Sample deliverable. Fernway Outfitters is an illustrative company. The figures are invented but internally consistent, so you can see how a real plan reads.
We spent three weeks with your customer experience, catalog, fulfillment and finance teams. This plan recommends three changes to start now, four to validate first, and five to leave alone, with the evidence for each.
The short version
Fernway’s teams spend 58% of their week moving, finding and relaying information. Three changes give back about 320 hours a month.
People in scope
38
Time on routine information work
58%
Opportunities assessed
12
Net hours a month, recommended
260 h320 h373 h
Net annual value, recommended
$140K$202K$269K
Do first
3 changes, ready to start
O1Customer experience#1
Order-status answers with live order and 3PL data
Let the helpdesk answer “where’s my order?” from real order, carrier and warehouse data.
O1Order-status answers with live order and 3PL data$86K$108K$127K
O2Supplier files to review-ready product records$50K$82K$125K
O3Daily 3PL exception monitor and triage$36K$50K$65K
Time returned, at $38/h loaded cost Other benefits
Time returned
$119K$146K$170K
Other benefits
$53K$95K$147K
Run costs
−$32K$39K$48K
Net annual value
$140K$202K$269K
What we’d leave alone
Don’t automate refunds, and don’t replace Gorgias with a custom chatbot. Defer demand forecasting until product and sales data are cleaner. In each case the cost or risk outweighs the gain today.
Three weeks inside Fernway’s operation. We interviewed the people doing the work, watched it happen, sampled the data behind it and checked every idea against the tools you already pay for.
3 weeks
16 people
15 sessions
8 systems
The evidence base
People interviewed
16
Working sessions observed
9
Systems reviewed
8
Support tickets sampled
4,180
SKUs traced from supplier file to live
212
3PL exceptions analyzed
1,140
Scope
In scope
Customer experience: order, returns and product questions
Catalog: new product setup and updates
Fulfillment: 3PL exceptions and wholesale orders
Finance: invoice matching and the weekly trading report
Out of scope
Marketing automation, reviewed lightly
Retail stores and wholesale sales
Replatforming or website redesign
Headcount and organization design
Engagement timeline
3 weeks · 15 sessions
Week 1Sep 8–12Kickoff and shadowing
ML
Sep 8Kickoff
Kickoff and scope alignment
Maya Lindqvist, Chief Operating Officer · Tom Reyes, Director of Customer Experience
PN
Sep 9Shadowing
Order-status and returns shadow
Priya Nair, CX Team Lead · Jordan Blake, CX Agent
Gorgias
Shopify
ShipBob
ER
Sep 10Shadowing
New product setup shadow
Elena Ruiz, Merchandising Manager · Sam Okafor, Catalog Specialist
Airtable
Shopify
Sheets
MC
Sep 11Shadowing
3PL exception handling shadow
Marcus Chen, Operations Manager · Ava Thompson, Fulfillment Coordinator
ShipBob
Shopify
NetSuite
RK
Sep 12Interview
Invoice matching and trading report
Rachel Kim, Controller · Daniel Osei, FP&A Analyst · Grace Liu, AP Specialist
NetSuite
Excel
Week 2Sep 15–19Data and systems
Sep 15Analysis
Ticket sample: 4,180 tickets tagged
Gorgias
LH
Sep 16Shadowing
Wholesale PO intake shadow
Lena Hoffman, Wholesale Coordinator
NetSuite
Outlook
Excel
CP
Sep 17Interview
Systems and access review
Chris Patel, IT & Systems Lead
Shopify
NetSuite
ShipBob
Airtable
MJ
Sep 18Analysis
SKU trace: supplier file to live listing
Mia Johansson, Product Copywriter
Airtable
Shopify
NW
Sep 19Interview
Returns and product feedback
Noah Williams, Product Lead · Hannah Moore, CX Agent, Returns
Loop Returns
Week 3Sep 22–26Sizing and recommendations
Sep 22Analysis
Opportunity sizing and tool checks
Gorgias
Klaviyo
NetSuite
TR
Sep 23Working session
Working session: answer rules
Tom Reyes, Director of Customer Experience · Priya Nair, CX Team Lead
ER
Sep 24Working session
Working session: listing standards
Elena Ruiz, Merchandising Manager · Sam Okafor, Catalog Specialist
Sep 25Analysis
Roadmap and business case
ML
Sep 26Readout
Readout with leadership
Maya Lindqvist, Chief Operating Officer · Tom Reyes, Director of Customer Experience · Marcus Chen, Operations Manager · Rachel Kim, Controller
Exceptions visible only in portal exports. One shared admin login.
