Discover Journey Map: Storevine Procure-to-Renew

Scenario

Storevine sells AI inventory forecasting to mid-market e-commerce companies (200-2000 employees). The PM wants a journey map for the full procure-to-renew arc to find where deals stall and where renewals are won or lost. This is a B2B buying-group journey: the Ops manager champions it, but Finance controls budget and IT controls the data integration. The map uses the multi-actor pattern with parallel tables per actor. All figures and quotes are illustrative and tagged [fictional].

Complexity warning: this is a multi-actor journey, which is harder to validate than a single-persona map. Research depth here prioritizes the primary actor (Ops manager); Finance and IT tracks are lighter and carry more Hypothesis-labeled entries. Mermaid is simplified to the primary actor’s path; the secondary actors intersect at the Evaluates and Decides stages.

Source Notes:

Gartner, “The B2B Buying Journey” (Brent Adamson et al.) - the model of a buying group of multiple stakeholders running parallel, non-linear “buying jobs” rather than one buyer moving down a funnel; this is why the map uses parallel actor tracks instead of a single line.
Brent Adamson, Matthew Dixon, Pat Spenner, Nick Toman, “The Challenger Customer” (Portfolio, 2015) - the role of the internal champion (Mobilizer) and the need to build consensus across a skeptical buying group.
Jim Kalbach, “Mapping Experiences” (O’Reilly, 2016) - the technique for mapping multiple actors with shared touchpoints and intersection points.

Prompt

/journey-map

map the full procure-to-renew journey for storevine (B2B AI inventory
forecasting, sold to mid-market e-commerce, 200-2000 employees).

it's a buying group, not one buyer:
- Ops manager = champion, lives in the forecasting pain daily
- Finance = approves the budget, cares about ROI and contract terms
- IT = owns the data integration and security review, can veto

stages: discovers -> evaluates (RFP + demo) -> pilots -> decides
(contract) -> onboards (data integration) -> uses (monthly forecast
cycle, recurring) -> renews (annual review, recurring).

the scary transition is pilot -> onboarding: the pilot uses clean sample
data and looks great, then real data integration is messy and the
accuracy people were promised wobbles. that's where trust is won or lost.

multi-actor, so use parallel tables per actor. the usage + renewal stages
are cyclical.

Output

Customer Journey Map: Storevine Procure-to-Renew - Multi-Actor Buying Group

Multi-actor journey. Primary actor: Ops manager (well-researched). Secondary actors: Finance and IT (lighter signal, more Hypothesis entries). Validate the secondary tracks before acting on them.

Executive Summary

This map covers a mid-market e-commerce buying group from first vendor research through the first annual renewal. Three actors run parallel buying jobs: the Ops manager champions the purchase, Finance gates the budget, and IT gates the integration and security review. The decisive moment of truth is the pilot-to-onboarding transition: the pilot runs on clean sample data and forecasts look excellent, but real data integration is messy and the promised accuracy wobbles. Deals and renewals are won or lost there. The largest opportunity is making onboarding accuracy match pilot accuracy by setting expectations during the pilot and de-risking the data integration. Grounded in 6 buyer interviews and 2 won/lost deal reviews [fictional].

Actors

Ops manager (primary / champion): owns the forecasting pain, runs the evaluation, will use the product daily.
Finance (budget approver): cares about ROI, payback period, and contract flexibility. Engages mainly at Evaluates and Decides.
IT / security (gatekeeper): owns data integration and the security review. Can veto. Engages at Evaluates (security review) and Onboards (integration).

Journey Scope

Journey type: Multi-actor; linear through Decides, then cyclical (Uses monthly, Renews annually)
Included: Vendor research through the first annual renewal (roughly a 12-15 month arc)
Excluded: Expansion / cross-sell to other teams, churn win-back

Stages (shared spine)

#	Stage	Primary goal (Ops)	Duration	Entry trigger	Exit criterion
1	Discovers	Find a fix for stockouts and overstock	Days-weeks	A bad forecasting quarter	Shortlists vendors
2	Evaluates	Prove this vendor is credible and safe	3-6 weeks	RFP issued	Pilot agreed or vendor dropped
3	Pilots	See accuracy on our own data	4-8 weeks	Pilot scoped	Pilot results reviewed
4	Decides	Get budget and integration sign-off	2-4 weeks	Pilot succeeds	Contract signed or no-deal
5	Onboards	Connect real data, hit live accuracy	3-6 weeks	Contract signed	First live forecast produced
6	Uses (loop)	Run a reliable monthly forecast cycle	Monthly, ongoing	Monthly data refresh	Forecast actioned
7	Renews (loop)	Justify the annual spend	Annual	Renewal date approaches	Renews or churns

Touchpoints per Stage (shared)

