Building Feedback Loops with Doozy: A Guide to Looped Learning

Why feedback loops matter in enablement

Leadership asks "did people actually understand this?" and nobody knows. The same training runs again next quarter. Gaps only surface after something breaks.

This is not a knowledge problem. It is a system problem.

Most training is built to be delivered once and forgotten. A course goes live, people complete it, and the team moves on. One-off training assumes people absorb information on the first pass, that knowledge stays current without maintenance, and that completion equals competence. None of these assumptions hold.

Feedback loops change the equation. Instead of treating learning as an event, you treat it as a system: one that continuously surfaces gaps, delivers targeted interventions, and validates whether those interventions worked.

What looped learning means in practice

Looped learning is a continuous cycle of insight, action, and validation. The goal is not to deliver content. The goal is to develop capability and know whether you succeeded.

This requires three things:

1. Signals that reveal where gaps exist.
2. Interventions targeted to the actual root cause.
3. Validation that confirms the intervention worked.

When these connect, learning becomes adaptive. It responds to change as fast as the business does.

Doozy's role in looped learning

Doozy is not just a learning platform. It is an insight layer. Most platforms track completion. Doozy tracks comprehension, and connects it to what happens next.

With Doozy, you can:

• Run diagnostic quizzes that reveal where knowledge gaps exist.
• Deliver micro-lessons in Slack, where work already happens.
• Track comprehension at individual, team, or organisation level.
• Connect learning signals to real-world performance.

The platform makes feedback loops practical at scale, not just possible in theory.

The core looped learning model

Looped learning follows a five-stage cycle. Each stage feeds into the next, creating a system that improves continuously.

This loop is not linear. You can enter it at any stage. The value comes from running it repeatedly, not from running it perfectly. Some signals come from outside Doozy. The loop is closed inside it.

1. Analyse: Start with performance signals

The loop begins with signals: data that tells you where things are not working as expected.

Signals come from operational metrics (error rates, rework, cycle time, quality thresholds), trend analysis (spikes after a product change or policy update), or manager observations.

The point is to look at outcomes, not activity. Completion rates tell you who showed up. Performance signals tell you whether training made a difference.

2. Identify: Pinpoint the knowledge gap

Once you have a signal, the next step is diagnosis.

Doozy quizzes are diagnostic tools. They surface what people actually understand, not what they claim to remember. A well-designed quiz reveals which topics are understood, where confusion exists, and whether the issue is missing knowledge or something else.

Without this visibility, you are guessing at root causes. With it, you can be precise. Run diagnostics at any level: individuals, teams, or entire organisations.

3. Develop: Deploy the right intervention

Once you know the gap, you choose the intervention. The right response depends on the root cause:

• Missing knowledge → Microlearning or documentation refresh.
• Process confusion → Workflow update or manager mentoring.
• Habit or consistency gap → Live session, reinforcement cadence, or manager check-in.
• Tool unfamiliarity → Quick reference guide or hands-on practice.

The goal is targeted action. If five people out of fifty are struggling, you support the five and move on. Doozy lets you assign learning to specific people or groups and schedule follow-ups automatically.

4. Reinforce: Confirm retention and behaviour change

Intervention is not the end. Memory fades. Habits revert. Without reinforcement, most of what people learn disappears within weeks.

Re-checking understanding after an intervention is essential. Doozy supports this through:

• Follow-up quizzes scheduled days or weeks after the initial learning.
• Spaced repetition using one-question-per-day delivery to combat the forgetting curve.
• Manager dashboards that flag who might need additional support.

This stage is about durability. You are not just checking completion. You are checking whether the learning stuck.

5. Validate: Close the loop with outcomes

The final stage connects learning back to the performance signals you started with.

Did the intervention work? Are the metrics improving? Are managers seeing better outcomes? Is the original problem resolving?

This validation is what makes the loop a loop. Without it, you are just delivering training and hoping for the best. With it, you have evidence, and you can make informed decisions about what to do next.

If the intervention worked, you move on. If it did not, you revisit the diagnosis. Maybe the root cause was different than expected. Maybe the intervention was too light. Maybe something else changed.

Either way, you learn, and the system gets smarter.

How Slack enables the feedback loop

Feedback loops need speed and visibility. Portal-based learning breaks both: it adds friction and hides participation. People forget to log in. Managers have no visibility. Loops slow down or stop entirely.

Slack-first learning is different. Learning happens where work happens. Managers see participation and results in real time. The loop stays observable, not buried in a system nobody checks.

Requests and signals in real time

Frontline teams know where training gaps exist before leadership does. They see the questions that come up repeatedly, the mistakes that happen after launches.

Slack makes it easy to capture this insight. Topic requests and feedback flow directly into the right channels without formal processes.