Action needed
AirtableProduct data
Acts as the product database; 12% of live SKUs disagree with Shopify.
Action needed
NetSuiteERP
Orders sync nightly; product data is keyed in separately from Shopify.
Manual handoffs
Loop ReturnsReturns
Return reasons captured as free text and never analyzed.
Manual handoffs
Excel & SheetsUsed everywhere
Trading report, PO macro, exception tracker and launch calendar.
Manual handoffs
Shopify PlusStorefront & orders
Reliable record of orders and customers. Tags used consistently.
Connected
GorgiasHelpdesk
Current macros and tags. AI agent is included in your plan but switched off.
Connected
KlaviyoEmail & SMS
Well configured. In scope only for content generation.
Connected
ConnectedManual handoffsAction needed
Chapter 02
Findings
Fernway’s operation as it really runs, organized so leadership can absorb it in a few minutes.
How 38 people spend their time
24%21%13%24%18%
Moving and re-keying information
Looking things up
Routine replies and status chasing
Exceptions, review and approvals
Improvement, analysis and relationships
58% of in-scope time goes to moving, finding and relaying information. Systems can absorb most of it. 24% is judgment that should stay with people, made faster. 18% is the work you hired them to do.
Wholesale order entryFulfillmentDocumented: 2 steps→Actual: 7 steps, 1 handoffs, 3 systems
As documented
Receive PO
Enter sales order
As it actually runs 4 manual steps
1PO arrives as PDF or emailOutlook
2Open retailer price listExcel
3Run personal reformatting macroExcel
4Check stockNetSuite
5Key sales orderNetSuite
6Confirm to retailerOutlook
1–3 days7Fix line mismatches
Most frequent exceptions
Exception
Per month
Resolution
Pattern
Owner
No carrier scan after 72 hours
410
1.8 days
Repeatable
Fulfillment
Split shipment confusion
260
0.6 days
Repeatable
CX
Address validation failure
180
1.2 days
Repeatable
Fulfillment
Backorder not communicated
140
2.4 days
Repeatable
CX
Missing attributes at review
120
4.5 days
Repeatable
Catalog
Damaged in transit
95
3.1 days
Judgment
CX
Invoice price mismatch
72
2.0 days
Repeatable
Finance
Return without authorization
64
1.5 days
Repeatable
Fulfillment
Wholesale PO line mismatch
19
2.2 days
Repeatable
Fulfillment
Suspected fraud or chargeback
14
5.0 days
Judgment
Finance
92% of exception volume follows a repeatable pattern that a system can resolve or prepare. Damage claims and fraud stay with people.
Risk register
Single point of failureCritical
Wholesale orders depend on one person’s macro
A personal spreadsheet macro reformats every retailer PO. When its owner is out, wholesale orders wait. Nothing is documented.
Customer experienceHigh
Customers find 3PL problems before you do
62% of 3PL exceptions are first reported by a customer. First response reaches 14 hours at peak.
Data integrityHigh
Three versions of every product
Attributes live in Airtable, Shopify and NetSuite. 12% of live SKUs disagree on at least one field, including 41 with wrong care instructions.
Revenue leakageHigh
Unmanaged goodwill credits
About $4,100 a month is credited without reason codes or approval limits.
AccessMedium
Shared 3PL admin login
Five people share one ShipBob admin account. Fix this before any automation is given access.
