Overview
Current Scenario
Marketers often face challenges to run and scale experimentation since the process is mostly manual and rely heavily on developers and analysts. Marketers face challenges like limited scalability, slow execution, and the inability to reuse insights from past experiments.
Impact
Slower decision-making and campaign optimization: The time-consuming process delays actionable insights, ultimately leading to slower iterations and lower marketing performance.
Inconsistent experiment setup and execution: Lack of standardization and automation causes inconsistencies in testing, affecting the reliability and comparability of results.
Reduced agility and scalability: Without the ability to quickly scale experiments and leverage past learnings, marketers struggle to adapt and optimize their strategies at the pace required for competitive advantage.
Solution: Fibr's AI Powered Hypothesis and Experimentation
Teams spend more time deciding what to test than actually testing.
Fibr.ai eliminates this gap by continuously analyzing behavior, traffic, and ad context to generate ready-to-test hypotheses.
Each hypothesis gives you answers three questions:
What’s underperforming?
Why might this be happening?
What can you change to fix it?
Data Inputs & Signals
Fibr uses multiple layers of data to generate meaningful hypotheses:
GA4 / Analytics
Session data, bounce rates, goals
Detect anomalies & trend shifts
On-page Behavior
Scrolls, clicks, heatmap data
Surface UI/UX issues
Brand Guidelines
Brand looks, tone, assets
Maintain brand uniformity
💡 Tips from the Fibr Team
Treat hypothesis as a conversation starter, not a final answer. Refine it before testing.
Connect Google Analytics (GA4) data to get more precise and higher-confidence ideas.
Focus on top 3–5 hypotheses; too many dilute test significance.
Fibr’s Hypothesis engine gives you data-led ideas you can act on immediately, reducing time-to-experiment and increasing test success rates.
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