dLocal saves FlexiPay $427K.
Then destroys $707K.
Once you price in re-integration, Brazil delay, approval-rate regression, tokenization churn, and dLocal's $25M/mo commitment trap — the headline savings invert. And that's before counting the $951K of incremental Yuno revenue FlexiPay unlocks in Brazil and Argentina under the base case.
Strategic Analysis
How I read this brief. MEDDPICC qualification, stakeholder power–interest matrix, Bain value pyramid, five principles, and a formal risk register. This is what drove every decision in the deliverables.
QBR Narrative
Data-driven review: TPV trends, the January dip explained, approval gap analysis, payment method efficiency, NPS verbatim decoded, support ticket trend, cost benchmarking — and three strategic insights.
Expansion: Brazil + Argentina
Three scenarios (Conservative / Base / Aggressive) with explicit assumptions. Base case: $951K Yuno revenue and $1.24M FlexiPay upside in Year 1.
Competitive Response
Point-by-point counter to dLocal's proposal. $1.13M switching cost waterfall. Performance risk, contract risk, 30-day action plan with owners.
Memo to Yuno VP
$75K of concessions to protect $2.06M ARR and unlock $951K expansion. Risk level HIGH. Decisions needed from VP in 72 hours. Plan B included.
KAM Operating System
Live decision support: 15 objection responses, Plan B simulator, QBR scripts, Concession Calculator with the Iceberg, and a sync engine that generates three outputs — Client QBR, Formal Proposal, VP memo — from one backend.
Situation
Before the four deliverables, here is the analysis that drove every decision. Frameworks used: MEDDPICC, Bain Value Pyramid, Power–Interest Matrix, and a structured Risk Register. This is what an Enterprise KAM does before the work, not after.
The Four Forces on this Account
Each force has a stakeholder, a data point, and a tension with the others. A good KAM holds all four in their head at once.
MEDDPICC Qualification
The Enterprise B2B qualification framework. Every letter is filled with specific evidence from the FlexiPay dossier, not generic placeholders.
Stakeholder Power–Interest Matrix
Three stakeholders, three strategies. The matrix tells you where to spend energy: empower champions, neutralize blockers, activate sleepers.
Bain Value Pyramid — Where Yuno vs dLocal Compete
dLocal competes only at the Functional level. Yuno wins Levels 2 and 3 structurally. The whole renewal conversation has to be moved up the pyramid.
Five Principles That Guided This Package
The hardest part of a renewal under pressure is holding discipline. These are the principles I committed to before touching the numbers.
Risk Register
Probability × Impact, scored, with owners and mitigations. Live tracking of everything that could break the deal, and what we do about each.
This analysis uses MEDDPICC (Lister et al., the B2B enterprise qualification standard), the Bain Value Pyramid of Elements of Value (B2B version, 40 elements across 5 tiers — I've collapsed to 3 for readability), a Stakeholder Power–Interest Matrix (Mendelow's grid), and a formal Risk Register with scored probability × impact. Everything traceable to the data in the FlexiPay dossier. The goal is not to use frameworks for their own sake — it's to demonstrate that under renewal pressure, this KAM doesn't improvise. Frameworks are the opposite of panic.
QBR Narrative
Data-driven performance review for the FlexiPay QBR meeting tomorrow. Same analysis I would hand Daniela for her CFO conversation. The Client QBR deck → is the artifact that gets presented to FlexiPay; this is the reasoning.
TPV Trajectory
Yuno Revenue Growth
The January dip is seasonal. Here's how I know.
Every candidate reads the −8.4% January drop as a churn signal. It isn't. Four data points rule out a structural problem:
Approval Rate Gap vs Benchmark
Monthly TPV Unlock at Benchmark
Local methods are fine. Cards are the bleed.
OXXO in MX runs at 91%. PSE in CO at 88%. Webpay in CL at 89%. These local methods are at or above LatAm benchmark — no action needed, protect the share. The entire approval-rate problem lives in cards, specifically in Peru and Colombia where routing depth is lowest. This is important because the fix is routing intelligence, not more payment methods.
Decoding the NPS verbatim · Daniela's words are a blueprint
Every clause in that sentence is a specific instruction:
Support tickets up 71% · this is a warning, not a reason to leave
Yuno is priced at the LatAm median. dLocal is at the bottom.
A 0.67% card take rate isn't a better deal — it's a different product. Single-acquirer PSPs price at the bottom of the band because they make margin elsewhere (FX spread, slower settlement, fewer acquirer fees). If you want to benchmark Yuno, benchmark performance, not price.
Three strategic insights
You're not paying for Yuno. You're paying for a routing brain.
Every 1 point of blended approval = $124K/year of FlexiPay net revenue. Yuno's multi-acquirer routing delivers 2–3 points of lift over single-acquirer baselines over 18 months. That's $248K–$372K/year of value dLocal's architecture can't reproduce — and the SLA we'll commit to in this renewal is the structural proof.
Brazil is not a cost question. It's a speed question.
Yuno already supports PIX, Boleto, and BR local acquiring — 0 weeks of new integration. dLocal requires full re-integration: 6–8 weeks of your 4-dev team. Every week of delay is $2.5M of deferred Base-case TPV and permanent share loss to competitors Sofia is racing against.
Your engineering constraint is the real story.
FlexiPay has 4 backend devs, 12 open roles, Brazil on the line. dLocal migration consumes 1,280 engineering hours of opportunity cost. Those same hours on Yuno's Brazil fast-track generate $990K of Base-case Y1 upside. The highest-ROI decision this quarter is the one that protects your engineering team, not the one that cuts your invoice.
Brazil + Argentina Expansion Model
Three scenarios · Explicit assumptions · Consistent across the whole package.
Brazil TPV Ramp
Argentina TPV Ramp
Year 1 Revenue Impact — Both Sides of the Deal
How we built these numbers
Every assumption is explicit and defensible. The reviewer and FlexiPay leadership should be able to challenge any row — the rationale column explains the reasoning.
Argentina-specific risk factors
vs. dLocal's "free 20 hours"
dLocal, Point by Point
Switching costs quantified. 30-day action plan. Every claim backed by numbers.
Switching Cost Waterfall
Point-by-point response to dLocal
30-day action plan
To: Yuno VP of Account Management
Honest risk, resource asks, 90-day plan with owners and plan B.
FlexiPay's 12-month renewal is in 45 days. dLocal has submitted a 24-month counter-proposal that is 18 bps cheaper on cards, waives the platform fee, and bundles "free" Brazil integration. CFO Carlos Mendoza is publicly leaning toward dLocal. Champion Daniela Ramos needs ammunition. The switching-cost math is overwhelming in our favor — we win if we move fast and give ground on the right lines. I need decisions in 72 hours.
Resources needed — decisions by EOD tomorrow
| # | Ask | Cost to Yuno | Why |
|---|---|---|---|
| 1 | Waive monthly platform fee ($2K/mo) | $24K/yr | Neutralizes CFO line-item objection |
| 2 | Match dLocal PIX at 0.50% (24-mo BR commit) | ~$36K/yr forgone | Brazil revenue still +$747K — concession pays itself 20× |
| 3 | Hold card take rate at 0.85% + introduce SLA | $0 | The SLA is the story. Don't drop the card rate. |
| 4 | 18-month term, no hard TPV minimum | Flexibility cost | Counters dLocal's commitment trap |
| 5 | 40h SE + 20h CSM (BR fast-track) | ~$15K one-time | Unlocks $951K Base-case Y1 expansion |
| 6 | Yuno CEO → Sofia executive sponsor call | 1h CEO time | Sofia breaks ties on strategic framing |
| 7 | Approval-rate performance SLA authorization | $50K max exposure | Single biggest differentiator vs dLocal |
| Total direct give | ~$75K/yr | Protects $2.06M ARR + unlocks $951K |
War Room
Built for the 15 minutes before the QBR meeting, and the 60 minutes inside it. Four AI-assisted modules that turn the entire strategy package into operational cue cards for a KAM under pressure.
Objection Handler
15 likely objections from Carlos, Daniela, and Sofia — each with a short answer, full response, and exact numbers to cite. Browse by category or stakeholder.
data/model.json,
three different outputs for three different audiences. No copy-paste errors. This is what a KAM operating
system looks like.