Monetization + Pricing Flashcards

Question

Value Metric Analysis

Answer 1

1. Define the Scope 1.1 What creates value for consumers? Look at the Why in our use case map 1.2 What metrics measure value? Generate proxies for the why 2. Survey Audience 2.1 Build your Max-Diff Survey 2.1.1 We typically also want to add a calibration question. This helps us get a sense of how much of the value metric our respondent might consume. E.g. How many leads do you typically get each month? 2.1.2 (# of times attribute was chosen most - # of times attribute was chosen least) / # of times attribute appear in the set 3 Analyze Results 3.1 Calculate Relative Preference Score 3.2 Analyze Score 3.3 Segment Results 3.3.1 Use Case, What attributes are our use cases created and differentiated on? 3.3.2 Demographics or Firmographics. What are our consumers' ages, genders or household incomes? What is their company's size, industry, status? 3.3.3 Geography. Where in the country or world do our consumers live?

Answer 2

Van Westendorp Pros: Anybody can do it even without a lot of customers or users Cons: All based on survey results versus the actual behavior of the target audience Conjoint Pros: Simulates the actual behavior Cons: Incomplete picture of how users evaluate different options - only understand the best/worst Live Testing Pros: Simulates the actual behavior Cons: Need a significant amount of volume and sophisticated infrastructure to handle and resolve different types of conflicts

Answer 3

Van Westendorp At what (monthly) price point does [product] become ... 1. Build Survey 1.1 Describe the product 1.2 Define the what (maybe value metrics) 1.3 Willingness to pay question 1.3.1 too expensive that you would never consider purchasing it? [Too Expensive] 1.3.2 starting to become expensive, but you would still consider purchasing it? [Not a Bargain] 1.3.3 a really good deal? [Not Expensive] 1.3.4 too cheap that you question the quality of it? [Too Cheap] 2. Analyze Result 2.1 Visualize results, what percentage of users think this is [too expensive] at each price point on the X axis? This is a cumulative chart. 2.2 Analyze overall Lower Bound = intersect between Not a Bargain and Too Cheap Upper Bound = Too Expensive and Not Expensive Optimal Price Point = Not Expensive and Not a Bargain 2.3 Segmentation

Answer 4

Scale Business Hypothesis: How does price scale? Consumer View: How does value scale for them? What Business Hypothesis: What do we charge for? Consumer View: What features and benefits do they value? Amount Business Hypothesis: How much do we charge? Consumer View: How much are they willing to pay? When Business Hypothesis: When do we charge? Consumer View: When do they want to pay?

Answer 5

1. Value metric strategies, changing how price scales 2. Packaging Strategies, Changing what we charge for 3. Pricing Strategies, Changes in price 4. When Strategies, Changing when we charge

Answer 6

1. Vertically, Offering the new use case as an additional choice, another price tier for example 2. Horizontally, Introduce a new use case as an add-on for all use cases

Answer 7

1. Consumer view for use case not aligned with business hypothesis 2. Growth loops for use case not enabled by business hypothesis 3. Cost of serving use case not balanced with revenue from use case

Answer 8

1. Some segments have a different view than the business hypothesis 2. Growth loops for some segments not enabled by business hypothesis 3. The cost of serving some segments is higher than revenue from the business hypothesis 4. Expand the target audience and serve some new personas and problems

Answer 9

Involves changing how our pricing scales: 1. Change from feature differentiated to a value metric (usage or outcome) 1.1 Drives expansion, Products with value metrics can drive expansion more organically than feature differentiated products. 1.2 Minimizes churn, Companies with value metric pricing have lower churn than feature differentiated pricing. 1.3 Value of features is declining, Most competing products in an industry offer similar features, making it harder to differentiate based on just features 1.4 Comfort with lower predictability, since it aligns with their outputs, they are willing to factor in the cost variability. 2. Change our value metric 3. Add more value metrics

Answer 10

1. Outcome vs Usage, sometimes use usage as a proxy for the outcome. Look at the why in our use case map 2. Continuous vs Banded 3. Single vs Multiple

