business data analytics · business data analytics lecture 4 ... marketing and sales customer...
TRANSCRIPT
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Business Data Analytics
Lecture 4
MTAT.03.319
The slides are available under creative common license.
The original owner of these slides is the University of Tartu.
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Marketing and Sales
Customer Lifecycle
Management:
Regression Problems
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Customer lifecycle
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Customer lifecycle
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Moving companies grow not
because they force people to move
more often, but by attracting new
customers
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Relationships based on
commitment
event-based subscription-based
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Relationships based on
commitment
• Packers and Movers
• Wedding Planners
Event-based Subscription-based
• Telco
• Banks
• Retail (Walmart, Konsume,
etc)
• Hairdressers
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition
Customer lifecycle
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Customer lifecycle
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Customer lifecycle
(Returns)
https://www.sas.com/content/dam/SAS/en_us/doc/analystreport/forrester-analytics-drives-customer-life-cycle-management-108033.pdf
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Customer Lifecycle
(Techniques/Approaches)
• Start with understanding of
your existing customers
(Segment the customers)
• Acquire the customers more
profitable
• Understanding future behavior
with Propensity Model
• Convince/Influencing your
customers to Spend more
• Clustering (Lecture 3)
• Regression techniques
(Present, Lecture 4)
• Classification (Lecture 5)
• Cross-selling/Up-selling
(Lecture ?)
Problems Solutions
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Customer lifetime value
(CLV)
Describes the amount of revenue or profit a customer generates
over his or her entire lifetime (10-20 years)
We attempt to minimize “cost per acquisition” (CAC) and
keeping any given customer.
or CLTV, lifetime customer value (LCV), or life-time value (LTV) .
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Predictive lifetime value: projects what new
customers will spend over their entire lifetime.
CLV is often referred to two forms of lifetime value analysis:
Historical lifetime value: simply sums up
revenue or profit per customer.
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time
Predicting purchase journeys
?
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“Algorithms predict purchase frequency, average order value,
and propensity to churn to create an estimate of the value of the
customer to the business. Predictive CLV is extremely useful for
evaluating acquisition channel performance, using modeling to
target high value customers, and identifying and cultivating VIP
customers early in their brand journey.”
custora.com
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Unsupervised learning Supervised learning
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Supervised vs. Unsupervised Learning
The goal of the supervised approach is to learn function that maps input
x to output y, given a labeled set of pairs
The goal of the unsupervised approach is to learn “interesting patterns”
given only an input
Categorize different types of customers ?
How many will leave ? (regression)
If a particular customer will leave or not ? (classification)
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Regression vs. Classification
If a particular customer will leave or not ?How many will leave ?
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Sleeping habits
4 hours of sleep 8 hours of sleep
exam performance
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Linear regression
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Linear regression
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Linear regression
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Simple linear regression
x
y
Task: given a list of observations find a line
that approximates the correspondence in the data
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Simple linear regression
output
(dependent variable,
response)
input
(independent variable,
feature,
explanatory variable, etc)
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Simple linear regression
intercept (bias)
coefficient (slope, or weight w)
noise (error term, residual)
shows how increases output
if input increases by one unit
mean of y when x=0
shows what we are not able to predict with x
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Simple linear regression
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Simple linear regression: example
Built-in R dataset: a collection of observations of the Old
Faithful geyser in the USA Yellowstone National Park
> data(faithful)
> head(faithful)
eruptions waiting
1 3.600 79
2 1.800 54
3 3.333 74
4 2.283 62
5 4.533 85
6 2.883 55
the duration of the geyser eruptions (in mins)
the length of the waiting period until the next one (in mins)
> model <- lm(data=faithful, eruptions ~ waiting)
What do we want to model here?
i.e. What is input and output?
> dim(faithful)
[1] 272 2
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Simple linear regression: example in R
The fitted model is: eruptions = -1.87 + 0.08 x waiting
What is the eruption time if
waiting was 70?
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Simple linear regression: example in R
The fitted model is: eruptions = -1.87 + 0.08 x waiting
> -1.874016 + 70*0.075628
[1] 3.419944
> coef(model)[[1]] + coef(model)[[2]]*70
What is the eruption time if
waiting was 70?
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Data
Machine learning “secret sauce”
Test
Train
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Prediction Problem
Training Data
(with labeled information)
X 1 -> Y1
X2 -> Y2
:
X100 -> Y200
X101 -> ?
X102 -> ?
:
X110 -> ?
Test Data
(no labeled information)
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Simple linear regression
x
y
Task: given a list of observations find a line
that approximates the correspondence in the data
Predict
this Label
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Simple linear regression: example in R
train_idx <- sample(nrow(faithful), 172)
train <- faithful[train_idx,]
test <- faithful[-train_idx,]
model <- lm(data=train, eruptions ~ waiting)
test$predictions <- predict(newdata=test, model)
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Multiple linear regression
all the same, but instead of one feature, x is a k-dimensional vector
the model is the linear combination of all features:
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Multiple linear regression
model_1 <- lm(data=train[,-1], AmountPerCust_2 ~ AmountPerCust_1
+ TransPerCustomer_1 + AmountPerTr_1 + gender + age +
discount_proposed + clicks_in_eshop)
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Multiple linear regression
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -121.71416 5.57368 -21.837 < 2e-16 ***
AmountPerCust_1 -0.11450 0.07461 -1.535 0.1259
TransPerCustomer_1 1.10197 0.68570 1.607
0.1090
AmountPerTr_1 0.18432 0.11310 1.630 0.1041
gender1 -1.24804 0.58533 -2.132 0.0337 *
age 0.20375 0.03775 5.397 0.000000132 ***
discount_proposed1 59.90655 2.02840 29.534 < 2e-
16 ***
clicks_in_eshop 23.98903 1.04957 22.856 < 2e-16
***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.948 on 322 degrees of freedom
Multiple R-squared: 0.8529, Adjusted R-squared: 0.8497
F-statistic: 266.7 on 7 and 322 DF, p-value: < 2.2e-16
Interpret
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Quality Assessment
• Mean Absolute Error• Mean Square Error• Root Mean Square Error• R2
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MAE and (R)MSE
Reference: https://medium.com/human-in-a-machine-world/mae-and-rmse-which-
metric-is-better-e60ac3bde13d
Loss function/distance:
MAE is more robust to outliers since it
does not make use of square.
With errors, MAE is steady
MSE is more useful if we are
concerned about large errors.
With increase in errors, RMSE
increases as the variance associated
with the frequency distribution of error
magnitudes also increases.
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RMSE Vs. MAE
https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d
RMSE should be more useful when large errors are particularly undesirable.
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RMSE Vs. MAE
https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d
RMSE does not necessarily increase with the variance of the errors. RMSE
increases with the variance of the frequency distribution of error magnitudes
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Simple linear regression: example in R
train_idx <- sample(nrow(faithful), 172)
train <- faithful[train_idx,]
test <- faithful[-train_idx,]
model <- lm(data=train, eruptions ~ waiting)
test$predictions <- predict(newdata=test, model)
MSE <- (1/nrow(test))*sum((test$eruptions - test$predictions)^2)
> MSE
[1] 0.2742695
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R2: Goodness of fit
Reference: https://www.youtube.com/watch?v=w2FKXOa0HGA
R2 -> 1, better as compared to when R2-> 0var(mean) – var(model)
R2 = var(mean)
*Var = variation
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R2 and more
• R-squared cannot determine whether the coefficient estimates and
predictions are biased, which is why you must assess the residual
plots.
• “Adjusted R-square” penalizes you for adding variables which do
not improve your existing model.
• Typically, the more non-significant variables you add into the
model, the gap in R-squared and Adjusted R-squared increases.
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Demo time!
https://courses.cs.ut.ee/2018/bda/fall/Main/Practice