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data-driven product management - Coggle Diagram
data-driven product management
key metrics:
adoption- NEW usage
engagement: involvement of usage from customers + how FREQUENTLY they use the product
key metric 1: adoption
customer journey - how does the new component/feature affect the customer journey?
define at what POINT in the user workflow the adoption occurs - then measure THIS
metrics!
adoption rate/percentage of first-time users during specified time period
time to first action: mean time for new users to try an existing feature - or - time for existing users to try a new feature for the first time
efficiency: why isn't adoption as high as what you expect/need? examine the workflow - how many steps are required in order to reach the adoption metric we're focused on? At what point in the customer journey are we losing customers? Analyze the customer funnel
key metric 2: engagement
active users
usage frequency
usage recency
depth of use
time spent
active users: DAU, WAU, MAU
usage frequency:
what is our expected frequency of use? What's reasonable? What's ambitious? Define this so that when we measure it, we can assess whether frequency is low or high
usage recency:
number of days/months since customer used last
again, define what's reasonable - expected - so that this can serve as a flag if it's especially low
depth of use:
what parts of the product are most frequently used? This shows where the VALUE is - so keep improving features that keep customers coming back to your product
time spent:
number of sessions + duration of each session
key metric 3: retention
retention curve: graph showing retention on y, timeline on x
number of users retained after a certain period of time - is the issue potentially onboarding (i.e., the dropoff occurs after around 1 month)?
cohort analysis: segment users into cohorts reflecting a shared characteristic
for ex, customers who complete a tutorial versus those who do not - is there higher retention among the cohort who completed the tutorial?
talk to customers who churn - learn about why, look for patterns among their responses - opportunities for improvement and increased retention
metrics framework
management: revenue, profitability, Net Promoter Score, retention, Customer Lifetime Value (CLV)
marketing: CLV, conversions, growth in DAU and MAUs
engineering: number of issues/bugs, click-through rate, velocity, NPS
customer: NPS, CSAT (Customer Satisfaction Score), Customer Effort Score (CES) - how easy was it for the customer to solve their problem w your product (rate on a scale or indicate good, OK, bad)
north star metric
for each metric that matters, establish how it feeds into the north star metric
role of data in proposing product updates
subsequent tracking requirements - how does tracking process need to change because of testing this new feature?
"hoped for"/anticipated positive change is specified QUANTITATIVELY - what will the update achieve, specifically, and outlining how we're going to determine whether the update is successful (metric achieved) or not