A Market Analytics Perspective of an Online/App Based

Recharge Company

Agenda• About the Firm - Model

• Objectives

• Data Analysis

• Inferences

• Recommendations

XYZ Ltd.

• A recharge portal

• Recharges largely for mobiles; data cards and DTH follow

• Offers discount coupons of popular food joints and

retailers for a small fee

Business Model

Commissions from telcos/DTH

providers for each recharge

done on the site

Small fee from customers when

they opt for coupons

Fees from coupon

merchants

Revenue Generation

Merchants account for the largest share of the company’s revenues (50%),

followed by customer fee for coupons (30%) while recharge commissions come last

(20%). The margins in the recharge business are low, lying in the range of 0.5% -

1%.

Recharge Process

Objectives• Identify points to increase revenue generation

• Evaluate performance of campaigns

The Data• Absence of customer – wise data (no transaction IDs); missing data

• Data cleaning – missing values filled in as 0s in some cases, and

‘Unknown’ in others (device type)

• Categorical variables (Source, device type) transformed into

continuous predictors

• Part of data converted into categorical variables – high rate of

‘Payment Success’ as a criteria

Techniques Used

Tool Predictor Variables Response Variable

Excel Pivots and Graphs N/A N/A

Naïve Bayes

Source campaign, Total Registrations, Coupon page visits,Coupon option, Payment attempt - Credit Card, Debit Card, Net Banking, HDFC ATM, Promo

Coupons Payment Success

Multiple Linear RegressionSource campaign, Installation, Total Registrations Coupon page visits, coupon

option, Payment attempt Payment Success

Data Analysis

Affiliate – 1 fares well on all elements of AIDA

Campaig

n - 1

Campaig

n - 2

Campaig

n - 3

Campaig

n - 4

Campaig

n - 5

Campaig

n - 6

Campaig

n - 7

Campaig

n - 8

Campaig

n - 9

Campaig

n - 10

Campaig

n - 11

Campaig

n - 12

Campaig

n - 13

Campaig

n - 14

Campaig

n - 15

Affiliate -

1

Partners

-10.00%

5.00%

10.00%

15.00%

20.00%

25.00%

7%6%

5%

10%

15%

3%

11%

14%

12%

21%

13%14%

12%

4% 5%

9%

16%

Campaign - wise Customer Revenue (Pay Success)

Analysis - Graphical

foodsto

re

lifesty

lecar

e

clothing

electr

onics gifts

travel

books kids

miscella

neous

enter

tainmen

t0

20000

40000

60000

80000

100000

120000

140000

160000 151023

34403

0 1818 2084 1 0 1790 0 0 1865368

Coupons Opted for by Customers

Analysis - Graphical

10.75%

57.20%

24.91%

0.02% 7.12%

Credit Card

Debit Card

Net Banking

HDFC Credit Card - ATM

Promo Coupons

Modes of Recharge Payment

56.37%32.35%

3.23%

1.27%

0.43%

0.84%0.06%

5.46%

Android PhoneAndroid TabiOS PhoneiOS TabDesktopWindows PhoneOthersUnknown

Devices Used

Analysis - Graphical

Naïve Bayes

• Post data cleaning, campaigns with pay success of 14% and

above were selected

• Excel random number generator - numbers between 1 and 10

were assigned to each row

• Rows with #5 were selected and used for categorical data

simulation

Analysis - Classification

• Since the data size was small, no pivots were used

• Data partitioning - Training data – 60%; Validation data – 40%

• Cut-off probability for success – 0.5

• 9 predictor variables

Analysis - Classification

Naive BayesSimulated data and Naïve Bayes Model

Classification Confusion Matrix Predicted Class

Actual Class 0 1

0 193 12

1 5 26

Analysis - ClassificationClassification Confusion Matrix

Predicted Class

Actual Class 0 1

0 125 12

1 2 18

Training Data Scoring Validation Data Scoring

Error rate: (5+12)/236 = 7.2%

Accuracy rate: (1 - .072) = 92.8%

Sensitivity: 26/(26+5) = 83.9%

Specificity: 193/(193+12) = 94.1%

False positive rate: 12/(12+26) = 31.6%

False negative rate: 5/(5+193) = 2.5%

Error rate: (2+12)/157 = 8.9%

Accuracy rate: (1 - .089) = 91.1%

Sensitivity: 18/(18+2) = 90%

Specificity: 193/(193+12) = 91.2%

False positive rate: 12/(12+18) = 40%

False negative rate: 2/(2+125) = 1.6%

Analysis - Predictive

Multiple Linear Regression

• Post data cleaning, ‘Transform Categorical Data’ was

applied on predictor variables Source and Device Type

• Data was partitioned into 60% training and 40% validation

• 5 predictor variables usedMLR

Multiple Linear Regression Model

Analysis - PredictiveInput

Variables Coefficient Std. Error t-Statistic P-Value

pay_attempt 0.9037 0.0032 278.2815 0

Source_red 0.1428 0.0467 3.0565 0.0023

Total_Registrations 0.0003 0.0006 0.4961 0.6199

Installation -0.0002 0.0006 -0.4294 0.6677

Coupon_page -0.1656 0.0017 -95.9321 0

Intercept -1.2571 0.5486 -2.2917 0.022

Residual DF 3062R² 0.9959Adjusted R² 0.9959Std. Error Estimate 14.3005RSS 626191

Due to their low p-values, we consider pay_attempt, source and

coupon_page as important predictor variables.

Pay attempt and Pay success are positively and highly correlated.

Inferences• Affiliate 1 seems to be a fairly good campaign in terms of

creating awareness, converting awareness to registrations

and subsequent steps, leading to successful payment

• The main pain point seems to occur at after the total visits

to coupon page. Not many customers end up attempting to

pay

• Food coupons account for approximately 77% of total

coupons selected by customers

• Debit card payments account for more than half the recharge

payments made

• The android OS based devices are more popularly associated

with the usage of the recharge app

Inferences

• The Naïve Bayes model can be used to classify users into

those who’s payments would successful and those who’s

wouldn’t, based on the payment modes

• The MLR can be used to best predict payment success.

Payment attempt is the best predictor of payment success

Inferences

Recommendations• Campaigns modelled on Affiliate – 1 could be conducted

• Vendors for food campaigns could be charged a higher

commission as the demand for these coupons is high

• Explore reasons as to why conversion rate from Coupon Page

visits to Payment Attempt is low – coupons of choice not

available, preferred payment mode not available, transaction

did not go through, etc.

• Make the app more user friendly for iOS and Windows phone

users to leverage these segments

• Using the Naïve Bayes model, specific campaigns can be

designed and emailed to the target users to boost customer

recharge revenue

Recommendations

• The MLR model can be used to predict the effectiveness of

future campaigns based on their similarities to the campaigns

presented in the data

Recommendations

Thank You