1. Software Product Management Metrics
Why measure?
What should we measure?
What should we do with the data?

2. Format & Goals Full disclosures: Format: Goals
I wrote this deck during the lunch break
My thoughts are based on my education & experience…but they’re not the whole picture so please share yours too!
Format:
Introduction
Quick presentation
Share stories & workshop solutions together
Goals
Learn Something New
Continue the Conversation

3. About Me (Dan Heinig)
Over 20 years of professional experience in technology, marketing/advertising, and software product management
MBA & multiple degrees in marketing, advertising management, information systems management (and cognitive psychology in progress)
Also university instructor of business & technology courses

4. Why Measure? To Learn Something New To Test Hypothesis
To Understand our “Current State”
To Understand the Impact of Change
To Predict Something About the Future
Rules of Thumb:
If you can't measure it, it’s not worth doing.
The data has to be worth more than the cost to get it.

5. What Should Product Management Measure?
Demand Validation (customer & stakeholder wants & needs)
Execution (product development & delivery)
Utilization & Engagement
Value: “How Much Is This Product/Feature Worth?”
Customer “value” (should reflect delivery against customer wants/needs)
Business “value” (should reflect positive impact on goals & objectives)

6. Key Performance Metrics: Demand Validation
Customer “Payment” for products/features
Leads (“notify me”)
Subscriptions (payment/pre-payment)
Time (customer research, beta testing, etc…)
Stakeholder “Payment” for products/features
Willingness to allocate time (writing business cases, estimating value, defining measurement, participation in planning activities)
Willingness to allocate real dollars (budget)
Willingness to allocate resources (trade-offs on prioritization & execution)

7. Examples: Demand Validation
Customers/stakeholders have to be willing to “pay” to demonstrate demand
Bad Demand Validation:
A survey/micropoll that asks “Would you use this feature if we built it?”
A survey that asks “How important is personalization to you?”
Good Demand Validation:
Ask users to “sign up to be notified…” (turbotax)
Ask stakeholders to write a “one page business case”

8. Key Performance Metrics: Execution
Lead Time to Development (in days/weeks)
a.k.a “ready for grooming/dev”
Lead Time to Deploy (in days/weeks)
a.k.a. “ready to merge”
Lead Time to Release (in days/weeks)
a.k.a “business readiness”
Cost of Change/Delivery (in $$$’s)

9. Examples: Execution Metrics
It takes the Product Management team an average of 3 weeks to get a new feature into the backlog (“ready for dev”)
It takes the Software Development team an average of 1 sprint (2 weeks) to develop a new feature (ready to merge/deploy)
It takes the rest of the organization (sales, marketing, training) an average of 2 weeks to release a new feature (“business readiness”)

10. Key Performance Metrics: Utilization/Engagement
% of target customer adoption
Number of customers we build the product/feature for, divided by
Number of customers that actually try the product/feature
Are they using it as often as we expected them to?
Active daily users
Are they using it the way we thought they would?
Feature analytics

11. Examples: Utilization/Engagement Metrics
We currently have 4,000 active users of a product:
We built a new features and 1,000 of the users tried it in the first 30 days
We achieved 25% adoption
We released a new feature in Q1:
90 days later, we have 1,300 active daily users (they use the features at least once per day) and on average, customers use the features 1.7 times/day.
We released a new feature in Q3:
80% of customers that use the feature use it the way we expected
20% of customers that use the feature appear to be using in a different way, or for a different reason

12. Key Performance Metrics: Value
Return on Investment (basic cost/benefit in $$$)
Should be able to measure against business goals & objectives
What impact did we think this would have?
What as the actual vs. estimated impact?
Explain the gap.
Should impact customer revenue generating activities
Acquisition
Retention
“Consumption” (purchases if that’s part of your revenue model)

13. Examples: Value Metrics
We built a new product for 2019.
It “cost” $1.2M
It generated $2.4M in new customer revenue
We achieved 100% return on our investment.
We have a business goal of “increasing new customer acquisitions by 25% in 2019”
The new product/feature resulted in a 12% increase in new customer acquisitions.
The new product/feature also resulted in a 2% increase in customer retention
The new product/features also reduced COA and increased CLV

14. What Should We Do With The Data?
Validation Data:
Informs our product/feature roadmaps
Execution Data:
Informs prioritization “How soon can we start to benefit from this product/feature?” (cost of delay)
Utilization Data:
Informs “go-to-market” release activities
Informs “sunsetting” of products/features
Value Data:
Informs “weight” of insights
Helps us predict/innovate
Ultimately helps us “keep doing what’s working” and “stop doing what’s not working”

15. Quick Q&A
Any questions so far?

16. Share Stores & Workshop Solutions
Spend a moment thinking about some of these scenarios you may have encountered:
“We want to build a product/feature but we don’t know how to validate whether it’s worth it or not”
We want to build a product/feature but don’t know how much it will cost or when it will be done
We built a product/feature and have no idea if it mattered/made a difference…
Others?
Please share your examples and we’ll see if we can have a discussion about how measurement can help us make better decisions.

17. Thank you! Thank you for participating.
If you’re not already involved in local PDX Product Management Meetups, please give it a shot.
Let’s keep growing the PDX Product Community…it attracts investors, creates jobs, and improves the quality of life for all of us.
Feel free to connect with me on LinkedIn.