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INTRODUCTION
BharatMatrimony is an online matrimony service and a part
of Matrimony.com. It was founded in 1997 by Murukavel
Janakiraman.
Bharat Matrimony registrations broadly happens through 3
sources:
1. Desktop browser registration
2. Mobile browser registration
3. Mobile application registration
Revenue generation for a month depends on the following
1. Conversion Rate
2. Average Transaction Value
3. Registration Per month
Registrants/ Customers who could be converted into sales are
divided into 3 buckets of relevant bases
1. Less than 40 Vintage days (LT40)
2. Greater Than 40 Vintage days (GT40)
3. Customers who could renew their product
INTERNSHIP OBJECTIVE
Build a datamart With 3 subcomponents of data in
order to improve the prediction model of the
Matrimony.com business model :
• FORECAST ON REGISTRATIONS/DAY ON MONTHLY
BASIS
• TELEMARKETING HOLIDAY IMPACT ON MONTHLY
SALES IN 2015
• GREATER THAN 40 RELEVANT BASE FOR THE
MONTHS FROM JANUARY 2016 TILL MAY 2016
FORECAST ON REGISTRANTS/DAY
A
B
TELEMARKETING HOLIDAY IMPACT ON MONTHLY
SALES
Objective
To find the holiday impact of telemarketing on monthly sales in
the year 2015 (Overall as well as Branch wise)
Impact
Telemarketing Holiday impact could be used to increase the
accuracy of monthly sales prediction of the business model
Implementation
Overall Holiday Impact
BranchWise Holiday Impact
Revised Predicted Sales(April)=
[Sales Per Day(April)/Sales Per Day(March)] *
[Sales(March)/Weighted Average Days(March)]*
[Weighted Average Days(April)]
• Holiday Impact is included in the calculation of Weighted
average days(April)
Outcome
Inclusion of Telemarketing Holiday Impact on sales in the
sales prediction model has improved the accuracy of the
predicted sales figure by 7.77%
GREATER THAN 40 RELEVANT BASE
Objective
To do the time series analysis on registrants/day in a given month
and forecast for the forthcoming months by adopting method with
least Mean Absolute Percentage Error.
Impact
1.Forecast on Registrants/day could be used by board members
to implement marketing campaign to enhance sales conversion.
2.Gave idea on how to move further in other projects.
Implementation
Data from Jan 2009 till April 2016 available.
Exponential Smoothing
Ft+1 = α*Yt + (1- α)*Ft
Holt Winters Method
•Level : Lt = α*Yt (St-s) + (1-α)*(Lt-1+Bt-1 )
•Trend : Bt = β* (Lt-Lt-1) + (1-β)*Bt-1
•Seasonal : St = γ*Yt/Lt + (1- γ)*St-s
•Forecast : Ft+m = (Lt + Bt*m) * St-s+m
s => Length Of seasonality
t+m => Forecast for ‘m’ period ahead
Exponential Smoothing including Autocorrelated Factor
Yt-11 had autocorrelation factor of 0.66
Ft+1 = (1-α- β )*Yt + α*Ft+ β*Yt-11
Figure: Mean Absolute Percentage Error against each method
adopted
Conclusion
Exponential smoothing including Autocorrelated factor is
the best fit time series forecast method than Exponential
Smoothing, Holt Winters Method and Excel Default
forecasting methods.
Take VolumeFlag sum on daily basis.
(Pivot – PaymentDate and VolumeFlag)
Find average sales on working days in a month(A)
Find average sales on Restricted Holidays in a
month(B)
Find average sales on Festive Holidays in a month(C)
Find (B)/ (A) –Restricted Holiday Sales in the month
with respect to Working day sales in the month
Find (C)/(A) –Festive Holiday Sales in the month with
respect to Working day sales in the month
Remove ‘Add on’ Products
Include only ‘Basic’
Remove ‘Add on’ Products
Include only ‘Basic’
Divide into sectors based on branch to sector mapping.
Take VolumeFlag sum on daily basis.
(Pivot – PaymentDate and VolumeFlag)
Find average sales on working days in a month(A)
Find average sales on Restricted Holidays in a
month(B)
Find average sales on Festive Holidays in a month(C)
Find (B)/ (A) –Restricted Holiday Sales in the month
with respect to Working day sales in the month
Find (C)/(A) –Festive Holiday Sales in the month with
respect to Working day sales in the month.
Objective
To find the monthly contribution to the Greater Than 40(GT40)
relevant base for a particular month.
For instance, GT 40 relevant base for May:
Impact
Monthly contribution to GT40 relevant base of a month could be
used to forecast forthcoming month’s GT40 relevant base.
Implementation
GT40 base contains registrants/customers who
• Logged in once in the month
• Completed 40 days after validation atleast before end of
the month
• If paid during the month,
• payment date – validation date >40 days
Data was fetched on monthly basis, from January 2016 till
May 2016.
.
Figure: Dataframes and columns
MatriId : Unique Id assigned to each registrant
PaymentTime : Date and time at which a registrant makes
payment.
ReversalOrNot : Whether the payment has been refunded
before the month start
ValidatedDate : The date at which the registrant’s validation
happened
MayPaidOrNot :Whether the payment happened in May
VintageAbove40OrNot : Whether the vintage days after validation
has crossed 40 days or not
GreaterThan40Part: Part of GT40 relevant base or not.
Outcome
Previous (chronological) months’ contribution of each
month to May month GT40 relevant base could be used
to forecast forthcoming month’s (June) GT40 relevant
base.
• Improved logical ability and numerical ability skills
• Expertise in Python modules like pandas, pymysql, xlwt
• Expertise in SQL database queries
• Basic knowledge in R programming
• Expertise in MS Excel and VBA
Month Monthly Registration Monthly
Contribution to
‘May GT40’
Percentage
Contribution
February 1100 50 4.545%
March 1000 350 35.000%
April 700 215 30.714%
MatriId Logged In the month (DataFrame 1)
Fresh profiles which logged in during the month with
Vintage above 40 at the end of the month (DataFrame 2)
Fresh profiles which logged in during the month with
Vintage above 40 at the end of the month, and paid after
the month, or not at all paid, or paid during the month. If
paid during the month (DataFrame 3)
DataFrame 1
• MatriId
DataFrame 2
• MatriId
• PaymentTime
• VolumeFlag
• ReversalOrNot
• ValidatedDate
• MayPaidOrNot
• VintageAbove4
0OrNot
DataFrame 3
• MatriId
• PaymentTime
• VolumeFlag
• ReversalOrNot
• ValidatedDate
• MayPaidOrNot
• VintageAbove4
0OrNot
• GreaterThan40
Part
DATAMART FOR THE MATRIMONY.COM
BUSINESS MODEL
By Arun Rajan, Amrita School Of Business
Industrial Mentor : Anirudh, Team Lead-Data Science Team, Matrimony.com
Faculty Mentor : Prof. A V Shyam, Amrita School Of Business, Coimbatore
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Excel Default Forecast Exponential Smoothing Holt Winters Method Exponential Smoothing
with Autocorrelation
Factor
Mean Absolute Percentage Error
LEARNINGS AND SKILLS ACQUIRED
INTERNSHIP OBJECTIVE