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Retro with Coda Brain
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Retro with Coda Brain

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Coda Brain prompt
Analyze each pillar’s work during Q4 2024. Look across Jira, Github, Slack, Coda, Gmail, Asana, Salesforce, and Zoom recordings and pull out the top 1-3 items for each of the following categories:
What went well
What could’ve gone better
What was unexpected
A key lesson for next time

Coda Brain Q4 Retro
Pillar name
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✨ Q4 Retro by Coda Brain
Core Product
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​

What went well

Real-time chart rendering success: The new real-time chart rendering was approved and merged smoothly, enhancing load times. (GitHub:
link
⁠
)
UI/UX collaboration improvement: Emily and Mark’s collaboration on UI/UX updates for the Insights Panel cut redesign cycle time by 20%. (Zoom:
link
⁠
)
Efficient backlog management: The prioritization of key bug fixes in the TrendPro release kept the development pipeline moving smoothly. (Jira:
link
⁠
)

What could’ve gone better

API integration confusion: There was confusion over the scope of API integration for the SmartData project, leading to duplicated efforts among Sophie and Alex. (Slack:
link
⁠
)
Delayed code review cycle: The code review process for legacy module updates took longer than planned, impacting deployment timelines. (GitHub:
link
⁠
)
QA resource constraints: Limited QA availability led to rushed testing before the major release. (Asana:
link
⁠
)

What was unexpected

Late-stage testing bug: A major bug surfaced during late-stage testing related to legacy code conflicts in TrendPro. (Jira:
link
⁠
)
User feedback on complexity: Beta testers, including the BetaHub team, found the new analytics configuration too complex, sparking unexpected revisions. (Coda:
link
⁠
)
Competitor update impact: A sudden feature release from InsightMax influenced client expectations mid-development. (Gong:
link
⁠
)

Key lesson for next time

Review legacy dependencies earlier: Ensure cross-functional teams, including QA led by Rahul, review legacy dependencies earlier in the sprint cycle. (Coda:
link
⁠
)
Increase user testing: Allocate more time for user testing phases to catch potential complexity issues early. (Zoom:
link
⁠
)
Buffer for competitor responses: Prepare a rapid response plan for unexpected competitor feature launches. (Slack:
link
⁠
)
Data
Ask Coda Brain
​

What went well

User engagement analysis completed: Completion of the user engagement heatmap analysis by Julia and Noah helped identify key trends across premium users. (Asana:
link
⁠
)
Improved data reporting: Coordination with the Core Product team, specifically with Jake, improved the accuracy of user activity data reports. (Slack:
link
⁠
)
Successful rollout of data pipelines: The new real-time data feed integration for the MarketPulse project was deployed without major issues. (GitHub:
link
⁠
)

What could’ve gone better

Cohort alignment delays: Delays occurred when aligning datasets from different user cohorts for the Monthly Insights Overview, impacting timelines. (Jira:
link
⁠
)
Inconsistent data validation: A few reports for ClientX showed discrepancies due to missed data validation checks. (Asana:
link
⁠
)
Communication gaps: There was occasional miscommunication on data format requirements between the Data and Experiment teams. (Slack:
link
⁠
)

What was unexpected

Client request pivot: Lisa from TechForward Inc. requested a new data aggregation feature mid-sprint, prompting a quick pivot by the data analysis team. (Gong:
link
⁠
)
New data anomaly discovered: An unexpected spike in user metrics due to a third-party integration glitch from DataLinkPro. (Zoom:
link
⁠
)
Positive client feedback on new report: A major client, FinStat Solutions, praised the real-time insights feature, exceeding initial expectations. (Gmail:
link
⁠
)

Key lesson for next time

Plan for client request buffers: Build buffer time into sprints for unexpected large client requests. (Zoom:
link
⁠
)
Enhance validation protocols: Strengthen data validation protocols to ensure report consistency. (Coda:
link
⁠
)
Cross-team clarity: Improve communication around data format requirements to prevent misalignment. (Slack:
link
⁠
)
Experiment
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​

Growth
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​

LT
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​

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