​Onboarding Teardown

Teardown Pdf

EatClub Onbaording Teardown.pdf​

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User goals, ICP, and JTBD

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Use Case 1: Quick Lunch

- JTBD: "I need a quick and easy way to order lunch so I can focus on work."

- Pain Points: Limited time, don't want to cook, want variety, offers.

- Desired Outcomes: Fast delivery, convenient ordering, variety of options.

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Use Case 2: Family Dinner

- JTBD: "I want to feed my family a delicious and convenient dinner without cooking."

- Pain Points: Limited time, want healthy options, want to please everyone.

- Desired Outcomes: Easy ordering, healthy options, variety of cuisines.

Use Case 3: Late-Night Cravings

- JTBD: "I need a satisfying late-night snack or meal that's easily accessible."

- Pain Points: Limited options, don't want to cook, want convenience.

- Desired Outcomes: Late-night delivery, variety of options, quick satisfaction.

Use Case 4: Social Gatherings

- JTBD: "I want to order food for a group of friends or colleagues without hassle."

- Pain Points: Coordinating orders, limited options, want variety.

- Desired Outcomes: Easy group ordering, variety of options, convenient delivery.

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​Use Case 5: Healthy Eating

- JTBD: "I want to maintain a healthy diet by ordering nutritious food that's easily accessible."

- Pain Points: Limited healthy options, want convenience, want tracking.

- Desired Outcomes: Healthy options, nutrition tracking, convenient ordering

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EatClub Activation Metrics

Step 1: Hypothesize Activation Metrics for Eat Club

1. Hypothesis 1: First-time order completion rate

Reasoning: This metric indicates whether new users are successfully completing their first order, which is crucial for activation. If users face issues during their initial experience, they may not return. A high completion rate suggests a smooth onboarding process.

2. Hypothesis 2: Average time to first order

Reasoning: This metric reveals how quickly new users are placing their first order. A shorter time indicates a more engaging onboarding process and higher activation likelihood.

3. Hypothesis 3: Percentage of users who add a payment method during sign-up

Reasoning: Adding a payment method during sign-up increases the likelihood of completing a first order. This metric helps evaluate the effectiveness of the payment method addition flow.

4. Hypothesis 4: First-order value (average)

Reasoning: A higher first-order value indicates users are more invested in the platform, increasing the chances of retention and repeat business.

5. Hypothesis 5: Percentage of users who order within 7 days of sign-up

Reasoning: This metric measures whether users are returning to the platform within a week of signing up, indicating early engagement and activation.

Step 2: Tracking Metrics and Rationale

Activation Metrics

1. First-time order completion rate

2. Average time to first order

3. Percentage of users who add a payment method during sign-up

4. First-order value (average)

5. Percentage of users who order within 7 days of sign-up

Retention Metrics

1. Repeat order rate (within 30 days)

2. Average order frequency

3. Customer lifetime value (CLV)

4. Net promoter score (NPS)

Engagement Metrics

1. Average session length

2. Bounce rate

3. Pages per session

4. Search queries (to measure discovery)

Funnel Metrics

1. Sign-up completion rate

2. Payment method addition rate

3. First-order conversion rate

4. Delivery address addition rate

User Experience Metrics

1. App rating (in-store)

2. Customer support ticket volume

3. Resolution rate for customer support tickets

By tracking these metrics, Eat Club can:

- Monitor activation and retention rates

- Identify funnel drop-offs and optimize the user journey

- Measure engagement and user experience

- Inform data-driven decisions to improve the platform and customer satisfaction

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