ReportingMedium
Two definitions of net revenue
Finance and merchandising report different numbers for the same week, so meetings start by reconciling.
What’s working: keep these
Clean order data
Shopify is a reliable record of orders and customers, with consistent tags.
A well-kept helpdesk
Current Gorgias macros give an AI agent good material from day one.
A written listing standard
Catalog already has a style guide and attribute list. Rare, and exactly what automation needs.
Appetite for change
11 of the 16 people we interviewed asked for help with the same three tasks.
Chapter 03
Opportunities
Twelve opportunities, each sized from the evidence and checked against the tools you already have. Values are net of the review and exception work each change introduces.
Value against effort
Positions use expected annual value. Colour shows the verdict, which also weighs confidence and risk.
How we decide
Do first3
Clear value, proven approach, ready to start.
Validate first4
Promising. One question to answer before building.
Defer3
Not now. Revisit when the conditions change.
Not recommended2
Little gain, or the risk belongs with people.
Value
Net hours returned each month, at $38 an hour fully loaded, plus costs avoided. Shown for conservative, expected and upside cases.
Effort
Build complexity, systems to connect and how much the work itself has to change.
Confidence
How strong the evidence is: measured, sampled or estimated.
All 12 opportunities
O1Order-status answers with live order and 3PL dataCustomer experience · Let the helpdesk answer “where’s my order?” from real order, carrier and warehouse data.Value / yr$86K$108K$127KHours / mo150 h180 h205 hEffortLowDo first
Today
3,720 order-status tickets a month, 38% of volume. Agents copy details from Shopify, the carrier site and the ShipBob portal. First response reaches 14 hours at peak.
Proposed
Switch on Gorgias’ AI agent for order-status questions, grounded in Shopify orders and carrier tracking. Add a small integration so it can see ShipBob exceptions, and hand anything unusual to an agent with the facts attached.
Where people stay in control
Refunds, address changes, damaged items and VIP customers always go to an agent. Your CX lead approves the answer rules before launch and reviews a weekly sample.
Approach
Configure existing
Connect systems
Live in week 5Confidence: High
The value, worked through
150 h180 h205 h a month × 12 × $38
$68K$82K$93K
Seasonal temp cover not needed
$18K$26K$34K
Annual value
$86K$108K$127K
Assumptions
Order status stays near 38% of tickets outside peak (sampled June–August).
4.5 minutes average handle time, from Gorgias reporting.
60–80% fully resolved without an agent, from our ticket sample and helpdesk benchmarks.
About 10% of the time saved goes to review and exceptions.
Data it needs
Shopify orders and fulfillments
Carrier tracking events
ShipBob exception feed
Gorgias macros and tags
Risks to manage
Two regional carriers lack scan events
Wrong delivery promises if data is stale
Customers who prefer a person
How we’ll measure it
First response time
Resolved without an agent
Repeat contact within 7 days
Satisfaction on automated answers
O2Supplier files to review-ready product recordsCatalog · Read supplier sheets, fill your product schema and flag gaps before review instead of after.Value / yr$50K$82K$125KHours / mo65 h82 h98 hEffortMediumDo first
Today
2,600 new SKUs a year arrive in more than 30 supplier formats. Each takes about 42 minutes to key, check and publish; launches wait 19 days on average for product data.
Proposed
An extraction workflow that reads supplier files, maps attributes to your listing standard, drafts titles and descriptions in your voice and flags missing care, sizing or material details at intake.
Where people stay in control
Merchandisers review every record before publication. Material, sustainability and safety claims require a source document.
Approach
Custom build
Connect systems
First category in week 9Confidence: Medium
The value, worked through
65 h82 h98 h a month × 12 × $38
$30K$37K$45K
Margin from launching sooner
$20K$45K$80K
Annual value
$50K$82K$125K
Assumptions
About 2,600 new SKUs a year across spring, fall and drops.
42 minutes per SKU, from shadowing three people on 24 SKUs.
45–65% of preparation time saved after review.
