1. Concept Generation, Evaluation, and Testing
Innovation Management, GSB 2013
Stefan Wuyts

3. Agenda Concept Generation Concept Evaluation Concept Testing
What is a product concept?
Lead User Analysis and other approaches to generate new product concepts
Concept Evaluation
Element of cost and element of risk
Planning the evaluation system
Quantifying evaluation: A-T-A-R model
Concept Testing
Why concept testing
Full screen

4. Concept Generation What is a product concept
Concept Generation What is a product concept? Lead User Analysis and other approaches to generate new product concepts

5. Good Concepts?
“We can use a new chemical extraction process that isolates and separates chemicals from foods to make decaffeinated espresso coffee.”
“Consumers want decaffeinated espresso that tastes identical to regular.”
“We can make a darker, thicker, Turkish- coffee-like espresso.”

6. Three Basic Inputs to the Creation Process
Form (the physical thing created, or, for a service, the set of steps by which the service will be created)
Technology (the source by which the form is to be attained)
Benefit/Need (benefit to the customer for which the customer sees a need or desire)
Technology permits us to develop a form that provides the benefit.

7. Alternative Patterns
Customer need  firm develops technology  produces form
Firm develops technology  finds match to a need in a customer segment  produces form
Firm envisions form  develops technology to produce form  tests with customer to see what benefits are delivered
Third route is most risky one, sometimes works but not recommended (e.g. Dupont with Kevlar fiber)

8. Problem-based concept generation
Customer need  firm develops technology  produces form
Sources and methodologies
Experts (internal & external)
Published sources
Internal records
Contacts with customers
Interviewing
Focus groups
Observation

9. Applications: cell phones
What problems do consumers identify?
Keeping the unit clean.
Breaks when I drop it.
Battery doesn’t stay charged long enough.
Finding it in dark.
Battery dies in mid- conversation.
Who “out there” hears me?
Dropped calls.
Looking up numbers.
Voice fades in and out.
Hard to hold.
Health risks?
Can’t cradle between ear and shoulder.
Flip cover breaks off.
Disruptive instrument.
Can’t see facial/body language.
Rings too loud/too soft.
Wrong numbers.
Fear of what ringing might be for.

10. Applications: pet owners’ problems
How important are the problems identified by consumers?

11. Applications: Dyson’s Air Multiplier Fan
Can we think of a technology that addresses major problems?
Conventional fan problems:
Spinning blades chop airflow
Hard to clean
Blades can be dangerous to children
Fan tips over
Energy inefficient

12. Air Multiplier: bladeless (uses technology adapted from hand dryers), and attractively designed.
Airstream is smooth and danger is eliminated
Low center of gravity eliminates tipping
Much more effective and efficient cooling
No blades to clean

13. What is a Product Concept ?

14. What is a Product Concept?
Statement of what is going to be changed and how the customer stands to gain or lose.
Potential customers do not have enough information to judge the worthiness of just an idea: the product concept gives them the required information.
Rule: You need at least two of the three basic inputs to have a feasible new product concept, and all three to have a new product.

15. Example: Decaf espresso
Benefit: “Consumers want decaffeinated espresso that tastes identical to regular.”
Form: “We should make a darker, thicker, Turkish- coffee-like espresso.”
Technology: “There’s a new chemical extraction process that isolates and separates chemicals from foods; maybe we can use that for decaffeinating espresso coffee.”
Why would each of these taken individually not be a product concept?

16. Example: Toilet Brush Idea: A new and improved toilet brush.
Concept: A toilet brush that contains detergent, refillable, and easy for the customer to attach to the handle.
Product (executions of this concept):
Lysol Ready Brush
Scrubbing Bubbles
Fresh Brush
Clorox Toilet Wand

17. Good concepts?
“Learning needs of computer users can be met by using online systems to let them see training CDs on the leading software packages.”
Good concept: need and technology are clear
“A new way to solve the in-home training or educational needs of PC users.”
Not a concept, need only
“Let’s develop a new line of instructional CDs.”
Technology only, lacking market need and form

18. Lead user analysis
Lead users are users whose present strong needs will become general in a market-place months or years in the future (Von Hippel)
Mostly used in technology-intensive markets; informed way to listen to the customer (recall the problems with listening to customers for creative solutions)
Why lead user analysis?
Lead users help identify future needs;
Lead users provide useful data regarding product concept and design.

