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Investment idea execution research
Identifies why users abandon key steps and turns those insights into product decisions.
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overview
This research explored why users of an investment service receive trading ideas but often do not execute them. The goal was to identify behavioral barriers, uncover recurring patterns, and generate product hypotheses that could improve execution rates.
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research
I conducted 12 phone interviews with affluent clients who had interacted with investment ideas. Each conversation lasted 10–20 minutes and focused on how users receive, evaluate, verify, and execute ideas in practice.
Insights were categorized manually, grouped by recurring themes, and translated into a structured set of findings and product hypotheses.
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key insights
The research revealed several recurring barriers to execution:
- push notifications are the main trigger for action
- email and SMS can be delayed or ignored
- users usually validate ideas manually before acting
- lack of available funds blocks execution even when the idea seems relevant
- unclear updates reduce trust and create suspicion
- technical issues and poor support damage confidence in the service
- idea quantity and variety affect perceived usefulness
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Behavior model
How users move from receiving an idea to deciding whether to act on it
journey flow
research data → insights → behavior → action
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delayed deliveryChannel trigger (push / sms / email)
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no time horizonInitial evaluation (horizon, clarity, trust)
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low trustManual validation (chart, spread, own judgment)
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no liquidityLiquidity check
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unclear updatesExecution decision (act / postpone / ignore)
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Post-evaluation (trust grows or declines)
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Insight pyramid
How interview data translated into product opportunities
Research evidence
“Users want to understand the time horizon before deciding whether to act.”
Insight
Users hesitate when the idea does not clearly indicate its expected duration.
Product implication
Show idea validity period in push notifications and idea cards.
Research evidence
“Push notifications are the fastest and most actionable channel.”
Insight
Push is the primary behavior trigger, while other channels mostly reinforce the signal.
Product implication
Prioritize push as the main delivery channel and use SMS / email as supporting channels.
Research evidence
“Even relevant ideas are ignored if there is no available cash at the moment.”
Insight
Execution depends not only on idea quality, but also on the user’s liquidity at the time of delivery.
Product implication
Experiment with sending ideas when the user has available funds.
Research evidence
“Users lose trust when idea updates are unclear or appear manipulated.”
Insight
Lack of explanation in idea updates creates suspicion and reduces confidence in the service.
Product implication
Add short explanations to idea updates, especially when assumptions change.
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Product hypotheses
The research led to six hypotheses that could improve user engagement and execution rates:
- add idea validity period to notifications and idea cards
- distribute ideas via messaging platforms such as Telegram
- send ideas when the user has available funds
- explain why an idea was updated
- combine push and SMS notifications
- increase the quantity and variety of ideas
Each hypothesis was tied to measurable product metrics such as CTR, execution rate, retention, and user trust.
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business impact
The findings connected behavioral barriers with product levers that could increase trade execution. For a brokerage product, faster and more frequent execution directly contributes to higher trading activity and commission revenue.
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Artifacts
Based on interview coding, frequency mapping, and manual synthesis.
insight frequency table
Placeholder for a table showing recurring insight themes across coded interviews.
interview excerpts
expand placeholder
Placeholder for selected interview fragments illustrating trust, delays, liquidity, and manual validation patterns.
decision flow diagram
Placeholder for a diagram mapping signal → evaluation → validation → liquidity → decision.
hypothesis framework
Placeholder for a matrix of hypotheses, metrics, expected behavior change, and business relevance.
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Methodological notes
The study was based on manual qualitative analysis: interview notes were coded, grouped into recurring themes, and translated into actionable product hypotheses. The final output combined behavioral patterns, insight frequency, and hypothesis framing for product teams.