Why Your App Keeps Failing

Business-focused summary (Kviva / agentic measurement)

Core strategic shift

• Kviva (Conviva) moved from measuring streaming video experiences to enabling “agentic” experiences.
• The argument: businesses shouldn’t optimize for systems-agents being “happy,” but for consumer outcomes.
• The company says it pivoted ~1 year ago to focus on AI + agents as a “next big wave” (larger than the TV-to-internet shift).

Value proposition / operating thesis

• Agents will fail like early streaming did if companies optimize only for internal signals (system health, agent success) instead of real consumer experience.
• Kviva’s mission: make agents smarter about people using a feedback loop from consumer behavior and real-time experience monitoring.

Key principle

• “Measure every consumer experience” (not samples), using a consumer behavior + outcome loop.

Frameworks / playbooks emphasized

• Experience-first measurement loop (video → agents)

  1. Collect real sessions/behavior
  2. Convert into behavior/stateful patterns
  3. Monitor in real time to detect “rogue” behavior
  4. Tie patterns → outcomes (conversion, abandonment), not just agent/system performance

• Pattern-based customer journey modeling

  • Replace “sequential funnel” analytics with stateful patterns that capture loops like bounce → research → cart → research.

• Mature-then-launch agent playbook (reduce risk)

  • Train/load behind the scenes from historical/current product interactions
  • Test until a “testing score” is reached on conversion/conduct (no numeric target specified)
  • Deploy into production only after passing that threshold

Product & differentiators (moat)

Kviva claims its moat is “deep measurement + scale,” specifically:

1. Consumer-scale measurement
   • Measures across 8 billion devices globally (human-scale).
2. Real-time collection & processing
   • Processes each consumer’s sessions in real time to react immediately.
3. Semantic matching (not word-for-word)
   • Extracts meaning from conversations/questions to compare across different phrasing.
4. Pattern database + real-time trend monitoring
   • Produces a real-time pattern database showing trends, drops, and emerging issues (including viral moments).

Concrete stats / metrics mentioned

• ~67–69% of consumers do not buy in a sequential funnel
  • Implication: many analytics miss journeys because people bounce between research, cart, reviews, price comparisons, and search.
• Operational/product testing target concept
  • Agents move to production “as soon as” a “testing score” on consumer conversion rate is reached (no numeric target provided).

No explicit CAC/LTV/churn/revenue figures were provided.

Example scenarios used to explain personalization

• Frequent flyer vs vacation researcher

  • Frequent flyer: fast booking (e.g., 2-minute decision)
  • Vacation planner: longer exploration (e.g., hour-long research)
  • Lesson: agents must learn different shoppers’ states or they’ll frustrate users and increase churn/abandonment.

• Cart abandonment misconception

  • “Abandoned the cart” might actually mean the same user bounced back into search/research.
  • Analytics must model patterns, not rigid funnel steps.

Go-to-market / ICP and customer strategy

• Primary ICP focus: enterprise e-commerce
  • Rationale: highest revenue at stake; higher risk if an immature agent mishandles transactions.
  • They argue large retailers will “fail” without this kind of measurement/experience feedback.
• Solution applies beyond e-commerce
  • Small sellers can adopt too, but Kviva emphasizes enterprise first due to risk and scale.
• Industry extension
  • The mission suggests broader business disruption, but execution examples are mainly framed around e-commerce agent launches.

Actionable recommendations Kviva advocates to customers

• Don’t launch an immature agent in production
  • Treat the initial agent like a “toddler” that will learn during transactions (too costly).
• Load patterns behind the scenes first
  • Feed the agent product-derived consumer patterns, then:
    • Monitor as it improves
    • Move to production when consumer metrics indicate readiness
• Measure consumers, not only agents
  • Align agent optimization with real consumer satisfaction and return behavior.

Internal transformation & leadership tactics (Zeb Chavich)

Organizational productivity mandate

• Leadership issued a directive: by end of a near-term period (“earlier this year,” exact month not recalled), each leader had to provide a plan to either:
  • Cut the organization in half, or
  • Deliver 5x productivity
• Engineering example:
  • 220 engineers → 110 (half-team)
  • Or ~3x improvement, described qualitatively as leaders achieving reductions and productivity gains.

Operating model change

• Reorganized product + engineering into “AI-centric / AI-first built pods.”
  • Co-founders led initial pods, then replicated the model.
• Cultural shift framing:
  • “It’s a mentality thing, not a skill set anymore.”
• Sales/marketing acceleration via AI agents:
  • Example: sales/marketing created personalized customer videos (logo/name personalization) in about an hour (demo claim).
• Training approach (implicit):
  • “Learn while everyone else is learning,” and start early to avoid organizational lag.

Talent philosophy (hiring & roles)

• Hire for system thinkers across functions (marketing, sales, ops), not just function-specific executors.
• Reframe roles as building cross-functional AI-enabled systems:
  • Marketing example: shift from a “single marketing function” to a product marketing system where an agent generates multiple assets (LinkedIn posts, white papers, briefs, presentations).
• Upskilling expectation:
  • Leadership expects AI to improve nearly every corporate function (“any function … can be done better with AI”).

Business success factors they highlight

• Conviction and mandates to drive adoption and restructure teams
• Speed of learning (start early; don’t wait)
• Experience feedback loop to prevent costly “agent in the wild” failures
• Real-time measurement to detect issues immediately
• Pattern modeling to reflect non-linear customer behavior

Presenters / sources

• Jay Garin Verrial (host / interviewer)
• Zeb Chavich, CEO & President of Kviva (interviewee)
• Keith (appears as Zeb’s guest; referred to during the discussion by the host as a co-speaker/CEO context)