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Jobs Best Practices

Guidelines for creating effective Jobs that drive real outcomes.

Job Design Principles​

1. Outcome Over Steps​

❌ Bad: "Click Settings, then Profile, then Edit" ✅ Good: "Complete user profile"

Why: Rise figures out the steps. You define the destination.

2. Measurable Success​

❌ Bad: "User understands analytics" ✅ Good: "User views 3+ charts in Analytics dashboard"

Why: Clear metrics enable optimization and measurement.

3. User Value First​

❌ Bad: "Increase feature usage" ✅ Good: "Help users analyze their data faster"

Why: Users engage when they see personal benefit.

4. Contextual Relevance​

❌ Bad: "Show to all users always" ✅ Good: "Show to new users during first week"

Why: Right intervention, right time, right user.

Starting Your Jobs Strategy​

Week 1: Foundation​

Create 1-3 Critical Jobs:

  1. Primary Activation Job

    • Most important onboarding outcome
    • Highest correlation with retention
    • Example: "Create first project"

  2. Quick Win Job

    • Easy to complete
    • Immediate value
    • Builds user confidence
    • Example: "Invite first team member"

  3. Aha Moment Job

    • Core value proposition
    • Differentiating feature
    • Example: "Generate first AI insight"

Don't: Create 20 Jobs immediately. Start focused.

Week 2-4: Observation​

Let Rise Learn:

  • Don't micromanage
  • Review analytics daily
  • Note friction points
  • Trust the learning process

Monitor:

  • Completion rates
  • Time to completion
  • User feedback
  • Intervention acceptance

Month 2+: Expansion​

Add More Jobs:

  • Feature adoption Jobs
  • Retention Jobs
  • Expansion/upgrade Jobs

Optimize Existing:

  • Refine messaging
  • Adjust targeting
  • Improve automation

Common Patterns​

Onboarding Jobs​

Best Practices:

✅ Focus on activation metrics
✅ Time-bound (first 7 days)
✅ Sequential (one at a time)
✅ Quick wins first
✅ Celebrate completions

Example Structure:

Job 1: "Complete profile" (Day 1)
Job 2: "Create first project" (Day 1-2)
Job 3: "Invite team member" (Day 3-5)
Job 4: "Use core feature" (Day 5-7)

Feature Adoption Jobs​

Best Practices:

✅ Target users who'd benefit most
✅ Show value before asking effort
✅ Demonstrate with examples
✅ Offer templates or shortcuts

Targeting:

Good fit:
- Active users
- Haven't used feature yet
- Use related features
- In target segment

Poor fit:
- Inactive users
- Already use feature
- Wrong plan/tier
- Wrong use case

Retention Jobs​

Best Practices:

✅ Identify early warning signals
✅ Offer proactive help
✅ Re-engage with new value
✅ Make return easy

Trigger Patterns:

- User inactive for 7 days
- Decreased engagement (30% drop)
- Repeated friction in same area
- Approaching subscription renewal

Messaging Best Practices​

Tone & Voice​

Be Conversational: ❌ "Navigate to the settings interface to configure parameters" ✅ "Head to Settings to set this up"

Be Helpful, Not Pushy: ❌ "You must complete your profile now" ✅ "Complete your profile to help your team find you"

Show Value: ❌ "Click the Export button" ✅ "Export your data to analyze in Excel"

Message Length​

Keep it concise:

  • Headlines: 5-7 words
  • Body: 1-2 sentences
  • CTAs: 2-3 words

Good Example:

Headline: "Export your data"
Body: "Download as CSV, Excel, or PDF to analyze offline."
CTA: "Export Now"

Too Verbose:

Headline: "Did you know you can export data from Rise?"
Body: "Rise offers multiple export formats including CSV, Excel, and PDF. You can export any data you see on screen. This is useful for offline analysis, reporting, and sharing with stakeholders who don't use Rise."
CTA: "Click Here to Export"

Timing​

Contextual Triggers: ✅ Show export hint when user is viewing data ✅ Offer report template when user opens Reports ✅ Suggest shortcuts after user repeats task 3x

Avoid: ❌ Interrupting active workflows ❌ Multiple interventions at once ❌ Immediately after user dismissed similar hint

Targeting & Segmentation​

Audience Definition​

Be Specific:

❌ Too Broad: "All users"

  • Results in low relevance, high dismissal

✅ Well-Targeted: "Users who signed up 3-7 days ago, created 1+ project, haven't invited teammates"

