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​ Probability Models

Picture
Video – insights and discussion
We employ a “Buy Till You Die” model to predict future donation behaviors
The model only uses three inputs:
  • Recency (R)
  • Frequency (F)
  • Number of people for each combination of R/F
This requires a small amount of data and provides an easier structure to work  with (i.e., data are aggregated from individual-‐level to R/F groups)
By assuming certain probability distributions for donors’ propensities, we can  construct a robust model that is easy to implement on Excel
This “BTYD” modeling approach has a long track record of success in a variety of  different domains
 
Expected number of donations in 2002-2006 as a function of recency and frequency à
 



 


​
  • Looking at the Bob’s, we observe that despite making a donation in every year from 1995 to 2001 (100% donation rate), there is only 3.75 out of 5 times that the Bobs are likely to make a donation in the period of 2002 to 2006.
  • Looking at the Sarah’s. These people are likely to make a donation only 0.07 out of 5 times. However, the total number of Sarah’s is very high. As we can see below, it is 3,464 out of 11,104.
 
  • Mary and Chris have the same RF (6,4), so their expected number of donations going  forward is the same. Even though Mary and Chris have lower F than Sharmila, their higher R suggests that  they are Alive, thus they are 50% more valuable than Sharmila.
  • Sharmila (5,5), despite high donation rate, has likely lapsed.
 
We now take our table, keep the rows and average across the columns. We're going to take a weighted average across the columns.
 
We can see that the model predicts very well both as for recency and for frequency.
Let’s look at the frequency graph.
At the top of that graph would be the Bobs. That number at the top of that graph would be 3.75. That's the prediction according to the model. If we look just below that, we'll see the actual number associated with the Bobs = 3.53. Thus, the model over-forecasts the number of purchases that the Bobs would make in the future. However, it's quite close , say within 5%.
Let’s look at the recency graph.
This graph isn't quite as pretty and perfect as the other graph. Especially towards the left side of it. Those people who haven't made purchases for a while, the model thanks to be killing them off and underestimating how many purchases they will make but it's not bad.
It still does a pretty good job.
Here, we've taken the Bobs and the Marys together because they made their purchases in the most recent period. We're going to say, how many purchases do we expect people to make on the basis of when they made them as purchased? And how many purchases did they make? Again, the mapping is very good.
 
We now bring it all together and make overall statements about purchasing for the customer base as a whole.
 
The graph to the left shows you the cumulative number of purchases.
 
Example 4:
Using a larger dataset from a different non-profit firm that was monitored for a much longer period.
We can see a “heat map” that  shows which combinations of RF will likely yield the most valuable donors.
 
Columns à Recency
Rows à Frequency
Observations:
Equivalent of Bob’s on the bottom-right à those who donated at every opportunity
Here again, we see that recency trumps frequency
 
We now look at the likelihood that a customer like this with this particular RF pattern would indeed be alive.
 
 
 
There are many models that predict future donation behaviors; we believe our  method is different / superior because:
  • The model requires a very small amount of data (Recency and Frequency),  compared to other models that require a large dataset (typically detailed  individual-­‐level characteristics, e.g., demographics)
  • The model has demonstrated robust out-­‐of-­‐sample validation
  • The model can be generalized to other types of behaviors; it is not  excessively customized to the donation domain
  • The model can easily be implemented on Excel; it does not require any  proprietary or specialized software

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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas