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

Bryan Cassady Guest Professor, [email protected]

KU Leuven Master Class: New Product Marketing5/11 Forecasting & Business Planning

Learning System Thinking

To better predict the future


About this courseIt 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 [email protected] +32-475-860-757

http://www.fast-bridge.net/resources/new-product-marketing/

http://www.slideshare.net/bryancassady2/2009-course-new-product-management-by-bryan-cassady


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 underestimatedThe 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 marketThe Lemming effect


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utilities mortgage credit cards internet pension savings telephone personalloans

insurance

Lowest chanceof success

Highest chanceof success


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utilities mortgage credit cards internet pension savings telephone personalloans

insurance

Lowest chanceof success

Highest chanceof success


Group 2 3 4 5 6 8 9 TotalUtilities 55 69 77 106 42 76 49 72Credit cards 136 149 85 45 52 53 72 86Mortgage 96 43 44 144 106 101 79 89Pension 98 79 81 58 111 113 109 94Internet 67 51 181 96 98 129 93 102Savings 128 113 111 125 85 102 83 106Telephone 75 98 135 75 163 105 136 111Personal loans 114 132 79 141 129 78 144 114Insurance 133 177 100 109 130 144 136 129

CorrelationsGroup 0.42 0.58 0.50 0.48 0.40 0.40 0.34 0.45Class 0.17 0.32 0.13 0.12 0.36 0.22 0.35 0.24


Group 2 3 4 5 6 8 9 TotalUtilities 55 69 77 106 42 76 49 72Credit cards 136 149 85 45 52 53 72 86Mortgage 96 43 44 144 106 101 79 89Pension 98 79 81 58 111 113 109 94Internet 67 51 181 96 98 129 93 102Savings 128 113 111 125 85 102 83 106Telephone 75 98 135 75 163 105 136 111Personal loans 114 132 79 141 129 78 144 114Insurance 133 177 100 109 130 144 136 129

CorrelationsGroup 0.42 0.58 0.50 0.48 0.40 0.40 0.34 0.45Class 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 = 250KExponent = power = 1.5

Num Books Sales0 16,000,0000 8,000,0000 4,000,0003 2,000,000

A 12 1,000,00064 500,000

Given 96 250,000272 125,000

2,172 62,500B 49,152 31,250

3,145,728 15,625569,437,594 7,813

291,552,047,998 3,906CalculationsA 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 ROI1 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%


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1000%

1500%

2000%

2500%

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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 ActuallyObserved

Harvard MBA’s Trivia facts 2% 46%

Employees of a chemicalcompany

Chemical industry andcompany - 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 losersCooper & Kleinschmidt




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True accuracy

Part 1

Estimated accuracy

Amount of case study read by subjects

Part 2 Part4Part 3

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Items of information available to handicappers405 2010

40%

20%

10%

30% Confidence Does Increase

Accuracy Does Not Increase

Perfo

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Actu

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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 questionYou’ll get more ideas…(about 50% more on average)


Be humble1

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For a good introduction seeThe Fifth Discipline, Peter Senge


Turn knob Measure Change

Filling a glass of water


Soviet Arms Threat to Americans

Need to BuildUS Arms

US Arms Threat to Soviets

Need to BuildUSSR 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 ofLeads

What clients willpay

Type of clients

RepeatVisits

Page visitsTrust


Number of visits

Experience

Partner sites

Number of leads

Client conversion costs

Quality ofLeads

What clients willpay

Type of clients

RepeatVisits

Page visitsTrust


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IssuesCan we increase

profitability with a new production process?

Sub-IssuesWill it reduce our costs?

Sub-IssuesCan our organization

implement the changes?

Sub-IssuesWill our quality level fall

while making the changeover?



Events

Patterns

Structures

**Mckinscy&Company



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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 clickSearches 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 NetherlandsData from google adwordsAre 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


BelgiumMortage New House

Households 4,000,000.00 % Move/ year 0.08Total Movers 320000Renters 0.4Net 192000% take loan 0.75Market Size 144000

Re-mortgageHouseholds 4000000% second mortage 0.04Market Size 160000

Total Market 304000Avg. Value of a new loan 5000Market Value 1,520,000,000.00

Available marketTotal Loans 304,000

% Internet 60%

% would use internet 50%Available market 91,200


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





Costs of productionNumber Fixed Costs First 50 K After 50 K Total Costs Avg. Cost25,000.00 100,000 150 40 3,850,000 154.050,000.00 100,000 150 40 7,600,000 152.075,000.00 100,000 150 40 8,600,000 114.7

100,000.00 100,000 150 40 9,600,000 96.0125,000.00 100,000 150 40 10,600,000 84.8150,000.00 100,000 150 40 11,600,000 77.3175,000.00 100,000 150 40 12,600,000 72.0200,000.00 100,000 150 40 13,600,000 68.0225,000.00 100,000 150 40 14,600,110 64.9250,000.00 100,000 150 40 15,600,220 62.4275,000.00 100,000 150 40 16,600,330 60.4


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