Launch benefit: margin on sales pulled forward about 13 days, applied to 30% of new SKUs.
Data it needs
Two seasons of supplier files
Airtable product base
Shopify product schema
Listing standard and style guide
Risks to manage
Supplier formats change without notice
Invented attributes, prevented by source-required fields
Inconsistent brand voice
How we’ll measure it
Time per SKU
Corrections in review
Attribute completeness
Days from receipt to live
O3Daily 3PL exception monitor and triageFulfillment · Find stuck orders before customers do, with the fix already drafted.Value / yr$36K$50K$65KHours / mo45 h58 h70 hEffortMediumDo first
Today
About 1,140 orders a month hit a 3PL exception. Ops finds them by exporting reports twice a day; 62% surface only when a customer writes in.
Proposed
A daily monitor that joins ShipBob, Shopify and carrier events, classifies each exception, drafts the fix and the customer update, and posts a prioritized queue to Ops every morning.
Where people stay in control
Ops approves reships, refunds and anything over $150. Customer messages use approved templates.
Approach
Connect systems
Custom build
Live in week 7Confidence: High
The value, worked through
45 h58 h70 h a month × 12 × $38
$21K$26K$32K
Goodwill credits and reships avoided
$15K$24K$33K
Annual value
$36K$50K$65K
Assumptions
Ops spends about 14 hours a week finding and chasing exceptions; CX about 7.
Half of today’s credits are avoidable when the problem is caught early.
Also reduces order-status tickets for O1; that effect isn’t counted twice.
Data it needs
ShipBob orders and exceptions API
Shopify fulfillments
Carrier events
Exception tracker history
Risks to manage
3PL API rate limits
Needs the shared admin login replaced first
How we’ll measure it
Exceptions found before the customer
Time to resolve
Credits issued
Order-status tickets
O4Automated weekly trading reportFinance · Reconciled numbers and drafted commentary by Monday morning.Value / yr$16K$20K$24KHours / mo36 h44 h52 hEffortMediumValidate
Why validate first. Two definitions of net revenue are in use. Automating now would automate the disagreement.
To validate: Finance and merchandising agree one definition of net revenue and of returns timing.
Today
The Monday report takes two days of an analyst’s time and reaches leadership on Tuesday afternoon.
Proposed
Agree metric definitions, automate the data pull and reconciliation, and draft commentary on the biggest movements with links to the underlying numbers.
Where people stay in control
The FP&A analyst edits the commentary; the Controller signs off before circulation.
Approach
Process change
Custom build
4 weeks after definitionsConfidence: Medium
The value, worked through
36 h44 h52 h a month × 12 × $38
$16K$20K$24K
Annual value
$16K$20K$24K
Assumptions
Two days a week of preparation today.
80–90% of assembly automatable once definitions are fixed.
Data it needs
Shopify, NetSuite and Loop exports
Current report workbook
Risks to manage
Definitions drift again without an owner
How we’ll measure it
Preparation time
Delivery time
Questions answered in the meeting
O5Invoice and PO matchingFinance · Match invoices to POs and receipts automatically; route mismatches with context.Value / yr$17K$25K$34KHours / mo28 h36 h44 hEffortLowValidate
Why validate first. This is a solved problem. Buying beats building, if a product fits your NetSuite setup.
To validate: Confirm two shortlisted products handle your multi-currency suppliers.
Today
About 520 supplier invoices a month are matched by hand. 14% have price or quantity mismatches that take a day or more.
Proposed
Evaluate two AP automation products that integrate with NetSuite. Configure matching rules and route mismatches to the buyer with the PO and receipt attached.
Where people stay in control
AP approves every payment; buyers resolve mismatches.
Approach
Buy a product
Configure existing
6 weeks after selectionConfidence: Medium
The value, worked through
28 h36 h44 h a month × 12 × $38
$13K$16K$20K
Early-payment discounts captured
$4K$9K$14K
Annual value
$17K$25K$34K
Assumptions
520 invoices a month at about 6 minutes each, plus mismatch work.
Product cost excluded; compared in the evaluation.
Data it needs
NetSuite POs, receipts and bills
Supplier terms
Risks to manage
Implementation effort varies by vendor
How we’ll measure it
Invoices matched without touch
Days to pay
Discounts captured
O6Return-reason analysis for product and sourcingCustomer experience · Turn free-text return reasons into a weekly fit and quality brief.Value / yr$14K$35K$67KHours / mo8 h12 h16 hEffortLowValidate
Why validate first. The value depends on whether the free text holds a usable signal. A short pilot will tell.
To validate: A two-week tagging pilot on one season’s returns.
Today
Return reasons are free text in Loop. Nobody reads them systematically, so fit and quality issues reach product teams months late.
Proposed
Classify return reasons weekly, link them to SKUs and factories, and send a short brief to product and sourcing.
Where people stay in control
Product decides what to change; the brief only surfaces patterns.
Approach
Custom build
Pilot in 2 weeksConfidence: Low
The value, worked through
8 h12 h16 h a month × 12 × $38
$3.6K$5.5K$7.3K
Returns avoided by earlier fixes
$10K$30K$60K
Annual value
$14K$35K$67K
Assumptions
Return rate of 14%; a 0.5–2 point reduction on affected styles.
Data it needs
Loop Returns export
SKU and factory mapping
Risks to manage
Free-text reasons are too vague to act on
How we’ll measure it
Issues found
Return rate on fixed SKUs
O7Wholesale PO intakeFulfillment · Read retailer POs and create draft sales orders for confirmation.Value / yr$10K$14K$17KHours / mo22 h30 h38 hEffortMediumValidate
Why validate first. Modest volume today. Worth it if wholesale grows as planned; the single-person risk needs fixing either way.
To validate: Confirm the 2027 wholesale forecast. Document the macro now regardless.
Today
310 POs a month arrive as PDFs and emails. One coordinator keys them into NetSuite with a personal macro.
Proposed
Extract line items from incoming POs, check them against price lists and stock, and create draft sales orders for the coordinator to confirm.
Where people stay in control
The coordinator confirms every order and handles retailer exceptions.
Approach
Custom build
Connect systems
6 weeksConfidence: Medium
The value, worked through
22 h30 h38 h a month × 12 × $38
$10K$14K$17K
Annual value
$10K$14K$17K
Assumptions
18 minutes per PO today; 60–75% saved.
Data it needs
Sample POs from top 10 retailers
Price lists
NetSuite items and stock
Risks to manage
Retailer formats vary widely
How we’ll measure it
Minutes per PO
Corrections
Orders entered same day
O8Demand forecasting modelPlanning · Forecast demand by style and size to cut stockouts and overstock.Value / yr$4.6K$69K$164KHours / mo10 h20 h30 hEffortHighDefer
Why defer. Eighteen months of reliable history and stockout-distorted data make a custom model unreliable today. Use NetSuite’s planning features and clean SKU data first; revisit next year.
Today
Buying uses spreadsheets and last year’s sales; frequent stockouts distort the history.
Proposed
A custom forecasting model trained on sales, traffic and seasonality.
Where people stay in control
Buyers own every purchase decision.
Approach
Custom build
6+ monthsConfidence: Low
The value, worked through
10 h20 h30 h a month × 12 × $38
$4.6K$9.1K$14K
Lower carrying cost and fewer stockouts
$0$60K$150K
Annual value
$4.6K$69K$164K
Assumptions
Value is potential, not expected, until data quality improves.
Data it needs
Sales history
Stockout log
Traffic
Risks to manage
Unreliable history
High cost to build and maintain
How we’ll measure it
Forecast accuracy
Stockout rate
O9AI-generated lifestyle imageryCreative · Generate lifestyle scenes for new products without a shoot.Value / yr$2.7K$13K$26KHours / mo6 h10 h14 hEffortMediumDefer
Why defer. Small time saving and real brand risk. Your creative standards for generated imagery aren’t set yet; revisit once they are.
