Product Marketing Assignment — AI-Powered Supply Chain Optimization

SEC 1

GTM Strategy

1A · Ideal Customer Profile (ICP)

Dimension	Target Profile	How to source it (public)
Segment	US omnichannel retailers — apparel, footwear, specialty, home, outdoor	Apollo / Clay industry filters; LinkedIn Sales Navigator
Revenue	$250M – $3B	Apollo / Clay revenue field; 10-K for public companies
Operational scale	Distribution centers; store + ecommerce + ship-from-store	10-K "Properties" section; store-locator; DC/fulfillment job posts; checkout shipping options
Tech fragmentation (qualifier)	Disconnected ERP + planning module + WMS + ecommerce, with no integration layer	BuiltWith / Clay tech-enrichment; job posts naming SAP, Oracle, Manhattan, NetSuite
In-house data capability	No mature, dedicated supply-chain data-science team	Sales Navigator headcount-by-function; data roles on careers page

"Why Now" triggers (publicly sourceable)

Margin / inventory pressure — earnings-call transcripts (keywords: "markdown," "inventory," "promotional," "margin pressure").
Tariff / sourcing exposure — 10-K Item 1A risk factors.
Operational trigger — new DC opening, ship-from-store rollout, newly hired Director of Demand Planning (Clay / LinkedIn hiring signals).

ICP Scoring Rubric (score 0–2 per signal; 7+ = Tier A, 4–6 = Tier B, under 4 = drop)

Signal	0	1	2	Tool
Revenue fit	Outside band	Edge of band	Squarely $250M–$3B	Apollo / Clay
Channel complexity	Single channel	Omnichannel	Omnichannel + ship-from-store	Website / store-locator
System fragmentation	Unified stack	ERP + one bolt-on	Multiple disconnected systems	BuiltWith + job posts
Why-now trigger	None	One soft signal	Public earnings / 10-K / hiring signal	Transcripts / 10-K / Clay
In-house data capability	Many data roles	A few analytics roles	None	Sales Navigator / careers page

Anti-ICP (do not chase):

Sub-$250M (can't fund a services engagement).
Over $3B giants (in-house data org + Big-4 ties; cycle too long for 90 days).
Already-unified planning stacks (RELEX, o9, Blue Yonder, SAP IBP fully deployed).
Single-channel / low-complexity retailers.

1B · Buyer Personas

The Sponsor

VP / Chief Supply Chain Officer (or COO)

Nav filter: "VP Supply Chain," "Chief Supply Chain Officer," "VP Operations"

Core job: Hit fulfillment SLAs, optimize working capital, defend margin to the board.
Measured on: Inventory turns, gross margin, stockout rate, cash tied up in inventory.
2 AM nightmare: "Eight figures frozen in slow-moving stock in one DC while we stock out on our highest-margin line in another — and the board wants to know why margin slipped."
Core objection: "We already have a forecasting tool. Why would this be different?"

The line that flips them: "Your forecast isn't the problem — it never reaching the shelf is. We connect planning, ERP and WMS to improve inventory optimization, freeing meaningful working capital within a quarter."

The Champion

Director of Demand Planning / S&OP Manager

Nav filter: "Demand Planning," "S&OP," "Inventory Planning"

Core job: Produce the forecast, run S&OP, defend the numbers to merchants and finance.
Measured on: Forecast accuracy (MAPE), planner productivity, fill rate.
2 AM nightmare: "I fix the system's forecast by hand every week, and I'm still the one who gets blamed when we stock out or over-buy."
Core objection: "I've seen 'AI forecasting' before — long setup, then I'm stuck defending a number I can't explain."

The line that flips them: "You stay in control. The model does the heavy lifting and flags shifts early, but you make the final call — and you can see the reason behind every recommendation, so you walk into S&OP with a number you can defend."

The Gatekeeper

CIO / VP of IT (with Head of Data)

Nav filter: "CIO," "VP IT," "Head of Data"

Core job: Keep core systems stable and secure, manage technical debt, prevent failed implementations.
Measured on: Uptime, security posture, on-time / on-budget delivery.
2 AM nightmare: "Another vendor wants to wire an unvetted AI engine into our system of record. If it breaks, the floor stops — and it's my name on the report."
Core objection: "I won't give an external model write-access to my system of record."

