1. Paid membership and online matrimony portals - an
Indian study
Sucanyaa Iyer, Naren Mohan, Sangeetha Gunasekar and Deepak Gupta
Amrita School of Business
Amrita Vishwa Vidyapeetham (University)
Coimbatore, India
sucanyaa.iyer@gmail.com
Abstract—Technological innovations and advent of e-
commerce has given rise to the internet portals in recent times.
One of such development can be seen in the field of matrimony.
The evident cultural shift in the outlook of marriage in India
and adoption to new technology has enabled the growth of
matrimonial service portals. Dispersed families adapt to online
matrimonial services to seek all the marriage-related
information. Most of the matrimonial portals that hold the
major market share work on freemium model. The present
study attempts to analyze the influences that motivate online
matrimony service users to become a paid member rather than
being a free member. We explore the question using a logistic
regression model on data from one of India’s premier
matrimony portals. Our results show significant gender based
differences in terms of factors that influence the proclivity to
take a paid membership. By this attempt the study aims to help
the matrimonial service providers to target their
customers/members more efficiently and effectively.
Keywords—Matrimony; ecommerce; technology; payment;
willingness to pay
I. INTRODUCTION
Matrimonial service providers have completely transformed
the way weddings happen in India today. In 2015, India is
estimated to possess 7% share of the total internet users of
the world and is ranked 4th in the world in terms of internet
usage. 11% of the share is estimated to come from the
internet users who use matrimonial service portals. The total
increase in growth of matrimonial sites is pegged to be
1500% by 2017[1].
Online matrimonial space in India is currently populated by
several 100 national, regional and local level players.
Among them the major national players are Shaadi.com,
BharatMatrimony.com, JeevanSaathi.com, and
SimplyMarry.com.
There are now over 12 million users who use online
matrimonial service providers. The hundreds of matrimonial
sites operating at the regional as well as national level have
sprouted up to meet the demand for marriage-related
information. The present study collates data from one of the
four major players of India with over 2.5 million customers
spread across 22 different states operating in 15 different
languages. The services include services like wedding
directory, pre-marriage counselling services, express
matrimony services, blood testing services, among others.
While the selected matrimonial web service provider for the
study is one of the largest in India, yet only about 11% of
the total customers are found to be paid members. This
gives us an interesting prospect to attempt a detailed study
on the attributes that motivate freemium mode customers to
become a paid member of the selected matrimonial web
service provider.
II. LITERATURE REVIEW
Growth of matrimonial web services:
Customers accept a new technology if they find it easy to
use. The advent of internet, social media and web
techniques have enabled to facilitate easier use and better
access to the customers using online portals to access the
information they require[2]. Evolution of IT and Web2.0
has enabled the matrimonial companies to identify their
customers and give them better search results. Further the
dispersion of Indians across the world has led to an increase
in the adoption to these matrimonial portals across the
globe[3]. The website such as Bharatmatrimony.com,
Jeevansathi.com, shaadi.com, simplymarry.com are the
leading national matrimonial services providers for Indian
globally[4]. KPMG’s study finds that there are about 53.4
million people who are in the marriageable age and can be a
part of these online matrimonial services in 2014[5].
Extant literature focusing on the increasing usage of
matrimonial service providersindicates that perceived ease
of use plays a vital role in determining the acceptance of
matrimonial websites, and thus the companies also ensure
that they develop websites that are easier to use [6].
Consumers evaluate the factors such as design and
interaction with the site before they start using the site[7].
The factors that are highlighted in the literature as important
for customers to choose a particular platform are
performance expectancy, effort expectancy, social

2. influence, trust and awareness of service[8].[9]indicates that
among all the people who use the online matrimonial
services, 70% of them seem to be well satisfied with the
services provided and 45% of them believe and trust the
website they were using. With regard to willingness to pay
for online a service in general, [10]finds that trust is the
most important factor that determines the willingness to
pay. [11]further emphasises that higher the trust on the
website greater the probability to pay by the customer.
While there are studies that have focused on the willingness
to pay for customers using online services like online music,
there are no studies in the literature that have attempted to
focus on the factors influencing customers to become a paid
member in the matrimonial services. Our study attempts to
do this with the data collected from one of the major online
matrimonial service provider.
III. RESEARCH METHODOLOGY
The study seeks to differentiate the non-payers from payers
and identify the factors that influence members to become a
paid member. In attempting this, the data was acquired from
the matrimonial website restricted only to first time payers.
Two factors that were hypothesised to be important
influences were gender and caste. The data was collected
across gender covering six major castes fromthe major
regions in India.
Sample size was based on the thumb rule of having a
minimum of 10 to 20 records per variable considered. 55
variables were considered for the study. Thus the final
sample size was 10900 record/members registered on
January 1st, 2014. Details of the matrix of sample size is
given in Table I
Sample size was calculated as follows:
Caste 1 male = 55* 10 = 550 or 55 *20 = 1100
Thus a sample between 550 – 1100 was considered
appropriate. Similar calculations were done for all the other
caste gender-wise.
