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Real-time Market Basket Analysis for Retail with Hadoop

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Real-Time Market Basket Analysis for Retail with Hadoop Simone Ferruzzi and Marco Mantovani Iconsulting Spa
@IconsultingBI Real-Time Market Basket Analysis for Retail with Hadoop
@IconsultingBI ICONSULTING ICONSULTING IS AN INDEPENDENT CONSULTING COMPANY SPECIALIZED IN DWH,BI & PM Strong expertise on all the market leading technologies INNOVATIVE SPECIALIZED DEVELOPING SKILLS VENDOR INDEPENDENT 2 3 41 WHO WE ARE More than 300 projects; more than 100 customers Professorship in main Italian Universities and Business Schools In-house Academy providing education services to professionals who need to develop their skills Spin-off of a major Research University Consortium 25% of our time invested in R&D Certified Partner of the main Business Intelligence software vendors # Data Warehouse # Business Intelligence # Performance Management
@IconsultingBI PROCEDURES & OPERATING INSTRUCTIONS ACCORDING TO ISO 9001:2008 STEP BY STEP APPROACH PROJECT REQUIREMENT & RESTRAINTS SERVICE QUALITY TIME & COSTS EXECUTION MEETING DEADLINES PROBLEMS & RISKS MANAGEMENT COMMUNICATION AMONG STAKEHOLDERS AGILE DESIGN THINKING METHODOLOGY ICONSULTING Methodology
@IconsultingBI Our CUSTOMERS MANUFACTURING ALFA WASSERMANN AMPLIFON ARISTON THERMO CAMAR SMA CANTIERI SANLORENZO CASE NEW HOLLAND FEDRIGONI G.D CISA (Ingersoll-Rand) DUCATI MOTOR HOLDING ESSECO FIAMM FONTANOT GRUPPO COESIA GRUPPO FABBRI ICF - LA FAENZA IGUZZINI I.M.A. INDUSTRIA MACCHINE AUTOMATICHE INTERTABA - PHILIP MORRIS KME KOMATSU LOWARA MAGNETI MARELLI MALAVOLTA CORPORATE MAPEI MARAZZI MARPOSS NEGRI BOSSI OVA BARGELLINI OTIS PHILIP MORRIS ITALIA PIRELLI POZZI GINORI ROSETTI MARINO SACMI SECI SONY EUROPA TEUCO GUZZINI UNO A ERRE VINAVIL MEDIA & PUBLISHING PANINI GROUP SKY ITALIA VODAFONE ZANICHELLI EDITORE GOVERNMENT & PUBLIC SECTOR MINISTERO DELL’INTERNO MINISTERO DEL LAVORO E DELLE POLITICHE SOCIALI REGIONE EMILIA ROMAGNA REGIONE CALABRIA REGIONE VENETO AGREA ARPA ARPAT CESIA COMUNE DI BOLOGNA COMUNE DI REGGIO EMILIA ERVET INVITALIA I.S.P.R.A. AMBIENTE ISTITUTO NAZIONALE FISICA NUCLEARE LEPIDA PROV. AUTONOMA DI BOLZANO PROV. AUTONOMA DI TRENTO PROVINCIA DI RIMINI UNIVERSITA’ DI BOLOGNA SERVICES DAY RISTOSERVICE GRUPPO SOCIETA’ GAS RIMINI MOBY RINA SIENAMBIENTE SOFIS FASHION CALZEDONIA DIESEL GEOX GUCCI IMAX LOTTO MILAR FINANCIAL SERVICES CREDIT SUISSE DEXIA CREDIOP FGA CAPITAL (GRUPPO FIAT) UNIPOL BANCA FOOD BIRRA PERONI ERIDANIA SADAM GRANDI SALUMIFICI ITALIANI MASSIMO ZANETTI BEVERAGE GROUP MONTENEGRO SALUMIFICIO FRATELLI BERETTA SEGAFREDO LARGE SCALE RETAIL CONAD ADRIATICO LA RINASCENTE SMA (SIMPLY MARKET) VIP CATERING
@IconsultingBI Business Intelligence Turning data into Information Historicize and Organize Information Facilitating access to information Evolution Trends (Big Data) + end users + informations + performance Connect analysis to Action Analyze data in Real Time Self-service BI Advanced visualization (mapping, etc.) New data type (unstructured data / text) Information Discovery on Big Data New channels of access (Mobile) Collaboration & Social
@IconsultingBI Market Basket Analysis for Retail Client:Major Italian fashion company (3000+ points of sales worldwide) Need:Market Basket Analysis on sold items. • Input: single invoice lines. • Output: Associative Rules to verify marketing campaigns, seasonal shopping habits, layouts of shops, etc. Solution: • Based on Hadoop ecosystem • Fully integrated with Business Intelligence platform (Oracle Business Intelligence Enterprise Edition)
