • Reddit, Twitter, Instagram comments?

  6. What integrations exist between Gorgias and other systems?

    • Shopify: Can agents see order history in Gorgias?
    • ReCharge: Are subscription details visible?
    • Klaviyo: Any email workflow integrations?
    • Stord/fulfillment: Shipping status visibility?

Data Availability & Access

  1. What data fields and attributes are available in Gorgias?

    • Ticket status (open, pending, closed)?
    • Tags or categories used to classify tickets?
    • Priority levels?
    • Timestamps (created, first response, resolved)?
    • Agent assignments?

  2. Is CSAT (Customer Satisfaction) scoring captured in Gorgias?

    • Post-resolution surveys?
    • How is satisfaction data collected (if at all)?
    • What’s the response rate?

  3. Where does CX data live today?

    • Only inside Gorgias?
    • Exported to Google Sheets?
    • Visible in Source Medium dashboards?
    • Any other BI tools or databases?

  4. What access does the CX team have to Source Medium dashboards?

    • How often do you use the CX Looker dashboard?
    • What data is available there?
    • Any gaps or limitations?

  5. How frequently is CX data reviewed or exported?

    • Daily operational reviews?
    • Weekly team meetings?
    • Monthly leadership reports?

  6. Can support tickets be linked to Shopify customer IDs or order IDs?

    • Is there a unique identifier that connects tickets to customers?
    • Can you pull order details for a ticket?
    • Is the linkage reliable and complete?

Current Usage & Reporting

  1. How is CX data used today - operationally, analytically, or both?

    • Operationally: Managing daily ticket queues, agent workloads
    • Analytically: Identifying trends, patterns, performance

  2. Are there any existing reports or dashboards the CX team uses?

    • Source Medium CX dashboard - what’s in it?
    • Google Sheets reports?
    • Gorgias native reporting?
    • Any other tools?

  3. What manual processes exist for extracting CX insights?

    • Do you export data from Gorgias manually?
    • Any spreadsheet manipulation or analysis?
    • How time-consuming is this?

  4. Is data used reactively (case-by-case) or for trend analysis?

    • Responding to individual customer issues?
    • Or also looking at aggregate patterns (e.g., spike in shipping complaints)?

  5. How do you currently measure CX team performance?

    • Agent metrics (tickets handled, response time)?
    • Team-level metrics (backlog, resolution rate)?
    • Quality metrics (CSAT, customer feedback)?

  6. What CX information gets reported to leadership?

    • Monthly reports to James/exec team?
    • Which metrics matter most to leadership?
    • How are these reports compiled today?

Customer & Order-Level Analysis

  1. Can support tickets be tied to specific customers, orders, or products?

    • Is there a Shopify customer ID in Gorgias?
    • Can you look up all tickets for a given customer?
    • Can you see which products a ticket is about?

  2. Is there visibility into customers with repeated or negative issues?

    • Can you identify “problem” customers with many tickets?
    • Do you track customers with escalations?
    • Any flags for high-effort customers?

  3. Can you identify customers who continue ordering after negative CX experiences?

    • Is there any analysis on loyalty despite issues?
    • Do you track whether resolved issues lead to repeat purchases?

  4. Can you identify customers who stopped ordering after unresolved tickets?

    • Any visibility into churn related to CX?
    • Do you track last order date vs. last ticket date?
    • Can you see if customers with open tickets stop buying?

  5. Is there any customer satisfaction or sentiment scoring today?

    • Beyond CSAT surveys - any sentiment analysis on ticket content?
    • NPS scores tracked?
    • Any customer health scoring?

  6. How do you track repeat contact rate or escalations?

    • Multiple tickets from same customer in short period?
    • Escalation tracking (to supervisor, to management)?
    • Re-opened tickets?

Metrics & Insights of Interest

Ticket Metrics:

  1. Which ticket metrics are most important to the CX team?

    • Time to first response (TTFR)?
    • Time to resolution (TTR)?
    • First contact resolution rate?