  • Higher relevance, better outcomes

Progressive Disclosure​

Complexity Gradient:

Beginner Users:
- Simple, guided Jobs
- More hand-holding
- Celebratory feedback

Intermediate Users:
- Feature discovery Jobs
- Shortcut suggestions
- Efficiency improvements

Advanced Users:
- Advanced feature Jobs
- Automation opportunities
- Power user tips

Exclude Appropriately​

Who to exclude:

- Users who already completed the Job
- Users outside the use case
- Users on wrong plan/tier
- Recently churned/inactive users

Performance Optimization​

Key Metrics to Track​

Completion Rate

Target: >60% for onboarding Jobs
Target: >40% for feature adoption Jobs

Time to Completion

Monitor: Median time, not average (outliers skew average)
Goal: Reduce over time as Rise optimizes

Intervention Acceptance

Acceptance Rate: % who engage with Rise's help
Target: >70% (if lower, intervention may be poorly timed)

Drop-off Points

Where users abandon the Job
Indicates: Friction, confusion, or low value perception

Iteration Strategy​

Weekly Reviews:

  1. Check completion rates
  2. Review friction heatmaps
  3. Read user feedback
  4. Adjust messaging or targeting

Monthly Deep Dives:

  1. A/B test messaging variants
  2. Experiment with different triggers
  3. Refine success criteria
  4. Update audience segments

When to Pause or Archive​

Pause if:

  • Completion rate < 20% after 2 weeks
  • High dismissal rate (>80%)
  • Negative user feedback
  • No longer aligns with product strategy

Archive if:

  • Feature deprecated
  • Job objective achieved org-wide
  • Replaced by better Job

Multi-Job Coordination​

Job Sequencing​

Good Sequence:

1. Quick win (easy completion) ✓
2. Core value (aha moment) ✓
3. Habit formation (repeated action) ✓
4. Advanced feature (power user) ✓

Poor Sequence:

1. Complex advanced feature ✗
2. Another advanced feature ✗
3. Simple task (feels like regression) ✗

Avoid Job Overload​

Rules:

  • Max 1 intervention per page view
  • Max 3 active Jobs per user
  • Space interventions 10+ minutes apart
  • Prioritize by importance

Priority Handling:

Critical Job active: Pause lower priority Jobs
User completed Job: Wait 30 min before next intervention
User dismissed 2x: Pause all Jobs for 24 hours

A/B Testing Jobs​

What to Test​

Messaging:

  • Formal vs casual tone
  • Benefit-focused vs feature-focused
  • Short vs descriptive

Timing:

  • Immediate vs delayed trigger
  • Time of day
  • Days into journey

Automation Level:

  • Guidance only
  • Prefill + guidance
  • Full automation

Test Structure​

Job: Complete Profile Setup
Variant A (Control):
  Message: "Complete your profile"
  Timing: Immediately after signup
  Automation: None

Variant B:
  Message: "Help your team find you—complete your profile"
  Timing: After first project created
  Automation: Prefill company from email domain

Measure:
  - Completion rate
  - Time to completion
  - User satisfaction

Common Mistakes​

1. ❌ Too Many Jobs Too Soon​

Problem: User overwhelmed with interventions Solution: Start with 1-3 critical Jobs

2. ❌ Micromanaging Steps​

Problem: Hardcoding every click like old DAPs Solution: Define outcome, let Rise figure out how

3. ❌ Poor Targeting​

Problem: Showing irrelevant Jobs to wrong users Solution: Carefully define audience segments

4. ❌ No Success Metrics​

Problem: Can't measure or optimize Solution: Define clear, measurable success criteria

5. ❌ Ignoring Analytics​

Problem: Not learning from data Solution: Weekly review of Job performance

6. ❌ Set and Forget​

Problem: Jobs become stale or irrelevant Solution: Regular iteration and updates

7. ❌ Forcing Users​

Problem: Mandatory, blocking interventions Solution: Helpful suggestions, not requirements

Checklist: Before Activating a Job​

[ ] Clear, specific goal defined
[ ] Measurable success criteria
[ ] Target audience well-defined
[ ] Appropriate priority level
[ ] Contextual trigger identified
[ ] Messaging is concise and valuable
[ ] Automation level appropriate
[ ] Success metrics defined
[ ] Tested in preview/sandbox
[ ] Rollout plan determined
[ ] Monitoring dashboard set up

Next Steps​