@IconsultingBI Market Basket Analysis key concepts • Market Basket Analysis (MBA) is an application of data mining algorithms aimed at identifying frequent patterns and co-occurrence relationships. • Given a set of input data, the MBA returns a set of association rules like A B The meaning of which is «If A occurs, then B is likely to occur» (in this case, «If you buy product A, you will also buy B») • Each rule is associated with two values that measure the degree of interest: – Support: the percentage of cases in which the two events A and B occur together on the total of the considered cases (e.g., the number of receipts in which A and B appear together divided by the total number of receipts); – Confidence: the percentage of cases in which the two events A and B occur together on the total of cases where A occurs (e.g., the number of receipts that contain both products A and B divided by the total number of receipts where A appears).
@IconsultingBI Example of associative rule • Easywear Underwear • Support: 9% • Confidence: 50% • In 9% of cases Easywear and Underwear products are sold together. • In 50% of cases when someone purchases an Easywear item, an Underwear item is also purchased.
@IconsultingBI Case study: MBA for Retail • Italian company leader in the Fashion industry • Sales data from the last three years • More than 100 million receipts • The results obtained can be used as an indicator for: – Defining new promotional initiatives – Identifying optimal schemes for the layout of goods in stores – etc.
@IconsultingBI Architecture Receipts Associative Rules Interactive Dashboards MBA job Job Management Console Email Number of sold items & Associative Rules
@IconsultingBI MBA Algorithm Steps Job 1 Job 2 Job 3 List of single sold items (receipt lines) Items list aggregated for receipts Support of the itemsets Map Reduce Map Reduce Map Reduce Receipt key, item value Combination of items inside the same receipt Calculation of all possible Association Rules that meet minimum Support criteria Association Rules that meet minimum Confidence criteria
@IconsultingBI Job Management Interface • Interface integrated with standard BI tool • MBA Algorithm can run on different data sets • Each user can perform custom analysis • Algorithm parameters (minimum support and confidence) can be set by end users • Examples of different analyses: – what types of products are sold together with a discounted item? – are there different association rules between products sold in city-center stores and those in outlets?
@IconsultingBI Job Management Interface Analysis Description Time filters Point of Sales filters Product filters Attributes used for association rules Support & Confidence parameters Run MBA
@IconsultingBI Results Dashboard Support Confidence
@IconsultingBI Analysis Examples • From 01/09/2013 to 31/12/2013 marketing campaign of a new type of bra • All Italian points of sales located in city centers • Analysis between all types of item except knitwear • Min. support 35%, min. confidence 50% Meaning: 36% of considered receipts contain all those items; when the new bra is purchased, 52 times out of 100 a slip and a babydoll are also purchased Same configuration as before, but considering only PoS in shopping centers Meaning: in shopping centers, the sales of easywear drive the sales of the new bra. Rules found: new bra slip, babydoll support: 36% confidence: 52% Rules found: Easywear new bra support: 50% confidence: 60%
@IconsultingBI Conclusions and future work Conclusions • Now business users can deeply investigate on the effectiveness of marketing and advertising campaigns and figure out whether shop windows and in-store layouts reach desired goals. • Market Basket Analysis algorithm can be customized on users’ needs. • Transparent interaction between Hadoop Cluster and Business Intelligence platform. Future work: from project to solution: • Complete framework to run complex Data Mining algorithms on Big Data. • Hadoop to exploit parallel execution and Distributed File System. • Seamless integration with standard Business Intelligence tools. • More user independence on data integration.