Answer 11

There are two things we want to understand to set the right value metric. 1. How does value scale for them? (value metric Max-Diff Survey) 1.1 Ideally, the more value a user gets, the more they pay 2. How much are they willing to pay? (Van Wetendorp) The key question is Does willingness to pay scale with the value metric? Check Scaling Our Value Metric below

Answer 12

1. Calculate Scaled Willingness to Pay 1.1 Willingness to pay = avg(Not a bargain + Not expensive) from responses Van Westendorp 1.2 Scaled Willingness to Pay = Willingness to pay * the number of the value metric. E.g. # Landing pages/month * Willingness to pay/page/month 2. Visualize on a Graph 3. Analyze Results 4. Segment Results

Answer 13

Involves changing what we charge for in a given use case, whether that is a single feature or combining multiple features into a package When to use packaging strategies 1. Align with features consumers value, When there are gaps between what features consumers value and what we offer 2. New Feature Development, When we need to understand the right user case and price point to place new features Packaging Strategies Scenarios 1. Add features to a use case (with or without changing price) 2. Move features between use cases (with or without changing price & Move up or down)

Answer 14

Define the scope 1. What value propositions or benefits do users value? 2. What product features or attributes do users value? Survey Audience Build your Max-Diff Survey Analyze Results 1. Calculate Relative Preference Score 2. Analyze Score 3. Segment

Answer 15

1. Setup Matrix 2. Analyze Matrix 2.1 Don't overfit this analysis to use cases. Jamming features into the perfect tier can add friction to the decision making process 2.2 Segment results to avoid underfitting analysis. only looking at the overall results without segmenting will give you an approximate picture. 3. Segment Results

Answer 16

X axis is Relative Preference Score Y axis is Willingness to Pay Both from -1 to 1 For the willingness to pay, use the following to steps to convert it to -1 and 1 range 1. Calculate Median WTP 2. For each feature, calculate the absolute deviation 3. The absolute deviation / Median WTP = % deviation Then plot them into the matrix, we have 4 quadrants 1. Low Value Low Willingness to Pay (Not Valued by Use Case) 1.1 What purpose does this feature serve? Important for setup or activation? Business values it but customers don't? 2. High Value Low Willingness to Pay (Table Stakes for Use Case) 2.1 Maybe they are available across competition or alternatives for free. 3. Low Value High Willingness to Pay (Add-ons) 3.1 Offer these as add-ons 4. High Value High Willingness to Pay (Expansion Triggers) 4.1 These features offer a great opportunity to differentiate some of our use case. 4.2 We are more likely to see features end up in this quadrant as we segment our results and look different segments value different features differently.

Answer 17

This involves changing the amount we charge for an existing use case. When to use pricing strategies 1. Align pricing with the monetization triad. Change price to align wit consumer view, growth loops or cost of revenue 2. Align pricing with changes to other elements of the monetization model. Change price to align with changing value metric, packaging or when we charge Increase price or decrease price. Most of the time we need to increase the price. Inflation is one factor. It also depends on whether the increase in depth or decrease in breadth is strong. Vice versus If there is a trade-off between consumer view and cost, [consumer view tends to win]

Answer 18

Too Cheap > Not a Bargain > Not Expensive > Too Expensive Lower Bound = intersect between Not a Bargain and Too Cheap Upper Bound = Too Expensive and Not Expensive Optimal Price Point = Not Expensive and Not a Bargain

Answer 19

1. Free vs Paid 1.1 Free to paid is rare since consumers who aren't used to paying for something have very low WTP 1.2 Paid to free usually happens in response to wider changes in the industry or product 2. Transactional vs Subscription 2.1 Requires a change in all other elements of the monetization model 2.2 Industry-wide shifts 3. Term of Subscription

