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Smart Strategies for Business Intelligence Design and Implementation July 31, 2010 Presenter : Vaibhav Dhawan Country Director Agenda An Introduction to Lunexa Business Processes and Technology Solutions Related Lunexa Case Studies Best Practices Methodology Technical Overview Complementary, End-to-End Advisory & Implementation Services Lunexa is a consulting firm focused on providing advisory and implementation services to help clients unlock opportunities from their data assets. Corporations at the leading edge of business intelligence development choose to work with Lunexa because we offer unique, end-to-end expertise in all aspects of the data warehouse technology stack: T ech Execs Business intelligence, reporting and analysis Database design and development Enterprise data integration Lunexa’s offerings emphasize the importance of advisory services that complement implementation efforts for each project: Architecture planning and design Benchmarking Best practice methodology Business process analysis Development and deployment strategy End-to-end impact analysis Project audits Tuning and optimization Vendor collaboration Project M anagers & Architects D evelopers Strategic consulting at the executive levels, that can be followed by complementary advisory and implementation services at the project management and developer levels Lunexa Consultants’ Customer Experience Lunexa consultants’ experience with a wide array of applications will allow clients to get a head start on planning and development efforts. Rather than waste time to re-define the generic aspects of these applications, customers can focus on the requirements for their own unique business models. An Introduction to Lunexa Business Processes and Technology Solutions Related Lunexa Case Studies Best Practices Methodology Technical Overview Business Processes and Technology Solutions Business Process analysis should initiate the design and development of any technology solution Analytical applications require the definition of a business decision architecture Operational applications must be designed with use cases and activity diagrams IT Strategy necessitates the modeling of broader business processes, both internal and external (involving customer and partner interactions), to determine the role that technology can play within the business processes Business Processes and Technology Solutions: Analytical Applications and Business Decision Architecture Breakdown the different steps of a business process What decisions need to be made at each step of the business process? What information is needed to make the decision? What questions need to be answered to make the decision? Business / marketing Intelligence constructs Reporting and analysis components Business Processes and Technology Solutions: Operational Applications, Use Cases and Activity Diagrams Different Levels of Detail for Business Process Definition Steps 1-3 1. Marketing and Merchant initiate a campaign and define an offer. 2. Marketing assigns a campaign code. 3. Issuers for register for the campaign. CAMPAIGN PLANNING Define Campaign Objectives Define Available Offers Establish Budget Define Preliminary Targeting Outline Creative Select Channels Establish Project Plan Obtain Approvals Step 5 Campaign Development Step 4 4. The Campaign Planning Phase produces Targeting Brief as a prerequisite to the Campaign Development Phase. 5. Programs opt in or out based on business, market, creative, or other conflicts. Create New Campaign Information Management «uses» Geo-Coding Preprocessor «extends» Register Campaign Objectives CAMPAIGN DEVELOPMENT Issuer Registration Creative Development Step 9 Create New Campaign Register Campaign Objectives Develop Models Perform Preliminary Segmentation Perform Final Targeting Define Offer Packages Assign Cells Generate Intermediate Mail File 9. Issuer registration is closed and an Offer Detail Extract (ODE) is generated. Steps 14-17 14. CIR appends cardholder name and address and generates Final Mail File. 15. Campaign Offer File is sent to Issuers 16. Final Mail File is sent to Mail House 17. Mail House sends Mail Report 18. Transaction data and/or response data Is evaluated to determine campaign effectiveness. POST-CAMPAIGN ANALYTICS Receive Redemption Data Generate Campaign Evaluation Receive Campaign-Specific Files [preliminary segmentation] ODS Steps 6-8 Develop Models 6. Optionally, location data is received from Merchants for geo-coding. 7. Preliminary segmentation begins. One or more Target Program Lists (TPL) are requested based on likely opt-in/opt-out scenarios. 8. Transaction data in ODS is used to build Propensity models. Receive Target Program List «uses» Merchant Receive Targeted Program List Marketing Perform Preliminary Segmentation Rewards Program Manager «uses» Evaluate TPL Counts Receive ODE File [evaluation not satisfactory] Transaction DW Final Targeting Exit «uses» [evaluation satisfactory] Issuer Apply TPL to working segment Generate Test Mail File Steps 10-13 Step 18 CIR Database Cardholder Repository (CIR) CAMPAIGN EXECUTION Generate Final Mail File Drop Mail Tag Account Profiles Send Issuer Offer Files Receive Mail Report «uses» 10. ODE is received and loaded. 