Loan Seeking on the Web - case study by STEM/MARK

Loan Seeking on the Web

Using online data to track consumer searching habits in

the e-loan market

Jan Lajka | August 2016

Key Challenges

When planning an advertising campaign on Internet, marketing managers need to know how potential

buyers use web based media to get information for their products.

Ideally, they would love to know the answers to the following questions:

• Where would a potential consumer go on the Web to search for information about the available

products in the market?

• How efficient are the different information services available on the web?

• How many visits to its product page each market player is capable to generate via the existing

variety of web services?

A common solution applied for solving this task when dealing with classical media advertising

campaigns is the use of telemetry for measuring the media audience.

Here a pilot study project is presented where Wakoopa's passive metering technologies (S/Marlowe)

were implemented to explore the efficiency of interactive web media in attracting potential customers

to commercial pages offering bank or non-bank loans on the Web.

Loan Seeking on the Web 2

Main Findings 1/2

The banks with the largest number of uninduced visitors to their loan pages are:

• ČSOB

• Moneta (formerly GE Money bank) and,

• Airbank (also the largest rate of page visits per visitor)

Most frequently, the referrers to these pages are search engines (google.cz, seznam.cz).

Among the respondents, who made final decision about loan, prevail the ones who chose

Airbank (20%) and Moneta (20%), while the rest chose ČSOB (10%), Česká Spořitelna (10%),

KB (10%), Equa bank (10%), Fio (10%), non-banking loan (10%).

Raiffeisenbank and Zuno have the highest advertising clickthrough rates, followed by Cofidis

and Proficredit. Most often, these are transactional email advertisements (Zumail), or pay-per-

click ads (Google AdWords, Sklik).


Main Findings 2/2

A vivid distinction is observed between the visit rates of banks reached via email advertisements

(i.e. Raiffeisen, Zuno) and of banks reached via search engines (e.g. ČSOB, mBank, Airbank).

However, when parallel analysis is applied to the visits only that are not induced by email ads,

Raiffeisen and Zuno lose their lead in the visit rates. This outcome implies that some

respondents from the online panels have had higher affinity towards transactional email

advertising which seemingly biased the preliminary results in favor of the email-induced visits.

Finally, a clear strategy is recognized for certain banks to provide their loan products (possibly

differentiated) through multiple domains:

• Česká Spořitelna: cspujcky.cz, pujckacs.cz, pujckabezpapiru.cz

• ČSOB: csob.cz, pujcka.csob.cz

• Equa bank: equabank.cz, https://equabanking.cz/cashloans


Research objective

To study how people use internet when choosing a loan

Target group

Online population in the Czech Republic

Panelists recruited

Loan seekers (treatment group), N=59: plan to take loan within the

coming 3 months, will choose from several loan suppliers, will search

for information on internet and will make decision independently.

Control group, N=51: respondents recruited for a study on the

popularity of the European Football Championship 2016 (whose

interested in taking a loan should follow natural spread in population).

Field Research Description

Monitoring the internet behavior of PC/tablet/mobile users by means

of Wakoopa technology (installed locally on panelists’ devices)

Data collection period: from 23. 5. to 23. 6. 2016

51%

49%

19%

41%

33%

6%

3%

8%

56%

33%

67%

33%

12%

25%

35%

27%

6%

24%

37%

33% 0% 20% 40% 60% 80% 100%

Men

Women

18-29

30-44

45-59

60+

Primary

Secondary

High-school

University

Sex

Ag

eEd

uca

tio

n

Loan sekers

Control group

Loan seekers, n=59, Control group, n=51 [in %]


Study Parameters

Research methodology

Data collected via Wakoopa S/Marlowe

• List of the web addresses (URLs) of the visited pages in the sequence in which they were visited by

each panelist

• Duration of the stay on each page

Information extracted from the data

• Domain – e.g. google.cz, airbank.cz

• Key phrases contained in the URL – e.g. ‘loan’, ‘ibs’

• UTM parameter in the URL – identifier of the source and type of ad (email, pay-per-click banner

etc.) on which user clicked to be re-directed to the advertiser’s page

Identification and categorization of the visited pages

• banking ‘loan’, non-banking ‘loan’, internet banking, non-loan

• search engine, email, web directory, web site, transactional email, news server, etc.

