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Product analytics Flashcards

1
Q

Common goals for product analytics

A
  1. Build the right products and features
  2. Reduce friction
  3. Increase revenue
  4. Drive more innovation
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2
Q

Types of product metrics

A
  1. Business outcomes
  2. Product usage
  3. Product quality
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3
Q

Business outcomes

A

How the product influences key business and financial outcomes.

Examples:
1. Net revenue retention
2. Churn

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4
Q

Product usage

A

How users behave inside the product, which features the users use the most
and here they are getting stuck

Examples:
1. Feature adoption
2. Stickiness

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5
Q
A

How the product is doing what it supposes to do

  1. Response time
  2. Number of bugs per feature
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6
Q

Lagging indicators

A

Metrics that you want to move for the health of the business, but may be harder to see results in a short timeframe

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7
Q

Leading indicators

A

Metrics that are measurable in a shorter time frame, and have a high probability of affecting your lagging indicators

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8
Q

OKRs

A

Objectives and Key results

‘What are we focused on right now?’

Consists of an objective and three to five supporting key results that map to it.

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9
Q

KPIs

A

Key performance indicators

‘Are we ok?’

A longer-term view of performance, and typically a set of numbers that you can consistently watch over time

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10
Q

One Metric That Matters

A

‘Does what we are working on contribute to this one metric?’

Focus on a single metric that matters the most for the success of the business at different stages

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11
Q

North Star Metric

A

‘Does what we are working on contribute to our primary metric?’

A single metric that is an indicator of achieving user value over a period of time

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12
Q

Check metrics (Balance metrics)

A

‘Will focusing too much on this one metric have drawbacks?’

Metrics you track to ensure you aren’t over-indexing on any one metric — for example, your primary North Star — and causing unintended negative consequences

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13
Q

Product analytics strategy includes

A
  1. The questions you are asking
  2. The goals you’re working towards
  3. The metrics you use
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14
Q

Major goal of product analytics

A

Use quantitative data to identify people from whom you then want to collect qualitative data

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15
Q

Breadth

A

A measure of how many total users you have, as well as how many users you have per account

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16
Q

Depth

A

How much of the product your customers are using — specifically how many key features or areas of the product they’re utilizing

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17
Q

Frequency

A

Measures how often users — or users from a specific account — log in to your product in a given timeframe

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18
Q

Product Analytics
Hierarchy of Needs

A
  1. Collect data
  2. Refine data into metrics
  3. Build metrics into reports
  4. Take action
  5. Data Actualization
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19
Q

Aha moments

A

When users recognize the distinct value of your product and become committed to a long haul

20
Q

Popular aha moments

A
  1. Facebook: when a user connects with 7 friends in first 10 days
  2. Slack: When teams send 2000 messages
  3. Dropbox: when a user saves 1 file into 1 folder at a 1 device
21
Q

How to take action on feature adoption data

A

Tactic 1: Increase the visibility of the feature
Tactic 2: Improve adoption so more users are aware of the feature
Tactic 3: Make improvements to the feature

22
Q

Friction

A

Anything that gets in the way of a user’s ability to achieve their objective or job to be done

23
Q

Examples of frictions

A
  1. Interface copy that doesn’t make sense
  2. Pages where the next action is not obvious
24
Q

Signs of friction

A
  1. Users dropping out of workflows
  2. Low usage of key features
25
Q

Users dropping out of workflows

A
  1. Use funnels to see how customers move through your product
  2. Use session replay to review a visitor’s journey in your product and see where and how they’re dropping off
  3. Support tickets topics
26
Q

Low usage of key features

A

Solicit feedback from users to understand why usage is lower or what’s causing them to experience friction

27
Q

Benefits of sunsets

A
  1. More streamlined user experience
  2. Increased efficiency
28
Q

Types of business cases

A
  1. Investing to usability (metric — stickiness)
  2. Investing in innovation
  3. Where to focus adoption efforts
29
Q

Key questions for a business case

A
  1. Who is the target audience
  2. What pain is this addressing
  3. What is the desired outcome
30
Q

Outcomes to target

A
  1. Revenue: how will this idea help increase revenue by affecting customer acquisition or retention?
  2. Cost: how does this recommendation reduce cost by adding new efficiencies?
  3. Risk: how does the idea mitigates business, economics, or market risks?
31
Q

AARRR metrics pyramid

A

Acquisition, Activation, Retention, Revenue, Referral

+ Engagement

32
Q

Acquisition Metrics short

A

1 Bounce Rate

1.2 Conversion Rate

1.3 Landing Page Conversion Rate

1.4 Cost of Customer Acquisition (CAC)

1.5 Channel Effectiveness

1.6 Traffic Source Distribution

33
Q

Acquisition metrics explained

A

1 Bounce Rate

The percentage of visitors who leave your website after viewing just one page. A high bounce rate may indicate issues with the landing page (e.g., messaging) or targeting.