Answer 20

First, do customers want to play? 1. Are customers willing to pay? if No, then free If Yes We go into Time of Transaction or Recurring? 1. What is their natural frequency of usage? 1.1 Low leads to Transaction 1.2 High leads to Recurring 2. What is the variance in their natural frequency of usage? 2.1 High leads to Transaction 2.2 Low leads to Recurring If it is Recurring, What is the term of Recurrence? 1. What is the natural frequency of adoption? How often does our audience make a decision about which product to use? 1.1 High leads to Short Term 1.2 Low leads to Long Term 2. How long does it take to form a habit? We want to avoid charging them again before forming a habit 2.1 Less Time leads to Short Term 2.2 More Time leads to Long Term

Answer 21

Qualitative Survey Asking users questions on frequency 1. How often do you face this problem? 2. How often do you use the alternative? Quantitative Analysis 1. Create a frequency histogram 1.1 Look at a long enough period to identify recurring patterns of usage. E.g if it is weekly, we need at least 1 month of data, if it is monthly, it needs at least 3 months 1.2 How many users are active N days out of that period 2. Analyze frequency 2.1 Look for peaks 3. Analyze variance 3.1 Single spike - low variance 3.2 Uniform Distribution - high variance 3.3 Multiple Spikes - high variance

Answer 22

1. What problem does the product solve for you? 2. When was the last time you had this problem? 3. How did you solve it then? 4. When was the last time you considered [alternatives] to solve this problem? 5. Before you knew about this product, did you ever have this problem before?

Answer 23

Qualitative Survey Asking users open-ended research questions? 1. What do you find most useful about the product? 2. How often do you use the product? 3. When did you feel like the product became an-essential/useful part of your life? 4. How long did it take you to get to this point? Quantitative Analysis 1. Define the habit moment 1.1 Action, what action do they do 1.2 Frequency, How often do they do it? 1.3 Time Frame, In what time frame do they do it? 1.4 Way to arrive at the habit moment 1.4.1 Qualitative Inputs, When did you feel the product was useful to you? or when did it become essential? 1.4.2 Correlation Analysis, What actions and frequency have a positive correlation to high retention, and a negative correlation to non-retention. 1.4.3 Advanced Analytics, Machine learning algorithms can help us identify where the turning point was 2. Identify successful users 3. Calculate time to habit 3.1 Identify the starting point, user sign up or registers as a user 4. Segmentation

Answer 24

Qualitative <-> Quantitative 1. Access to Customer, users and customers are the same group, Low <-> High 2. Audience Reach, Small <-> Large 3. Product Maturity, [Pre-PMF, Mature] <-> Growth, Growth is the best phase to do the quantitative survey 4. Industry Maturity, Mature <-> New 5. Research Question Type, Open-ended <-> Specific 5.1 When an organization wants to understand how many people feel a particular way about some aspect of the product, they are looking to quantify customer sentiment. On the other hand, when an organization is concerned with how people feel about something, or why they feel the way they do about something in the product, the research needs are more qualitative. 5.2 Qualitative research should precede quantitative research

Answer 25

What are you solving for? Who can answer your questions? Defensive Play: 100% Existing Customers New Feature: 80% Existing Customers 20% Prospective Customers Product Diversification: 50% Existing Customers 50% Prospective Customers Adjacent Expansion: 20% Existing Customers 80% Prospective Customers New Market: 100% Prospective Customers

Answer 26

MaxDiff Survey Attributes: Key Considerations 1. Are the attributes comparable? Don't compare features to categories 2. Do the attributes represent a realistic decision? Don't ask people to compare attributes that are dependent upon each another 3. Are we asking the audience to compare essential attributes? Don't compare the product's core attributes, because users will always need access to them

Answer 27

Outsource Pros: More Expertise Less engineering effort Limited bias Easier to recruit non-customers Cons: More expensive Make take longer, while still requiring input and management Less control over data quality and follow-up More difficult to associate customer profiles into In-House Pros: Less expensive Usually faster Can associate customer profile Cons: Less expertise Potentially bias responses More difficult to recruit outside customers Internal alignment and effort

Answer 28

Vertical 1. When consumers of a single use case segment value the new use case 2. When the value scales the same as existing use cases 3. When a vertical strategy doesn't increase friction in growth loops 4. When the cost to serve the use case is low Horizontally 1. When consumers across use case segments value the new use case 2. When the value scales differently from the existing use cases 3. When a vertical strategy adds significant friction in growth loops 4. When cost to serve the use case is high

