BIG DATA Opinion Analisis SABM LATAM 201404

[Big Data] Simple Exercise of Consumer
Preferences Analysis Based on Twits
for SAB Miller LATAM Brands By Gustavo Pabón – May 2014

Our group mission:
To own and nurture local and
international brands that are the
first choice of the consumer

Our group mission:
How to measure the
consumer preferences?

Our group mission:
How to measure the
consumer preferences? Twitter may help

Here it is presented the result of a simple
exercise of consumer preferences analysis
based on twits from 2nd of April of 2014 to
26th of April of 2014.

Here it is presented the result of a simple
exercise of consumer preferences analysis
based on twits from 2nd of April of 2014 to
26th of April of 2014.
On a scale from 1 to 5*, the weighted average of SAB
Miller LATAM consumer preference was:
4.76
* The scale will be explained in next slide

© SABMiller plc 2012
Internal Use / Confidential / Secret
Exercise Summary
Twits sample streaming
range of dates and filter
Twits were streamed from 04/02/2014 to 04/26/2014 with a GAP from 04/12/2014 to
04/15/2014. The GAP was due to a technical issue on the streaming program. The
filter used was based on keywords presented on the next slide.
Twits sample size
The raw data size was 33.853 twits.
First step of data selection was filtering twits not related to SAB Miller LATAM / Global
Brands using bag of words technique. The result was a reduction to 20.044 twits.
Second step of data selection was filtering twits not related to consumer preferences
using crowd sourcing (Amazon Mechanical Turk). The result was a reduction to 3669
twits.
Consumer preference
scale from 1 to 5
Using crowd sourcing, each twit were classified by 10 different people in three
categories: 1 negative preference, 5 positive preference, 3 neutral. If more than 6
people agree on the preference, the twit was classified on such preference, if not, the
twit was classified as neutral.
Why crowd sourcing?
It is very difficult for an automatic sentiment analysis program to work with twits. They
are usually not well written, have a lot of slangs and sarcasms. In addition Spanish
internet language is not as studied as English. Human raters typically agree 79% of the
time*, while a program is at most 70% accurate. The first run of an automatic
sentiment analysis were able to classify just 151 twits.
8Presentation information in footer
* Taken from Ogneva, M. "How Companies Can Use Sentiment Analysis to Improve Their Business". Retrieved 2012-12-13.

Keywords used for streaming filter (1 of 2)
 Global brands
@Grolsch, #Grolsch, @Miller_Global, @MillerCoors, #MillerGenuineDraft, @Birra_Peroni, @peroniclub,
#PeroniNastroAzzurro, #peroni, @Pilsner_Urquell, #PilsnerUrquell, @MillerLite, #millerlite, @MGD_Argentina,
@MillerLiteAR, @MillerLiteCol, @millerlitehn, @MillerPanama, @MillerLitepa, @Miller_SLV.
 Argentina’s brands
@CervezaIsenbeck, #isenbeck, @Warsteiner, @WarsteinerAR, #Warsteiner.
 Colombia’s brands
@CervezaAguila, #AguilaLight, #aguila, #CervezaAguila, @clubcolombia, #ClubColombia, #clubcolombiadorada,
#clubcolombiaroja, #clubcolombianegra, @cervezacostena, #CervezaCosteña, #CervezaCostena, @PilsenCerveza,
#Pilsen, @CervezaPoker, #CervezaPoker, @pokerligera, #pokerligera, #colaypola, @ReddsColombia, #redds.
 Ecuador’s brands
@ClubPremiumEc, #ClubPremium, #ClubPremiumRoja, #ClubPremiumNegra, @cervezaconquer, @PilsenerEcuador,
@Miller_Ecuador

Keywords used for streaming filter (2 of 2)
 Salvador’s brands
@BarenaHN, #Barena, @Barena_Peru, @PilsenerSV, @PilsenerLiteSV, #PilsenerLite, #Pilsener, @RegiaSV,
#regiaextra, @SupremaSV, #cervezasuprema, @GoldenSV, #cervezagolden
 Honduras’ brands
@ImperialHN, #CervezaImperial, #imperialhn, @PortRoyalhn_com, @SalvaVidaHn, #salvavida, #salvavidahn,
#cervezasalvavida, @BarenaHN, #Barena
 Panamá’s brands
@cervezaaltlas, #cervezaatlas, @Cerveza_BALBOA, #cervezabalboa
 Peru’s brands
@cerarequipena, #cervezaarequipena, #cervezaarequipeña, #arequipeña, #arequipena, #Barena, @Barena_Peru,
@CristalPeru, #cervezacristal, @cusquenaperu, #cusqueñaperu, #cusquenaperu, #cervezacusqueña,
#cervezacusquena, #cusqueñamalta, @Pilsen_Callao, #PilsenCallao, @Pilsen_Trujillo, #PilsenTrujillo,
#CervezaSanJuan, @Backus_Ice, #BackusIce

Consumer preference rating consolidated by country

Number of twits

Average consumer
preference rate

Using SSD (sum of squared distance) from “Number of twits”
and “rate”, Salvador did have the highest rate: 4,83.

Using SSD, Argentina did
have the lowest rate: 4,32

Consumer preference rating consolidated by brand

Using SSD, Pilsener
Salvador did have the
highest rate: 4,85

Using SSD, Barena did
have the lowest rate: 3,94

Consumer preference rating consolidated by date and day of week

Consumer preference rating consolidated by date
GAP due to a technical issue
on the streamer program

Consumer preference rating consolidated by day of week

From Wednesday to Friday the
number of twits increases as
well as the rate.

From Friday to Sunday the
number of twits decreases as
well as the rate.

Consumer preference rating consolidated by hour

Consumer preference rating consolidated by hour
From 10 am to 1 am, most
of twits were posted. But,
in average, rates do not
change much.

Word picture of common words of positive preference twits.

Word picture of common words of negative preference twits.

Conclusion
I could conclude from this simple exercise that
sentiment and opinion analyses on twits related
to SAB Miller LATAM brands can be an
alternative tool to effectively measure
customer preferences.

BIG DATA Opinion Analisis SABM LATAM 201404

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