"Marketing Engineering" - Digest 5

Study of cases

• Chapter 3: Segmentation and targeting
• Case problem: 
• How to find key drivers of customers' choice on products based survey data of customer preference and their choice
• How to win loyalty of customer and how to reduce customer churn rate based on key driver found in above questions
• Case solution: 
• Use logit model to derive relationship between propensity to buy with their preference on features
• Cluster customer based on propensity to buy. Focus on competitive customer (slight prefer ABB product instead of competitors) and switchable customers (slight prefer ABB's competitor product instead of ABB's and ABB is the second)
• Based on the formula, find independent variable with statistic significance
• Investigate impact of these key drives to competitive customer and switchable customers and figure out how to win loyalty of customer and how to reduce customer churn rate
• Why the author put this case after the chapter
• Customer segmentation based on propensity to buy is very useful to identify customers that worth sales' efforts on them
• How can the case be implemented into real situation
• It might not be easy to identify a few variable that really represent customers' attitude toward the product. The starting point might a find key variable from lots of variables through ANOVA.

"Marketing Engineering" - Digest 4

Study of cases

• Chapter 3: Segmentation and targeting
• Case problem: 
• How to segment customers based on data of segmentation basis (data about customer preference on product features and price)
• How to categorize customer to different segment based on data of segmentation descriptor (data about customer demographics) 
• Case solution: 
• Cluster analysis (KNN maybe) to segment customers based on data of segmentation basis.
• Based on cluster information, use discriminant analysis (multi regression maybe) to derive formula to categorize cluster
• Why the author put this case after the chapter
• Emphasize the difference between two variables, basis and descriptor
• Emphasize segmentation should focus on customer needs instead of profiles
• How can the case be implemented into real situation
• Though data of segmentation descriptor can be get easily, data of segmentation basis designed and collected purposely.
• For different purpose of segmentation, different data of segmentation basis should be collected. Here lists some in the book
• for position studies
• product use
• product preference
• benefits sought
• a hybird of the variables above
• for price decisions
• price sensitivity
• deal proneness
• price sensitivity by purchase/use patterns
• for advertising decision
• benefit sought
• media use
• psycho-graphic/lifestyle
• a hybrid
• for distribution decision
• store loyalty and patronage
• benefits sought in store selection

"Marketing Engineering" - Digest 3

Topics:
Back to the book of "Marketing Engineering". Look at the book, I'm not sure if I will read the book from cover to cover. Case in the book seems practical. And algorithm looks useful as well, but time consuming to dive into since I don't have the software with the book. Maybe the easy thing to do is to go through cases at the end of each chapter and figure out the idea behind it.

Framework
The following framework is designed to construct the study of cases after each chapter.

• Chapter
• Case problem
• Case solution
• Why the author put this case after the chapter
• How can the case be implemented into real situation

Study of cases

• Chapter 2: Tools for marketing engineering: market response models
• Case problem: 
• How to spend promotion budget? 
• The relationship between promotional spending and sales units are given and the price and cost of each unit are provided
• Case solution: 

• Use Adbudg model to curve fit the relationship between promotional spending and sales units.
• Maximize profit based on the derived curve.
• Why the author put this case after the chapter
• Emphasize saturation of sales due to promotional spending
• How can the case be implemented into real situation
• One challenge to implement it is that relationship between sales units and promotional units. 
• What if it is a new products?
• What if it is a new promotional method?
• One possible answer for this might be to get the relationship through trials and errors
• The other challenge is that parameters in promotional spending might be more than one
• maybe it is the problem of strategy for advertising

Summary of my knowledge about statistics or hypothesis test in particular

Reason for summary
I feel it's time to stop looking into more statistics or hypothesis test in particular. Anyway I'm not going to work as a statistician. I should position myself as marketing analyst with solid knowledge of statistics and capable of conducting complicated hypothesis test for marketing test.

List of my knowledge about hypothesis test

• parametric testing
• one sample test (z test and t test)
• two sample test (mean test and standard deviation test)
• one way ANOVA
• two way ANOVA (full factorial design and fractal factorial design - Taguchi method)
• non parametric testing (normal distribution test)

The next question is what is the value to marketing. Maybe it is easy to explain it in design of experiment. hypothesis test is one step of design of experiment

Marketing campaign can be considered as one type of experiment with input as marketing mix (4P, price, place, promotion, and product), output as goals (revenue, profit, market share and etc) and market itself as black box. After you have insight about the market, you might find a few key factors. You will try to find the optimal combination to maximize your goal with minimum cost. That's where hypothesis test comes in and help it.