1.2 Conversion Rate

The percentage of users who take a desired action, like signing up for a newsletter.

1.3 Landing Page Conversion Rate

The percentage of visitors who take a desired action on a specific landing page, like signing up or starting a trial, on a specific landing page.

1.4 Cost of Customer Acquisition (CAC)

The cost of acquiring a new customer through marketing and sales efforts.

1.5 Channel Effectiveness

The success of each acquisition channel in driving traffic, sign-ups, or purchases.

1.6 Traffic Source Distribution

The breakdown of incoming user traffic by different sources, such as organic search, referrals, or paid ads.

34
Q

Activation metrics (short)

A

2.1 Time to Value (TTV)

2.2 Onboarding Completion Rate

2.3 User Activation Rate

2.4 Trial-to-Paid Conversion Rate

2.5 First-time User Conversion Rate

2.6 Product Qualified Accounts (PQA)

2.7 Product Qualified Leads (PQL)

35
Q

Activation metrics (detailed)

A

2.1 Time to Value (TTV)

The time it takes for a user to experience the core benefits of your product after starting to use it. A shorter TTV leads to higher user satisfaction, engagement, and retention. In product-led growth, optimizing TTV is crucial to ensure users quickly understand the value your product delivers.

2.2 Onboarding Completion Rate

The percentage of users who complete the onboarding process successfully.

2.3 User Activation Rate

The percentage of users who successfully complete a certain milestone in your onboarding process.

2.4 Trial-to-Paid Conversion Rate

The percentage of trial users who convert into paying customers.

2.5 First-time User Conversion Rate

The percentage of first-time users who complete a desired action, such as creating an account or purchasing. This metric helps assess the effectiveness of the onboarding process.

2.6 Product Qualified Accounts (PQA)

“In product-led sales, the product determines Product Qualified Accounts (PQA) to indicate when an account is prepared for sales engagement and potential conversion.” – Elena Verna, source

2.7 Product Qualified Leads (PQL)

“PQLs, or Product Qualified Leads, are the people within the existing self-serve user base with buying power.” – Elena Verna, source

36
Q

Engagement metrics (short)

A

Engagement Metrics can be considered part of the Retention and, depending on the context, Activation (e.g., Session Length). I presented them as a separate category to emphasize the distinct metrics focusing on user interaction with the product.

3.1 Daily Active Users (DAU)

3.2 Monthly Active Users (MAU)

3.3 Stickiness

3.4 User Satisfaction (CSAT)

3.5 Session Length

3.6 Session Frequency

3.7 Feature Usage

3.8 Customer Effort Score (CES)

3.9 Task Success Rate

3.10 User Feedback Score

37
Q

Engagement metrics (detailed)

A

Engagement Metrics can be considered part of the Retention and, depending on the context, Activation (e.g., Session Length). I presented them as a separate category to emphasize the distinct metrics focusing on user interaction with the product.

3.1 Daily Active Users (DAU)

The number of unique users who engage with the product daily.

3.2 Monthly Active Users (MAU)

The number of unique users who engage with the product monthly.

3.3 Stickiness

The ratio of daily active users (DAU) to monthly active users (MAU), which indicates how often users engage with the product.

3.4 User Satisfaction (CSAT)

A measure of how satisfied users are with the product, often determined through surveys or in-app feedback (e.g., Pendo, Gainsight).

3.5 Session Length

The duration of a user’s interaction with the product during a single session.

3.6 Session Frequency

The average number of sessions per user within a specific time frame.

3.7 Feature Usage

The frequency and depth of usage for specific product features.

3.8 Customer Effort Score (CES)

Measures the ease with which customers can interact with your product or service. It is often determined by asking users to rate the effort required to accomplish a task or resolve an issue on a scale from very low to very high effort.

A lower CES indicates a more user-friendly product, which can lead to higher user satisfaction and loyalty.

3.9 Task Success Rate

The percentage of users who successfully complete a specific task or set of tasks within your product. This metric helps assess the usability and effectiveness of your product’s features.

3.10 User Feedback Score

A quantitative measure of user satisfaction gathered through surveys, ratings, or reviews.

There isn’t a single standardized method or rating scale. This could be a numeric scale (e.g., 1 to 5 or 1 to 10), a star rating, or a qualitative scale (e.g., poor, average, excellent).

38
Q

Retention metrics (short)

A

4.1 Churn Rate

4.2 User Retention Rate

4.3 User Renewal Rate

4.4 Customer Lifetime

4.5 Customer Health Score

4.6 Product Adoption Rate

39
Q

Retention metrics (detailed)

A

4.1 Churn Rate

The percentage of users who stop using the product within a specific period, e.g., monthly.

