New Product Marketing: Learning System Thinking... To better predict the future!

Bryan Cassady Guest Professor, Bryan@fast-bridge.com
KU Leuven Master Class: New Product Marketing
5/11 Forecasting & Business Planning
Learning System Thinking
To better predict the future

About this course
It is a sad fact that most new businesses, products and service fail. Some
estimate the failure rate is as high as 90%. This course is about why products
fail and what you can do to increase your odds of success.
This lecture is a part of series of 12 lectures. In my classes I use a lot of
videos. If you’d like to see the presentations with videos, go to:
http://www.fast-bridge.net/resources/new-product-marketing/
I hope in the pages that follow you will find new ideas and inspiration… If
you’d like to download the whole class go to:
http://www.slideshare.net/bryancassady2/2009-course-new-product-
management-by-bryan-cassady
If you have ideas on ways to improve this course or would like help with your
new products, I’d love to here from you…
Bryan Cassady
bryan@fast-bridge.com
+32-475-860-757

A less-than-optimal "configuration" of product or
service attributes and benefits is selected.
Lack of a strong sustainable position in the
market
Marketers fall in love with a product no one else
loves
The marketing plan for the new product or
service is not well implemented in the real
world.
Marketers assess the marketing climate
inadequately.
The plan is too complicated
A failure to ask the right questions and a belief
that everything is a big idea
No Support to get things done
A questionable pricing strategy is implemented. A weak positioning strategy is used.
Cannibalization underestimated
The advertising campaign generates an
insufficient level of new product/new service
awareness.
Over-optimism about the marketing plan leads
to a forecast that cannot be sustained in the real
world.
Too focused on the internal game not enough
on the market
The Lemming effect

0
1
2
3
4
5
6
7
8
9
10
utilities mortgage credit cards internet pension savings telephone personal
loans
insurance
Lowest chance
of success
Highest chance
of success

Group 2 3 4 5 6 8 9 Total
Utilities 55 69 77 106 42 76 49 72
Credit cards 136 149 85 45 52 53 72 86
Mortgage 96 43 44 144 106 101 79 89
Pension 98 79 81 58 111 113 109 94
Internet 67 51 181 96 98 129 93 102
Savings 128 113 111 125 85 102 83 106
Telephone 75 98 135 75 163 105 136 111
Personal loans 114 132 79 141 129 78 144 114
Insurance 133 177 100 109 130 144 136 129
Correlations
Group 0.42 0.58 0.50 0.48 0.40 0.40 0.34 0.45
Class 0.17 0.32 0.13 0.12 0.36 0.22 0.35 0.24

* Not accurate, but for description

Number of hits on website 1.4
Number of books sold in the U.S. 1.5
Telephone calls received 1.22
Magnitude of earthquakes 2.8
Diameter of moon craters 2.14
Intensity of solar flares 0.8
Intensity of wars 0.8
Net worth of Americans 1.1
Number of persons per family name 1
Population of U.S. cities 1.3
Market moves 3 (or lower)
Company sizes 1.5
Exceedance = breaking point = 250K
Exponent = power = 1.5
Num Books Sales
0 16,000,000
0 8,000,000
0 4,000,000
3 2,000,000
A 12 1,000,000
64 500,000
Given 96 250,000
272 125,000
2,172 62,500
B 49,152 31,250
3,145,728 15,625
569,437,594 7,813
291,552,047,998 3,906
Calculations
A 96/((1,000,000/250,000)^-1.5)
B 96* ((250,000/31,250))^1.5)

Exponent Share of the top 1% Share of the top 20%
1 99.99% 99.99%
1.1 66% 86%
1.2 47% 76%
1.3 34% 69%
1.4 27% 63%
1.5 22% 58%
2 10% 45%
2.5 6% 38%
3 4.6% 34%
* Clearly, you do not observe 100 percent in a finite sample.

Investment Investment Return ROI Cum invest Cum return Cum ROI
1 100 2000 2000% 100 2000 2000%
2 100 150 150% 200 2150 1075%
3 100 100 100% 300 2250 750%
4 100 100 100% 400 2350 588%
5 100 50 50% 500 2400 480%
6 100 50 50% 600 2450 408%
7 100 25 25% 700 2475 354%
8 100 25 25% 800 2500 313%
9 100 10 10% 900 2510 279%
10 100 10 10% 1000 2520 252%
11 100 5 5% 1100 2525 230%
12 100 4 4% 1200 2529 211%
13 100 3 3% 1300 2532 195%
14 100 2 2% 1400 2534 181%
15 100 1 1% 1500 2535 169%
16 100 1 1% 1600 2536 159%
17 100 1 1% 1700 2537 149%
18 100 0 0% 1800 2537 141%
19 100 0 0% 1900 2537 134%
20 100 0 0% 2000 2537 127%
Totals 2000 2537 127%

0%
500%
1000%
1500%
2000%
2500%
0%
500%
1000%
1500%
2000%
2500%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cumulative
ROI
ROI

Output Output Output
20.0% 80.0%
*20%
36.0% 64.0%
*20%
48.8% 51.2%
20.0% 51.2%

For each of the following ten items, provide a low and high guess such that you are 90
percent sure the correct answer falls between the two. Your challenge is to be neither too
narrow (i.e., overconfident) nor too wide (i.e., underconfident). If you successfully meet this
challenge you should have 10 percent misses– that is, exactly one miss.
Low High Correct
1. Martin Luther King's age at death _________ _________ _________
2. Length of the Nile River (km) _________ _________ _________
3. Number of countries that are members of OPEC _________ _________ _________
4. Number of books in the Old Testament _________ _________ _________
5. Diameter of the moon (km) _________ _________ _________
6. Weight of an empty Boeing 747 (kg) _________ _________ _________
7. Year in which Wolfgang Amadeus Mozart was born _________ _________ _________
8. Gestation period (in days) of an Asian elephant _________ _________ _________
9. Air distance from London to Tokyo _________ _________ _________
10.Deepest (known) point in the ocean (meters) _________ _________ _________

