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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 - PowerPoint PPT Presentation
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Market Basket & Advanced Analytics at Dunkin Brands
Mahesh Jagannath, Prasanna Palanisamy
Oct 1, 2014
Confidential information: Copying, dissemination or distribution of this information is strictly prohibited.
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
Confidential information: Copying, dissemination or distribution of this information is strictly prohibited.
3
Disclaimer
All data used is sample data for presentation purposes only and is not actual corporate sales or consumer data
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About Dunkin Brands
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BI Program At Dunkin Brands
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• 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.
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BI/DW Architecture at Dunkin Brands
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Franchisees(above store)
RPS Archive
Oracle BI
Enterprise Data Warehouse
RPS BluecubePAR
Oracle EBS
DBI Corporate Users
StetonSMG
Other DBI Data
Intl. POS
Hyperion Users
PAR Terminals
Social Media
Loyalty / CRM
Hyperion
Radiant Sales Data
Exadata Exalytics
R
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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
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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
Strengths
• Powerful & Extensible
• Graphical & Extensive statistics
• Free—open source
Challenges• Memory constrained
• Single threaded
• Outer loop—slows down process
• Not industrial strength
R environment
R—Widely PopularR is a statistics language similar to Base SAS or SPSS statistics
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Oracle Advanced Analytics Oracle R Enterprise Component Architecture
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Oracle Advanced AnalyticsOracle R Enterprise Compute Engines
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Advanced Analytics Methodology
Identify Business Objective
Understand Data
Prepare data
Develop modelTest Model
Deploy Model
Monitor Performance &
re-calibrate
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ORE Advanced Analytics Framework
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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
Confidential information: Copying, dissemination or distribution of this information is strictly prohibited.
15
Market Basket AnalysisIdentify
Business Objective
Understand Data
Prepare data
Develop modelTest Model
Deploy Model
Monitor Performance &
re-calibrate
•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
•Pairwise association model similar to Apriori, custom SQL implementation
•Output to Star schema suitable for OBIEE consumption
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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?
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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 window data with 1 day latency
• Measures are non-additive along the Product Hierarchy
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Design : Approaches considered
1.Use Oracle Data Mining / Oracle R Enterprise Association Rules
2.Use Frequent Itemset table function in Oracle 11g to compute Item-sets
3.Custom SQL Development
• Approach Chosen
• Oracle Advanced Analytics for exploration / Ad-Hoc
• Custom SQL for repeatable basket computation
• OBIEE for reporting
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High-level Design
Transaction Data
Data Model/ Pre-processing
Rule Development Measure
Calculation
UI / Reports
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4 Key Reports
Transaction Detail: Product of
Interest
Related Product Pairings
Single Item Transactions: % of transactions when
products are purchased alone.
% of Transactions
containing related items
Related Item
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What beverages are sold most often with PM
Flats?
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POI Transaction Detail
Transaction Detail: Product of
Interest
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Related Purchases
Related Product Pairings
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!
Single Item Transactions
Click on to drill down for more detail
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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
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27
• Customer Profiling
• Clustering / Segmentation
• Customer Churn Prediction
• Targeted Promotions
Current Areas Of Interest
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Customer ProfilingIdentify
Business Objective
Understand Data
Prepare data
Develop modelTest Model
Deploy Model
Monitor Performance &
re-calibrate
• Compute behavioral variables
• Create Customer record
• Data Exploration in R
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Customer Profiling: Attributes
Descriptive Spend/ Check Transaction/Frequency
Store Features Historical Purchase
1. Customer ID2. City3. State4. DMA5. Age6. Profession
1. Min Check 2. Max Check3. Total Spend4. Average Weekly
Spend5. Total points earned6. % Points redeemed7. Total No. of coupons
redeemed8. Total discount
amount (Coupons)9. Avg weekly coupon
redeemed
1. Start Date2. Last transaction date3. Days since last
transaction4. Total
transactions/Visits5. Average weekly visits6. % discounted visits7. Top Day part8. Daypart - % Visits9. Preferred Store10. Multi Store flag11. Average DD Card
Recharge Amount12. Average DD Card
Recharge Frequency13. Days since last
recharge14. Current card balance15. Transaction Activity in
weeks
1. POS: drive thru or not2. Combo or not3. Wifi
1. Total Spend /Category
2. % spend on each Category
3. % spend Sub category
4. Average number of items per transaction
5. Preferred item combo
List of customer attributes used as-is or derived from their transactional history
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Customer Segmentation / ClusteringIdentify
Business Objective
Understand Data
Prepare data
Develop modelTest Model
Deploy Model
Monitor Performance &
re-calibrate
• To understand your customers
• Targeted Marketing• Design Promotions
• Compute behavioral variables
• Create Customer record
• Data Exploration in R
• Identify variables for clustering,
• Normalize data for Clustering
• K-Means Clustering used to cluster Customers and find individual cluster characteristics
• Model displays cluster means – Cluster properties
• Number of Customers in a cluster
• Deployed for targeted Marketing and Monitoring Customer behavior
• Re-run the model periodically to update the new clusters
• Indicates any shift in the customer behavior
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Customer Segmentation / Clustering
Clustering Algorithm
Customer Data Profiles
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
- Coffee Regulars - Avg weekly visits are 5.45
- Avg coffee transactions 80.29%
- High Spenders, Frequent visitors- Avg weekly spend ($35.12)- Avg. weekly visits (7.44)
- Coffee and Food in basket (Avg items per transaction 2.4
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Customer Churn AnalysisIdentify
Business Objective
Understand Data
Prepare data
Develop modelTest Model
Deploy Model
Monitor Performance &
re-calibrate
• Define Churn & Active Customer
• Identify Churn Customer patterns
• Is the churn pattern localized or National?
• Compute behavioral variables
• Create Customer record
• Data Exploration in R
• Create Training data set• Should have equal
distribution of churn and usual customers
• Model to derive churn risk score.
• SVM• Logistic
regression• Naïve Bayes
Classifier
• Test the model on test data set, for which outcome is known
• Select threshold for model selection
• Confusion Matrix for the best Model
• Model will calculate the churn score for existing customers
• Flag customers with high risk, low risk based on churn score
• Monitor the response and re-calibrate by updating training data or model parameters
• Calculate the metrics for model evaluation
Class Active Churn
Active 71.93% 28.07%
Churn 15.37% 84.63%
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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
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Q & A
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