19. 3 important traits of good lead users
1. … face needs well before the marketplace does;
2. … would benefit significantly from solution to these needs;
3. … are knowledgeable and innovative.

20. Steps in Lead User analysis

21. Other approaches to generate new product concepts: Brainstorming
Group Creativity Method
Principles of Brainstorming:
Deferral of Judgment
Quantity Breeds Quality
Rules for a Brainstorming Session:
No criticism allowed.
Freewheeling -- the wilder the better.
Nothing should slow the session down.
Combination and improvement of ideas.
Brainstorming techniques
Brainstorming circle
Reverse brainstorming
Phillips 66 groups (buzz groups)
Delphi method
Electronic brainstorming

22. Delphi method

23. Other approaches to generate new product concepts: Use New Media
Listening-in
Listen to the voice of the customer
Monitor public communities and blogs to spot new trends and opportunities
Build electronic communities
+ Establish rapport with customers, enable customer support, build emotional bonds with the customer
- Costly and time consuming (hire moderators & facilitators); takes time for the community to mature; privacy, confidentiality, content ownership, and other legal issues.

24. Example: Del Monte Pet Food Division
Working with MarketTools, analyzed data from millions of blogs, forums, and message boards,
Identified biggest concerns of pet owners and a new customer segment (“Dogs Are People, Too”)
Created invitation-only online community to encourage customer innovation (500 consumers)
Community generated and refined ideas for new breakfast product  Sausage Breakfast Bites
Idea-to-store process: 6 months instead of 1 year.
Source:

25. Concept evaluation Element of cost and element of risk Planning the evaluation system Quantifying evaluation: A-T-A-R model

26. Element of cost: Cumulative expenditures curve
Many high-tech
products
Many consumer
products
Time

27. Element of risk: Risk/Payoff matrix
Cells AA and BB are “correct” decisions.
Cells BA and AB are errors, but they have different cost and probability dimensions.
Usually BA (the “go” error) is much more costly – but don’t forget opportunity costs!
Consider how “new-to-the-world” the product is as that has an impact on the risk level

28. Real options logic as response to risk
Source of real options approach: early discussions on exploration versus exploitation (Schumpeter 1934; March 1991)
Exploitation: refinement, choice, efficiency, selection, implementation, execution  Clear results, short-term
Exploration: search, variation, risk, experimenting, flexibility, discovery  More uncertainty regarding results, long-term
Problem exploitation: suboptimal (stable) equilibria;
Problems with exploration: insufficiently developed ideas, not reaping the benefits of experimentation (more search than application), no distinctive competence;
Problems of both: they are self-reinforcing.
Challenge: find the right balance.
Real options logic: postpone the decision until uncertainty is reduced

29. Probability distribution
Imagine two investment alternatives (A1 and A2), for which the probability of payoff (in $) is initially unknown. The decision maker can either:
Gather extra information, postpone choice  improving future returns;
Use current information, choose  improving current returns.
Probability distribution A2
After further exploration in A2 (scenario 1) A2
After further exploration in A2 (scenario 2)
Before further exploration in A2 A2 A1
Pay-off ($) -9 -6 -3 3 6 9

30. Real options logic summarized:
Bet on different horses: option on >1 strategic alternative;
Postpone the selection decision until there is more clarity as to the market potential of different concepts.
Real options logic is advisable when:
There is great uncertainty regarding link between current investments and future outcomes;
Decisions today strongly determine opportunities tomorrow (path-dependence)
Abandoning and executing options later on is feasible.
Applied increasingly in high-tech environments.

31. The overall evaluation system

32. Planning the evaluation system
Everything is tentative
Financial analysis
Marketing
Identify potholes
Damaging problems,
anticipate major difficulties
Keep in mind that you are dealing with people
Example: it is very difficult to pull the plug.
versus

33. Example of pothole: Complexity
IT IS ROCKET SCIENCE
PHILIPS ADMITS WHAT EVERYONE KNOWS: DIGITAL GADGETS ARE WAY TOO COMPLICATED FOR THE AVERAGE CONSUMER (Newsweek 2004)
NEWSWEEK: Is it true you ran a test, giving 100 of your top managers one weekend at home to get various Philips gadgets operating?
KLEISTERLEE (CEO Philips): Yes, we did. And indeed, a number failed, returned frustrated and some even angry; another group that succeeded returned quite proud. It strengthened our conviction that we must start making things easier for consumers or we will never see the real promise of the digital revolution come to life. And we must do it now.

