Chapter 9 Flashcards

(43 cards)

1
Q

Q: Who on the new-product team is chiefly responsible for the sales forecast?

A

The marketing member.

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2
Q
  1. Q: Why is sales forecasting one of the hardest challenges in new-product financial analysis?
A

A: Because it depends on many uncertain factors: marketing effort, customer adoption, and competitor reaction.

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3
Q

Q: Name two reasons a product with huge potential might still miss its sales forecast.

A

(1) Insufficient marketing effort; (2) Strong competitive moves

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4
Q

What does A-T-A-R stand for?

A

Awareness, Trial, Availability, Repeat.

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5
Q

In ATAR, what input often comes from post-trial data?

A

Repeat (R) percentages.

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6
Q

Q: Formula for long-run market share (MS) in ATAR?

A

MS=A×T×Av×R.

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7
Q

Q: Name two reasons a product with huge potential might still miss its sales forecast.

A

(1) Insufficient marketing effort; (2) Strong competitive moves.

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8
Q

What two things drive long-term sales growth after trial?

A

Repeat purchase and favorable word of mouth.

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9
Q

Q: Besides time and cost, what major factor guides the choice of a forecasting model?

A

A: Product/market newness.

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10
Q
  1. Q: Define Rs in the switching model.
A

A: Proportion of customers who switch to the new product when it launches.

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11
Q
  1. Q: Define Rr in the switching model.
A

A: Proportion of repeat purchasers of the new product.

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12
Q
  1. Q: Switching-model formula for repeat rate
    𝑅
    R?
A

R = R_s/(1+R_s - R_r)

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13
Q
  1. Q: List the five adopter categories in order.
A

A: Innovators → Early adopters → Early majority → Late majority → Laggards.

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14
Q
  1. Q: Why are early adopters critical?
A

A: Their behavior influences later segments.

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15
Q

Bass sales functions: s(t) = … depends on which two parameters?

A

p (coefficient of innovation)
q (coefficient of imitation)

Base sales functions mathematical expression that predicts the number of units sold over time before you layer on special marketing events or promotions.

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16
Q
  1. Q: Give two common problems with new-product sales forecasts.
A

A: (1) Users don’t fully grasp the product; (2) Competitors change strategies.

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17
Q
  1. Q: What-if analysis helps when data are _____?
A

A: Not very accurate (high uncertainty).

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18
Q
  1. Q: One way managers can reduce dependence on poor forecasts?
A

A: “Forecast what you know” or approve situations, not numbers.

19
Q
  1. Q: Why defer detailed financial analysis until later stages?
A

A: Early numbers are highly uncertain; waiting reduces wasted effort

20
Q
  1. Q: General rule: the riskier a project, the _____ the required rate of return.
A

A: Higher.

Rate of return (RoR) is the percentage of profit or loss made on an investment over a specific period of time.

21
Q
  1. Q: What is real-options analysis in product development?
A

A: Valuing a new concept like a financial option to estimate NPV under uncertainty.

21
Q
  1. Q: Top-down portfolio approach—explain in one line.
A

Starts with its overall business strategy and then allocates resources across different types of innovation projects that align with that strategy.

22
Q
  1. Sales analysis
A

Straightforward forecast using historical/purchase-intention data.

23
Q
  1. Q: Bottom-up approach—explain in one line.
A

Individual project ideas emerge first — often from employees, R&D, or market feedback — and the company then builds its innovation strategy around the most promising opportunities.

24
26. Purchase intention
Survey measure used to estimate future trial or repeat.
25
27. What-if analysis
Sensitivity testing of uncertain forecast inputs.
26
28. Bass parameters (p, q)
Innovator & imitator coefficients in diffusion.
27
29. Market potential (m)
Total number of eventual adopters in Bass.
28
Peak sales s*
Maximum sales rate predicted by Bass model.
29
Repeat purchase (R)
Percentage of triers who buy again.
30
Word of mouth
Informal customer communication that boosts trial.
30
Simulated test market
Lab or virtual environment using A-T-A-R.
31
Forecast horizon
Time span over which sales are projected.
32
Lifecycle concept of financial analysis
Forecast cash flows across introduction, growth, maturity, decline
33
Forecast cash flows across introduction, growth, maturity, decline
Approach to lessen risk from forecast errors.
34
Isolate critics –
Tactic to prevent over-cautious gatekeepers from killing ideas prematurely.
35
Market-testing rollout
Gradual geographic/segment launch for learning.
36
Strategic criteria
Non-financial filters tied to the PIC.
37
Competitive response
Competitive response
38
Post-trial data
Consumer info collected after first use of prototype.
39
Slea peak timim t*
When diffusion model predicts maximum sales.
40
NPV
Present value of future cash flows minus costs.
41
Product/market newness –
Degree to which product or market is unfamiliar, guiding model choice.