Solved – R package for marketing

I'm taking an intro to marketing class this semester and the course requires us to buy a software which looks nothing more than a bunch of excel macros. From the software's page

Marketing Engineering for Excel provides tools to help address the most common marketing problems Segmentation, Targeting and Positioning (STP); New Product Decision; Sales Forecasting; Advertising and Communication Decisions; Salesforce and Channel Decisions; Pricing; and Sales Promotion Decisions. Users will be able to harness the power of world-class analytics to run quick and simple analyses… all from within Excel.

I'll buy the license for the software (40 bucks for 6 months)… but can anyone tell me if there are R packages that do similar stuff?

Best Answer

One clarifying question would be: how familiar are you with R already?

To be honest, not buying the macros is probably too risky. Will the teacher expect your assignments to exactly match the results of the macros? Might they even expect you to submit Excel spreadsheets as your answers? Are the techniques used in the macros straight out of your textbook (i.e. you could reproduce them in R) or are you going to have to reverse engineer what the macros do (without having them in hand) to duplicate the results in R? If you need help, will the teacher, teaching assistants, classmates only be able to speak in terms of Excel?

I used R in a machine learning class where the teacher highly recommended Matlab, and I was the only one who used R. (I think one or two folks used Java, maybe one Python.) I'd been working in R for years, though, and was quite confident I could do what needed to be done, even though I could not use the recommended Matlab Toolkits. I did well in the class, but you really have to have your eyes open going in and feel comfortable in R and in finding the right R packages to accomplish what you need to do.

If you buy the macros, you can still try to replicate the results in R. (Check your results against the macros.) That way, you'll be preparing for the future. Your situation is sort of like where the professor requires you to buy their book for the course: sometimes it's easier to pay the ransom than to fight.

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Solved – R package for combining factor levels for datamining

It seems it's just a matter of "releveling" the factor; no need to compute partial sums or make a copy of the original vector. E.g.,

set.seed(101)
a <- factor(LETTERS[sample(5, 150, replace=TRUE, 
                           prob=c(.1, .15, rep(.75/3,3)))])
p <- 1/5
lf <- names(which(prop.table(table(a)) < p))
levels(a)[levels(a) %in% lf] <- "Other"

Here, the original factor levels are distributed as follows:

 A  B  C  D  E 
18 23 35 36 38

and then it becomes

Other     C     D     E 
   41    35    36    38

It may be conveniently wrapped into a function. There is a combine_factor() function in the reshape package, so I guess it could be useful too.

Also, as you seem interested in data mining, you might have a look at the caret package. It has a lot of useful features for data preprocessing, including functions like nearZeroVar() that allows to flag predictors with very imbalanced distribution of observed values (See the vignette, example data, pre-processing functions, visualizations and other functions, p. 5, for example of use).

Solved – Optimization for marketing allocation in R

This is a typical linear programming problem, where both objective and constraints are linear. R has many functions to solve this, for example optim() or package BB. However your decision variables are binary and N is probably big. So you may want to try to solve the dual problem, instead of solving the primal directly.

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Solved – R package for combining factor levels for datamining

Solved – Optimization for marketing allocation in R

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