Slides Mike Claiborne recently used in his discussion w/ mentees of The Product Mentor. The Product Mentor is a program designed to pair Product Mentors and Mentees from around the World, across all industries, from start-up to enterprise, guided by the fundamental goals…Better Decisions. Better Products. Better Product People. Throughout the program, each mentor leads a conversation in an area of their expertise that is live streamed and available to both mentee and the broader product community. http://TheProductMentor.com

1. Financial Analysis for
Product Managers
2014-11-18
Mike Claiborne

2. Agenda & Audience
Agenda:
1. Evaluation of a investment opportunity
2. General tips, tricks, advice
3. An example
Audience:
Product managers who don’t have a formal education or
background in financial analysis

3. PdMs use analysis to support decisions
Quant
● Financial analysis
● Website / app traffic
● Customer service calls
● Customer survey
Qual
● Customer interview
campaign
● Competitive analysis -
features
● Analyzing new laws /
regulations

4. Examples of FA supporting decisions
Decisions Supporting Analysis
Do we make this investment? Payback
ROI
P&L Forecast
Is this product line healthy?
Where do we reduce costs?
P&L Historical
Trend
Cost Driver
How should we set pricing?
Marginal Cost

5. 1)Evaluate an investment opportunity
Approach
A. Cost projections
B. Revenue projections
C. Return analyses
D. Scenario /
sensitivity
analyses

6. 1.A Costs - Consider all incremental
Initial Investment
● Software development
● Content development
● Domain name
Ongoing
● ITOps (hosting, bandwidth, ...)
● Licensing of content, software
● Customer support
● Physical goods, incl. holding & shipping
● Software maintenance
● Customer acquisition / marcoms
● Sales commission
Understand
Drivers

7. 1.B Revenue - Art & science
● Often hardest - predicting the future
● Don’t be a “ten-percenter”,
ground your assumptions
in facts
10%

8. Revenue analysis - From total to your
take
Total Market
Addressable Market
Your Share of
Market

9. Estimating total market
Much easier for existing markets
● Government (BLS, Fed Bank, DoE, …)
● Industry news (blogs, magazines, etc)
● Interviews and news releases from
competitors -> source
● Research reports
For new markets, do your own research
● Start with what you can find (see
above)
● Conduct qual and quant research
Examples
● Market-specific
(Nielsen &
Kagan, industry
groups)
● Investor
(investment
banks, ...)
● Tech (Forrester,
Gartner, ...)
● Blogs (Business
Insider, GigaOm,
VentureBeat, …)

10. Estimating addressable
Markets are segmented - you have product-market fit with
only some segment(s)
Segmented on (examples):
● Feature
● Distribution
● Pricing
● Availability of complimentary services / products
Be realistic about what’s in your addressable market
May need to reconsider which segment(s) to target

11. Example of sizing addressable
– 10%’er approach
● Product: 1:1 iOS app for students to write essays for
grades 3-8
● Total market: 3.9M kids per grade for 5 grades ->
23.4M
● 10-Percenter’s estimate of our market share is 10% of
total market = 2.3M students
Wooohooo!!!

12. Example of sizing addressable
– Your approach
● 4 states didn’t adopt Common Core
(12% of US students)
● Only 90% in gen ed in public school
districts
● 1:1 tablet use is 5% today; research
reports predict 20% in 5 years
● iOS is 95% today but predicted by
research reports to be 60% in 5 years
Addressable market in 5 years is
only 2.2M
Segment on
content feature
needs
Segment on
distro channel
Segment on tech
platforms in use

13. Win rates the heart of the model
Look for data - any data - to
support your expectations
• Do research with customers
• Qual (e.g. interviews)
can give directional info
• Really want quant, e.g. a
survey
• Past product launches
• Penetration of similar
competitors
6-Year Penetration of Past Launches
15%
14%
13%
12%
11%
10%
9%
8%
7%
6%
5%
4%
3%
2%
1%
0%
Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

14. Your market share will be higher when…
● You have a differentiated and
defensible value proposition
● You are entering an existing
market that’s not concentrated
● Customer switching costs are
low
● You have a reliable /
established way to sell / distro
● You already have customer
relationship (e.g. upsell)
● Other reasons?
Concentrated
Top 3 firms control > 70%
Not Concentrated
Top 5 firms control only 40%

15. 1.C Return analysis quantifies “bang for
your buck”
Analysis Description Calculation
ROI
(Return on
Investment)
• Percentage return on
original investment
• Usually in terms of 3-year or
5-year
ROI% = (gains – investment) * 100
Payback
Years
• Number of years of margin
required to pay back initial
investment
(investment)
(usually custom)
Note: IRR, NPV and others rarely used in software product management

16. ROI example: 5-year ROI for new SAAS product
5yr Gains
5yr Revenue
License revenue
5yr Costs
IT Ops
Cust. svc.
Mktg
Sales commissions
Mtce. Development
Content licensing
minus minus
Initial Investment
Software dev.
Marketing launch
Customer dev.
Domain purchase
Trademark
Content dev.
etc
Initial Investment
(See above)

17. What is good?
• It depends on your situation
• What is attractive at a mature
company won’t be acceptable for a
VC-backed start-up
• Can use the metrics to compare
different projects
• At a mature company
• ROI > Cost of capital (usually 10-
13%)
• Opportunity cost (what would you do
if you didn’t do this project)
• Want payback in 2-4 year range

18. 1.D Scenario / sensitivity analyses
• We can’t predict the future – the
best models are just guesses
• Use these to explore your
assumptions:
• Scenarios – Potential outcomes
• Sensitivity – How sensitive is
your mode to certain
assumptions (e.g. win rates,
content licensing costs)

19. Scenarios: Realistic versus High-Low
• Usually for scenarios high–medium–low works
• For each scenario, we adjust the model assumptions
(win rate, price, growth rate, etc.)
• Don’t just arbitrarily set the assumptions H/M/L across
the scenarios
• Instead, create realistic H/M/L scenarios and set
assumptions around those stories
• E.g. an L scenario might be: Our product is not well-differentiated
and was launched 2 years late
• Price is lower (we can’t support a high price)
• Win rate & growth rate are lower (not differentiated)
• Investment costs higher (2 extra years of dev)
• Sales start 2 years later

20. 2 General Tips and Advice
A. Financial:
• Ignore general inflation
• Factor in a ramp-up to your penetration rate over an
amount of time appropriate to your situation – could be
many years
• Factor in churn in a recurring business model - you will
lose a % of customers each year
B. 95% of time Google Spreadsheets is sufficient. The
auto-backup, collaboration are great

21. General Tips and Advice
C. Financial modeling is a lot like coding:
• Start with clear requirements. What questions do
you need to answer and what analysis does that
require?
• First build an MVP. You can add more
complexity later if needed (often it is not)
• Manage scope. Don’t spend a week modeling a
$30K investment decision

22. General Tips and Advice
C. Financial modeling is a lot like coding: (continued)
• Design before coding. For more complex models, a
flowchart diagram of data and calculations reduces rework

23. General Tips and Advice
C. Financial modeling is a lot like coding: (continued)
• Build components with interfaces rather than a
monolith. Arrange model into logical sections
• Organize assumptions in one place
• QA, manual and automated. QA the model well with
test data. You can also set up automated checks that
calc figures a second way and compare results
• Clearly document for future use
• Label everything in the model. Use note and
description fields
• Color code
• Never embed constants in fields

24. 3. An example
A representative example of a *very simple* model
Notice
• Model organized into separate sheets
• Color coding of data
• Data sources documented
Somebody else should be able to use this model with
relative ease