Table I: Sample size matrix - Gender-wise and Caste-wise
Caste Count Paid Not- Paid
Paid
Rati
o
(%)
Paid
Rati
o
(%)
Paid
Rati
o
Tota
l
M F M F M F M F (%)
Cast
e 1
852 580 154 186 698 394 18.1 32.1 23.7
Cast
e 2
1016 511 360 251 656 260 35.4 49.1 40.0
Cast
e 3
1074 414 126 95 948 319 11.7 22.9 14.9
Cast
e 4
1367 530 72 106 1295 424 5.3 20.0 9.4
Cast
e 5
1565 708 383 331 1182 377 24.5 46.8 31.4
Cast
e 6
1417 892 188 186 1229 706 13.3 20.9 16.2
729
1
363
5
128
3
115
5
600
8
248
0
17.6 31.8 22.3
The dependent variable is binary variable with values 1 or 0.
Paid members are identified as 1 against free or non-paid
members as 0. Logistic regression estimation method was
used to estimate the models.
Logistic regression model is represented as:
Log (pi/(1-pi)) = ᵝo + ᵝ1Xi1 + ᵝ2Xi2 + . . . . + ᵝpXip+ ui
Log (pi/(1-pi)) =ᵝo + ᵝ1Xi
’
+ ᵝ2Zi
’
+ui
Where:
Xi
’
=55 variables of interest. Refer Table IV.
Zi’= Control variables covering the demographic features of
the customers.
ui = The random error component.
IV. RESULTS AND DISCUSSION
SPSS modeller, the statistical tool, was used to run the
logistic regression. All the 55 variablesare seen to have an
impact on the behaviour of the members. Table II gives the
impact of the variables that have higher odds ratio across
both the genders.
TABLE II: Variables with higher odds ratio across gender
VARIABLES ODDS RATIO
Male Female
>1 (In favour of)
Number of Photos 1.386 1.512
Payment page Visit 1.174 1.149
Number of Profiles Shortlisted 1.013 1.019
Personal Information 1.002 1
<1 (Not in favour of)
Age 1.072 0.985
Number of interested Received 0.999 1.014
Number of Profiles viewed 0.996 0.995
Eating Habit 0.673 0.419
Ancestral Origin 0.643 0.417
=1 (Equal probability)
Parents occupation 0.211 0.298
Horoscope Generation 0.088 0.13
Differential probability
Birth Details 1 0.217
Odds ratio varies across male and female as seen from Table
II. The variables from the top have almost equal odds ratio
impacting the Y variable (paid/free). Members who post
higher “number of photos” have higher probability of
becoming a paid member irrespective of gender. Such

3. probability can also be seen for members who have higher
“payment page visit”.
As we move along the table the odds ratio across gender
vary. While there is a higher of probability of male members
who give “birth details” to become a paid member, the
probability in the female gender is in the favour of those
who don’t.
There are few variables that are significant for a particular
gender alone. Table III gives the list of variables that are
significant but affects only one gender.
TABLE III: Variables impacting only one gender
Male (not for female) Female (not for male)
Family values Dhosham
Horoscope Eating habits
Photos like received Occupation details
Family values Eating habits
Number of logins Family status
Male members who have horoscope, higher photo likes and
higher numbers of login have higher probability to pay
whereasfemale members who have mentioned their eating
habits, occupational details and belong to a higher family
status (rich) have higher probability to pay.
Detailed tabulation of all the 55 variables have been listed in
Table IV. Significant variables are highlighted.