34. Example of Dealing with People: Hard to pull the plug.
Why? One of the most-cited reasons: “Escalation of Commitment”, the tendency to continue investing in a strategy, despite negative feedback, continuation in a failing course of action.
Solutions: How can we reduce escalation of commitment bias? (Boulding, Morgan & Staelin 1997)
More information? No (problem of information distortion)
Emphasize uncertainty or external causes of failure? No
Calculate Net Present Value of continuing to invest versus NPV of not continuing to invest? No
Establish a rule a priori that determines when to stop
Sequential decision decoupling (people who decide on continuation ≠ people who are strongly involved)
If at first you don’t succeed, try, try again. Then quit. No use being a damn fool about it. -- W.C. Fields

35. Quantifying evaluation: the A-T-A-R model
Profits = Units Sold x Profit Per Unit
Units Sold = Number of buying units
x % Aware of product
x % who would Try product if they can get it
x % to whom product is Available
x Repeat measure
Where repeat measure = 1 + (% of triers who repurchase x # additional units bought by repeaters)
Profit Per Unit = Revenue per unit - cost per unit
Example I showed in class:
1000 households, 50% awareness, 80% try, 80% availability, 75% repurchase (2 units)
1000 * 50% = 500; 500 * 80% = 400; 400 * 80% = 320; 320 * (1 + 2*0.75) = 800 units

36. Concept Testing Why concept testing Full screen

37. Why concept testing? We are not starting from scratch:
We already had a Product Innovation Charter (PIC) approved as a guide to select ideas;
We already have gained first insight into the market area singled out by the PIC;
Concepts always undergo some initial reaction by management (based on heuristics or method like A-T-A-R).
Concept testing precedes technical work (e.g. “prototype testing”, see later). Concept testing is intended to (1) remove poor concepts, (2) get first idea of purchasing likelihood, and (3) make the concept more concrete (attributes).

38. Concept testing is not feasible if…
…prime benefit is personal sense (e.g. taste).
…concept involves new art and entertainment.
…concept embodies new technology that users cannot visualize or addresses needs that customers cannot articulate (e.g. first microwave).

39. Make choices: preliminary indication of price; select the concept test format; specify respondent group and response situation; prepare interviews

40. Application (ATAR model)
Example Nestlé Refrigerated Foods (Contadina Pasta)
24% “definitely buy” + 51% probably “would buy”
Adjusted trial, rule of thumb: 80% of the “definitely” + 30% of the “probably” will actually buy, or: (0.8 x 24%) + (0.3 x 51%) = 34.5%
Assuming 48% awareness and 70% availability, we get :
AW x T x AV = 0.48 x 34.5% x 0.70 = 11.6%
Target households x trial rate = 77.4 million x 11.6% = 9 million
Repeat for similar products = 39%; average customer repeat = 2.5 times; No. of units bought per repeat purchase occasion = 1.4
Additional sales because of repeats: 39% x 2.5 x 1.4 = 136.5%
Hence, the “R” in the A-T-A-R model = 1 + (.39 x 2.5 x 1.4) = 2.365
A-T-A-R Sales prediction: 9 million x = million.
(note deviation from book)

41. The Full Screen
Refine and rank-order the concepts (on basis of mathematical models, graphics, checklists)
Forces feasibility evaluation along technical and commercial dimensions, and summarizes what must be done.
Cross-functional: involve major functions (marketing, technical, operations, finance), new products managers, staff specialists (IT, distribution, procurement, PR, HR)
 Decide whether or not to allocate further resources to each concept

42. Full screen: example (simple) mathematical model: profile analysis
I = T*C*P/D
I = index of attractiveness
T = probability of successful technological development
C = prob. of commercial success if technological success
P = Likely profit
D = development cost
Keep it simple;
Need for qualitative input (accuracy, error);
Pay attention to deliberate underestimation of costs;
Select several ideas => higher returns.

43. Full screen: example graphic: profile sheet

44. Full screen: example scoring Model: Industrial Research Institute
Technical success factors:
Proprietary Position
Competencies/Skills
Technical Complexity
Access to and Effective Use of External Technology
Manufacturing Capability
Commercial success factors:
Customer/Market Need
Market/Brand Recognition
Channels to Market
Customer Strength
Raw Materials/Components Supply
Safety, Health and Environmental Risks
Source: John Davis, Alan Fusfield, Eric Scriven, and Gary Tritle, “Determining a Project’s Probability of Success,” Research-Technology Management, May-June 2001, pp