Customer Analytics in SPSS

Name: Customer Analytics in SPSS
Rating: 4.2 (107 reviews)

Identify your best customers and increase response rates, customer loyalty and profits

Created byBogdan Anastasiei

Last updated 11/2018

English

What you'll learn

Perform RFM analyses (recency, frequency, monetary value)
Perform complex market segmentations using an advanced clustering method
Generate profiles of the customers who responded to the past offers
Identify the top responding geographical areas (postal codes)
Estimate the contact probability of purchase and select the contacts with the greatest probabilities
Predict the probability of purchase for new customers
Compare campaign effectiveness (in terms of response rate)

Course content

8 sections • 33 lectures • 2h 57m total length

Introduction5:55
What are you going to learn in this course, exactly.

Introduction to RFM5:03
Just in case you're not familiar with the RFM concept in marketing, I explain here this powerful segmentation technique.
Independent RFM2:04
What is the independent RFM analysis method - where the recency, frequency and monetary scores are independent of each other.
Nested RFM4:54
What is the nested RFM method - where the recency, frequency and monetary scores depend on each other. The advantages and disadvantages of this method.
Our Example Files3:39
In this lecture I describe in detail the data sets we are going to use for our practical examples of RFM analysis.
Executing the Independent RFM Analysis When Data Are Customers5:55
How to perform the independent RFM analysis procedure in SPSS when our data represent unique customers.
Interpreting the Independent RFM Analysis When Data Are Customers12:09
Here I explain thoroughly how to interpret the output of an independent RFM analysis when our data represent unique customers.
Performing the Nested RFM Analysis When Data Are Customers9:07
How to execute the nested RFM analysis when our data represent unique customers and how to interpret the output.
Executing the Independent RFM Analysis When Data Are Transactions4:14
How to execute the independent RFM analysis when our data represent unique transactions.
Interpreting the Independent RFM Analysis When Data Are Transactions2:27
How to interpret the results of an independent RFM analysis when our data represent unique transactions.
Performing the Nested RFM Analysis When Data Are Transactions3:34
How to run the nested RFM analysis when our data represent unique transactions, and how to interpret the output.
Practical Exercises0:28
Here you can find the practical exercises for the RFM analysis.

Two-Step Cluster Technique - Introduction2:25
In this lecture we present the basics of the two-step cluster method, that is used by the Direct Marketing module to segment your customers or contacts into homogeneous classes.
Performing the Cluster Analysis (1)12:10
How to execute the two-step cluster method in the Direct Marketing module and how to interpret the main output.
Performing the Cluster Analysis (2)14:05
More details about the output of the two-step cluster analysis.
Practical Exercises0:31
Here you can find the practical exercises for the cluster analysis

Executing the Procedure5:33
How to run the procedure that generates prospect profiles (based on the most important prospect characteristics) and computes the response rate for each profile.
Interpreting the Results7:18
How to identify your best profiles based on the output provided by the SPSS program.
Practical Exercises0:34
Here you can find the practical exercises for the profile generating technique

Running the Procedure6:50
How to execute the procedure that helps identifying the postal codes with the highest response rate for your next direct mailing campaign.
Interpreting the Results8:48
The SPSS program computes a number of indicators for each postal code. We'll learn how to interpret them and how to select the postla codes with the best customers.
Setting a Maximum Number of Contacts3:20
How to run a postal code analysis when we have to restrain the number of contacts we send the message to (it can happen frequently, for budget reasons for instance).
Practical Exercises0:13
Here you can find the practical exercises for the postal codes analysis