Today
Two lifestyle shoots a season cover hero products only.
Proposed
Generate supporting lifestyle images from product photography.
Where people stay in control
Creative approves every image.
Approach
Buy a product
—Confidence: Low
The value, worked through
6 h10 h14 h a month × 12 × $38
$2.7K$4.6K$6.4K
Shoot costs reduced
$0$8K$20K
Annual value
$2.7K$13K$26K
Assumptions
Limited to supporting images, never product accuracy shots.
Data it needs
Product photography
Risks to manage
Brand and authenticity
Product misrepresentation
How we’ll measure it
Approval rate
Conversion on affected pages
O10Personalized email contentMarketing · Generate subject lines and content variants for segments.Value / yr$1.8K$7.7K$14KHours / mo4 h6 h8 hEffortLowDefer
Why defer. Not a bottleneck, and Klaviyo already includes it. Your marketing team can trial it without us.
Today
Marketing writes three to four campaigns a week with light segmentation.
Proposed
Trial Klaviyo’s built-in AI features for subject lines and variants.
Where people stay in control
Marketing approves all sends.
Approach
Configure existing
—Confidence: Medium
The value, worked through
4 h6 h8 h a month × 12 × $38
$1.8K$2.7K$3.6K
Incremental email revenue
$0$5K$10K
Annual value
$1.8K$7.7K$14K
Assumptions
Uses existing Klaviyo plan features.
Data it needs
Klaviyo account
Risks to manage
Off-brand copy
How we’ll measure it
Open and click rates
O11Fully automated refundsCustomer experience · Approve and issue refunds without an agent.Value / yr$4.6K$6.4K$8.2KHours / mo10 h14 h18 hEffortMediumDon’t do
Why not recommended. Refunds are a small share of tickets but carry most of the fraud and policy risk. Keep them with agents; O1 and O3 give agents the context to decide faster.
Today
Refunds are 4% of tickets and are approved by agents within policy limits.
Proposed
Automatic approval of refunds that meet policy rules.
Where people stay in control
Agents approve all refunds today.
Approach
Custom build
—Confidence: High
The value, worked through
10 h14 h18 h a month × 12 × $38
$4.6K$6.4K$8.2K
Annual value
$4.6K$6.4K$8.2K
Assumptions
Hours shown for comparison only.
Data it needs
Refund history
Risks to manage
Fraud
Policy abuse
How we’ll measure it
—
O12Custom chatbot to replace the helpdeskCustomer experience · Build a bespoke support assistant instead of using Gorgias.Value / yr$0Hours / mo0 hEffortHighDon’t do
Why not recommended. Gorgias’ own AI agent covers the need (O1). A custom build adds cost and maintenance with no clear gain.
Today
Gorgias handles every channel and already includes an AI agent.
Proposed
A custom assistant integrated with Shopify and ShipBob.
Where people stay in control
—
Approach
Custom build
—Confidence: High
The value, worked through
0 h a month × 12 × $38
$0
Annual value
$0
Assumptions
No gain over O1.
Data it needs
—
Risks to manage
Ongoing maintenance
Duplicated tooling
How we’ll measure it
—
Chapter 04
Plan
A 13-week plan to deliver the three recommended changes and settle the four open questions. Each phase ends with a measured result and a decision about what comes next.
Phase 1Weeks 1–5
Quick win
Order-status answers live and measured.
Baselines and access
Answer rules approved
O1 live on all carriers
Phase 2Weeks 4–9
Core workflows
Exceptions caught early; first category through the pipeline.
O3 exception queue
O2 outerwear category
Shared login replaced
Phase 3Weeks 8–13
Extend and validate
Your team running the pipeline; open questions answered.
