Financial Analysis for

Product Managers

2014-11-18

Mike Claiborne

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

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

Examples of FA supporting decisions

Decisions Supporting Analysis

P&L Forecast

ROI

PaybackDo we make this investment?

Is this product line healthy?

Where do we reduce costs?

P&L Historical

Trend

Cost Driver

How should we set pricing?Marginal Cost

1)Evaluate an investment opportunity

Approach

A. Cost projections

B. Revenue projections

C. Return analyses

D. Scenario /

sensitivity

analyses

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

1.B Revenue - Art & science

● Often hardest - predicting the future

● Don’t be a “ten-percenter”,

ground your assumptions

in facts

10%

Revenue analysis - From total to your

take

Total Market

Addressable Market

Your Share of

Market

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, …)

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

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!!!

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

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

competitors0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

11%

12%

13%

14%

15%

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

6-Year Penetration of Past Launches

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%

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

(usually custom)

(investment)

Note: IRR, NPV and others rarely used in software product management

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)

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

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)

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

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

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

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

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

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