The line that flips them: "You don't have to. Read-only via governed APIs, recommendations out, any write-back human-approved — exactly the governance Gartner flags as the top AI risk for 2026."

1C · Value Proposition + 3 Key Messages

Core Value Proposition: "We turn fragmented retail data into predictable margins — by layering AI demand forecasting onto the systems you already run and connecting prediction to replenishment, so insight actually reaches the shelf. No rip-and-replace. No autonomous control of your systems. First insights in weeks via a diagnostic; integration phased over a defined rollout."

Message 1 · Sponsor

"Your AI spend may be failing silently."

Pitch: A sharper forecast that never reaches your replenishment systems changes nothing. We connect prediction to action across stores and channels, so the number becomes a stocking decision.
Proof: The industry's own diagnosis — execution systems are the weakest link; rigid rules overwrite AI insight before it reaches the store.
Outcome: Meaningful trapped cash freed within a quarter.
Kills the objection: "We already have a forecasting tool."

Message 2 · Gatekeeper

"AI value without the AI risk."

Pitch: No rip-and-replace, no autonomous control. We read your data through governed APIs and return recommendations; any write-back is human-approved.
Proof: Gartner names AI and decision-governance failure a top strategic risk for 2026.
Outcome: Adopt AI with a clean audit trail and no new single point of failure.
Kills the objection: "I won't give a model write-access to my system of record."

Message 3 · Champion

"End the weekly override grind."

Pitch: Trade manual overrides for a model that flags demand shifts and at-risk SKUs early, so you manage exceptions instead of rebuilding the plan.
Proof: Planners today spend most of their time massaging data; a large share of that work is automatable.
Outcome: Hours back each week, fewer overrides, a forecast defensible in S&OP.
Kills the objection: "Will this replace me or pile on more work?"

1D · 30-60-90 Day GTM Plan

Targets a small, high-value account set. With 12 enterprise accounts and long 6–12 month retail sales cycles, the 90-day goal is not a closed deal — it is 2–3 readiness-kit fills, a trust-building webinar, and a scoped proposal that seeds pipeline for the following quarters.

Days 1–30 — Foundation & Targeting

Objective: lock the account list and ready the assets.

Activity	Asset	Tool / Channel
Build the 12-account list in Clay (revenue + tech + hiring + trigger enrichment), score via rubric	Tier-A list with per-account trigger	Clay / Apollo / Sales Navigator
Adapt hero asset: Supply Chain AI Readiness Assessment	10-minute gated scorecard	Web / LinkedIn
Build the Demand Planner's Discovery Playbook	Discovery guide	Internal

Exit gate: 12 accounts scored Tier-A, 36 contacts mapped in Sales Navigator, hero asset live.

Days 31–60 — Account-Based Outreach

Objective: earn first conversations through specific, non-generic insight.

Activity	Asset	Channel
1-to-few ads matched to the 12 company domains	Execution-gap teardown creative	LinkedIn
Synchronized LinkedIn + 3-step email to all 3 personas	Outreach sequences	LinkedIn + email
Publish 2 credibility pieces (Gatekeeper: safe integration; Champion: forecasting)	2 articles	LinkedIn / blog

Exit gate: 4–6 of 12 accounts meaningfully engaged; 2–3 Readiness Assessments completed; 2–3 discovery meetings booked (stretch: 4–5).

Days 61–90 — Prove Value & Seed Pipeline

Objective: convert a discovery into a low-risk paid proof and a scoped proposal.

Activity	Asset	Channel
Convert qualified discoveries into scoped opportunities via a short results review of their Readiness Kit findings	Per-account findings deck	Direct
Host a webinar inviting all engaged + cold prospects, moderated by 1–2 external industry leaders alongside our heads, framing the forecast-to-shelf problem and how peers are scoping it	Webinar + benchmark share	Virtual event
Issue scoped proposals; equip the Champion with the CFO business case	SOW + 1-page ROI case	Direct

Exit gate: 1–2 scoped opportunities created; 1 webinar run with external moderators; 1 scoped proposal issued. First close expected in a following quarter, in line with enterprise retail sales cycles.

