Case: Analysis of Purchase Decision Survey, Electrical Switches and Sockets

The second case study we present regards the analysis of a survey conducted to find out factors that shape the purchase decision of electrical switches and sockets. The survey, prepared for a well-known manufacturer and distributor of electrical switches and sockets, resulted in a capable data set of answers; however we considered its default statistical analysis as poor and we attempted to put our hands on it. What follows is the results of our own analysis, demonstrative of the value of our approach, which you may find of interest.

The initial data set is comprised of 253 questions (attributes, all of structured textual nature) and answers from 309 customers’ answers (instances). The question titles are clustered and presented in the following Table 1.

Table 1: Question titles (attributes)

Table 2 presents the characteristics of the attribute that served as the target to our analysis, namely the decision to buy or not the products of the under study brand.

Table 2: Target attribute details

Advanced filtering and prioritization algorithms, fine tuned for the data set under focus, essentially contributed a ranking of the available attributes, in regards to their information value on predicting the target attribute price. The 10 most important attributes are presented in Table 3.

Table 3: Questions of most importance

With valuable inputs like these, we put into use an extended set of advanced machine learning algorithms to finally come up with the underlying structure and patterns that cannot but remain unseen under a typical statistical analysis. We present and discuss a few out of these patterns hereafter, while you may find a more extended selection out of these results in the full report, which is available or download below.

Rule 1: if known_brand_brand= absolutely_agree then brandbougth_brand=yes (88% success)

Rule 1 comes up with the very significant -especially under a marketing perspective- pattern that a customer who is well informed about the under focus brand, is expected to select its products for purchase, with a significant certainty of 88%.

Rule 2:  if most important_char_ endurance= yes & advertising_in_magazines = yes then brandbought_brand= yes (85% success)

Rule 2 reveals that a customer who considers endurance as a significant characteristic and has viewed at least one of the brand’s advertisements in magazines will buy the brand’s products. This rule came up with a success rate of 85% across the data set.

Rule 3: if most important_char_quality=yes & important_char_endurance=yes  & important_char_security=yes then brandbought_brand= yes (74% success)

Rule 3 suggests with a certainty of 74% that a customer who considers quality, endurance and security as important factors that shape her purchase decision, is expected to select the under focus brand, versus the other ones available. Yes, the brand seems to be very strong, at least that is what the data set and the data mining analysis performed indicates.

The full report presents 31 such rules and you may download it here (.pdf). And we do believe that such insights, remaining latent in most of your data sets, consist a solid potential for you to upgrade your inputs to decision making. So, why omit such insights, when you can just mine your knowledge? Send us your data sets right now, and just wait for the very treasures of your data to be revealed!

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