@IconsultingBI Real-Time Market Basket Analysis for Retail with Hadoop
Real-time Market Basket Analysis for Retail with Hadoop

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Real-time Market Basket Analysis for Retail with Hadoop

  • 1. Real-Time Market Basket Analysis for Retail with Hadoop Simone Ferruzzi and Marco Mantovani Iconsulting Spa
  • 3. @IconsultingBI ICONSULTING ICONSULTING IS AN INDEPENDENT CONSULTING COMPANY SPECIALIZED IN DWH,BI & PM Strong expertise on all the market leading technologies INNOVATIVE SPECIALIZED DEVELOPING SKILLS VENDOR INDEPENDENT 2 3 41 WHO WE ARE More than 300 projects; more than 100 customers Professorship in main Italian Universities and Business Schools In-house Academy providing education services to professionals who need to develop their skills Spin-off of a major Research University Consortium 25% of our time invested in R&D Certified Partner of the main Business Intelligence software vendors # Data Warehouse # Business Intelligence # Performance Management
  • 4. @IconsultingBI PROCEDURES & OPERATING INSTRUCTIONS ACCORDING TO ISO 9001:2008 STEP BY STEP APPROACH PROJECT REQUIREMENT & RESTRAINTS SERVICE QUALITY TIME & COSTS EXECUTION MEETING DEADLINES PROBLEMS & RISKS MANAGEMENT COMMUNICATION AMONG STAKEHOLDERS AGILE DESIGN THINKING METHODOLOGY ICONSULTING Methodology
  • 5. @IconsultingBI Our CUSTOMERS MANUFACTURING ALFA WASSERMANN AMPLIFON ARISTON THERMO CAMAR SMA CANTIERI SANLORENZO CASE NEW HOLLAND FEDRIGONI G.D CISA (Ingersoll-Rand) DUCATI MOTOR HOLDING ESSECO FIAMM FONTANOT GRUPPO COESIA GRUPPO FABBRI ICF - LA FAENZA IGUZZINI I.M.A. INDUSTRIA MACCHINE AUTOMATICHE INTERTABA - PHILIP MORRIS KME KOMATSU LOWARA MAGNETI MARELLI MALAVOLTA CORPORATE MAPEI MARAZZI MARPOSS NEGRI BOSSI OVA BARGELLINI OTIS PHILIP MORRIS ITALIA PIRELLI POZZI GINORI ROSETTI MARINO SACMI SECI SONY EUROPA TEUCO GUZZINI UNO A ERRE VINAVIL MEDIA & PUBLISHING PANINI GROUP SKY ITALIA VODAFONE ZANICHELLI EDITORE GOVERNMENT & PUBLIC SECTOR MINISTERO DELL’INTERNO MINISTERO DEL LAVORO E DELLE POLITICHE SOCIALI REGIONE EMILIA ROMAGNA REGIONE CALABRIA REGIONE VENETO AGREA ARPA ARPAT CESIA COMUNE DI BOLOGNA COMUNE DI REGGIO EMILIA ERVET INVITALIA I.S.P.R.A. AMBIENTE ISTITUTO NAZIONALE FISICA NUCLEARE LEPIDA PROV. AUTONOMA DI BOLZANO PROV. AUTONOMA DI TRENTO PROVINCIA DI RIMINI UNIVERSITA’ DI BOLOGNA SERVICES DAY RISTOSERVICE GRUPPO SOCIETA’ GAS RIMINI MOBY RINA SIENAMBIENTE SOFIS FASHION CALZEDONIA DIESEL GEOX GUCCI IMAX LOTTO MILAR FINANCIAL SERVICES CREDIT SUISSE DEXIA CREDIOP FGA CAPITAL (GRUPPO FIAT) UNIPOL BANCA FOOD BIRRA PERONI ERIDANIA SADAM GRANDI SALUMIFICI ITALIANI MASSIMO ZANETTI BEVERAGE GROUP MONTENEGRO SALUMIFICIO FRATELLI BERETTA SEGAFREDO LARGE SCALE RETAIL CONAD ADRIATICO LA RINASCENTE SMA (SIMPLY MARKET) VIP CATERING
  • 6. @IconsultingBI Business Intelligence Turning data into Information Historicize and Organize Information Facilitating access to information Evolution Trends (Big Data) + end users + informations + performance Connect analysis to Action Analyze data in Real Time Self-service BI Advanced visualization (mapping, etc.) New data type (unstructured data / text) Information Discovery on Big Data New channels of access (Mobile) Collaboration & Social
  • 7. @IconsultingBI Market Basket Analysis for Retail Client:Major Italian fashion company (3000+ points of sales worldwide) Need:Market Basket Analysis on sold items. • Input: single invoice lines. • Output: Associative Rules to verify marketing campaigns, seasonal shopping habits, layouts of shops, etc. Solution: • Based on Hadoop ecosystem • Fully integrated with Business Intelligence platform (Oracle Business Intelligence Enterprise Edition)
