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 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
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…
Intelligenza artificiale… 44
Intelligenza artificiale… 45
Grazie per l’attenzione! firstname.lastname@example.org
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