Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale

 
Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
Prof. Francesco Sacco

Università dell’Insubria e SDA Bocconi

Big Data:
che si può fare?

2018 05 07
Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
Big data…
Un minuto di Internet nel 2018

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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
Cosa si può fare…
Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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Big Data: che si può fare? - 2018 05 07 Prof. Francesco Sacco - Impara Digitale
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4   Entrare sul mercato californiano?

          www.voicesfromtheblogs.com | we look into the data, not at the data
5
    Il Giappone e l’olio di oliva

         www.voicesfromtheblogs.com | we look into the data, not at the data
6     Travel Volumes, Fares
        and the Economy
                                                                                                                      BUSINESS TRAVEL
                                                                                                                      SURVEY 2017

                                                                                                                      MARZO 2017 - RELEASE DIGITALE
                       2015: …

                         …                                               …                                           …

                                                                                                                                         UVET AMERICAN EXPRESS

                       I DATI PIÙ SIGNIFICATIVI DEL PRIMO SEMESTRE 2014

                         UVET TRAVEL INDEX            TREND DI SPESA                 BIGLIETTERIA AEREA             HOTELLERIE                RAIL
                         …                            …                              …                              …                         ….

                       ANDAMENTO DEL NUMERO DI TRASFERTE E DELLE SPESE DI VIAGGIO
                       Numero indice base 1° Semestre 2013

                          120                                                                       115

                                                             100
                                                                                                                             99                      108
                          100                                      100                              105
                                                                               88

                                                                                                                             93
                           80                                                   87
                                                        1° Sem. 2014         2° Sem. 2014       1° Sem. 2015              2° Sem. 2015          1° Sem. 2016

                                                                                                               BUSINESS TRAVEL SURVEY di Uvet American Express

                       SOMMARIO

                          BUSINESS TRAVEL SURVEY 2017                                                                    MARZO 2017- RELEASE DIGITALE

    Using Big Data to inform the industry of Business Travel
Travel from Italy only: does Economic
6       growth drive Business Travel?
                      Destination            Lag (number of quarters)
                                                                                          Spear

                                                                                         man

                                                                                         Correlat

                                                                                         ion
                   United States                             3                                   0.93**
                   Japan                                     1                                   0.92**
                          Poland                             2                                   0.92**
                    Chile                                    1                                   0.85**
                     Netherlands                             1                                   0.72**
                    Israel                                   3                                   0.71**
                                 Correlation between GDP and volume of flights
                        Portugal                             2
                       **: statistical significance at 95%; *: statistical significance at 90%
                                                                                                 0.71**
                       Germany                               3                                   0.67**
                        Denmark                              1                                   0.63**
    Data Source:
6   The prediction for 2016 Q4: USA

          ● Quarterly real volume
          ● Prediction for 2016 Q4 with macroeconomic variables (GDP, etc)
          ● Prediction for 2016 Q4 without macroeconomic variables

         Base 2014 Q1 = 100, data Source:
The prediction for 2017 Q1 & Q2 (as of
6              December 2016)

      ●Quarterly real volume
      ●Prediction for 2017 Q1 & Q2 with macroeconomic variables

       Base 2014 Q1 = 100, data Source:
Patterns in Air Fares? An example:
6
                                             Milan to New York
                                      baseline = Q1
                                                                            54%

    Average % Variation
                                          30%
                                                            15%                                               17%
                                                                                              8%                         9%
                                                           peak season    peak season

                                                  Q2                                    Q3                          Q4

                                                                     Economy                       Business
                                       baseline = Q1
               Average % Variation

                                                                           peak season
                                                                             39%
                                           24%
                                                                                                              17%
                                                              11%                             9%                         7%
                                                            peak season    peak season

                                                      Q2                                 Q3                         Q4
Patterns in Air Fares in Asia? An example:
6                              Milan to Shanghai

                          baseline = Q1
                                                       8%
                                                                                                5%
    Average % Variation

                                           3%
                               0%                   peak season                                peak season

                                                                                    -9%
                                                                       -11%
                                      Q2                          Q3                      Q4

                                                Economy                  Business

                                        Lowest fare = Q4, no longer Q1
                                     Business class behaviour also changes
6
6
6   Mix all information
                                   Air Fare Pattern
    GDP - Flight Prediction

                              +
            Competitors Analysis
                                       Pre buy seats

    +                              =    & Resell for
                                       better service
                                         (or profit)
7   Nowcasting the economic cycle
7                   Nowcasting the economic cycle
                                                 Lavoro
                                                                   De
                                                                        sk
                                                                             t op                               Correlation
                                y
                           ac
                    P   riv

                                                                                            WN
      mia

                                                                                             I
    Ec ono

                                                                                                o
                                                                                             lo g ic
                                                                                            Ec o
         Wi
             r ed

                                                                                                       Data Source
                                                                                       re
                                                                                                       ◉   WNI (Voices)
                                    I br                                       So
                                                                                  la
                                                                                                       ◉   Wired =“web visits” (wired.it)
                                           ide
                                                                                                       ◉   ibride (Unrae)
                                                          Futuro                                       ◉   other = GoogleTrends
8          Make profit out of nowcasting
    Example: looking at trend switching of

        WNI, speculate on the market.
Make profits on machine learning

                                       Algo Finance Sagl
9
11
Cos’altro si può fare…
Wireframe of future VOICES ANALYTICS® (Prototype version, may change)
VOICES ANALYTICS® PRODUCTS

It reports the s e ntiment
and describes the m ain
    opinions around a
  brand/product/topic.

  It also provides the
reasons for expressing a
  positive or negative
       sentiment.
VOICES ANALYTICS® PRODUCTS

       It performs a
  comparison across the
    brands active in a
     peculiar market,
   showing the net rate
 between promoters and
        detractors.
 This give s an ide a about
  of the online “word of
   mouth” around each
           brand.
VOICES ANALYTICS® PRODUCTS

It investigates the words
     and the concepts
      associated to a
  product/market/topic
  and allows to identify
 unsatisfied n e e d s of the
consumers or n e w trends
   in consumption and
    opinions combining
   sentiment with topic
     discovery models.
VOICES ANALYTICS® PRODUCTS

 It applies social network
  analysis to identify the
influencers and the “hub”
 active on conversations
   around a brand/topic.

This allows to s e lect users
 that mus t b e engaged for
  marketing activities and
to spread communication.
VOICES ANALYTICS® PRODUCTS

This promptly builds indexes
  of mood/views/attitudes
showing the variation across
       tim e and space.

 It can b e used for content
 generation purposes or as
  fire-alarm. The index can
 integrate sen timent with
    other sources of data.
VOICES ANALYTICS® PRODUCTS

It combines sentiment analysis
with other sou rces of data (e.g.
 google query, websites visits,
  daily revenues or other own
             data).

 It performs time series analysis
 to identify breakpoints, trends,
 predict out c o me s and evaluate
which time series can anticipate
an increase/decrease in the level
 of another o n e in order to take
               action.
VOICES ANALYTICS® PRODUCTS

      It m o nitors the
     effective n e s s of
advertising/communication
 campaigns by combining
 reputation analysis with
   predictive analytics.

  This allows to take action
 adjusting the frame of the
  campaign and evaluating
its effects going beyond the
      number of “like s ”.
ANALISI GRANDI EVENTI
ANALISI GRANDI EVENTI
ANALISI GRANDI EVENTI
Cos’altro si può fare con altre tecnologie…
Linked data…
Linked data…
Intelligenza artificiale…

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Intelligenza artificiale…

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Grazie per l’attenzione!

      francesco.sacco@unibocconi.it
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