The webinar doubles as a recall and education play: it re-engages cold prospects who ignored 1:1 outreach, borrows credibility from external industry leaders, and helps attendees understand the scope and applicability for their own companies — warming the wider list while paid audits advance the hottest accounts.

SEC 2

ABM Plan

2A · Target Accounts

Each account is sourced and scored using the Section 1 rubric. Revenue and systems are estimates — confirm via 10-K, Apollo/Clay and job posts before outreach; swap any that exceed $3B or already run a unified planning stack.

#	Account	Segment	Entry Persona
1	Boot Barn	Western/work apparel & footwear	Sponsor
2	Designer Brands (DSW)	Footwear	Champion
3	The Container Store	Home/storage	Sponsor
4	Duluth Trading	Apparel DTC + retail	Champion
5	Build-A-Bear	Experiential retail	Champion
6	Citi Trends	Value apparel	Sponsor
7	Shoe Carnival	Footwear, multi-banner	Gatekeeper
8	Sportsman's Warehouse	Outdoor	Champion
9	Torrid	Apparel omnichannel	Champion
10	Ollie's Bargain Outlet	Closeout	Sponsor
11	Lands' End	Apparel DTC→omnichannel	Champion
12	Kirkland's	Seasonal home décor	Sponsor

The Account Drill (run before outreach)

Job listings — capture named systems (SAP, Oracle, NetSuite, Manhattan WMS, ecommerce platform) and open demand-planning / ERP-architect / S&OP roles.
10-K Item 1A — flag single-source suppliers, inventory obsolescence, markdown pressure, fulfillment risk.
Two competitors — identify one peer move (new DC, AI initiative, omnichannel push) to anchor the copy.
LinkedIn org map (Sales Navigator) — name the Sponsor, Champion, Gatekeeper; note recent hires/promotions.
Trigger validation — confirm the "why now" is live within the last 1–2 quarters before sending.
Read reports, investments, understand the system of the org.

2B · Multi-Channel Plan

Channels run as coordinated waves against the same 12 accounts, built around three assets: the AI Readiness Kit (10-min assessment + scorecard), the Demand Planner's Playbook, and the Webinar.

Wave	Window	Channel	Action	Asset / CTA
1 — Air cover	Days 1–14	LinkedIn ads (1-to-few, 12 domains)	Awareness on the planning→ERP→WMS disconnect	AI Readiness Kit
2 — Direct 1:1	Days 7–30	LinkedIn + 3-step email	Synchronized outreach to all 3 personas per account	Readiness Kit → discovery call
3 — Educate	Days 21–45	Email + LinkedIn	Share the Playbook with engaged-but-undecided contacts	Demand Planner's Playbook
4 — Recall & convene	Days 45–90	Webinar	Invite all engaged + cold prospects; external leaders + our heads moderate	Attend → discovery call

Channel roles

LinkedIn ads = awareness only (12 accounts is too small a pool to optimize on).
Email + LinkedIn 1:1 = primary conversation driver; the Readiness Kit is the low-friction entry offer.
Playbook = educational nurture for contacts who engaged but haven't booked.
Webinar = recall & education play; re-engages cold prospects, borrows credibility from external leaders.

Cadence per persona: Day 1 email + LinkedIn connect → Day 4 email 2 → Day 8 email 3 → Day 14 LinkedIn touch (Playbook) → Day 45–90 webinar invite for non-responders.

2C · LinkedIn Outreach Message

Targeting: Sponsor (VP Supply Chain / COO)

Hi [First Name] — hope all's well. I'm reaching out from [Your Company].

We help retailers connect demand forecasting to the systems that actually execute it. Our solution links Plan, ERP and WMS so the forecast becomes a stocking decision instead of a report nobody acts on. No rip-and-replace, no loss of control.

We've been seeing a lot more interest from omnichannel retailers lately — they already own the forecasting tools, but the number never reaches the shelf, and the cash stays trapped.

That's why I think this could be a strong fit for [Company] — especially if your team is carrying excess safety stock to cover demand you can't see, stocking out on your best SKUs while dead stock sits in another DC, or finding out about supplier delays only after the shelf goes empty.

Worth a quick look? I can send our 10-minute Supply Chain AI Readiness Kit — it shows how connected planning, ERP and WMS really are, flags the gaps driving overstock and stockouts, and benchmarks against peers.

