Ge#ng The Product Right:
Understanding Customer Choice
May, 2009
Strategic Business Development, ExecuAon, M&A
Page 1

Overview
Defining the problem
•
Common approaches
•
The feature‐value method
•
An example
•
Applying the method
•
Time line
•
Summary
•
© 2009 Andrew Haines

Costly Mistake
• A soMware start up spent 9 months in beta
test before discovering one of the most
important feature requirements for their
product
• This is something that you can avoid
© 2009 Andrew Haines

Understanding Customer Needs
Do customers value feature A over B?
•
What is the right price point?
•
What market share can you expect?
•
Will feature A be worth the cost?
•
How compeAAve will the product be?
•
© 2009 Andrew Haines

Common Approaches
• Gathering input
– Ask customers
– Ask the sales team
– MarkeAng & engineering dialogue
• Obstacles to understanding
– Hard to judge qualitaAve data
– Customers are cagey on price
– In standard surveys, customers ask for everything at a
low price
– No handle on compeAAveness
© 2009 Andrew Haines

The Feature‐Value Method
• Show customers sample product
configuraAons that include price
• Introduces trade‐off decisions
• Correctly done, this yields:
– RelaAve value of the features
– Demand curve as a funcAon of price
– Preference share
– ROI for various alternaAves
© 2009 Andrew Haines

Design AutomaAon
Product Example
• Interviewed 30 engineers drawn from top 100
customers
• Each customer asked to rate the a\\racAveness
of 16 hypotheAcal products
• Each customer asked to rate purchase
probability of selected product

Possible Features
 Turnaround  Data Acquisition
Less 1 hour
 Remote

Several hours
 Local

Over night
 Buffered

One day (24
 Conditional

hours)
 Stimulus  Display
Fast Write and Slow Graphical
 
Write Textual

 Fast Write only Legacy

 Slow Write only Tabular

 System Only

What’s Most Important?
• Turn around is key feature 50%
45%
• Turn around Ame 40%
Percent of Ra,ng
accounts for 45% of all 35%
the change in 30%
25%
a\\racAveness raAngs 20%
15%
• Price is important but not 10%
as important as turn 5%
around 0%
Turn Around
Acquisition
Stimulus
Price
Display
• Display, SAmulus and
Data
Data AcquisiAon rank in
that order of importance

How Much Is Enough?
Turn Around Time
 The Feature-Value method
1
provides insight into the
Change In A2rac,veness Ra,ng
how much is enough for
0.5
each feature
0
 In this example, several
Less than 1 Several Over night One Day
hours are almost as good
hour hours (24 Hrs)
‐0.5
as less than 1 hour
‐1  This data allows return on
engineering effort to be
‐1.5 optimized
‐2

OpAmal Feature Set
One hour turnaround Ame
•
Fast and slow write
•
Graphical display
•
Remote data acquisiAon
•
Looking at the data reveals the feature set that
offers the most a\\racAve product with the least effort

What Will They Pay?
Price ($)
 Trade-offs judgments
1.2
required of customers by
1
this method provide more
Change In A2rac,veness
0.8
reliable data about price
0.6
 In this example, after a
0.4
steep drop between $17K
0.2
and $35K, the curve
0
flattens
15K 35K 55K 75K
‐0.2
‐0.4
‐0.6
‐0.8

Price OpAmizaAon
Expected Revenue Per Prospect
4.5
4
3.5
3
2.5
K$
2
1.5
1
0.5
0
15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
Price (K$)
The opAmal revenue generaAng price can be
esAmated from individual respondent raAngs

Calculate ROI And More
• Using simulated market share, price and total
available market data, esAmaAon of expected
returns is straight forward
• Comparing returns against the cost of
developing and markeAng provides ROI
• Market segmentaAon analysis can be done if
the data sets are sufficiently large (>150)
© 2009 Andrew Haines

Results
• The Feature‐Value method idenAfied key
product feature characterisAc prior to product
development
• Start‐up in same area spent 9 months in beta
test before discovering this fact
• Product line conAnues to generate revenue,
differenAaAon and forms basis for new market
penetraAon strategies
© 2009 Andrew Haines

Applying The Method
Focus On Features
• Understand the feature set
– Customer discussions
– ApplicaAons/Sales/Engineering input
– Survey
• Select features
Ignore features that every product must have
–
Focus on either/or features
–
Focus on high cost features
–
Select 3 to 5 features
–
Each feature is usually represented at 3 values
–
© 2009 Andrew Haines

Applying The Method
Collect The Data
• Key customer calls or visits
– Focus on those who drive your business
– Sample size must be about 20
• Internet survey
– Broad coverage for diverse markets
– Opportunity to discover segmentaAon strategies
© 2009 Andrew Haines

Timeline
Select Feature Set
Survey
Analyze
0 2 4 6 8 10 12
Weeks

Proven Method
• Wide spread use in consumer research
– 30 years of use
• Some noteworthy examples
Marriot Ame‐share units –decors, services, and price
–
MasterCard and Diner’s Club –travel & entertainment features
–
Polaroid’s instant camera design –consumer reacAons
–
Tagamet (SKF) and Zantac (Glaxo) ulcer drugs –pricing
–
• ApplicaAons in semiconductors and EDA
– FPGA feature set, DSP design environment features, RTL source level
debugger features
© 2009 Andrew Haines

About The Road Warrior Group
The Road Warrior Group (RWG) is a team of experienced
internaAonal execuAves with the business and
technical skills to provide....
• Tools to analyze, measure and improve results for
your customer
• ExperAse to evaluate, advise and execute market
driven M&A acAviAes
• EffecAve markeAng methods to improve product
feature selecAon and ROI
• Experience to re‐structure and manage cost effecAve
sales organizaAons
© 2009 Andrew Haines

Summary
• Make sounder product decisions
• Improve:
– CompeAAveness
– Market Share
– Be\\er ROI
• For more info:
– andyh@roadwarriorgroup.com
– www.roadwarriorgroup.com
© 2009 Andrew Haines

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