How to Hang on to Conviction in an Emerging Market – Braze’s Long Game to PMF

Start by locking in a single, testable PMF hypothesis for this market and validate it with a lightweight encrypted feedback loop across accelerators. Establish a clear link from product signals to action, and set a priority on learning velocity over vanity metrics.

Set policies that govern experiments during the early months, with silent tests to reveal signal without shaping feedback. Use a weighted model: 60% activation, 40% retention, and align outcomes with a duizendstraal multi-cohort view that aggregates across groups over decades of data.

Craft a contrast narrative between early-market traction and mature markets, and keep the story grounded in data. Use a duizendstraal lens to align features with stripe integrations and with monzo-style growth patterns, ensuring the link between activation and retention stays visible across groups and channels.

Translate insight into concrete changes: implement onboarding accelerators, tighten policies around data sharing, and invest in a modular stack that supports rapid iteration. Build a lightweight data lake that preserves privacy with encrypted signals and a clear path from events to revenue, enabling controlled experiments during scale-up.

Looking ahead, define a 12-quarter plan with milestones for core segment PMF, extension to adjacent segments, and governance gates that keep long-term conviction while avoiding overfitting. Maintain a silent cadence for feedback cycles, and ensure each decision ties back to a single, measurable outcome across decades of context and a group of cross-functional stakeholders.

Orchestrate resources around a small, repeatable learning loop and empower a cross-functional group to own the long game. This disciplined pattern keeps conviction intact as you scale in an emerging market, with a clear link from experiments to PMF and a duizendstraal-driven view across decades of data.

Bearing the long arc: practical steps to stay committed while pursuing product-market fit

Start with a concrete 90-day plan: define three PMF hypotheses, design nine experiments, and schedule weekly reviews. Create decks that distill each hypothesis, the experiment, expected signals, and decision rules. Assign owners and ensure vidal, alexis, and nina participate in the review with clear inputs. Use a register to track new signups, activation, and wide usage patterns. Link each experiment to a concrete decision: pivot, persevere, or stop. Set a simple scorecard for every metric to keep focus.

Evaluate signals across usage, orders, and engagement. Already collected data from prior cycles; compare cohorts by day 7, 14, and 30. Track trends in activation, retention, and repeat orders. Build a mirror of the value path: onboarding leads to first value within 3 sessions; if not, adjust. Set extremely actionable thresholds: activation rate above 12% within 14 days warrants scaling the experiment; below 4% after 21 days triggers a pivot. Nowadays operators and product teams must run short loops and fast tests.

Define a hierarchy of decisions and a lightweight contract among founders and the early team. Capture efforts and burn in a weekly dashboard. Began with a lean scope; keep salary tied to milestone completion. If the core KPI misses targets for two consecutive sprints, reallocate budget and manpower.

Practical steps for execution: picking a small set of tools, registering experiments, and aligning with services and partners. Focus on supporting data and judgment for decisions. Build dashboards that mirror customer touchpoints and track orders, activation, and revenue signals. Apply learnings to product, pricing, and go-to-market tactics, and appreciate the contributions from alexis and nina.

Keep the long arc in view by documenting what works in a central place and using it to train the team. Use feedback loops across departments to avoid silos; the leader should pick priorities, and the team doing the work should feed back the results. Review every Friday with the ops team and adjust the plan to reflect current trends, operator feedback, and customer signals.

Identify a narrowly defined ICP and validate with micro-segments quickly

Define ICP narrowly: these midwest-based direct-to-consumer brands in home goods, with 1–5M in annual revenue, 1,000–5,000 orders per month, AOV $40–$90, and a Shopify or BigCommerce stack. Validate quickly by proving a core value message can convert an onboarding instance into a paying account within 2 weeks. Focus on these signals: first-party data, repeat purchase rate, and a simple onboarding workflow that delivers a tangible outcome by day 10.

Micro-segments to validate fast: segment these by behavior and fit across these axes: devices (these devices: mobile and desktop), onboarding completion rate by source channel, product focus (fruit lines vs non-fruit lines), midwest metro clusters (Chicago, Minneapolis, Detroit, Kansas City, Indianapolis), buyer type (first-time buyers vs repeat buyers), and channel preference (paid social vs search). Segment by whatever product category you test to uncover unique hooks. Each segment yields a distinct messaging shard, and these shards feed the core workflow without overhauling the tech stack.

