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Market Basket & Advanced Analytics at Dunkin Brands. Mahesh Jagannath, Prasanna Palanisamy Oct 1, 2014. Agenda. About Dunkin Brands Inc. BI Program at Dunkin Brands BI Architecture at Dunkin Brands Advanced Analytics Architecture & Methodology Advanced Analytics Use Cases at Dunkin

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Market Basket & Advanced Analytics at Dunkin Brands


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    1. Market Basket & Advanced Analytics at Dunkin Brands Mahesh Jagannath, Prasanna Palanisamy Oct 1, 2014

    2. Agenda • About Dunkin Brands Inc. • BI Program at Dunkin Brands • BI Architecture at Dunkin Brands • Advanced Analytics Architecture & Methodology • Advanced Analytics Use Cases at Dunkin • Market Basket • Customer Analytics • Q & A

    3. Disclaimer All data used is sample data for presentation purposes only and is not actual corporate sales or consumer data

    4. About Dunkin Brands

    5. BI Program At Dunkin Brands • First launched at DBI in 2007 • 1350 BI users today with role based access to 504 dashboard pages • Mature governance process • Domestic POS sales analysis to increase comparable store sales and profitability of DD and BR in U.S. • Store development dashboards to identify opportunities to continue DD U.S. contiguous store expansion • International reported sales analysis to drive accelerated international growth across both brands.

    6. BI/DW Architecture at Dunkin Brands Other DBI Data R Exadata Exalytics Hyperion Users Enterprise Data Warehouse Oracle BI Oracle EBS Hyperion DBI Corporate Users Intl. POS Franchisees (above store) Social Media Radiant Sales Data Loyalty / CRM Steton SMG PAR RPS Bluecube PAR Terminals RPS Archive

    7. Agenda • About Dunkin Brands Inc. • BI Program at Dunkin Brands • BI Architecture at Dunkin Brands • Advanced Analytics Architecture & Methodology • Advanced Analytics Use Cases at Dunkin • Market Basket • Customer Analytics • Q & A

    8. Advanced Analytics platform • Products Considered • Oracle Advanced Analytics / Oracle R Enterprise (ORE) • Open Source R • IBM SPSS • Chose Oracle Advanced Analytics • Excellent fit with existing analytics infrastructure • All the benefits of Open source R • Scalability of Oracle 11G on engineered systems

    9. R—Widely PopularR is a statistics language similar to Base SAS or SPSS statistics • R environment Strengths • Powerful & Extensible • Graphical & Extensive statistics • Free—open source Challenges • Memory constrained • Single threaded • Outer loop—slows down process • Not industrial strength

    10. Oracle Advanced Analytics Oracle R Enterprise Component Architecture

    11. Oracle Advanced AnalyticsOracle R Enterprise Compute Engines

    12. Advanced Analytics Methodology

    13. ORE Advanced Analytics Framework

    14. Agenda • About Dunkin Brands Inc. • BI Program at Dunkin Brands • BI Architecture at Dunkin Brands • Advanced Analytics Architecture & Methodology • Advanced Analytics Use Cases at Dunkin • Market Basket • Customer Analytics • Q & A

    15. Market Basket Analysis • Understand role of category and purchase behavior • Identify category marketing opportunities • Get richer insight into behavioral changes from promotions • Apply data validation rules • Transform POS data into MB input format • Output to Star schema suitable for OBIEE consumption • Pairwise association model similar to Apriori, custom SQL implementation

    16. Market Basket Business Questions Choose a Category: (Sub Category Level) Answer the following questions for that Item in a particular region last week. • What % of all transactions include [Product]? • What related items are sold most frequently with [Product]? • What is the average ticket $ amount when [Product] is present? • On Average how many [Product] are sold in each transaction? • What beverages are consumers buying most with [Product]? • In what % of [Product] transactions is [Product] the only product purchased?

