1. Innovation Management 2012 Stefan Wuyts
Sales forecasting
Innovation Management 2012
Stefan Wuyts

2. Agenda Adoption Diffusion Hedging against bad forecasts
Financial life cycle
Real options

3. Forecasting using A-T-A-R
Express T as the ultimate long-run trial rate (in %)
Express R as the ultimate long-run repeat rate (share of purchases among those who tried the product)
Use prior information on repeat rates, switching rates, etcetera to value the components of this model
Use what-if analysis to examine the sensitivity of the market share forecasts

4. Adoption AWARENESS INTEREST EVALUATION (mental rehearsal) TRIAL

5. Drivers of adoption: innovativeness
Innovativeness: predisposition to buy new and different products and brands rather than remain with previous choices and consumption patterns. (Steenkamp, ter Hofstede and Wedel 1999)
Note that innovativeness can also work against you! How?
Innovativeness is in turn determined by personal traits such as:
Conservative (security, conformity, tradition) versus Openness to Change (‘self-direction’, searching for stimuli)
Ethnocentrism: appropriateness to purchase foreign products
General attitude towards the past
Innovativeness is also determined by cultural traits:
Individualism (+); Uncertainty avoidance (-); Masculinity (+)

6. Drivers of adoption: study in CPG markets
Probability of trial in consumer packaged goods markets (Steenkamp & Gielens 2003) is a function of:
Innovativeness (+)
Importance of social-normative pressure in society (-)
Recent marketing communication (+)
Brand strength (+)
Intensive strategy regarding the marketing mix (+)
Characteristics of the category (e.g., competitive reactions, crowding, marketing intensity, intensity of new product introductions)
Novelty (nature of effect not so clear)

7. Drivers of adoption: role of complexity
Perceptions of usage difficulty, complexity (Wood and Moreau 2006):
Perceptions of usage difficulty delay purchase (48% of potential digital camera buyers) and actual usage difficulty increases returns of product to the shop (30% of home networking products).
Model of the influence of complexity expectations on innovation evaluation:
Updating expectations
Step 1:
Complexity
expectations
Step 2:
(Dis)confirmed
expectations
Step 3:
Experienced
emotions
Step 4:
Innovation
evaluation
Step 5:
Usage
diffusion
Awareness
Early use of innovation (Trial)
Adoption

8. Drivers of adoption: network effects
The value of a product to its users increases with the installed base (number of users)
Three sources (Lee and O’Connor 2003):
Direct effects—the relationship of the product to its customer base (e.g., fax machine)
Indirect effects—the relationship of product compatibility to product utility (e.g., operation system and application software)
The standards issue (battle of competing standards, e.g. VHS vs. Betamax)
Network effects lead to lock-in and “winner-take-all” competition

9. Drivers of adoption: network effects
Consumers derive intrinsic value from features/attributes and extrinsic value from installed base and availability of complementary products
Positive feedback of network effects in the PC industry:

10. Diffusion (adoption over time) (Rogers 1995)
100
Laggards (16%)
Late majority (34%)
Early majority (34%)
Early adopters (13.5%)
Innovators (2.5%)
Time

11. Roger’s classification of adopters:
Innovators
Entrepreneurial, open to new ideas, higher income
Early
adopters
Opinion leaders, link to early majority, social networks
Majority
Less leadership, more risk-averse, social networks
Late
majority
Often economic/social pressure to adopt, less embedded in social networks
Laggards
Not open to change, often adoption after new versions or substitute products are already entering the market

12. Some examples of diffusion curves
(Source: Tellis, Stremersch and Yin 2003)

13. Moore’s chasm model for
discontinuous high-tech innovations

14. Modeling the diffusion process–General diffusion model
Cumulative # adopters
Market potential
Diffusion speed at time t
Adoption rate

15. Modeling the diffusion process–General diffusion model
In diffusion model g(t) can take on different forms:
g(t) = p: the innovation coefficient (sometimes called initial trial probability).
g(t) = qY(t): late adopters learn from early adopters. q is called the imitation coefficient.
g(t) = p + qY(t): adoption rate is function of both consumers’ innovativeness and imitation.
Bass model: g(t) = p + (q/M) Y(t)