4.2 User Retention Rate

The percentage of users who continue using the product after a specific period. Often monthly.

4.3 User Renewal Rate

The percentage of users who renew their subscription or continue using the product after their initial contract period.

4.4 Customer Lifetime

The average time it takes for a user to stop using the product.

4.5 Customer Health Score

A composite metric that combines multiple indicators, such as usage, satisfaction, and support interactions, to provide an overall assessment of the customer’s relationship with the product.

4.6 Product Adoption Rate

The percentage of users who adopt new features or functionality within a certain time frame after release.

40
Q

Revenue metrics (short)

A

5.1 Average Revenue Per Account (ARPA)

The average revenue generated per account (customer) within a specific time frame. For example, monthly.

5.2 Customer Lifetime Value (CLV/LTV)

The total revenue a user generates during their entire relationship with the product.

5.3 Customer Profitability

The difference between the lifetime value of a customer (LTV) and the cost of acquiring them (CAC).

5.4 Monthly Recurring Revenue (MRR)

The predictable revenue generated by a subscription-based product every month.

5.5 Expansion Revenue

Additional revenue generated from existing customers through upsells, cross-sells, or add-on purchases.

5.6 Net Revenue Churn

The revenue lost due to customer cancellations, downgrades, or non-renewals within a specific period, typically a month/year.

5.7 Net Revenue Retention

The cumulative sum of retained, contracted, and expanded revenue over a specific period, typically a month or year.

5.8 Average Contract Value (ACV)

The average revenue generated from each customer contract, which can help assess the effectiveness of pricing and packaging strategies.

41
Q

Revenue metrics (detailed)

A

5.1 Average Revenue Per Account (ARPA)

The average revenue generated per account (customer) within a specific time frame. For example, monthly.

5.2 Customer Lifetime Value (CLV/LTV)

The total revenue a user generates during their entire relationship with the product.

5.3 Customer Profitability

The difference between the lifetime value of a customer (LTV) and the cost of acquiring them (CAC).

5.4 Monthly Recurring Revenue (MRR)

The predictable revenue generated by a subscription-based product every month.

5.5 Expansion Revenue

Additional revenue generated from existing customers through upsells, cross-sells, or add-on purchases.

5.6 Net Revenue Churn

The revenue lost due to customer cancellations, downgrades, or non-renewals within a specific period, typically a month/year.

5.7 Net Revenue Retention

The cumulative sum of retained, contracted, and expanded revenue over a specific period, typically a month or year.

5.8 Average Contract Value (ACV)

The average revenue generated from each customer contract, which can help assess the effectiveness of pricing and packaging strategies.

42
Q

Referral Metrics (short)

A

6.1 Virality Coefficient

The number of new users acquired through referrals by existing users. Often expressed as a ratio (<1, 1, >1).

6.2 Customer Referral Rate

The percentage of customers who refer others to the product.

6.3 Referral Conversion Rate

The percentage of referrals that convert into active users.

6.4 Net Promoter Score (NPS)

A measure of customer satisfaction and loyalty based on how likely users are to recommend the product to others.

Warning: NPS measures customer attitude and sentiment, not the actual behavior.

43
Q

Referral metrics (detailed)

A

6.1 Virality Coefficient

The number of new users acquired through referrals by existing users. Often expressed as a ratio (<1, 1, >1).

6.2 Customer Referral Rate

The percentage of customers who refer others to the product.

6.3 Referral Conversion Rate

The percentage of referrals that convert into active users.

6.4 Net Promoter Score (NPS)

A measure of customer satisfaction and loyalty based on how likely users are to recommend the product to others.

Warning: NPS measures customer attitude and sentiment, not the actual behavior.

44
Q

Activation metrics that matter

A
  1. Time to Value (TTV)

The time it takes for a user to experience the core benefits of your product after starting to use it.

  1. User Activation Rate

The percentage of users who successfully complete a certain milestone in your onboarding process.

45
Q

Engagement metrics that matters

A
  1. DAU / MAU / WAU
  2. Stickiness (DAU/MAU)
  3. User satisfaction (surveys /in app feedback)
46
Q

Retention metrics that matter

A
  1. User retention rate
    The percentage of users who continue using the product after a specific period. Often monthly.
  2. Product adoption rate
    The percentage of users who adopt new features or functionality within a certain time frame after release.
47
Q

Referral metrics that matter

A
  1. Virality Coefficient
    The number of new users acquired through referrals by existing users. Often expressed as a ratio (<1, 1, >1).
  2. Net Promoter Score (NPS)

A measure of customer satisfaction and loyalty based on how likely users are to recommend the product to others.

Warning: NPS measures customer attitude and sentiment, not the actual behavior.

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