  • 8. @IconsultingBI Market Basket Analysis key concepts • Market Basket Analysis (MBA) is an application of data mining algorithms aimed at identifying frequent patterns and co-occurrence relationships. • Given a set of input data, the MBA returns a set of association rules like A B The meaning of which is «If A occurs, then B is likely to occur» (in this case, «If you buy product A, you will also buy B») • Each rule is associated with two values that measure the degree of interest: – Support: the percentage of cases in which the two events A and B occur together on the total of the considered cases (e.g., the number of receipts in which A and B appear together divided by the total number of receipts); – Confidence: the percentage of cases in which the two events A and B occur together on the total of cases where A occurs (e.g., the number of receipts that contain both products A and B divided by the total number of receipts where A appears).
  • 9. @IconsultingBI Example of associative rule • Easywear Underwear • Support: 9% • Confidence: 50% • In 9% of cases Easywear and Underwear products are sold together. • In 50% of cases when someone purchases an Easywear item, an Underwear item is also purchased.
  • 10. @IconsultingBI Case study: MBA for Retail • Italian company leader in the Fashion industry • Sales data from the last three years • More than 100 million receipts • The results obtained can be used as an indicator for: – Defining new promotional initiatives – Identifying optimal schemes for the layout of goods in stores – etc.
  • 11. @IconsultingBI Architecture Receipts Associative Rules Interactive Dashboards MBA job Job Management Console Email Number of sold items & Associative Rules
  • 12. @IconsultingBI MBA Algorithm Steps Job 1 Job 2 Job 3 List of single sold items (receipt lines) Items list aggregated for receipts Support of the itemsets Map Reduce Map Reduce Map Reduce Receipt key, item value Combination of items inside the same receipt Calculation of all possible Association Rules that meet minimum Support criteria Association Rules that meet minimum Confidence criteria
  • 13. @IconsultingBI Job Management Interface • Interface integrated with standard BI tool • MBA Algorithm can run on different data sets • Each user can perform custom analysis • Algorithm parameters (minimum support and confidence) can be set by end users • Examples of different analyses: – what types of products are sold together with a discounted item? – are there different association rules between products sold in city-center stores and those in outlets?
  • 14. @IconsultingBI Job Management Interface Analysis Description Time filters Point of Sales filters Product filters Attributes used for association rules Support & Confidence parameters Run MBA
  • 16. @IconsultingBI Analysis Examples • From 01/09/2013 to 31/12/2013 marketing campaign of a new type of bra • All Italian points of sales located in city centers • Analysis between all types of item except knitwear • Min. support 35%, min. confidence 50% Meaning: 36% of considered receipts contain all those items; when the new bra is purchased, 52 times out of 100 a slip and a babydoll are also purchased Same configuration as before, but considering only PoS in shopping centers Meaning: in shopping centers, the sales of easywear drive the sales of the new bra. Rules found: new bra slip, babydoll support: 36% confidence: 52% Rules found: Easywear new bra support: 50% confidence: 60%
  • 17. @IconsultingBI Conclusions and future work Conclusions • Now business users can deeply investigate on the effectiveness of marketing and advertising campaigns and figure out whether shop windows and in-store layouts reach desired goals. • Market Basket Analysis algorithm can be customized on users’ needs. • Transparent interaction between Hadoop Cluster and Business Intelligence platform. Future work: from project to solution: • Complete framework to run complex Data Mining algorithms on Big Data. • Hadoop to exploit parallel execution and Distributed File System. • Seamless integration with standard Business Intelligence tools. • More user independence on data integration.