Validation plan: Run two 14-day experiments with three messaging variants across onboarding emails, push, and in-app prompts. This framework creates a fast feedback loop and yields onboarding-to-activated-account rate, time-to-first-purchase, and 7-day retention. One-on-one conversations ensure the buyer speaks plainly about pain points; schedule 10–15 minute sessions with 5–7 target accounts to gather realization and truth about fit, and provide a direct response to a request for data within 24 hours. Use a simple ICP score to rank each account on core criteria: stack match (Shopify/BigCommerce), revenue band, AOV, midwest fit, and growth signals. Nearly all tests inform where to focus next, and happen only when you see signals that validate the core premise.

Milestones and execution: shortlist 15 target accounts, secure 5 one-on-one conversations, reach onboarding completion rate of at least 60% for the tested segment, finalize core messaging, publish a 2-page onboarding playbook, and implement a direct-to-consumer-specific workflow that scales to 50 accounts per month. Share outcomes with stakeholders on behalf of the team, and prepare for scalable expansion while keeping the bond with customers intact.

Outcome: you gain a validated ICP and micro-segments that reveal truth about PMF. The fruit of rapid validation is a clear path forward, and the bond with customers strengthens because onboarding experiences are tailored to these segments. When a segment hits milestones, allocate resources to that account to drive sustainable growth. We cared enough to test quickly, and if signals point to wrong fit, pivot fast rather than chasing vanity metrics. Additionally, monitor shares and referrals as evidence of product-market resonance.

Design short, disciplined experiments with measurable signals

Run a 2-week pilot on a single metric and iterate fast. Define the baseline, target signal, and stopping criteria before you begin; otherwise you risk chasing noise. Initially, pick activation rate or 7-day retention as the signal, and track it for every cohort. Involve product, data, and marketing in a single cross-functional team to keep accountability tight. Youd want to keep the scope narrow to improve the likelihood of a clean read and avoid left bias. This bold approach works in western markets and barcelona teams, and it helps former habits give way to sticking, measurable gains. Pendulums of momentum may swing, but disciplined experiments help you keep them balanced.

Elements guide every test: hypothesis, signal, decision rule. The hypothesis should be explicit: if we deliver a targeted onboarding prompt, activation within 7 days rises. Use intentional signals such as completion rate, time-to-value, and repeat usage to separate noise from signal. The decision rule states whether to roll out, pause, or learn from the next cohort. Keep the scope narrow, else results become noisy.

Measurement plan: set a sample large enough to detect a lift of 10% with 95% confidence and 80% power. Solidified data readouts avoid cherry-picking. Track signal stability across two consecutive cohorts to confirm; if the signal holds, scale; if not, pivot.

Fintech case: test bold onboarding tweaks for a housing fintech product that helps manage debt. Measure the proportion who complete onboarding and start a housing loan application within 14 days. If the metric improves, champion the change with your entity and barcelona‑style team; else, pivot to another experiment. The exercise contributes to your broader strategy and strengthens your standing as a living proof of concept, becoming a reference point for where to invest next.

Execution and culture: assign a small, autonomous entity responsible for the test; ensure data access and privacy. If the effort contributes to your growth, your team becomes more bold and takes ownership as a champion of becoming a data-driven unit. Shoulder the risk, log results transparently, and share learnings across western and non‑western contexts. As said, data beats guesses, and sticking with disciplined loops turns tentative bets into solidified signals that you can scale with confidence.

Guardrails for resource allocation aligned with a long-term trajectory

Allocate 40% of the annual operating plan to long-horizon product-learning and market experiments, with explicit exit criteria at 12 and 24 months and a staged build-out timeline for core capabilities. Establish a bench of metrics that determine reallocation and require monthly reporting against this bench.

Create a cross-functional guardrail: a quarterly spending review led by the head of product, marketing, and data, anchored by a domestic hub. Maintain mysql-backed dashboards that track progress by region, product line, and customer cohort; surface updates through a single portal. headquartered teams own the core bets, while international units execute controlled pilots to diversify risk.

Whereas tempting short-term fixes lure teams toward incumbents’ quick wins, execute with a long-horizon tilt to grow world-class capabilities and prove traction across relative segments.

To minimize frustration and misalignment, tie resource allocation to a calendarized PMF timeline and to external benchmarks. Contrast internal projections with observed results in the portal, and use zildjian-named experiments to keep teams focused on durable impact rather than noise from one-off wins.

Measurement and governance: anchor decisions to three leading indicators–activation rate, retention lift, and revenue per user–and reallocate only when two of three meet threshold. Viewed across markets, this approach prevents overcommitment to a single channel and keeps the long trajectory in view. Aligns with values that customers benefit from reliable performance and with reporting discipline that keeps stakeholders informed with compliments and candid feedback from the field.

