Movie magic case_study

Movie MagicImplementing Strategy with Analytics

BUSINESS SCENARIO

BUSINESS QUESTIONS

APPROACHMovieMagic 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.

DATA ARCHITECTURE

Subscription ID Description

1 Online Yearly

2 Offline Yearly

3 Online Monthly

4 Offline Monthly

Customer ID

Name Address Email Joined Date

*Customer Type

Subscription ID

Subscription Date

Customer ID

Media ID

Date of Rent

Period Quantity Price

Media ID

Media Name

Type Category ID

Inventory (Q)

Date Time Stamp

Rented Quantity

[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

Sub

scri

ptio

n D

B

Media Database

Med

ia C

ateg

ory

DB

MODEL DEVELOPMENT

DETAILED PROCESSIllustrated 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)

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..)

Cluster results by k means with 3 classes on output

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 CategoryMedia Type

-Browsing history

Cluster Function of:

-DatetimeStamp-Geography

-Media Category-Media Type-No. of times

bought-Browsing history

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.