Category: R

Abhijit Telang introduces the concept of conjoint analysis and shows how you can implement this in R:

We will need to typically transform the problem of utility modeling from its intangible, abstract form to something that is measurable. That is, we wish to assign a numeric value to the perceived utility by the consumer, and we want to measure that accurately and precisely (as much as possible).

This is where survey design comes in, where, as a market researcher, we must design inputs (in the form of questionnaires) to have respondents do the hard work of transforming their qualitative, habitual, perceptual opinions into  simplified, summarized aggregate values which are expressed either as a numeric value or on a rank scale.

I tend to shy away from this kind of analysis because it runs a huge risk of trying to turn ordinal utility rankings into cardinal functions.

Miroslav Rajter builds a Monty Hall problem simulator using R:

The original and most simple scenario of the Monty Hall problem is this: You are in a prize contest and in front of you there are three doors (A, B and C). Behind one of the doors is a prize (Car), while behind others is a loss (Goat). You first choose a door (let’s say door A). The contest host then opens another door behind which is a goat (let’s say door B), and then he ask you will you stay behind your original choice or will you switch the door. The question behind this is what is the better strategy?

This is something that puzzled me for a very long time. This is fundamentally a Bayesian problem built around processing new information, and once I understood that, the answer was a lot clearer. H/T R-Bloggers.

Allison Koenecke takes us through the arulesSequences package in R:

In the following tutorial, we answer both questions using the R package arulesSequences [4], which implements the SPADE algorithm [5]. Concretely, given data in an Excel spreadsheet containing historical customer service purchase data, we produce two separate Excel sheet deliverables: a list of service bundles, and a set of temporal rules showing how service bundles evolve over time.  We will focus on interpreting the latter result by showing how to use temporal rules in making predictive sales recommendations.

Our running example below is inspired by the need for Microsoft’s Azure Services salespeople to suggest which additional products to recommend to customers, given the customers’ current cloud product consumption services mix.  We’d like to know, for instance, if customers who have implemented web services also purchase web analytics within the next month.  Actual Azure Service names have been removed for confidentiality reasons.

Market basket analysis is an interesting topic, though in my limited experience, it really falls apart when you have a large number of products to compare, so it tends to work better with toy examples or limited product selections because when you have a 50,000+ SKU inventory, the lift of any individual combination of products rarely gets above the level of noise.