Online complaints posted by your customers have a silent yet powerful influence on your brand.
One, they negatively influence a prospective customer’s judgement when he is researching the web for a brand.
According to one Google survey, 2 out of every 3 customer in India searched the web before buying a financial product like a loan or an insurance cover. Further, 66 per cent of Indians shared their good experience with a brand with others all the time, according to the American Express 2014 Customer Service Barometer study. Nielsen 2011 study reveals over 70 per cent of web users trust online comments. Read on to get the full report on how online customer comments could alter your brand image.
1. F i r s t C a m p u s D i g i t a l , P l o t n o 7 0 , M o o n r a m K a t t a l a i C o l o n y ,
K u n d r a t h u r , C h e n n a i 6 0 0 1 2 8
2015
How online customer complaints destroy
your brand more than offline yelling
Danny Goodman & team
First Campus Digital
2. First Campus Digital Page 1
Introduction: Onlinecomplaints posted by your customers havea silent yet
powerfulinfluence on your brand.
One, they negatively influence a prospectivecustomer’s judgementwhen he is
researching on the web for a brand.
According to one Google survey, 2 out of every 3 customer in India searched
the web before buying a financial product like a loan or an insurancecover.
Further, 66 per cent of Indians shared their good experience with a brand with
others all the time, according to the American Express 2014 Customer Service
Barometer study. Nielsen 2011 study reveals over 70 per cent of web users
trustonline comments.
Two, adversecustomer comments on the web could seriously alter the image
of your brand. Whattook years and a fortuneto build via traditional media like
TV/print could be irreversibly altered by harsh customer feedback posted on
the web.
The following presentation analysis both positive & negative words used by
real life consumers against select housing loan providers. How adverseonline
comments subtly alter the brand image of mortgage lenders, compelling loan
seekers to sign up with a competitor.
3. First Campus Digital Page 2
Semantic analysis of real home
loan customers’ comments on
complaint registries like
www.mouthshut.com
4. First Campus Digital Page 3
Methodology:
- Words that were used by current housing loan customers at
least 50 times for FY 2012 & 13 were chosen
- They were selected from complaint registries like
www.mouthshut.com
- Loan disbursement figures for FY 2011 – 2012 and FY 2012 –
2013 were used in the calculation of ratios
- Complaints and compliments on social media platforms like
Facebook, Twitter, Linked in and Google circles were not
included. Since most housing loan companies actively address
complaints on social media
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Tracking errors
- There could be doublecounting of different words that
convey the same meaning in a paragraph.Eg: goons and
harassment, cheaters and liars
- This semantic exercise pertainsto feedback from retail
customers only
- Each bank or housing financingcompany calculates&
publishes its loan book differently:
For instance, IndiaBullsloansincludedisbursements for
commercial vehicles, buildersetc. We have avoided
IndiaBulls.It is unlikelythat builderswould go onlineto
register their complaint
- HDFC loandisbursement figures includemortgages
that are sold to other banks in the previous12 months.
Thus, some of the financialfigures used for calculating
ratios are not truly representative of retail banking
- PNBHFL’s loandisbursement figures for FY 2012-2013
is Rs 3682 crores. No standalonefigures could be found
for the earlier year. Thus, we have avoidedPNBHFL
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Calculating the ratios. Example:
Satisfaction index calculation for SBI:
All figures in crores
Total retail loan disbursals for FY 2011-2012 and FY 2012-2013 = Rs
1,02,789 + Rs 1,19,467 = Rs 2,22,206
Total number of positive words earned for the 2 respective FYs = 805
For Rs 100 crores = 0.36
Dissatisfaction index calculation for SBI:
Total retail loan disbursal Rs 2,22,206
Total number of negative words = 905
For Rs 100 crores = 0.40
Likewise ratios are calculated for other housing loan providers and
plotted on the graph.
