SPSS for Data Analysis - Part 3

SPSS for DATA ANALYSIS – PART 3

It must be established that the primary objective of the data we are using is “to predict who will respond to an offer for a product or service.”

Approaching this problem requires some insights into the data and we can only achieve this by leveraging on “Exploratory Data Analysis.” We shall be making use of excellent visualization feature of SPSS together with other statistical techniques embedded in SPSS.

Wait a minute… something just crossed my mind! Can you in few minutes recall what we treated in the last two blogs on data analysis using SPSS? If you can, then move on to this part. If you can’t, I sincerely encourage you to get the concepts in the earlier blogs before you proceed to the present one.

Great... let’s push it forward!

Note: if you have not gone through part 2 of this tutorial, please do so first via this link

EXPLORATORY DATA ANALYSIS continues….

In this section we shall be exploring another feature of analysis offered by SPSS namely:

In Figure 24, how to navigate in SPSS to produce charts is shown. Click on “Graphs” and it will bring a list of option where “Chart Builder…” is among. Click on “Chart Builder…” and Figure 25 displays.

In Figure 25, the selection of different charts is shown. Drag any type of chart you want to plot. In this case, we are plotting “clustered bar chart” drag the chart (3) into the box above and a default plot is displayed (see Figure 26).

In the “Variables” list, select the variable for “Y-axis” (5) (in this case “Income”), “X-axis” (4) (in this case “Education”) and “Cluster on X” (6) (in this case “AcceptedCmp2”). The primary aim for producing this chart is to study the relationship between the “Income” and the “Education” by the “AcceptedCmp2.” Both “Income” and “Education” remained constant while the “Offer Acceptance” vary.

The results for the participants’ Income and Education by the “Offer Acceptance” are shown below:

The figure above shows the trend of the customers’ offer acceptance by their income and educational level. For the offer 1 (a), it can be observed that there were more participants in all education level (except in “Basic”) that accepted the offer in the first campaign. In “Basic” education level, none of the customer with Basic education level accepted the offer in the first campaign. Similar trend can be seen in the second campaign (b). But in the second campaign, the customers that are “Masters” holders received the highest number of the offer.

Some differences can be spotted in the third campaign: (1) for “Graduation” and “Master” there was nearly the same number of customers that accepted and the customers that rejected the offer in the third campaign. Exceptionally, for the customers with “Basic” education level, there was the same number of the customers that accepted and rejected the offer made in the third campaign. Whereas the offer acceptance/rejection trends in the fourth and fifth campaign are similar; significant number of the customers accepted the offer made in the fourth and fifth campaign.

Conclusion

In this section of SPSS for Data Analysis, visualization in SPSS was covered. The clustered bar chart was used to visualize the trend between the “Income” and “Education” of the customers by “Offer Acceptance.” There appeared a significant number of the customers that accepted the offer than the ones that rejected.

Now that we have an idea of these trends, we then can proceed to correlation analysis to mathematically state the relationship between the variables.

NEXT: SPSS for DATA ANALYSIS - Part 4