• main bank revelation based on the most visited internet banking page by each consumer


Visit generators (loan seekers)


The graph describes from which

domain panelists were referred to a

“loan” domain and where they went to

next.

The domain that serves as a referrer in

a reference connection is marked by a

larger indent from the circuit

(e.g. More people visited a ‘loan’ page after following

a reference from google.cz (see the blue reference

connection) than the other way around (see the green

reference connection))

Referrers to ‘bank loan domains’ relations


151 transits, 36 loan seekers

preceding

bank loan domains

(ad-driven visit)

following

bank loan domains

(uninduced visit)

The most common reference types are

ad emails (transactional + direct) and

search results (ppc + uninduced)

google.cz

email.seznam

.cz

zumail.cz

ermail.cz

mam

email.cz

facebook.com

usetreno.cz

cz.unicreditbanking.net

mail.google.com

google.com

s.cw

stats.cz

login.szn.cz

seznam

.cz

search.seznam

.cz

pokorunce.cz

ermail.cz

servis.karatsoftware.cz

moneymail.cz

facebook.com

google.com

ibs.internetbanka.cz

pujcka.csob.cz

them

ail.cz

seznam

.cz

zlateslevy.cz

zumailfeed.cz

49 44 13 13 9 7 6 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3

rb.cz 53 1 8 6 0 7 0 3 0 0 0 0 0 0 0 0 3 0 0 0 2 0 0 0 1 2 2

zuno.cz 32 1 5 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 3 0 1 1

gemoney.cz 23 2 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 2 1 0 0 0 0

cofidis.cz 22 1 1 0 10 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

airbank.cz 21 5 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0

pujckomat.cz 18 0 3 0 0 0 1 0 0 0 1 4 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0

homecredit.cz 17 8 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

az-pujcky.cz 15 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

mbank.cz 13 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0

cetelem.cz 13 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

prontopujcka.cz 13 0 0 0 0 0 1 0 0 0 0 0 1 0 0 4 0 0 0 0 0 0 0 0 0 0 0

finpujcka24.cz 10 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0

pujcky.name 10 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

proficredit.cz 10 1 3 0 2 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

bbpujcka.cz 9 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

crediton.cz 9 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0

equabank.cz 8 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

aaapujcky.eu 7 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

pujckanaop.cz 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

csob.cz 5 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0

japonskapujcka.cz 5 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ferratum.cz 4 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

akutnipujcka.cz 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

pujcka.biz 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

onpujcka.cz 3 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

prijemna-pujcka.cz 3 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

uzasnapujcka.cz 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

multipujcka.pujcky.cz 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

pujcky1uvery.cz 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

bez-registru-pujcky.cz 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


10

8

6

4

2

0

Referrers to ‘loan domains’ relations

Where they go

Where they

search

The table to the left presents main

relations* between:

• referring web sites (by columns)

• referred loan domains (by rows)

* For better visualization, only a fraction of the whole table is depicted

which includes solely referred domains and referrers with more than 2

visits (resp. references).

The greener is the cell connecting a row

with a column, the stronger is the relation

between the two.

For instance, see the fourth cell on the

fourth row. It implies that 10 out of the total

22 visitors of cofidis.cz were referred to it by

ermail.cz which is the strongest relation

supported by data.

36 35

9

20

50

18

7

25

cpc email affiliate jiné

loan seekers

control group

Loan seekers, n=59; Control group, n=51 [in %]

Ad campaigns


23

21

11

8

8

7

7

16

15

10

19

10

3

8

24

10

google/adwords

zumail

direct_mail

usetreno.cz

espoluprace

ermail

seznam/sklik

jiné

AD medium AD source

2 main AD/marketing channels: emailing (transactional + direct email),

cpc ads (adwords and sklik)

References to ‘loan’ domains by transactional mail

11



Transactional email appears

to be very efficient web tool for

generation of visits not only to non-

bank but also to bank loan domains,

e.g. zuno.cz and rb.cz

PPC ads in search engines leading to ‘loan’ domains

12



PPC ads in

search engines

This way of promotion

is used by two bank domains

(Raiffeisen and AirBank)

Search result references to ‘loan’ domains

13



Non-ad search engine links

drove loan-seekers to visit

11 separate loan domains, only two of

which were bank domains

(i.e. Era and Equa bank)

non-ad search

engine links

Loan pages visited – TOP15 The exclusion of email-induced visits leads to

decrease in differential between the top 5 banks and

the rest. Furthermore, Zuno and ČSOB lose their top

ranks in favor of Home Credit, mBank, Airbank and

Česká Spořitelna.