TABLE IV: Gender wise significant variables
Female
Male
Female
Male
Predicted accuracy
90.30
%
92.50
%
Predicted accuracy
90.30
%
92.50
%
Nagelkerke 0.742 0.699 Nagelkerke 0.742 0.699
Variables B B Variables B B
Age -0.015 0.07
Horoscope Request
Receive
0.081 0.089
AncestralOrigin(1) -0.874 -0.442 Horoscope Request Sent -0.118 -0.076
AnnualIncomeinINR 0 0 HoroscopeAvailable
BirthDetailsAvailable
(1)
-1.527 -2.37 HoroscopeAvailable(1) -0.518 -1.115
BodyType(1) 0.567 0.478 HoroscopeAvailable(2) -2.042 -2.19
Complexion(1) -0.139 0.52 Ignored 0.006 -0.015
Dosham(1) 0.618 0.175 Login -0.002 -0.007
DrinkingHabits(1) 0.85 -0.065 NoOfPhotos 0.413 0.327
EatingHabits(1) -0.871 -0.396 OccupationCatDesc
EducationInDetail(1) -0.401 -0.259 OccupationCatDesc(1) -0.973 -17.08
Family Info
Modification
-0.286 -0.236 OccupationCatDesc(2) -0.429 -0.212
FamilydescriptionLe
ngth
0.004 0.003 OccupationCatDesc(3) 2.089 -0.002
FamilyStatus OccupationCatDesc(4) 0.585 -0.27
FamilyStatus(1) 0.123 OccupationCatDesc(5) 0.469 -0.92
FamilyStatus(2) -0.545 0.2 OccupationCatDesc(6) 0.448 -0.352
FamilyStatus(3) -0.011 -0.356 OccupationInDetail(1) -0.413 -0.715
FamilyStatus(4) 0.431 -18.08 ParentsOccupation(1) -1.21 -1.555
FamilyStatus(5) 0.259 PartnerPrefSet(1) -1.362 -1.54
FamilyType PaymentPgVisits 0.139 0.16
FamilyType(1) -0.887
Personal Message
Receive
0.019 0.048
FamilyType(2) -0.95 0.457
Phone View Request
Receive
0.07 0.245
FamilyType(3) 0.836 Photo Likes (Received) -0.07 -0.045
FamilyValue Photo Request Receive -0.064 0.055
FamilyValue(1) -0.34 -18.92 Photo Request Sent -0.081 -0.002
FamilyValue(2) -0.041 0.304 Photos Liked (Sent) -0.031 -0.089
FamilyValue(3) 0.012 0.174 Profile Modification -0.091 -0.071
FamilyValue(4) 19.81
ProfileDescriptionLengt
h
0 0.002
FamilyValue(5) -0.197 Raasi(1) 0.444 0.145
Hobbies Modification -0.405 -0.552 Receive Interest 0.014 -0.001
HobbiesAvailable(1) -0.375 -0.476
Reference Request
Receive
0.087 -0.038
Horoscope
Generation
-2.039 -2.436 Reference Request Sent 0.014 0.012
ReferenceAvailable(1
)
-1.69 -1.451 SmokingHabits(1) -1.785 -0.694
Sent Interest 0.002 -0.004 Star(1) -0.911 -0.495
ShortlistedProfiles 0.018 0.013 ViewProfile -0.005 -0.004
Siblings(1) -0.077 -0.09 Weight(1) 0.308 -0.138
SiblingsMarried(1) 0.36 0.205 ZodiacSign(1) -0.375 -0.223
V. CONCLUSION
Since almost all the famous matrimonial services function
on the same base, we can extrapolate from our results to
give general suggestions that will motivate the free
members to pay. Any matrimonial service company works
on a trust factor. The members who register on the portal
need to believe that they will meet their soul mate and only
then will pay. Thus they need to be given the feeling that
they are in the right place to find their soul mate. This can
be done if the member
● Receives more relevant profiles on their search
page.
● If they receive request from profiles that match
their partner preference stated. Thus matrimony
service providers can refine their search algorithm
from broad variables to specific ones like caste,
location of the partner searched etc.
● When the member is constantly exposed to the
payment page then he/she eventually makes a
payment. Thus make that as a landing page on the
portal than a page that opens only when searched
for specific lead services.

4. ● Age is an important factor determining the
seriousness of marriage, thus the matrimonial
website can target all the members above a certain
age say 27 for females and 30+ for male members
are highly potential customers.
● When a profile has more number of photos then the
members are considered to be serious about
marriage, thus encourage the members to add more
photos when they log in.
● Birth details are filled for male customers.
● Show them appropriate matches which will reduce
the search time on the page. Thus increasing the
probability to pay
● Re-direct them the payment page after generation
genuine interest by ways of relevant offers.
VI. LIMITATIONS AND SCOPE FOR
IMPROVEMENT
Although the study has adequate insights for the
matrimonial service providers as to how to attract or make
the non payers pay the following are the few limitations of
this study.
● Due to low availability of sample the holdout
sample technique was not done to validate the
results.
● If these six castes are behaving differently gender-
wise and caste wise, extrapolating the results to all
the other caste which has a different outlook
towards marriage will become difficult.
Areas that can be included for further studies are as follows:
● There are about 300+ castes in India; this study
considers only 6 popular castes that are assumed to
be significant in their respective areas thus similar
study on other castes can give specific insights to
the caste chosen.
● Few variables such as “Who created the profile”
can be included. This might showthe impact of
profile owner and the probability to pay. For
example; Are the profiles created by family or self
has impact on payment?
VII. ACKNOWLEDGEMENT
The formulation of this report is the results of the efforts put
by us along with the productive inputs form our guides,
employees, concerned matrimony portal users and my
respected professors. We would like to take this opportunity
to thank and acknowledge each and everyone involved in
this project directly or indirectly. We are grateful to each
one of them.
We must emphasise on the powerful contribution made by
our project guides Dr. Sangeetha G and Dr. Deepak Gupta,
professors of our institute who provided a great support
intellectually and emotionally.
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