Woosh Discussion

matched

Fri, Jan 9, 2026, 11:45 AM • —

Client

Altitude Create

Participants

0 attendees

Action Items

0 pending

AI Meeting Analysis

AI GENERATED

Client Sentiment

[^]POSITIVE

The discussion shows alignment on key product principles, agreement on sample size/session design, and proactive focus on improving UX and differentiation, with no notable conflict or dissatisfaction indicated.

Meeting Quality

EFFECTIVENESS82%

EXCELLENT

Relationship Status

healthy

Executive Summary

The team aligned on core product principles for Woosh, emphasizing that randomness in practice is necessary for meaningful skill development even if it slightly complicates the user experience. They also converged on a practical data/UX framework (putt categorization, minimum sample size, and session length) and identified performance improvements (faster analysis via batching and server-side trimming) as key to adoption and differentiation.

Key Topics Discussed

Practice randomness vs user experience tradeoffsPutt categorization framework (direction and distance buckets)Minimum viable sample size and session designFreemium monetization strategy and upgrade triggersAnalysis speed improvements (batch processing, server-side video trimming)Market differentiation via accuracy, UI innovation, and brand partnerships

Decisions Made

✓Randomness should be a foundational element of the practice experience to prevent users from making superficial, non-transferable adjustments.
✓Putts should be categorized by direction and distance, starting with 3–15 feet and then using 5-foot increments for pattern analysis.
✓A minimum viable sample size is 16 putts from four spots to generate actionable insights.
✓Target session length for casual users should be ~20 minutes.
✓Adopt a freemium model where the free tier limits usage and paid tiers unlock additional features based on user goals/engagement.
✓Prioritize reducing analysis time through batch processing and server-side video trimming to improve user flow.

Follow-up Items

→Define the exact distance buckets and direction taxonomy (e.g., left-to-right vs right-to-left, uphill/downhill if applicable) and document them for product/engineering.
→Translate the 16-putt/4-spot protocol into an in-app guided session flow (instructions, prompts, progress indicators, and completion criteria).
→Specify freemium tier limits (e.g., sessions per week, number of analyses, storage, export/share features) and map paid-tier value props to user personas (casual vs competitive).
→Create an engineering plan to implement batch processing and server-side video trimming, including performance targets (time-to-result) and instrumentation.
→Define UX changes that preserve randomness while minimizing frustration (e.g., explanation tooltips, “why this matters” messaging, adaptive difficulty).
→Develop a differentiation/positioning brief centered on accuracy, UI innovation, and partnership strategy with top golf brands.
→Identify and shortlist potential golf brand partners and outline a partnership outreach plan (targets, pitch, mutual value, timeline).

Open Questions

?How will randomness be implemented (fully random vs constrained random) while ensuring sessions remain comparable for analysis?
?What is the precise definition of “direction” categories (start line, break direction, miss direction, or all of the above)?
?Are additional contextual variables needed for analysis (green speed, slope, lie, indoor vs outdoor), and if so, how will they be captured without adding friction?
?What are the concrete thresholds for “actionable insights” from 16 putts (confidence levels, error bars, or minimum effect sizes)?
?What are the target performance benchmarks for analysis time (e.g., under X seconds per putt/session) and acceptable failure/retry behavior?
?What features will be reserved for paid tiers versus free to maximize conversion without harming activation?
?Which partnerships are most strategic first (OEMs, training academies, media/influencers), and what proof points are needed to secure them?

AI Recommendations

★The meeting produced clear decisions on methodology (randomness, categorization, sample size) and strategic direction (freemium, differentiation) plus identified concrete optimization areas. Effectiveness is reduced by the lack of explicitly assigned owners, timelines, and recorded action items.

Analysis generated: Jan 9, 2026, 1:01 PM