  2. How do you track open vs. closed ticket volume?

    • Backlog trending?
    • Queue health?
    • SLA compliance?

  3. Do you analyze ticket volume by category, issue type, or product?

    • What categories/tags exist today?
    • Most common issue types?
    • Any product-specific patterns (e.g., more issues with certain SKUs)?

  4. What’s the backlog situation?

    • Average tickets in queue?
    • Oldest open tickets?
    • Seasonal variations?

Agent Metrics:

  1. What agent-level metrics do you track?

    • Tickets per agent (daily/weekly)?
    • Resolution time by agent?
    • Quality scores?

  2. How do you measure agent performance and efficiency?

    • Any performance indicators or KPIs?
    • Peer comparison?
    • Coaching based on data?

Customer Experience Metrics:

  1. If CSAT is collected, how is it used?

    • Target CSAT score?
    • Trending over time?
    • By issue type or agent?

  2. Is repeat contact rate tracked?

    • Definition of repeat contact (same day, same week, same issue)?
    • Target or benchmark?

  3. Is there any analysis on CX impact on retention or reorder behavior?

    • Do customers with good CX experiences order more?
    • Any correlation data available?

Behavioral Analysis:

  1. Would you want to analyze time from ticket resolution to next order?
    • See if resolved tickets lead to continued purchasing
    • Compare to customers without tickets

Future Analytics & Modeling Expectations

  1. What questions does the CX team wish they could answer but can’t today?

    • What’s frustrating about current data visibility?
    • What would make your job easier?
    • What would you show leadership if you could?

  2. What insights would be most valuable for CX leadership?

    • Trends in customer issues?
    • Team performance over time?
    • Product quality insights from support data?
    • CX impact on business metrics?

  3. Are you interested in automated reporting vs. ad-hoc analysis?

    • Daily/weekly automated dashboards?
    • Self-service querying and exploration?
    • Both?

  4. Would deeper customer-level analytics be valuable?

    • Impact of CX on customer lifetime value (LTV)?
    • Segmenting customers by CX history?
    • High-value customers getting better/worse service?

  5. Is there interest in predictive or proactive CX insights?

    • Predicting which customers are at churn risk?
    • Identifying tickets likely to escalate?
    • Proactive outreach for customers with potential issues?

  6. How valuable would it be to integrate CX data with commerce/order/customer data?

    • See full customer journey (orders + tickets + subscriptions)?
    • Analyze CX alongside revenue metrics?
    • Understand customer segments by CX behavior?

Resources Mentioned/Requested

To be filled out during/after meeting

  • Access to Gorgias (view or export)
  • Gorgias tag/category documentation
  • Sample Gorgias data export
  • Source Medium CX dashboard access
  • Any Google Sheets CX reports
  • CSAT survey configuration details
  • Review platform access (if applicable)
  • CX team org chart / agent list
  • [ ]
  • [ ]

Action Items

To be filled out during/after meeting

Landon (CX)

  • [ ]

Brainforge (Uttam/Awaish)

  • [ ]

Shivani

  • [ ]

Notes

Space for additional notes during the meeting

Key Insights

Gorgias Data Structure

  • Tags used:
  • Categories:
  • Ticket statuses:
  • Fields available:

Integration Details

  • Shopify connection:
  • ReCharge connection:
  • Source Medium data:

Current Pain Points

Quick Win Ideas

Quotes/Verbatim

Data Modeling Considerations

To inform future CX data modeling in dbt

Potential Source Tables (Gorgias)

  • Tickets
  • Customers
  • Messages/Conversations
  • Tags
  • Agents/Users
  • CSAT Responses

Key Joins Needed

  • Gorgias Customer → Shopify Customer (via email or ID)
  • Ticket → Order (via order ID if available)
  • Ticket → Product (via SKU or product ID)

Priority Metrics for Data Platform

To be filled based on meeting outcomes

MetricDefinitionSourcePriority
TBDTBDTBDTBD

Document Change Log

DateAuthorChange
Jan 13, 2026Awaish KumarInitial creation of CX discovery agenda

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