Best, [Name] · [Book a time]

2D · Email Sequence (3 Steps)

Step 1 · Day 1 — the "especially if you're…" anchor

Subject: The forecast you're already paying for

Hi [First Name],

I'm reaching out from [Your Company]. We help mid-market retailers connect demand forecasting to the systems that actually execute it — planning, ERP and WMS — so the forecast turns into a stocking decision instead of a report nobody acts on.

We've been seeing a lot more interest from omnichannel retailers lately — they already own the forecasting tools, but the number never reaches the shelf, and the cash stays trapped.

That's why I think this could be a fit for [Company] — especially if your team is:

carrying excess safety stock to cover demand you can't see across channels,
stocking out on your best SKUs while dead stock sits in another DC, or
learning about supplier delays only after the shelf goes empty.

If any of those land, I'll send our 10-minute AI Readiness Kit — it scores where the gaps are before we ever talk. Worth a look this quarter?

Best, [Name]

Step 2 · Day 4 (reply thread) — the risk objection + educate

Subject: RE: The forecast you're already paying for

Hi [First Name],

Following up. Usually the hesitation here isn't whether AI forecasting works — it's the fear of a long, risky build wired into the core ERP.

That's exactly what we designed around — especially if you're hearing from IT that the last "AI" project turned into a multi-year integration, that no one wants an external model writing to the system of record, or that a broken pipeline could stop the warehouse floor. We sit read-only: ingest via governed APIs, return recommendations, any write-back human-approved. Core systems untouched, first insights in weeks.

I'll also send our Demand Planner's Playbook — it lays out how we scope this safely, stage by stage. Want it?

Best, [Name]

Step 3 · Day 8 (reply thread) — the low-friction close

Subject: RE: The forecast you're already paying for

Hi [First Name],

I'll keep this short. If the Readiness Kit surfaced gaps — or you just want a read on where your biggest cash leaks sit — the fastest next step is a short results review, walking through your findings against what we see across similar retailers. Open to a quick look this week?

Best, [Name]

2E · Success Metrics

Volume metrics are discarded for a 12-account pool. We track account penetration, progression and pipeline against realistic enterprise conversion over a long sales cycle.

Metric	Definition	Target (90 days)
Account penetration	≥3 personas engaged within the same account (Sponsor + Champion + Gatekeeper)	4–6 of 12
Multi-threading depth	Avg. engaged contacts per active account	≥3
Readiness Kits completed	10-min assessment completed by a target exec	2–3
Playbook downloads	Engaged contacts taking the nurture asset	4–6 contacts
Discovery meetings booked	Short review calls across the 12 accounts	2–3 (stretch 4–5)
Webinar re-engagement	Cold/non-responsive accounts attending the webinar	3–5 accounts
Scoped opportunities	Discovery calls advancing to a scoped/qualified opportunity	1–2
Net pipeline value	Total Contract Value sourced from the cohort	Tracked cumulatively
First close	Expected in a following quarter, per 6–12 month retail cycles	0–1 in 90 days

SEC 3

Sales Enablement

The full designed pitch deck is available as a separate file — 📑 open the Pitch Deck (PDF). The outline below is the talk track behind it.

3A · Pitch Deck (7 slides — content + talk track)

