![]() Prospect Profiles. This technique uses results from a previous or test campaign to create descriptive profiles.For example, it can identify different groups of customers based on various demographic and purchasing characteristics. Cluster Analysis. This is an exploratory tool designed to reveal natural groupings (or clusters) within your data.RFM Analysis. This technique identifies existing customers who are most likely to respond to a new offer.The Direct Marketing add-on option allows organizations to ensure their marketing programs are as effective as possible, through techniques specifically designed for direct marketing, including: So what else can you do in Marketing Analytics with SPSS 19. ![]() It is simple, and yet powerful into turning what used to be a purely statistical software for nerds into a beautiful easy to implement tool for business users. Note here I am evaluating RFM as a marketing technique (which is well known) but also the GUI of IBM SPSS 19 Marketing Analytics. 9) Comparative plots placed side by side with easy to understand explanation (in the output word doc not shown here) 10) Auto generated scores attached to data table to enhance usage. Note the effect that a scale of color shades have in visual representation of data. 8) An attractive heatmap to represent monetization for customers. Auto generated options for customized standard graphs. Rather than pseudo techie jargon 5) Note the clean design of the GUI in specifying data input type 6) Again multiple options to export results in a very user friendly manner with options to customize business report 7) Graphical output conveniently pasted inside a word document rather than a jumble of images. Here are some screenshots to describe the features.ġ) A simple dashboard to show functionality (with room for improvement for visual appeal)Ģ) Simple Intuitive design to inputting data 3) Some options in creating marketing scorecards 4) Easy to understand features for a business audiences I just got myself an evaluation copy of a fully loaded IBM SPSS 19 Module and did some RFM Analysis on some data- the way SPSS recent version is it makes it very very useful even to non statistical tool- but an extremely useful one to a business or marketing user. Such categories may be arrived at by applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks.Įven if you dont know what or how to do a RFM, see below for an easy to do way. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days between 91 and 365 days and longer than 365 days. To create an RFM analysis, one creates categories for each attribute. ![]() Monetary Value – How much does he spend?.Recency – How recently a customer has purchased?.It is commonly used in database marketing and direct marketing and has received particular attention in retail. RFM is a method used for analyzing customer behavior and defining market segments. RFM Analysis can help you cut through and focus on the real customer that drives your profit. You may be a retail player with thousands of customers or a enterprise software seller with only two dozen customers. ![]() Recency Frequency Monetization is basically a technique to classify your entire customer list.
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