Banking and social media

Banking and Web-talk:
how online conversations boost the retail banking
industry, evidence from Webank

Filippo Corbelli | Bocconi University| 12.17.2011 Share

Credits
✤  Prof. Stefano Caselli
Full professor at the Department of Finance at Bocconi University, Milan

✤  Mrs. Vittoria La Porta
PR and Brand Reputation Manager at Webank SpA

✤  Mr. Pietro Di Giacomo
Engagement Manager at Nuvó Consulting Srl


First of all …

Financial crisis Social Media revolution Banking & Social Media


As a matter of fact,
How does online word-of-mouth What factors drive banking
influence your banking decisions? decisions?

Past 2 years Past 3-5 years
Age
5%

65+ 26%

Others
31%

Age
7%

45-64 14%

Online WOM
7%

Age
12%

28%

30-44 Face-to-face
27%

Age
26%

32%

18-29 Past experience
35%


Why this research?
To understand whether social media are relevant to the retail banking industry.

In other words, to investigate...

...to which extent online word-of-mouth
impacts the users’ decision to open an online
bank account?


Let’s hypothesise that:
Online word-of-mouth and online accounts are
correlated

WOM volume has a positive impact on online accounts

WOM valence influences online accounts

WOM variance influences online accounts

WOM venue-specificity influences online accounts

The amount of online accounts opened in prior years is
informative of future conversations


The ingredients are:
✤  DAY From Jan 2011 to Jun 2011

Natural logarithm of the number of AOL
✤  LNaol
opened in day t

Number of conversations about Webank
✤  CONV
in day t

Volatility in the number of AOL opened
✤  VOLaol
between t +1 and t

Dummy variable to identify the days t at
✤  POST
higher posting frequency

Dummy variable to identify the days t at
✤  SUBJ
higher brand sentiment

Dummy variable to identify the venue-
✤  VENUE
specificity per day t


3D Analysis:
1. LINEAR REGRESSION
Forward stepwise approach

2. GRANGER’S CAUSALITY
Vector Autoregressive (VAR) model

3. PREDICTION-FIT

Static and dynamic forecasting


Linear Regression

R-squared Breusch-Pagan Engle’s Lagrange Durbin Alternative Breusch-Godfrey
41.63% 0.3109 0.06 0.9617 0.9608

partial NO NO NO 1-order NO higher-order
correlation heteroscedasticity ARCH effects serial correlation serial correlation

Stationary variables
IS IT
NO!
SPURIOUS?!
Augmented Dickey-Fuller test


How to interpret the results:
CONV: +0.8% in AOL (same day)
WOM affects AOL =

POST: +21.39% in AOL (6 days after)

SUBJECTIVITY and SUBJ: +12.08% in AOL (3 days after)
VENUE affect AOL =
VENUE: -19.26% in AOL (3 days after)

270 more AOL today predict on
average 1 more AOL tomorrow
PAST AOL
affect FUTURE AOL =
the larger the fluctuations in AOL,
the higher the impact for Webank


Granger’s causality
M1: 1 lag M2: Lütkepohl (2 lags)

UNIDIRECTIONAL BIDIRECTIONAL

Users talk about Webank as they
WOM can predict future revenue have opened an online account


Does the model predictably fit?

Linear Regression Vector Autoregressive Model

Static Forecasting Dynamic Forecasting


Static
✤ RMSE: 0.3523

✤ “White” Residuals


Dynamic
Scenario A Scenario B

forecast June 2011 forecast July 2011


So what?
So What?

“I open a bank …to satisfy a self-
…and I talk about Webank
account as I have presentational need
as I have opened a Webank
heard about Webank and gain social
account…!
online…! currency!”!

Monitoring conversations to predict future AOL;

Subjective or non-informative comments should be favoured; and

Services and content should be easy to be shared.


Q&A

Thanks.
“The best way to predict the future is to create it”
P. Drucker

@filippocorbelli

Filippo Corbelli


Banking and social media

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