O2 remaining categories
O4 definitions agreed
O5 product selected
13-week roadmap
Built for you
Built with you
Your team
WorkstreamW1W2W3W4W5W6W7W8W9W10W11W12W13
Baselines and access: weeks 1 to 2, built for you
O1Answer rules and configuration: weeks 1 to 3, built for you
O1Pilot on two carriers, then all: weeks 3 to 5, built for you
O33PL exception feed: weeks 2 to 4, built for you
O3Daily triage queue: weeks 4 to 7, built for you
O2Schema mapping and standards: weeks 5 to 6, built with you
O2Pipeline: outerwear category: weeks 6 to 9, built with you
O2Remaining categories, your team leads: weeks 10 to 13, built with you
O4Net revenue definitions: weeks 8 to 9, your team
O5AP product evaluation: weeks 9 to 11, your team
Review and next-quarter plan: weeks 12 to 13, built for you
Milestones
Week 2Baselines agreed
Week 5Order-status answers live
Week 7Exception queue live
Week 9First category published
Week 13Quarter review
Capacity, gross to net
Hours a month
Time recoveredBefore any new work300 h368 h425 h
Less review and quality checksWeekly samples, record review−25 h30 h32 h
Less new exception handlingHand-offs from automation−10 h12 h14 h
Less maintenance and tuningRules, prompts, supplier formats−5 h6 h6 h
Net usable capacity260 h320 h373 h
1.92.32.7 full-time roles of net capacity. We recommend using it for peak-season cover, faster launches and supplier management rather than reducing headcount.
Economics, per year
Time returnedNet hours × 12 × $38
$119K$146K$170K
Other benefitsSeasonal temp cover not needed · Margin from launching sooner · Goodwill credits and reships avoided
$53K$95K$147K
Helpdesk AI agent usagePer automated resolution; rises with success
−$25K$30K$36K
Model usage for extraction and triage
−$3K$4.5K$6K
Integration hosting and monitoring
−$3.6K$4.8K$6K
Net annual value
$140K$202K$269K
Conservative $140K · Upside $269K
Before build fees, which are fixed and quoted separately. Values are estimates from discovery; the plan measures each against a baseline.
How we’d deliver it
Built for you
We build and launch it. Your team approves and adopts it.
O1Order-status answers with live order and 3PL data
O3Daily 3PL exception monitor and triage
Integration-heavy work with a clear outcome. We build and launch it; your CX lead and Ops manager approve the rules and review results.
Built with you
Your team builds it with us, then owns it.
O2Supplier files to review-ready product records
Catalog work changes every season, so it should be owned in-house. Your merchandiser co-builds the first category with us, then leads the rest with our review.
Your team
Low-risk changes your team can make with our checklist.
O4Automated weekly trading report
O5Invoice and PO matching
O10Personalized email content
Definitions, a product evaluation and a feature trial. We provide a short checklist and review the outcome.
Who does what
Area
Keating & Co
Fernway Outfitters
Answer rules and tone
Draft, test against real tickets
CX lead approves before launch
Integrations and access
Build, document, monitor
Grant least-privilege access; IT reviews
Catalog pipeline
Co-build first category, then review
Merchandiser owns from week 10
Measures and reporting
Baselines and a fortnightly scorecard
COO reviews and decides next steps
Support after launch
60 days of close support included
Choose: your team, a partner or us
Decisions we need from you
Confirm phase-one scope (O1 and O3) and name the business owners.
Grant read access to Gorgias, Shopify, ShipBob and a NetSuite sandbox.
Agree the baseline period and the four success measures.
Nominate the merchandiser who will co-build O2.
Schedule the net revenue definitions session (O4).
Next steps
01
Questions and clarifications
Walk through this plan with your leadership team. One round of clarifications is included.
This weekBoth
02
Scope and proposal
A fixed scope and fee for phase one, with the discovery fee credited in full.
Within five daysKeating & Co
03
Kickoff
Access, baselines and answer rules in the first week.
Week 1Both
04
First result
Order-status answers live and measured.
Week 5Keating & Co
Investment
Complete
Discovery & Action Plan
This document.
Proposed
Build: weeks 1–13
Fixed scope and fee, quoted on approval. Discovery fee credited in full.
Optional
Run and improve
Monthly support after 60 days of close support, or a handover to your team.