Stage	Touchpoint	Channel	What happens
Discovers	Category research, peer referrals	Web, network	Builds a vendor shortlist
Evaluates	RFP response, sales demo	Doc, call	Sees forecasting on sample data
Evaluates	Security questionnaire	Doc / portal	IT reviews data handling
Pilots	Pilot environment	Product	Runs forecasts on a sample of real data
Decides	Business case + contract redline	Doc	Finance reviews ROI and terms
Onboards	Data integration / ETL setup	Product, IT	Connects live inventory + sales data
Uses	Monthly forecast run + dashboard	Product	Generates reorder recommendations
Renews	Annual business review (QBR-driven)	Call	Reviews realized value vs. spend

Emotional Curve - Ops manager (primary)

Stage	Dominant emotion	Confidence	Source
Discovers	Frustration with the status quo, hope	High	6 interviews; all cited a specific bad quarter [fictional]
Evaluates	Cautious interest, fear of overpromising	Medium	Interviews; demos seen as “too clean” [fictional]
Pilots	Excitement (pilot looks great)	High	Pilot accuracy on sample data impressed every interviewee [fictional]
Onboards	Anxiety, then either relief or betrayal	High	The pilot-to-live accuracy gap was the most-cited risk [fictional]
Uses	Routine confidence (if accuracy holds)	Medium	Hypothesis from 2 live customers [fictional]
Renews	Pride or quiet regret	Medium	Renewal intent tracked realized accuracy [fictional]

Emotional Curve - Finance and IT (secondary, lighter signal)

Actor	Stage	Dominant emotion	Confidence	Source
Finance	Decides	Skeptical scrutiny of ROI claims	Medium	2 deal reviews; ROI math was the sticking point [fictional]
Finance	Renews	Wants proof of realized savings	Low	Hypothesis; not directly interviewed [fictional]
IT	Evaluates	Guarded; security-review burden	Medium	Security questionnaire was a common delay [fictional]
IT	Onboards	Stress over data quality and integration effort	Low	Hypothesis; inferred from sales-cycle notes [fictional]

Pain Points and Moments of Truth

Stage	Actor	Pain / Moment of Truth	Severity (1-5)	Evidence	Implication
Evaluates	IT	Slow security review stalls the deal	4	IT review added weeks in 2 of 2 deals [fictional]	Pre-package security docs to remove the bottleneck
Pilots	Ops	Pilot accuracy on clean sample data	3	Pilots consistently impress [fictional]	Beware: pilot success sets an expectation live data may miss
Onboards	Ops + IT	Pilot-to-live accuracy gap	Moment of Truth (5)	Most-cited deal/renewal risk [fictional]	This decides trust; close the gap or set expectations during the pilot
Decides	Finance	ROI case feels speculative	4	ROI math stalled both reviewed deals [fictional]	Tie the business case to pilot-measured accuracy, not generic benchmarks
Uses	Ops	First live forecast is wrong and nobody flags it	4	Hypothesis [fictional]	Add a confidence/anomaly flag so a bad forecast is caught before it is actioned

Opportunities

Stage	Opportunity	Product / GTM change	Effort (rough)
Evaluates	Unblock IT review	Ship a pre-filled security packet (SOC 2, data-handling, DPA)	Small
Pilots	Set honest accuracy expectations	Show pilot results with a “live data will differ” band and explain why	Small
Onboards	Close the pilot-to-live gap	Guided data-quality check + an onboarding accuracy SLA with a named owner	Large
Decides	Make ROI concrete	Auto-generate a business case from the pilot’s measured accuracy and the customer’s own stockout cost	Medium
Uses	Catch bad forecasts early	Confidence score + anomaly flag on each monthly forecast	Medium

Visual (simplified to the primary actor)

timeline
    title Storevine Procure-to-Renew (Ops manager path)
    Discovers : Bad forecasting quarter : Shortlist vendors
    Evaluates : RFP : Demo : Security review
    Pilots : Forecast on sample data
    Decides : Business case : Contract
    Onboards : Live data integration (moment of truth)
    Uses : Monthly forecast cycle (recurring)
    Renews : Annual business review (recurring)

Cyclical usage and renewal loop:

flowchart LR
    A[Monthly data refresh] --> B[Forecast run]
    B --> C[Reorder actioned]
    C --> A
    C --> D[Annual review]
    D -->|renews| A

Finance intersects the spine at Decides and Renews; IT intersects at Evaluates and Onboards.

Research Gaps

The Finance and IT tracks are mostly Hypothesis: neither actor was interviewed directly, only inferred from deal reviews [fictional]. Interviewing 3-4 Finance and IT stakeholders would replace the weakest entries.
The Uses-stage emotions rest on 2 live customers [fictional]; not enough to generalize. A post-onboarding survey at 30 and 90 days would strengthen this.
The pilot-to-live accuracy gap is the load-bearing finding but is currently anecdotal. Instrumenting pilot accuracy vs. first-live-forecast accuracy across deals would quantify it and validate the central moment of truth.