Since only few respondents have opened a url

containing the key word “loan” on a mobile device,

below the “loan” domains they visited are simply

listed:

Loan seekers on a mobile device

csob.cz, imedia.cz, equabank.cz, pujcovnavranov.cz,

japonskapujcka.cz, ferpujckabezdolozeniprijmu.cz,

pekelnedobrapujcka.cz, google.cz, hyperpartner.cz,

slevoking.cz, velkemoznostiosobnipujcky.com,

vyplacenipujcek.cz, budejckadrbna.cz, cinska-

pujcka.cz, cspujcky.cz, gemoney.cz, i-form.cz,

mikropujcka.top, onlinepujckaihned.cz,

pujcka4500.cz, rapidpujcka.cz

Control group on a mobile device:

zuno.cz, pujckacs.cz, novinky.cz, seznam.cz,

aaaauto.cz, facebook.com, google.cz,

zlatakoruna.info Loan Seeking on the Web

14 14

39

31

31

24

19

17

15

15

14

14

14

14

12

10

10

9

13

0

0

2

9

0

2

11

0

4

4

2

loan seekers

control group

Loan seekers, n=59, Control group, n= 51 [in %]

email-induced visits

included 24

17

17

15

15

15

14

14

14

14

10

10

10

10

8

4

0

2

4

0

2

0

4

0

4

0

0

2


excluded

Desktop

Average number of visits on “loan” pages


It should not come as a surprise that a

loan seeker has visited more than six

times more pages on average than a

panelist from the control group.

The average number of non-bank loan

pages visited per panelist exceeds

almost twice the number of bank-loan

ones.

In absolute value, however, the negative

effect of the exclusion of the email-

induced visits from the average number

of visits per loan page is more

significant for bank loans than for non-

bank loans.

3,58

8,13

0,44 0,81

Bank loans Non-bank loans

Loan seekers Control group

Loan seekers, n=59, Control group, n= 51 [údaje v %]

2,37

6,21

0,29 0,53

Bank loans Non-bank loans


included


excluded

Ad driven visits to bank and non-bank ‘loan’ pages

Obviously, the domains (zuno.cz

and rb.cz) generating visits via

transactional emailing mainly lose

their top positions among the

control group.

The rank and share of Airbank

remain unaffected.

These discrepancies across

samples imply that transactional

emailing did not have the same

skewing effect on the results of

the control group as it had with

the loan seekers.


Loan-seekers, n=59 [in %]

23

13

10 8

5 4

2 2 2 2 2 2

cofi

dis

.cz

rb.c

z

pro

ficr

ed

it.c

z

use

tren

o.c

z

nejlep

si-

au

top

ujc

ka.c

z

air

ban

k.c

z

hyp

erf

inan

ce.c

z

ho

mecr

ed

it.c

z

pen

ize.c

z

po

rad

nap

ujc

ka.c

z

pu

jcka7.c

z

pu

jcko

mat.

cz

Control group, n=51 [in %]

15,2

13,2 12,2

6,3 5,3 4,7

3,9 3,4 2,8 2,2 2,2 2,0 1,8 1,6 1,4 1,2 1,2 1,2 1,2 1,0 0,8 0,8 0,6 0,6 0,6 0,6 0,6 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4

zun

o.c

z

rb.c

z

cofi

dis

.cz

pro

ficr

ed

it.c

z

pu

jcka.c

sob

.cz

air

ban

k.c

z

ho

mecr

ed

it.c

z

use

tren

o.c

z

pu

jcka7.c

z

mb

an

k.c

z

pu

jckacs

.cz

zon

ky.c

z

cete

lem

.cz

pu

jckab

ezp

ap

iru

.cz

pro

nto

pu

jcka.c

z

pu

jcko

mat.