Why Now "Demand has never been harder to predict." Three drivers: tariff volatility, omnichannel demand splintering, thin margins.
Talk track: "The forecasting playbook that worked five years ago is broken. Tariffs shift monthly, the same SKU now competes across store, online and ship-from-store, and margins are too thin to absorb a bad bet." · Visual: three icons; a margin line trending down.
The Hidden Cost A bad forecast costs twice: cash frozen in dead stock + lost sales from stockouts.
Talk track: "Capital trapped in slow movers in one DC, and stockouts on your best-margin SKUs in another. Most teams only measure the overstock. The lost-sales side is invisible — and usually bigger." · Visual: split screen, overstocked DC vs. empty shelf.
Why It Persists Your systems don't talk. Forecast (planning tool) can't reach replenishment (ERP), which can't see stock (WMS).
Talk track: "You don't have a forecasting problem. The number lives in one system and the replenishment rules live in another. The forecast dies before it reaches the shelf." · Visual: three disconnected system boxes, broken arrows.
The Reframe "A forecast your systems can't act on is worthless." The forecast-to-execution gap is the real problem.
Talk track: "A pure software vendor sells you a better number and walks away. We're a transformation firm. We connect the systems so the number becomes a decision." · Visual: the three boxes now connected by a clean intelligence layer.
How It Works (the Gatekeeper slide) Read (governed APIs) → Forecast & Recommend → Human-approved write-back. "Core systems untouched."
Talk track: "We read your data through governed, read-only APIs and return SKU- and store-level recommendations. Nothing changes in your system of record unless a human approves it." · Visual: architecture diagram with read-only + human-approval gates highlighted.
Proof Case study snapshot — 3–4 hard metrics + one customer quote.
Talk track: "A ~$900M apparel retailer in your exact position freed ~$3M in working capital and cut top-SKU stockouts from 12% to 7% in two quarters — without replacing a single system." · Visual: before/after metric tiles.
The Path AI Readiness Kit (10 min) → Discovery → Phased integration → Scale. CTA.
Talk track: "The first step costs you nothing and ten minutes: our Readiness Kit scores exactly where your forecast-to-shelf gap is leaking cash. From there we phase it, proving value on a slice before touching the network." · Visual: four-step horizontal path, step 1 highlighted.

3B · Discovery Questions (arm the rep before the deck)

"When your demand plan updates, how does that actually change what gets shipped? Is it automatic, or does someone re-key it?"
"How do you see ship-from-store demand in your forecast today?"
"When a stockout happens, do you find out before or after it hits the shelf?"
"How much of your planners' week goes to manual overrides versus actual analysis?"
"Last time you bought a planning tool, did margin actually move?"

3C · Objection Handling (the ten that decide the deal)

1 · "We already have a forecasting tool."

Most of our clients did too — that's exactly the point. The tool improved the number, but the number never reached replenishment. We connect what you already own.

2 · "I won't give a model access to my system of record."

You won't have to. We're read-only through governed APIs, and any write-back is human-approved. Your core system is never touched.

3 · "This sounds like a multi-year project."

First insights come in weeks via the Readiness check and a phased pilot. We prove value on one slice before we touch the network.

4 · "Migration and integration are a risk for us."

That's the right concern — which is why we avoid migration entirely. Nothing moves and nothing gets replaced; we connect to your existing systems through standard APIs and run alongside them. Switch us off and your systems are exactly as they were. We start with one DC or one category, prove it's stable, then expand.

5 · "What about data security and compliance?" (CISO)

We're read-only and we work within your security perimeter — SOC 2, your access controls, your data residency. We don't move data out; we read what you permit and return recommendations. Your security team reviews the architecture before anything connects.

6 · "How is this different from what SAP / Oracle / Blue Yonder already gives us?" (incumbent)

Those are the systems we connect to, not replace. The platform vendors optimize within their own walls; the value you're losing is in the handoff between them. We're the layer that makes them work together — complementary, not competing.

7 · "Do we lose headcount on the planning team?" (people / political)

No. Your planners stay in control and make the final call; the system removes the manual grunt work so they spend time on judgment, not reconciliation. It makes the team you have more effective.

8 · "How do we measure ROI / what's the business case?" (CFO)

We baseline before we start — excess inventory, stockout rate, working capital tied up — then measure against it each quarter. You see freed cash and stockout reduction in hard numbers. The Readiness check gives you the starting benchmark for free.

9 · "We don't have the internal bandwidth for another project." (capacity)

That's why the first phase is light on your side — the Readiness check needs no data and no time, and the pilot runs on one slice with our team doing the heavy lifting. We work around your calendar.

10 · "Why now? We can revisit next budget cycle." (timing)

Fair — but the cost of waiting isn't zero. Every quarter the gap persists is another quarter of trapped cash and lost sales, and tariff and demand volatility are widening it. The Readiness check costs nothing and tells you whether it's worth acting on now or later — so you decide with data, not defer blind.

3D · 30-Second Elevator Pitch

"Most retailers are sitting on millions in the wrong inventory — too much of what isn't selling, too little of what is — because the forecast lives in one system and replenishment runs in another, so the number never reaches the shelf. We're a digital transformation firm: we connect your planning, ERP and WMS so the forecast becomes an actual stocking decision, without replacing your systems and without giving up control. For a retailer your size, that's typically single-digit to low-tens of millions in freed cash within a quarter."