Assignment and follow-up

Doozy's Slack integration keeps ownership visible:

• Managers get notified when someone is assigned learning.
• Assignments appear in Slack DMs, not buried in a portal.
• Follow-up reminders keep things moving without manual tracking.

Nothing falls through the cracks.

Learning in the flow of work

A quiz appears in a DM. A micro-lesson shows up in a channel. The learner responds, and they are done. No portal. No login. No context switching.

This is what makes feedback loops sustainable. If learning is easy to deliver and easy to complete, you can run loops frequently. If it is hard, loops stall.

Structuring learning for scale

Structure prevents sprawl. As teams grow, you need clear categories so people know what applies to them and content owners know where things belong.

Doozy supports three types of learning:

Universal learning

Baseline knowledge everyone needs, regardless of role: company-wide launches, policy changes, compliance, culture onboarding.

Broad but shallow. The goal is shared context. Doozy tracks with organisation-wide triggers make this easy to manage.

Focused learning

Deeper content only for those whose work depends on it: role-specific skills, initiative-based training, time-bound priorities.

Narrow and deep. The goal is expertise where it matters most. Doozy tracks with targeted triggers and group filters deliver the right content to the right people.

Remediation learning

Targeted reinforcement driven by signals: individuals below threshold on a diagnostic, teams showing performance gaps, groups where a process change did not land.

Reactive but precise. The goal is to close gaps already identified. Doozy's manual assignment and follow-up features make this practical without administrative overhead.

Turning loop insights into strategy

Every loop generates data. Over time, that data reveals patterns.

You start to see which topics cause persistent confusion. You notice which teams adapt quickly and which need more support. You learn which interventions work and which do not.

This insight should shape your enablement roadmap.

Instead of guessing what to build next, you prioritise based on evidence:

• High-risk gaps get immediate attention.
• Recurring issues get durable content investments.
• One-time needs get lightweight interventions.

You stop building training for its own sake. You start building training that solves real problems, and you can prove it.

Operationalising looped learning

Feedback loops do not run themselves. Someone needs to own each stage.

A practical operating model assigns responsibility for:

• Signals: Who monitors performance data and flags issues?
• Diagnosis: Who runs diagnostic quizzes and interprets results?
• Intervention: Who creates or assigns the right learning?
• Reinforcement: Who schedules follow-ups and tracks retention?
• Validation: Who connects learning outcomes back to business metrics?

In smaller teams, one person might own the whole loop. In larger organisations, responsibilities might be distributed across L&D, managers, and subject-matter experts.

The key is clarity. When ownership is unclear, loops stall. Training becomes reactive again. Data exists but nobody acts on it.

Doozy helps by automating delivery, reminders, and tracking, so humans can focus on judgment calls: what matters, what to build, and when to intervene.

Managing your learning portfolio

Not all topics deserve the same investment. Over time, loop data helps you manage learning like a portfolio.

A strong prioritisation process combines:

• Operational signals: Where are errors or quality issues concentrated?
• Expert input: What do managers and SMEs see on the ground?
• Loop history: Which topics cause persistent confusion? Which interventions worked?

This is not diagnosis. This is investment strategy. You are deciding where to build durable content, where lightweight interventions suffice, and where to stop investing entirely.

The goal is proactive enablement: building content before gaps become problems, not reacting every time someone asks.

What a mature feedback loop looks like

When feedback loops run consistently, the results compound.

Faster response to change. When a product ships or a policy updates, you can deploy learning in hours, validate comprehension in days, and remediate gaps in a week. No more six-month content cycles.

Clear visibility into capability. At any moment, you know what people understand and where they struggle. You can answer leadership questions with data, not guesses.

Learning that evolves continuously. Content stays current because loops surface when it drifts. Interventions improve because you see what works. The system gets better over time.

Measurable improvement. You can draw a line from learning to outcomes. You can prove that enablement investments are paying off.

This is what it looks like when learning becomes a system, not a project.

Getting started with looped learning in Doozy

You do not need to transform everything at once. Start with one loop.

Pick one signal. Choose a metric that matters: error rate, onboarding ramp time, rework volume, whatever is visible and important.

Run one diagnostic. Build a short quiz that tests the knowledge most likely to affect that metric. Send it to the relevant team.

Take one action. Based on the results, deploy a targeted intervention. It might be a micro-lesson track, a documentation update, or a manager conversation.

Validate once. After the intervention, re-check. Did comprehension improve? Did the original signal move?

That is one loop. Learn from it. Then run another.

Over time, loops become habit. Diagnosis gets faster. Interventions get sharper. Validation becomes automatic.

You stop running training programs. You start running a learning system. Enablement stops being content production and becomes system operation. Leadership trusts it because it is measurable, responsive, and tied to outcomes.

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Ready to start? Add Doozy to Slack. Pick one metric. Send one diagnostic quiz. See what you learn.

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