Answer 29

1. Who are we targeting with our optimizations? 2. What will drive revenue growth from these different customers?

Answer 30

1. Potential Customers 1.1 Definition, Haven't transacted yet, but good candidates to be customers 1.2 Mindset, Curious about value and whether the product is worth the price 1.3 Uber example, Downloaded app, created account, no first ride 2. Existing Healthy Customers 2.1 Definition, paying customers who have a healthy engagement with the product 2.2 Mindset, Already happy with the product, might be wondering about what else the product can solve for me 2.3 Uber example, User who takes a ride once/twice every week 3. At-Risk Customers 3.1 Definition, Used to be existing health customers but are now at risk of churning 3.2 Mindset, Dissatisfied with their experience and might be considering switching to an alternative 3.3 Uber example, Riders who have given low ratings on two rides in a row 4. Churned Customers 4.1 Definition, Used to be existing healthy customers but have stopped transacting with us 4.2 Feel like the product didn't solve their problem, or that it's not worth the price, considering alternatives 4.3 Uber example, User who used to take rides weekly, but hasn't taken a ride in the past 2 weeks

Answer 31

1. How well am I converting users to the target state today? 2. Why have they not converted to the target state already? 3. Which users are candidates for conversion?

Answer 32

Potential Customers: New Revenue Existing Healthy Customers: Expansion At-Risk Customers: Existing + Contraction + Churn Churned Customers: Existing

Answer 33

1. Who, Customer persona attributes 2. What, Action taken in the product 3. When, External time-based triggers 3.1 Time of the Year 3.2 Seasonal Events

Answer 34

1. Awareness, Customers are not aware of the expansion path 2. Value, Customers are aware of the expansion path, but do not see the value 3. Conversion, Customers see the value, but there is too much friction to convert

Answer 35

1. Who, Who are these customers? 2. How, How did we acquire them? 3. What, What did they do before converting? 4. When What time factors drove conversion?

Answer 36

1. Negative actions, Identify key actions of existing healthy customers that suggest they might be at-risk 2. Negative Direction Of Engagement, Identify declining trend of engagement in existing healthy customers 3. Prediction Model, Build a prediction model that gives each user a score for how likely they are to churn.

Answer 37

1. Satisfied, Have gotten the needed value from the product, and don't need it anymore 2. Unsatisfied, Seeking an alternative because the product no longer satisfies their needs 3. Losing Habit, Losing the habit for external reasons

Answer 38

Perceived Value > Perceived Price + Friction Perceived Value : The way our users interpret and perceive the value of the target state with respect to the current state Perceived Price: The way our users understand the price relative to other factors (alternative options, products, use cases) Friction: Barriers to go from the current state to the target state

Answer 39

Users need to go through 3 stages 1. Educate, Customers learn how the target state can solve their problems 2. Convert, Customers follow the process of conversion to the target state 3. Activate, Customers establish a habit around the target state

Answer 40

A spectrum of required resources and effectiveness Low Resource <--> High Resource Low Effectiveness <--> High Effectiveness Tell < Show < Experience Tell In product Message Educational emails Show Explainer videos Product demos Case studies Experience Free trials Free credit promos

Answer 41

1. Message, What message do we want to send? 1.1 Personal Motivation 1.2 Financial Motivation 1.3 Social Motivation 2. Channel, What channel do we want to use? 2.1 Product [Exisiting Healthy] 2.2 Notifications [Exisiting Healthy] 2.3 Paid Media [Churned Customers] 2.4 People [Churned Customers] 3. Timing, When do we want to educate them? 3.1 Internal Triggers, In the product 3.1 External Triggers, Events, Holidays, or seasons that influence consumers's perceived value.