3E · Three Business Outcomes

Working capital released: 15–20% reduction in excess and obsolete inventory, freed without raising stockout risk.
Revenue protected: stockouts on high-margin SKUs cut by roughly a third through earlier, location-level demand signals.
AI adopted without disruption: first value in weeks, core systems untouched, zero autonomous control — the version IT and finance will actually approve.

3F · Simple Case Study Structure

Context: company type, scale, segment.
Trigger: the "why now" that opened the door.
The friction: the pain, quantified.
What we did: Readiness Kit → diagnosis → phased integration; no-disruption explicit.
Results: 3–4 hard metrics.
In their words: one short Champion or Sponsor quote.
What's next: the expansion path.

SEC 4

Content

4A · Three Blog Topics

The Hidden Cost of Safety Stock: How Disconnected Systems Quietly Trap Your Cash. Why a forecast in one system and replenishment in another forces over-buffering. (Sponsor.)
Layering AI on Systems You Already Run: A CIO's Guide to Read-Only Forecasting. A technical walkthrough of governed APIs and human-approved write-back, without core-system risk. (Gatekeeper.)
From Override to Insight: How Demand Planners Escape the Weekly Spreadsheet Grind. How connecting forecast to execution turns planners from system-babysitters into exception-managers. (Champion.)

4B · Webinar Topic

"The Forecast-to-Shelf Gap: Why Your AI Investment Isn't Moving Margin, and How Retail Leaders Are Closing It." Moderated with 1–2 external industry leaders and our heads; anonymized before/after examples of mid-market retailers connecting prediction to execution.

4C · LinkedIn Post (Sponsor / Champion)

If your business is carrying an extra 15–20% in "safety stock" right now, you're not managing inventory. You're subsidizing a forecast that can't reach the shelf.

Most mid-market retailers don't have a forecasting problem. They have a connection problem. The forecast lives in the planning tool. The replenishment rules live in the ERP. The stock lives in the WMS. None of them talk — so by the time a stockout shows up in a report, the wrong DC has already shipped the wrong thing.

The usual fix is more tools, disconnected, and more buffer capital thrown at the risk. There's a better one: connect the systems you already own, so the forecast becomes an actual stocking decision. No multi-year migration. No handing over control.

We built a 10-minute AI Readiness Kit that scores exactly where your forecast-to-shelf gap is leaking cash. Comment "readiness" and we'll send it over.

4D · Landing Page

🌐 Open the live landing page →

Headline

Turn fragmented retail data into predictable margins.

Subtext

Your forecast is only worth something if it reaches the shelf. We connect AI demand forecasting to the systems you already run — planning, ERP and WMS — so prediction becomes a stocking decision. Free up trapped working capital, cut stockouts on your best SKUs, and keep full control. First value in weeks, not a multi-year build.

Case Study (full)

Context	US omnichannel apparel retailer, ~$900M revenue, 300+ stores plus ecommerce and ship-from-store, running SAP ECC with a separate planning module and a third-party WMS.
Trigger	A new VP of Supply Chain inherited rising markdowns and a board question about why margins kept slipping despite a recent demand-planning tool purchase.
The friction	The forecast was improving on paper, but fixed ERP replenishment rules overwrote it. Result: ~$18M tied up in slow-moving stock across two DCs, a 12% stockout rate on top-margin SKUs, and planners spending most of each week on manual overrides they didn't trust.
What we did	Started with the AI Readiness Kit to score the gaps, then ran a phased integration connecting planning output to ERP replenishment via governed, read-only APIs. Recommendations surfaced to planners; write-back human-approved. No system replaced.
Results (first 2 quarters)	Excess & obsolete inventory down ~17%, releasing ~$3M in working capital. Top-margin SKU stockouts cut from 12% to ~7%. Forecast-to-replenishment cycle moved from weekly batch to near-daily. Planner override time cut roughly in half.
In their words	"We didn't need a new system. We needed our systems to talk to each other." — VP, Supply Chain.
What's next	Expanding from the two pilot DCs to the full network, and extending demand sensing to new-product launches.