    17. Data Analysis & Design Considerations • 8 M daily transactions, ~25M transaction detail lines • 20 TB data warehouse size, sales data about 10 TB • Hierarchies: 5 level Product, 2x4 level Org, 4 level regional ~1000 SKUs @Item Group/Size level • Exponential growth in combinations with each hierarchy • 2 years of pre-computed Market Baskets and associated sales measures for reporting • Nightly compute within ETL windowdata with 1 day latency • Measures are non-additive along the Product Hierarchy

    18. Design : Approaches considered • Use Oracle Data Mining / Oracle R Enterprise Association Rules • Use Frequent Itemset table function in Oracle 11g to compute Item-sets • Custom SQL Development • Approach Chosen • Oracle Advanced Analytics for exploration / Ad-Hoc • Custom SQL for repeatable basket computation • OBIEE for reporting

    19. High-level Design Rule Development Transaction Data UI / Reports Data Model/ Pre-processing Measure Calculation

    20. 4 Key Reports % of Transactions containing related items Single Item Transactions: % of transactions when products are purchased alone. Transaction Detail: Product of Interest Related Product Pairings 20

    21. Related Item What beverages are sold most often with PM Flats?

    22. POI Transaction Detail Transaction Detail: Product of Interest 22

    23. Related Purchases Related Product Pairings 23

    24. Related Transactions Non-additive measures 5+3+3 Don’t Equal 11 in this case because some medium and small coffees might be sold in the same transaction!

    25. Single Item Transactions Click on to drill down for more detail

    26. Agenda • About Dunkin Brands Inc. • BI Program at Dunkin Brands • BI Architecture at Dunkin Brands • Advanced Analytics Architecture & Methodology • Advanced Analytics Use Cases at Dunkin • Market Basket • Customer Analytics • Q & A

    27. Current Areas Of Interest Customer Profiling Clustering / Segmentation Customer Churn Prediction Targeted Promotions

    28. Customer Profiling • Compute behavioral variables • Create Customer record • Data Exploration in R

    29. Customer Profiling: Attributes List of customer attributes used as-is or derived from their transactional history

    30. Customer Segmentation / Clustering • To understand your customers • Targeted Marketing • Design Promotions • Re-run the model periodically to update the new clusters • Indicates any shift in the customer behavior • Compute behavioral variables • Create Customer record • Data Exploration in R • Model displays cluster means – Cluster properties • Number of Customers in a cluster • Deployed for targeted Marketing and Monitoring Customer behavior • Identify variables for clustering, • Normalize data for Clustering • K-Means Clustering used to cluster Customers and find individual cluster characteristics

    31. Customer Segmentation / Clustering Analyze Cluster means to Derive Cluster Properties • Regulars – avg weekly visits are 5 • 78.2% visits in morning • Mostly coffee drinker, but 25% times food buyers Clustering Algorithm • Coffee Regulars • Avgweekly visits are 5.45 • Avg coffee transactions 80.29% Customer Data Profiles • High Spenders, Frequent visitors • Avgweekly spend ($35.12) • Avg. weekly visits (7.44) • Coffee and Food in basket (Avg items per transaction 2.4

    32. Customer Churn Analysis • Define Churn & Active Customer • Identify Churn Customer patterns • Is the churn pattern localized or National? • Monitor the response and re-calibrate by updating training data or model parameters • Calculate the metrics for model evaluation • Compute behavioral variables • Create Customer record • Data Exploration in R • Model will calculate the churn score for existing customers • Flag customers with high risk, low risk based on churn score • Create Training data set • Should have equal distribution of churn and usual customers • Test the model on test data set, for which outcome is known • Select threshold for model selection • Confusion Matrix for the best Model • Model to derive churn risk score. • SVM • Logistic regression • Naïve Bayes Classifier

    33. Possible Future initiatives • Periodic Churn Rate Modeling – measure churn over time • Customer Segments based on buying pattern – what they buy, when they buy? • Identify customers who are more likely to respond to offers • Personalized promotions for retention • Customer Lifetime value • Customer Sentiment Analysis • Enrich customer profiles with modeling scores

    34. Q & A