Running the Procedure11:32
How to execute the procedure that estimates the buying (or responding) probability for each contact in our list - and how to save our model for later use.
Interpreting the Output15:26
How to select the best contacts based on their buying probability (or buying propensity).
Validating Our Model2:15
In this lecture we divide our sample of contacts in two sets: training set and test set. Next, we create the model in the training set and validate it in the test set (in other words, use it to estimate the buying probabilities of the contacts in the test set). This way we can know how our prediction model performs in an independent data set.
Interpreting Validation6:02
In this lecture we interpret the output of the validation procedure, comparing the success rate (percentage of buyers) in the training set and test set.
Predict the Propensity to Purchase For New Contacts8:53
How to use our model predict the buying probabilities for new contacts that were just added to our list. This is all-important, because that's why we have created the model in the first place.
Practical Exercises0:25
Here you can find the practical exercises for the propensity to purchase models

Requirements

Basic SPSS knowledge (how to work with menus and commands, how to read an output)
Basic marketing and direct marketing notions

Description

Learn how to get insights from your customer data, understand your customers deeply and target the right customers with the right products!

The SPSS program offers a comprehensive customer analytics tool – the Direct Marketing module. With this tool you can conduct powerful analyses without being an expert in statistics and data analysis.

The everyday interactions with your customer generates a high amount of valuable data. The customer marketing analysis is the best solution to transform these data into real knowledge. The goal of this analysis is to get you a precise view of your customers, identify the most profitable groups of customers and send them the most appropriate marketing messages.

The Direct Marketing toolkit in SPSS includes six practical analysis procedures. Each of these procedures has its own section in this course.

The RFM analysis allows you to classify your customers according to the recency, frequency, and monetary value of their purchases. You can pinpoint your most valuable customers (those who buy often and spend much money), as well as adapt your strategy for each RFM customers (e.g. encourage new customers to buy more, reward good customers with discounts and prizes, re-gain old customers that stopped buying from you etc.)
The cluster analysis procedure helps you segment your customers or prospects using their most relevant demographic, economic or behavioral characteristics. In each cluster you will find customers that are similar with eah other and different to the others. You can combine this procedure with other analyses, to identify the segments with the highest RFM values, for example, or to estimate the buying probability in each segment.
The customer profiling technique helps you detect the customer groups with the highest response rate, based on the results of previous campaign. This way you can know in advance which customers are more likely to respond to your future offers. In consequence, you can significantly improve the targeting of your future campaigns, reduce campaign costs and increase sales and ROI.
Another procedure allows you to identify the responses to your campaign by postal codes. This is extremely useful for direct mailing campaigns, because you can find out the geographical areas where most of your customers live. You can compare the response rate of each geographical zone to your target rate and decide where to send your future mailing packages so you can maximize your profits.
The Direct Marketing module in SPSS also helps you estimate the probability of purchase for each contact in your list, using an advanced prediction analysis method (binomial regression). You can send your future messages only to the prospects who are most likely to buy from you and remove the inactive prospects from your list. Moreover, you can predict the probability of purchasing for new customers, those freshly added to your list.
The Control Package Test method allows you to compare the effectiveness of two or more marketing campaigns. This is useful especially when you intend to test existing campaigns against new campaigns. The differences between the campaigns response rates are evaluated using the binomial test.

Most of the procedures above use sophisticated statistical analysis techniques to process your data. However, you don’t have to be a statistician in order to use them. You can get the results you need with a few clicks only, in a few seconds. This is what you will learn in this course.

Every procedure is explained live in SPSS, and the output is interpreted in detail. At the end of each section you can find a couple of practical exercises to strengthen your knowledge.

Join this course today and you will be able to analyze your customer data using state-of-the-art predictive techniques and make informed decisions!

Who this course is for:

marketing students
marketing professionals
direct marketing professionals
marketing data analysts
customer analysts
anyone interested in learning customer analytics with SPSS

Customer Analytics in SPSS

What you'll learn

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Course content

Introduction1 lecture • 6min

RFM Analysis11 lectures • 54min

Segmenting Customers4 lectures • 29min

Generating Customer Profiles3 lectures • 13min

Identifying the Top Responding Postal Codes4 lectures • 19min

Estimating Buying Probabilities6 lectures • 45min

Comparing Campaign Effectiveness3 lectures • 12min

Download Links1 lecture • 1min

Requirements

Description

Who this course is for: