RFM Analysis : Recency Frequency Monetary Model

1. Movie Magic
Implementing Strategy with Analytics

2. BUSINESS SCENARIO
BUSINESS QUESTIONS

3. APPROACH
MovieMagic offerings need to be analyzed with respect to usage
patterns, customer base and behavior, sales & product portfolio
and come up with strategies backed by customer understanding &
personalization.
DATA ARCHITECTURE MODEL DEVELOPMENT
DERIVING
STRATEGIES
- Databases are created
using various sources:
− Customer
− Media
− Usage
−Common linkages are
created using Primary
keys IDs
−New data definitions
are derived.eg.current
inventory, turn-around-
time etc.
Classification is done
based on Volume vs.
Value concept.
−Technique used:
− Cluster analysis
− RFM Technique
−Clusters/Groups
created based on
independent metrics
available for each of the
categories.
−Prediction models
developed for each
branch of fishbone.
Defining customers via
metrics in terms of
clusters created for each
branch.
–Personalization exercise
for each cluster.
–Strategy derived for each
segment:
– Financial
– Marketing
– Sales
– Supply Chain.

4. DATA ARCHITECTURE
Subscription ID Description
1 Online Yearly
2 Offline Yearly
3 Online Monthly
4 Offline Monthly
Custome
r ID
Name Address Email Joined
Date
*Custom
er Type
Subscrip
tion ID
Subscrip
tion
Date
Custome
r ID
Media
ID
Date of
Rent
Period Quantity Price
Media
ID
Media
Name
Type Categor
y ID
Inventor
y (Q)
Date
Time
Stamp
Rented
Quantit
y
[Movie/
Game
Name]
Movie/
Game
Cat. ID Descr. Class
1 Genre Movie
2 Starcast Movie
3 Released Movie
4 Language Movie
5 PC Game
6 PS Game
7 Genre Game
Customer Database
Rent Logbook DB
SubscriptionDB
Media Database
MediaCategoryDB

5. MODEL DEVELOPMENT

6. DETAILED PROCESS
Illustrated below is the detailed process of Clustering and RFM
RFM stands for Recency, Frequency & Monetary Analysis
Recency: When did the customer make their last purchase?
Frequency: How often does the customer make a purchase?
Monetary: How much money does the customer spend?
The following step by step process will be followed for the Modeling:
DATA:
It has the fields : (i) ID, (ii) Area, (iii) Country, (iv) Recency (Rcen),
(v) Frequency (Freq), (vi)Monetary (Money)
Only the 4 attributes, area, recency, frequency, monetary, and one class (output),loyalty, are used to
build the decision table.
STEP1:
Cluster customer value by K-means algorithm. This step the scaling of R–F–M attributes and yield
quantitative value of RFM attributes as input attributes, then cluster customer value by using K-means
algorithm. The detail process of this step is expressed into two sub-steps as follows: (..contd)

7. STEP1a:
Defining the scaling of R–F–M attributes.
This sub-step process is mainly divided into five parts introduced in the following:
(1) The R–F–M attributes are equal weight (i.e. 1:1:1).
(2) We define the scaling of three R–F–M attributes, which are 5, 4, 3, 2 and 1.
(3) Sort the data of three R–F–M attributes by descendant order.
(4) Partition the real data of R–F–M attributes respectively into 5 scaling in MovieMagic dataset
(5) Yield quantitative value of R–F–M attributes based on input attributes for each customer
Sample Table:
STEP1b:
Cluster customer value by K-means algorithm.
According to quantitative value of R–F–M attributes for each customer, partition data
into n clusters using K-means algorithm for clustering customer value. (contd..)

8. Cluster results by k means with 3 classes on output

9. METHODOLOGIES:CLUSTERING/PREDICT
RentRent SubscriptionSubscription Non-SubscribersNon-SubscribersSubscriptionSubscription
One Time Repeaters Yearly/Monthly Yearly/Monthly One Time Repeaters
For each of the Fishbone branch>>Subset of data obtained>Clustering/RFM Technique Used>Model developed
Cluster Function
of:
-DatetimeStamp
-Geography
-Media Category
-Media Type
Cluster Function
Of:
-DatetimeStamp
-No. of Times
Rented
-Geography
-Media Category
-Media Type
Cluster Function of:
-DatetimeStamp
-Geography
-Media Category
-Media Type
-Subscription type
-No. of times
subscribed
-No. of times
discontinued
Cluster Function of:
-DatetimeStamp
-Geography
-Media Category
-Media Type
-Subscription type
-No. of times
subscribed
-No. of times
Discontinued
-Browsing history
Cluster Function
of:
-DatetimeStamp
-Geography
-Media Category
Media Type
-Browsing history
Cluster Function
of:
-DatetimeStamp
-Geography
-Media Category
-Media Type
-No. of times
bought
-Browsing history

10. IMPLEMENTATION
Sales Strategy
•USAGE PATTERN: Within
X days of release, Y% of
extra streaming over base
and thereafter Z% of extra
rent over base value after X
days.
•COMBO PACKS: Creating
**combos of DVDs to push
sales of Slow Mover DVDs
Financial Strategy
•DISCOUNTING:
Discounting to clusters of
users based on profit
generation, less penetration,
opportunity index. Like Hike
Prices when demand more in
streamline for a
period,pattern of usage.
•SUPPLIER
NEGOTIATION: Based on
usage pattern, demand
forecasting, days of payable
outstanding can be
negotiated with the suppliers
Marketing Strategy
•GEO SPECIFIC
ADVERTISING:
More advertising in less
penetrating areas with
respect to the usage index,
competitive scenarios.
•TARGET MARKETING:
based on usage pattern &
specific demands, Customer
Lifetime Value.
Supply Chain Strategy
•STOCKOUT/BACKLOG:
predict inventory to avoid
stockouts, Calculate Adjusted
Turn Around Time based on
Consumption Pattern for each
branch of Fishbone.
•RED FLAGS: Flagging Users
which are probable unsubscriber/
discontinuation of usage based
on patterns of subscription
packages they used.
•DISTRIBUTION NETWORK:
local network at more
demanding areas.
**DVD Types:1 movie/game pack, N in 1 pack ,Combo Packs , Star Packs, Vintage packs etc.
Personalization: Creating portfolio of users at Individual level and implementing above strategies.