Operational discipline: set a living budget cap for each resource category and adjust quarterly; if a bet underperforms in two consecutive quarters, pause and reallocate to a proven vector. This stance, coupled with a transparent portal and bench, helps Braze compete with incumbents while remaining domestic- and world-class in execution.

Institutionalize decision logs and recurring conviction reviews

Implement a centralized decision log with a fixed cadence of conviction reviews across product, engineering, sales, and regional teams to lock in the long game toward PMF. Start a mini pilot in the Nordic region with a small cross-functional group, then scale as evidence accumulates.

These arent decisions taken lightly; they rely on test results, evidence, and clear alignment with target metrics. The logs capture the aspect of each decision, the context, and the resulting insight, traveling alongside field feedback to inform bets.

What to log

• Decision objective, date, and owners to establish accountability and ownership.
• Hypotheses, the target metric, and alignment with product strategy; note the specific aspect being tested.
• Evidence: quantitative data, qualitative feedback, experiments, and stories from traveling customers and frontline teams.
• Assumptions, risks, and mitigations; store mild or warm signal levels to indicate confidence.
• Alternatives considered and why they were not chosen; document lessons learned to avoid duplication.
• Decision outcome and next steps; then update the log to reflect progress and follow-on actions.
• Conviction level and rationale; include a fixed score and a plan to revisit if new evidence emerges.

Cadence, roles, and governance

• Set a federal-style governance cadence: weekly conviction reviews for the first six weeks, then monthly leadership checks to maintain momentum.
• Invite cross-functional participants: product, data, engineering, marketing, and regional leads; ensure stephane and lahtela are looped into relevant decisions as benchmarks.
• Use a documented decision log as a living artifact that developers and product teams can consult alongside roadmaps; this keeps a developer-focused lens on feasibility and impact.

How to use logs to preserve conviction

• If the log shows strong evidence and a warm signal from customers, proceed with the plan and publish a short insight brief to align teams.
• If the evidence is mild or inconclusive, explore alternative experiments in parallel; use traveling feedback as a guide and consider steering toward a different target.
• When a decision dominates other options, annotate the rationale clearly and share stories from Nordic markets to reinforce alignment across teams.
• Document outcomes in a way that naturally links to the next set of experiments or product bets; this creates a continuous feedback loop rather than a one-off decision.

Practical benchmarks and examples

• Look to combinator-style accelerators for how to structure decision logs as a lightweight playbook; the best teams keep the process simple yet rigorous.
• In explored bets, use lahtela’s approach of starting small, measuring impact, and expanding when early signals are favorable.
• Leverage stories from stephane and other regional leaders to validate that the documented evidence translates to real-world success across markets.
• Maintain a neutral tone in logs to avoid warmth masking risk; address risk with mild mitigations and clear thresholds for escalation.
• Target clearer alignment between product bets and GTM motions to ensure the long-game conviction persists even as markets travel and shift.

Scale pilots while preserving core product principles and customer value

Launch 4 pan-european pilots with a fixed core-principles rubric and a shared KPI set that tracks customer value over activation speed. Keep the scope concentric: start with the smallest viable functionality and, if targets are met, expand in controlled steps. The plan includes separate pilots per country to respect local nuance while safeguarding ours core principles embodied in the product.

Organizational alignment sits at the center: establish a lightweight governance scheme that ties product, success, and regional teams. The approach resonated with regional leaders because it preserves core functionality while enabling rapid learning. We tackle friction at critical touchpoints by maintaining a single, consistent user interface and hiding optional complexity behind clear defaults; this fuel momentum without diluting value. Input from samir helped shape the scheme, and beth and strazza contributed to field feedback and data modeling, identifying gaps to address in the next cycle.

Considering regional diversity, we set patient rollout cadences with internal milestones. Each pilot contains only the core features that deliver value, while extras stay on the shelf until scale criteria are met. The plan includes a contact channel for customers and a feedback loop to identify issues quickly and patch them in the next iteration. Metrics cover activation rate, feature usage, retention across countries, and qualitative feedback, all tracked in a single dashboard.

Internally, beth and samir identified friction points in onboarding, while strazza’s analysis highlighted data gaps that could hinder becoming a repeatable pattern. The pilot scheme includes clear steps to fuel organizational learning, with a scheme to become a scalable practice that preserves the core functionality we embody in ours product.