Answer 42

Motivational Boosts Boost and Examples 1. Trust/Credibility/Authority: Trust Seals, Trusted Organizational Affiliations, Press Logos, Trusted Endorsements 2. Social Proof: Testimonials, Reviews, Case Studies, Example Customers 3. Urgency: Deadlines, Countdowns 4. Scarcity: Limited Spots/Availability 5. Belonging: Pictures of Similar People, Popularity Tags 6. Completion: Status Bars, Checklists, Making Something Look Undone Price Perception Biases 1. Anchoring, Consumers understand price relative to other factors, not as an absolute number 2. Paradox of Choice, When presented with multiple options, consumers find it hard to make a decision 3. Number Biases, round numbers are easier for consumers to understand, and preferred. 4 or 9 are preferred

Answer 43

Conversion isn't the end of the journey. We need to avoid the risk of churning. Setup Moment -> Aha Moment -> Habit Moment

Answer 44

To move them into healthy existing customers We need to first understand why they have not convert 1. Awareness: Educate on core value of the paid use case 2. Value: Increase perceived value, decrease perceived price 3. Conversion: Minimize friction of conversion Optimization Considerations 1. Conversion Motion 1.1 Free to paid (freemium) [Spotify, Slack] 1.2 Direct to paid [Instacart, Uber] For Direct to Paid PVpaid > PP + F For Free to Paid PVpaid - PVfree > PP + F 2. Acquisition Parameters 2.1 Organic 2.2 Direct 2.3 Paid [LowPVPV] 2.4 Referral [High PV] Strategies for Educating High Starting PV can leverage Tell, Show Low Starting PV can use Experience

Answer 45

1. Deepen revenue from the current use case 2. Move the customer to a higher ARPC use case 3. Add on more use cases

Answer 46

1. Habit Reinforcement 1.1 Who: At Risk of losing the habit 1.2 What: Re-establish the habit with their current use case, focus on the activate step 2. Anti-Conversion Flow 2.1 Who: Unsatisfied, Losing Habit, on the fence and considering 2.2 What: Decrease PV, Increase PP, and Friction of churn. Need to balance the friction. 3. Use Case Transition 3.1 Who: Satisfied, Unsatisfied 3.2 What: Educate, convert and activate on the new use case

Answer 47

1. Resurrect to New Use Case 1.1 Who: Satisfied, Unsatisfied 1.2 What: Educate, convert and activate the new use case 2. Involuntary Churn Mitigation 2.1 Who: Involuntary Churn 2.2 What: Minimize Product and Payment Friction 3. Reactivation (hardest) 3.1 Who: Unsatisfied Voluntary 3.2 What: Educate, convert and activate the new use case

Answer 48

2 Reasons Product Issues Technical bugs Getting logged out Forgetting password Payment Issues Expired credit card Credit limit reached Address change Payments failures normally fall into 2 buckets Soft Decline Insufficient Funds Billing address changes Transaction flagged as fraudulent Hard Decline Credit card expired/cancelled Credit card number updated For minimizing the soft declines 1. Optimize Retry Cadence 2. Credit Card Processor Rotation 3. Reducing Restriction, Removing verifications to avoid triggering failed payments 4. Optimize Fraud Classification 5. Recurring Flags For minimizing the hard declines 1. Optimizing Payment Options 2. Account Update Programs 3. Incentivize User Updates

Answer 49

Any strategy that involves a temporary decrease in model friction, e,g, discounts, promotions, coupon codes, free trials, credits. etc

Answer 50

1. Incentives lower the consumer's view of value, discount fatigue 2. Incentives disable growth loops 3. Incentives make it harder to support the cost of revenue

Answer 51

Incentives that Drive Optimization 1. Convert potential customers to existing healthy customers 2. Expand revenue from existing healthy customers 3. Save at-risk customers and make them healthy again 4. Resurrect churned customers back to a healthy state

Answer 52

1. Consumer Sensitivity, Testing lower price for a use case might upset current customers 2. Cross Population, Untested consumers might be influenced if they hear about the test, giving murky result. 3. Time, Takes longer to get accurate results, not ideal when decisions need to be made quickly 4. Infrastructure Lift to Test, Testing will require touching multiple parts of the system, make it cumbersome