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Web dissatisfaction index
- Despite being the largest home loan lender, SBI has attracted the lowest number of criticism
from its customers. While LICHFL, being many times smaller than SBI has drawn in the
highest number of negative words online
- The reasons for SBI’s enviable online reputation could be many. One reason: being the last
to hike interest rates for floating rate customers. Resetting interest rates ( which means
increased EMI for home loan customers) is one major reason for online customer outburst.
Not many customers of SBI have complained about arbitrary hiking of interest rates.
- LICHFL’s unexplained hiking of interest rates on home loans, poor customer service at the
front end (yelling at customers) & an almost nil web sales support could be the key reasons
for earning the maximum number of online brickbats
0.4
0.53
0.6
0.9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SBI ICICI Bank HDFC LICHFL
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- With its largest home loan portfolio and a proportionately larger number of
complementary words, SBI has been ranked the lowest in customer satisfaction. Here’s one
reason: SBI attracted more than 800 positive words online for a loan portfolio of
approximately Rs 222,206 crores.
While LICHFL, with a loan portfolio of Rs 43,475 crores for the 2 financial years, earned
online praise words of about 600. When the 2 housing loan providers’ numbers are indexed
to Rs 100 crores, LICHFL has taken the top slot and SBI to the bottom
- Another probable reason. As indicated earlier, PSU banks like SBI are the last to hike
interest rates following RBI rate hikes. Then, SBI is the pioneer of teaser home loans where
interest rate is fixed for the first 3 years. An obvious draw for home loan hunters. These
moves by the nation’s lender could be reasons for its customer complacency, in not getting
on the web and posting their positive experiences about the lender. Effectively relegating
SBI to the lowest in customer satisfaction. Indian customers, at times, could be stingy in
praising a service provider on the web
Overall, LICHFL customers, some who have taken home loans against their life insurance
policies, are a harried lot
- Surprisingly, ICICI bank and HDFC despite having a robust web support team have not
inched up in the rankings
0.36
0.59 0.59
1.43
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
SBI HDFC ICICI Bank LICHFL
Web satisfaction Index
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The goodwill values are obtained by dividing the dissatisfaction valueby the
satisfaction value.
When values are lower than 1they mean the goodwill or satisfaction with the
brand is higher. When the values are more than 1they mean dissatisfaction
with the brand is higher.
0.13
0.63
0.89
0.99 1.01
1.12
0
0.2
0.4
0.6
0.8
1
1.2
PNBHFL LICHFL ICICI Bank IndiaBulls HDFC SBI
Web goodwill graph
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Assigning real personalities for HFCs/banks
SBI Sunil Gavaskar
HDFC MS Dhoni
ICICI Bank Sachin Tendulkar
PNBHFL RK Laxman
LICHFL Sreesanth
IndiaBulls Bookie
At First Campus Digital we conducted an informal survey of assigning
Indian cricketing personalities to the above listed housing loan
providers. Cricketing personalities from the vintage to the modern
era were suggested. The table above bears the results.
12. First Campus Digital Page 11
When a similar exercise of ascribing cricketing personalities to
housing loan providers (HLPs) were conducted - the 2nd
time using
the positive & negative words found online, the result was shocking.
Online words describing the services of housing loan providers
drastically altered the online image. The online image of a housing
loan provider depended on the search results thrown up by Google,
and the swift online response from the company.
Online brand image matters. Google’s survey indicates 2 out of every
3 Indian internet user conducted research on the web before
choosing a financial services provider.
0.13 (Gavaskar)
0.63(Sachin)
0.89 (Laxman)
0.99 (Dhoni) 1.01 Sreesanth
1.12 (Bookie)
0
0.2
0.4
0.6
0.8
1
1.2
PNBHFL LICHFL ICICI Bank IndiaBulls HDFC SBI
How HFCs/banks are perceived on the web -
string word (semantic) analysis
13. First Campus Digital Page 12
.
Conclusion: We have reached the end of the presentation. We would
love to hear from you. Feel free to write to us at email:
firstcampusdigital@gmail.com. Or call: 00 91 9445583361 or
9791328647Thanks.