16. Modeling the diffusion process– Bass diffusion model
St = sales at time t
p = innovation coefficient, initial trial
q = imitation coefficient,
M = total market potential
Yt= cumulative sales up to time t
M is a constant, considered to be “known”
Unknown parameters to be estimated, on basis of previous experiences if the product has not been launched yet: p & q
The Bass diffusion model has great predictive power

17. Modeling the diffusion process– Bass diffusion model: limitations
Marketing mix instruments are ignored
Network effects are ignored
Subsequent product generations are ignored
Valid only for first purchase
Population is assumed homogeneous. But population of potential adopters can be heterogeneous with some adopters being driven more by their intrinsic innovativeness and other adopters being driven more by imitation

18. Modeling the diffusion process– Bass diffusion model: extensions
Heterogeneity among adopters: influencers vs. imitators (Van den Bulte and Joshi 2007)
Some customers are more in touch with new developments and some (often same) have disproportionate influence on others.
If a proportion θ of the population consists of influentials (denoted with subscript 1) and the other 1- θ are imitators (denoted by subscript 2), one needs to account for heterogeneity in adoption rates:
g1(t) = p1 + q1Y1(t) (the influentials)
g2(t) = p2 + q2[wY1(t) + (1-w)Y2(t)] (the imitators)
Note the asymmetry! Also note: q1 and p2 need not be zero.
If p2 = 0, contagion from influencers to imitators is critical!
If p2 = 0 and also w is small, then the diffusion process is “bimodal”, i.e. the “chasm” pattern (see next sheet).

19. Modeling the diffusion process– Bass diffusion model: extensions
Application of this extension to the Bass diffusion model:
If: p1=0.01; p2=0; q1=0.5; q2=0.2; θ=0.15; w=0.01
Then: diffusion process becomes bimodal, with “chasm”
Adoptions
0.04
0.02
Time

20. Managing risk in financial analysis
(1) Use the Life Cycle concept of financial analysis

21. (2) Adopt real-options analysis in new product value assessment
Data (see Figure 11.7):
Startup costs in Year 0: $70,000.
The cash flows for Years 1 through 4 are estimated to be $40,000 in a high-demand scenario, or $10,000 in a low-demand scenario.
The probabilities of a high- or low-demand scenario are both 50 percent.
The product concept could be abandoned after Year 1, and the equipment could be sold for $38,000.
Discount rate = 12%.

22. Cash flow in Year 1 for each demand scenario:
Total
High
40,000
40,000/(1.12)
= 35,714
40,000/(1.12)2 = 31,888
40,000/(1.12)3 = 28,471
$136,073
Low
10,000
10,000/(1.12)
= 8,929
10,000/(1.12)2 = 7,972
10,000/(1.12)3 = 7,118
$34,018
Low demand scenario: cash flow in Year 1 if option taken to abandon project and equipment is sold:
Demand
Year 1
Take Option to Abandon and Sell Equipment
Total
Low
10,000
38,000
$48,000
Therefore the project would be abandoned after Year 1.

23. Expected value of investment is:
Now assess NPV for each demand scenario, assuming project is abandoned after Year 1 if demand is low.
Demand
Year 0
Year 1
Year 2
Year 3
Year 4
Total
High
-70,000
40,000/(1.12)
= 35,714
40,000/(1.12)2 = 31,888
40,000/(1.12)3 = 28,471
40,000/(1.12)4 = 25,421
$51,494
Low
48,000/(1.12)
= 42,857
-$27,143
Expected value of investment is:
(0.5)($51,494) + (0.5)(-27,143) = $12,176
Since this expected value is greater than 0, firm should make the investment.
Source: Edward Nelling, "Options and the Analysis of Technology Projects," in V. K. Narayanan and Gina C. O'Connor (eds.), Encyclopedia of Technology & Innovation Management, Chichester, UK: John Wiley, 2010, Chapter 8.