PERCENTAGE OF MISSES
Type of People Tested Type of Question Asked Ideal Target
Actually
Observed
Harvard MBA’s Trivia facts 2% 46%
Employees of a chemical
company
Chemical industry and
company - specific facts
10% 50%
50% 79%
Managers of a computer Co.
General business facts
Company - specific facts
5% 80%
5% 58%
Physicians
Probability that a patient has
pneumonia
0-20% 82%
Physicists
Scientific estimates like the
speed of light
32% 41%

(Source: Gary Larson, The Farside Gallery Chronicle Publishing Co.,1980)
“What do you mean ‘Your guess is as good as mine’?
My guess is a hell of a lot better than your guess!”

2 out of 10 real differences, the rest statistically significant
Yes
Yes
Source: New products what separates winners from losers
Cooper & Kleinschmidt

40
60
50
30
20
10
0
True accuracy
Part 1
Estimated accuracy
Amount of case study read by subjects
Part 2 Part4Part 3
Percentageofquestionscorrect

Items of information available to handicappers
405 2010
40%
20%
10%
30% Confidence Does Increase
Accuracy Does Not Increase
Performance

Actualprobability
Predicted probability (confidence)
Medical Diagnoses
Weather forecasts

 The British were sinking many
German subs.
 The assumptions:
 Codes could not be broken
 It must be a leak
 The right Q: How could they
have broken our code
 A false code released just in code
could have tested the assumption
and might have changed the way
 It is now 2 years after the launch,
Beatthatquote has failed
 Why did it fail…
 Just asking the question
You’ll get more ideas…
(about 50% more on average)

Be humble1
2
3 Look at the big picture
4

For a good introduction see
The Fifth Discipline, Peter Senge

Turn knob Measure Change
Filling a glass of water

Soviet Arms
Threat to
Americans
Need to Build
US Arms
US Arms
Threat to
Soviets
Need to Build
USSR Arms
USSR Arms
Threat To
Soviets
US Arms
Need To Build
US Arms
Threat To
Americas
Need to build
USSR ARMS

Number of
visits
Experience
Partner sites
Number of leads
Client conversion costs
Quality of
Leads
What clients will
pay
Type of clients
Repeat
Visits
Page visits
Trust

1
2
3
4

Issues
Can we increase
profitability with a new
production process?
Sub-Issues
Will it reduce our costs?
Sub-Issues
Can our organization
implement the changes?
Sub-Issues
Will our quality level fall
while making the
changeover?

Events
Patterns
Structures
**Mckinscy&Company

1
2
3
4
Look at the big picture

Key word_
English UK num
Key_Word_
NL BE num NL num
loan 7.87 4,090,000 lening 7.1 49,500 7.23 90,500
home mortgage 7.31 60,500 hypotheek 6.04 18,100 3.21 260
mortgage 11.72 5,000,000 hypotheek 6.04 18,100 3.21 450,000
Cost per click
Searches per month

Key word_
English UK num
Key_Word_
NL BE num NL num
loan 7.87 4,090,000 lening 7.1 49,500 7.23 90,500
home mortgage 7.31 60,500 hypotheek 6.04 18,100 3.21 260
mortgage 11.72 5,000,000 hypotheek 6.04 18,100 3.21 450,000
Belgium Netherlands
Data from google adwords
Are Belgian consumers really less interested ?
If yes, why is the price higher than the
Netherlands ?
Are they searching in a different way (eg. Using
google.nl instead of google.be)
Do you believe the numbers ?

Secondary source
Data
Calculation Result

Belgium
Mortage New House
Households 4,000,000.00
% Move/ year 0.08
Total Movers 320000
Renters 0.4
Net 192000
% take loan 0.75
Market Size 144000
Re-mortgage
Households 4000000
% second mortage 0.04
Market Size 160000
Total Market 304000
Avg. Value of a new loan 5000
Market Value 1,520,000,000.00
Available market
Total Loans 304,000
% Internet 60%
% would use internet 50%
Available market 91,200

Registered Minivans
Dodge Caravans 120,000
Ford Windstars 20,000
Toyota Sienas 8,000
Total Market Size 148,000

Costs of production
Number Fixed Costs First 50 K After 50 K Total Costs Avg. Cost
25,000.00 100,000 150 40 3,850,000 154.0
50,000.00 100,000 150 40 7,600,000 152.0
75,000.00 100,000 150 40 8,600,000 114.7
100,000.00 100,000 150 40 9,600,000 96.0
125,000.00 100,000 150 40 10,600,000 84.8
150,000.00 100,000 150 40 11,600,000 77.3
175,000.00 100,000 150 40 12,600,000 72.0
200,000.00 100,000 150 40 13,600,000 68.0
225,000.00 100,000 150 40 14,600,110 64.9
250,000.00 100,000 150 40 15,600,220 62.4
275,000.00 100,000 150 40 16,600,330 60.4

At companies like P&G, brand managers are trained
hard to stay in touch with their gut feelings…

New Product Marketing: Learning System Thinking... To better predict the future!

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