Answer 53

1. Define Scope and Objective 1.1 What is the change we are testing? 1.2 Success Metric, What metrics do we think it will impact? also Trade-off metrics, and Secondary Metrics 1.3 Impact, In which direction and by how much will the metric move? 2. Build the test 2.1 Define solution and control variations, What are our solution and control variations? 2.2 User assignment, Which users could we assign to our solution and control variations? 3. Launch the test 3.1 Timing, When should we run the test? 3.2 Duration, How long should run the test for? 4. Evaluate Results 4.1 Implement, Should we implement our test? 4.2 Don't Implement, Should we not implement our test and iterate on our solution instead? 4.3 Investigate Further, Do we need to run the test for longer or do more analysis? 5 Implementing Solution 5.1 Roll Out As-Is, Do we roll out solution variation or do we need to develop further? 5.2 Keep a Holdout Group, Should we keep a holdout group to track long-term effects or should we roll out to 100%. A holdout group is a small control group that is withheld from experiencing the solution once implemented. This provides a reference for how our success metrics and additional outcome metrics would have performed without the solution. 5.3 Managing Existing Users, How do we manage existing customers through this rollout?

Answer 54

Success Metric: Metric we want to move with our solution Trade-off metrics: Metrics that could be negatively impacted by our solution and affect revenue as a whole Secondary Metrics: Metrics that validate we are not doing any harm Leading Indicator: Metrics that give us early signals of long-term impact.

Answer 55

1. Painted Door, Feature or product appears to be functioning, but is actually just there to get a user to express intent 2. Wizard of Oz, An automated process is replaced by a manual/thrid-party process temporarily 3. Provisional Test, Breaking down our tests into smaller tests, especially when there are multiple smaller hypotheses/changes in solution 4. Targeted User Release, Instead of rolling out the test to all the users, we target a specific group of users for the test. 4.1 Solution will be affective for the target audience 4.2 Solution will not negatively affect other users

Answer 56

1. Offline communication (Cross Population) 2. Measuring network effects, NFX 3. Multiple Sessions, same user using different devices For 1 and 2, we could use Group Based Assignment, create and assign closed groups to solution and control variations Group Based Assignment Groups need to be closed to minimize cross-population and measure network effects Solution and control groups need to be matched to avoid biased results For Multiple Sessions, we could use 1. Visitor Level Tracking, User cookies to track returning visitors and put them in the variation they were initially assigned to 2. Net New Testing, Only include new users in a specific test and split them between solution and control variations

Answer 57

1. Seasonal Variation 2. Internal Promotions, by other teams 3. Product Specific Timing 4. External Market Factor

Answer 58

Considering all the metrics we defined before.

Answer 59

Grandfathering helps minimize negative experiences and hence churn of existing customers. How you communicate any change is incredibly important. 1. Message 1.1 What change are we making? 1.2 Why are we making the change? 1.3 Why is the change good for customers? 1.4 What is the best channel for customers to reach out with questions/concerns? 2. Channel 2.1 Not just Blog or Forum only 2.2 Email to customers 2.3 Widely distributed press release 2.4 Personal sales rep call to enterprise value customers 1 Drawbacks to Grandfathering 1.1 Potential Lost Revenue 1.2 Techincal and Resource Costs 1. Should we grandfather or not? 1.1 Is the revenue loss from churn worth the cost of grandfathering? Potential Revenue Loss > Cost to Grandfather Potential Revenue Loss = # of Existing Customers * Revenue per Customer * Probability of Churn Estimating Probability of Churn Alternatives & Why of the Use Case Existing Customer Data, How long have they been with us? How engaged? How have they responded to NPS? Modeling Impact of Change, Segmentation Test Results, Provides a good read of how customers will react by comparing the engagement and retention rate of solution and control variations. Cost to Grandfather How robust is your infrastructure? How much technical debt does grandfathering create? 2. How long should we grandfather for? 2.1 What is the natural churn cycle for our product? 2.2 How long can we sustain the cost of grandfathering? How should we transition these customers to a new solution? What can we do to minimize churn? The most common method is to incentivize.