cz

pekeln

ed

ob

rap

ujc

k…

mu

ltip

ujc

ka.p

ujc

ky.c

z

ww

w.z

un

o.c

z

m.p

ujc

ka7.c

z

cred

itair

.cz

uza

snap

ujc

ka.c

z

esh

op

.sb

erb

an

kcz

.cz

firm

y.c

z

pri

jem

na-p

ujc

ka.c

z

rsts

.cz

rozh

od

nu

to.c

z

cso

b.c

z

eq

uab

an

k.c

z

gem

on

ey.c

z

itesc

o.c

z

jap

on

skap

ujc

ka.c

z

bo

nu

s-p

ujc

ka.c

z

csp

ujc

ky.c

z

pekeln

e-d

ob

ra-…

s.im

ed

ia.c

z

uvero

db

uri

nky.c

z

uza

sna-p

ujc

ka.c

z

ww

w.v

elk

em

ozn

ost

i…

zvyh

od

nen

ap

ujc

ka.c

z

Uninduced versus ad-driven (AD) domain visitors

There are two main ways in

which ‘loan’ pages are visited:

• through clickable

advertisements (ad-driven)

• via search engines and

direct visits (uninduced)

ČSOB and Moneta (formerly

GE Money Bank) have the

largest number of unique

uninduced visitors, followed

by Airbank and Equabank.

On the other hand,

Raiffeisenbank and Zuno have

the largest share of visitors

whose visit was triggered by

an ad.


0 5 10 15 20 25

usetreno.cz

pujckacs.cz

investujeme.cz

banky.cz

m.gemoney.cz

online.gemoney.cz

csas.cz

muj.erasvet.cz

erasvet.cz

google.fi

finance.cz

zuno.cz

pujcky.cz

kb.cz

usetreno.cz

postovnisporitelna.cz

csob.cz

unicreditshop.cz

mbank.cz

rb.cz

eshop.sberbankcz.cz

cetelem.cz

equabank.cz

airbank.cz

epujcky.rb.cz

gemoney.cz

pujcka.csob.cz

Number of visitors with AD

Number of visitors without AD

Word of caution

Data collected via Wacoopa S/Marlowe enables identification of the sequential order in which panelists

were surfing from one page to another.

Passive web metering allows also for parallel control of the completeness and precision in data

collected from identical sample of respondents via online surveys. For instance, for a respondent who

reported visiting only non-banking loans while filling the parallel online survey, passive data collected

via Wacoopa S/Marlowe showed counterevidence for numerous visits of hers to various banking loan

pages.

On the other hand, the results from the web visit tracking should be interpreted with caution as well

because recorded sequence of pages does not need to imply causality between the sequential pages,

per se. To claim the latter with confidence, an additional analysis should be carried out based on ad hoc

intuition or ex post survey data.

In summary, combining survey data with passive metering data brings certainly higher informativeness

and clarity of conclusions than any partial analysis based on whichever of the two types of data only.


STEM/MARK is a leading Czech research agency, member of the World

association for market, social and opinion research (ESOMAR).

Founded in 1994, we have more than 20 years experience of implementing

full-service market and social research projects in various sectors like finance,

travel, media, pharmaceutics, public administration, FMCG etc.

We collect the data ourselves to ensure high quality data and total control of the collection process.

20

STEM/MARK and telemetry

We have more than 5 years experience of collecting and analyzing behavioral data.

Thanks to our innovative web telemetry technologies:

• custom-built web browser for PC and mobile devices

• wakoopa-based passive metering solution S/Marlowe

we are able to:

• collect web behavioral data from our own verified panel of respondents on the territory of the Czech Republic and Slovakia or globally via Wakoopa hub

• analyze behavioral data and provide main results and findings of the analysis, graphically visualized in a final report presented personally or online on the Internet

For more details on the services provided by STEM/MARK visit our web page.

The only way to do great work is to love what you do…

Steve Jobs

http://www.stemmark.cz/about-us/

KONTAKTNÍ OSOBA

@stemmark

slideshare.net/stemmark

www.stemmark.cz

STEM/MARK, a.s.

Chlumčanského 497/5

180 00 Praha 8

Jan Lajka

Deputy Director

+420 606 816 940

[email protected]

Jiří Axman

Client Service Manager

+420 225 986 882

[email protected]

Contact

http://www.stemmark.cz/



Marketing

Loan Seeking on the Web - case study by STEM/MARK