Week 11 Questions - Dustin Gregoire

Workbook for Chapter 12 - Developing New Products

Section A True/False:

1. Continuous and dynamically continuous innovations are higher on compatibility than
discontinuous innovations. True! Continuous innovations, from my understanding, are easier for consumers to feel compatible with than discontinuous innovations would be.

2. Innovators are “generic” in nature. False! If innovators were generic, that would go against the whole idea of innovation. Innovations should be unique, or at least at the minimum go for something new or different.

Section B Fill in the Blanks:

1. The degree to which customers perceive a new product/service as superior to similar existing
products determines the relative advantage.

2. The higher the degree of trialability, the greater would be the rate of
diffusion.

3. Now consider this: there are 3 types of people in this world: 

• The ones who play the game.
• The ones who watch the game.
• The ones who have no idea that the game is being played.

According to the Diffusion of Innovation curve, there are five types (segments) of consumers.

Innovators – The ones who play the game.
Early adopters – The ones who watch the game.
Early Majority – The ones who watch the game.
Late Majority – The ones who watch the game.
Laggards – The ones who have no idea that the game is being played.

Section C Multiple Choice Questions:

Innovators possess certain personality traits. Which is the trait that they do not possess?

a) Low on dogmatism
b) High on brand loyalty 
c) Variety novelty seekers
d) Risk takers

I believe innovators do not possess high brand loyalty. This is because an innovator should not be "loyal" to one specific brand, since that, in my opinion, goes against what an innovator is. An innovator would be able to jump brand to brand in order to remain being the first to certain products.

Section D: Please use a piece of scratch paper and a pencil for this exercise. Draw a product lifecycle first. Put any of the following 5 products or services on the Product Life Cycle (PLC). Once you are done, please take a picture of your graph and upload the picture to your blog.

Explain why it took less time for some products or services to achieve their saturation point. 

• Internet Telephones (WAP or 3G)
• MS-DOS
• Palmtop computers 
• Play Station 2
• Sega Megadrive
• Fax machines
• Facebook
• Snapchat
• Instagram
• Pinterest
• Myspace
• Twitter
• Vimeo
• Twitch 

Some products or services may take less time to achieve their saturation point due to there being less "substance" to their product. For example, how do you exactly add on to a service like Snapchat, which it's main focus is to snap a picture to chat with friends? Another reason some products or services may take less time to achieve their saturation is because they are easily replaced by a newer, better product. Take the Play Station 2, for example. The Play Station 2 thrived for many years until it was replaced with a newer, better version: the Play Station 3. Thus, the Play Station 2 reached it's saturation point.

E: Advanced Topics - New Product Diffusion Modeling:

In this exercise you will use a spreadsheet to understand new product diffusion models. You will build a simple model which forecasts the number of innovation/trial (or P) and imitation/repeat purchasers, and translate this figure to total sales. You might try out this model yourself (see the Excel Spreadsheet - Simulation – Diffusion on Innovation in Week 11) to estimate/simulate total sales.

The Bass model answers the following question:

How many customers will eventually adopt the new product and when?

Recall the basic Bass model (for the purpose of stimulating curiosity and love of learning)

S(t+1) = p*N + (q-p)*Q(t) - (q/N)*Q(t)^2

Where N = Market Size
s(t) = probability of trial adoption in period t Set s(0) = 0.
Q(t) = fraction of ultimate potential trial customers that have adopted through period t Set Q(0) = 0.
p = coefficient of innovation
q = coefficient of imitation

Sensitivity Analysis:

Scenario A: p = 0.0039, q=0.2

Scenario B: p = 0.0039, q=0.5

Q1. Change the repeat purchase parameter q from .2 to .5. What is the effect of this change on the sales forecast?

It appears in Scenario A, which q = .2, that sales gradually rise throughout the years instead of saturating. However, in Scenario B, which q = .5, sales peak around the year 2000, and then steadily decline, instead of gradually increasing like in A.

Q2. Any other observations?

It looks like the amount of cumulative sales is actually higher in Scenario B than in Scenario A!

Q3. "Essentially, all models are wrong, but some are useful."- From Box's widely cited book Statistics for Experimenters. Based on your perception, is this model useful? Why or why not?

This model can be useful in my opinion. It's usefulness lies in seeing how a product is bought - all at once, and then peaking/declining later, or gradually over time, while always increasing?

Section F: Reflection Questions for Week 12:

What did you know a lot about from this week’s class?

How/why firms create new products, different groups of adopters defined by the diffusion of innovation theory, the various stages involved in developing a new product/service, and finally the product life cycle (PLC).

What would you like to learn more about?

I would like to learn more about the product life cycle and it's applications.

Should I include Part E next year? Why or why not?

Sure, why not? It's always interesting to apply our learning in an actual example.