(Figures are illustrative and internally consistent for the assignment; replace with real data before external use.)

SEC 5

Metrics

The motion is run by a lean 5–6 person pod, so the model is built for leverage, not volume: 12 accounts worked deep by hand (Tier 1), ~40 worked wide on automation (Tier 2), and human time concentrated on the one stage that converts — discovery. Metrics lead with pipeline value, not logo count, because this is a high-ACV, long-cycle motion.

5A · KPIs by Discipline

Discipline	Primary KPI	What it measures	Realistic target (first 90 days)
GTM (headline)	Qualified pipeline value (TCV)	Total contract value of scoped opportunities created	$400K–$1.6M
GTM	Pipeline-coverage ratio	Qualified pipeline value ÷ fully loaded program cost	5× or better
GTM	Pipeline velocity	Days from first discovery call to a scoped opportunity	Baseline set this quarter; long retail cycle assumed
ABM (Tier 1)	Account penetration	% of the 12 deep accounts with 3+ personas engaged	4–6 of 12
ABM (Tier 1)	Multi-threading depth	Avg. engaged contacts per active account	3 or more
ABM (Tier 1)	Meeting-to-opportunity rate	% of discovery calls advancing to a scoped opportunity	~30%
Demand (Tier 2)	Wider reach engaged	Tier-2 accounts touched via webinar / content / ads	30–40 accounts
Demand (Tier 2)	Webinar registrants	Registrants across both tiers	40–80
Content	Asset-to-conversation rate	% of Readiness Kit completions + webinar attendees who book discovery	15–25%

5B · The Two-Tier Funnel

Tier 1 — Deep ABM (12 accounts, human-led). SDRs and PMM run 1:1 outreach, multi-threading and discovery. Small base, high intent — where near-term pipeline comes from.

Tier 2 — Wide demand (~40 accounts, automation-led). Same asset set delivered with near-zero extra labor. Lower intent, larger volume — fills Q2–Q3 pipeline while Tier 1 advances.

Stage	Definition	Tier 1 (deep)	Tier 2 (wide)
Accounts touched	In motion	12	~40
Accounts engaged	Multi-touch reply / interaction	4–6	10–15
MQL	Readiness Kit completed or webinar attended	2–3	8–12
SQL	Discovery call taken + trigger confirmed	2–3	1–2
Pipeline	Scoped opportunity created	1–2	0–1
Closed-won	Signed engagement	0–1 (likely next quarter)	nurtured to later quarters

The webinar is the bridge between tiers: one event, run by the existing team, pulls 40–80 registrants across both, re-engages cold Tier-1 accounts, and feeds the Tier-2 pipeline — the highest-leverage spend in the plan.

5C · MQL → SQL → Pipeline → ROI

Each stage is tied to a concrete, observable action — not a vanity signal.

MQL — an in-ICP executive (VP / Director / CXO at a $250M–$3B retailer) completes the AI Readiness Kit or attends the webinar. Fit plus intent, not a click.
SQL — that executive takes a discovery call and the account shows a confirmed "why now" trigger. The booked, attended conversation defines the stage.
Pipeline — the discovery advances to a scoped opportunity with an agreed next step. Counted at Total Contract Value.
ROI — contract value won from the cohort ÷ fully loaded program cost (ads, content, tools, rep time), measured over the full sales cycle.

ROI = Total Contract Value sourced ÷ (program spend + loaded labor)
Worked example: one closed engagement at ~$400K TCV against ~$80K fully loaded program cost returns ~5×, with $400K–$1.6M in scoped opportunities carried as pipeline coverage behind it. Conversion rates are assumptions, to be replaced with observed rates after the first cohort.

5D · 12-Month Trajectory (why the small base compounds)

The 90-day base is small because it is early, not because it is the ceiling. Each quarter adds a new Tier-1 cohort while prior Tier-2 accounts mature into Tier-1.

Horizon	Accounts in motion	Qualified pipeline (cumulative)	Closed-won
90 days	12 deep + 40 wide	$400K–$1.6M	0–1
6 months	+12 new deep cohort	$1.2M–$2.5M	1–2
12 months	3–4 cohorts run	$2.5M–$4M	3–5