Application,
One interesting application might be in Google ads optimization. Here is an very interesting youtube video to find the best ads with different heading, first description line, second description line and url. One thing to note is that, in practice, people care about the optimal level of the factor, rather than know the factor really has impact on the experiment.

P.S. From tomorrow, I need to focus more on marketing strategy and cases instead of techniques in statistics. Don't be a nerd.

Buzz words in job requirement - Taguchi method

The first time I heard "Taguchi method" is in the six sigma course. It's not covered by the intense course. But the professor did mention it several times.

I kept on searching for details about the method. It seems not well defined even in Wikipedia. Maybe the term itself is not clear or accurate. The exact term might be "Taguchi method in design of experiments". If that is the case, I think "Taguchi method" is just orthogonal analysis in fractal factorial design.

the Idea
In simple English. "Taguchi method" is the method to use smallest runs of experiment, which neglects effect of combination of factors.

If effect of combination of factors is not our interest, we can use 8 runs of experiment to represent an experiment with up to 7 facors, which is far more effective than full factorial case, 2⁷=128

Design of experiment
I'm not sure if should use this as a heading. It covers lots of topics and Fisher even wrote a book about it in 1935. Here I will copy the rule of thumbs about options in design of experiments from one online document
as the summary of this blog.

When do we use which method?

• Option 1: Factorial Design
• Small numbers of variables with few states (1 to 3)
• Interactions between variables are strong and important
• Every variable contributes significantly
• Option 2: Taguchi Method
• Intermediate numbers of variables (3 to 50)
• Few interactions between variables
• Only a few variables contributes significantly
• Option 3: Random Design
• Many variables (50+)
• Few interactions between variables
• Very few variables contributes significantly

Buzz words in job requirement - fractal factorial design

Here is the buzz word I used in my experience as project officer in charge of an experiment. To relate the experience to marketing test, I have to use to some buzz words. "fractal factorial design" seems complicate, and the professor of six sigma mentioned the course. So, it must be useful somewhere in some way.

First of all, definition from Wikipedia.

In statistics, fractional factorial designs are experimental designs consisting of a carefully chosen subset (fraction) of the experimental runs of a full factorial design. The subset is chosen so as to exploit the sparsity-of-effects principle to expose information about the most important features of the problem studied, while using a fraction of the effort of a full factorial design in terms of experimental runs and resources.

There are key terms here, "fraction" and "full factorial design".

"full factorial design" is the way to list all the combination of change in each factors to take all factors and combination and factors into consideration.
For an experiment with 2 factors, source of errors/difference in observation comes from X₁, X₂ and X₁X₂

For an experiment with 3 factors, source of errors/difference in observation comes from X₁,X₂,X₃,X₁X₂,X₁X₃,X₂X₃,and X₁X₂X₃

It's simple. But when number of factors increases, runs of experiment increase exponentially. it's 2ⁿ. In some cases, it's extremely expensive to conduct the full factorial design.

That's where "factual" comes. Number of runs does not increase when new variable comes in, but some combination of variable need to be compromised. Here is the example.

Conclusion of fractal factorial design, full factorial design is the dumb but expensive method. fractal factorial design is the cheap method with the trade off of cost.

P.S. I need to related it to the context of marketing. The technique can be used in design of marketing campaign test. For traditional marketing campaign, fractal factorial design might be necessary because it's expensive. But for online marketing campaign test, it seems full factorial design is possible.

P.S.2 Here is a youtube video that might be useful to explain fractal factorial design.

Buzz words in job requirement - ANOVA part 2

I am afraid there has to be ANOVA part 2 if change of more than one factor is conduct simultaneously. Another reason is that the title with part 2 is so cool. It sounds like a Kill Bill 2, Harry Potter and deathly hallows part2.

1. What problem can cool ANOVA part 2 can solve?
In one-way ANOVA, samples are different due to change of one variable. What if there are more than one factor/variable? In this case, there are 2-by-2 matrix, and each cell contains one sample with several observations.
In the context of marketing, imagine the marketer design a campaign with input of ads spending and discount level. He/she is not sure about how to fine tune the parameter, so he/she set different levels for each factor. impact of market is recorded. The question is if market really respond differently as factors change.

2. Idea of two-way ANOVA
Assume there are only two factors. And assume means of these samples are identical. Errors of each observation comes from four sources, factor A, factor B, interaction between A and B, randomness.

SS_A, SS_B and SS_AXB are all compared with SS_within. If they are relatively large, they causes difference more than randomness.

3. Formula of two-way ANOVA
Of course, there should be formula when there is no analyst supporting you. You can find formula here.