11. Final Targeting is completed. 12. Control groups are defined. 13. Preliminary Mail File is sent to CIR. TDB Evaluate Working Segment Counts Define Offer Packages Targeting Analyst Evaluate Working Segment Counts The high-level business process is broken down to use cases for different steps Assign Cells Develop Creative [not satisfactory] Revise Segmentation Model Generate Intermediate Mail File Creative Vendor [satisfactory] Create Preliminary Segmentation Report [] More detailed activity diagrams are then created for each step of a use case Submit for Approval Business Processes and Technology Solutions: IT Strategy and Broader Business Processes With the online division of a leading retail bank, Lunexa consultants worked with business process maps describing customers’ and prospects’ multi-channel interactions with the bank in order to define and implement KPIs and interactive dashboards that enabled the end-toend measurement of the business processes. Collect, Analyze & Deconstruct Metric Components Identify KPIs GDWG Reviews, Certifies & Deploy, Publish Publishes & Train Key Metrics Develop & Test Verify / Validate Standardize & Certify An Introduction to Lunexa Business Processes and Technology Solutions Related Lunexa Case Studies Best Practices Methodology Technical Overview Proposal Review: Related Lunexa Case Studies Leading Credit Card Company Highlights: • Gathered business requirements and defined the end-to-end detailed design for the campaign management platform including: Centralized customer database with 50+ million cardholders Direct marketing engine Integrated workflow using Aprimo Post-campaign analytics Creative and branding approval web interface • This is the first time the Credit Card Company has taken on such campaign management and loyalty marketing initiatives. These activities were outsourced in the past. • Facilitating and managing the workflow across numerous organizations – banks and merchants – presented a unique challenge. Solution Type: Campaign management platform Data Sources: Credit card transactions, cardholder data and campaign responses Related Technologies: Aprimo and MicroStrategy Lunexa Activities: Business requirements gathering, tool evaluation, detailed design Proposal Review: Related Lunexa Case Studies Leading Retail Bank Highlights: • Analyzed existing marketing automation and business intelligence technologies in use to determine gaps/overlaps and create a master inventory of business metrics. • Using the client’s business process maps as a foundation, interviewed the sales & marketing teams to consolidate 2,400+ metrics into 30 KPIs with detailed business requirements. • Established an ongoing approach and process to identify, validate and publish new KPIs and maintain existing ones. • Designed a metrics repository database to support the development of interactive performance dashboards on the initial set of KPIs. • Implemented the client’s first interactive performance dashboard to present executive management and product managers with a single integrated view across all 100+ banking and financial products. Solution Type: Business intelligence and online performance management Data Sources: Account and customer information, web traffic, inbound and outbound campaigns, sales activity Related Technologies: E.Piphany, Mediaplex, Omniture, Unica and Visual Sciences Lunexa Activities: Business requirements gathering, metric consolidation strategy, performance dashboard design and development Proposal Review: Related Lunexa Case Studies Leading Online Retailer Highlights: • Gathered business requirements for business intelligence from the CEO and heads of Marketing, Merchandising and Product Management. • Detailed end-to-end design for data integration, reporting and analytics. Process for identifying unique customers from named and anonymous purchases across multiple sites hosted by the Retailer. Customer segmentation is the key focus of the business intelligence design. • Currently implementing the enterprise data warehouse with web analytics, ecommerce and customer demographic data. • This will allow the Retailer to attribute purchase decisions to specific marketing activities. Solution Type: Business intelligence and enterprise data warehouse Data Sources: Web analytics, e-commerce transactions and customer demographics Related Technologies: Great Plains, Omniture and YesMail Lunexa Activities: Business requirements gathering, detailed design and development An Introduction to Lunexa Business Processes and Technology Solutions Related Lunexa Case Studies Best Practices Methodology Technical Overview Lessons Learned and Best Practices Business process requirements should drive technology solutions and not the other way around; technology should aid process improvements. Functional requirements must be assembled in an architecturally sound manner. Enterprise Marketing Management (EMM) comprises various components of marketing automation and intelligence, and is slow to be offered by the vendor community as an integrated solution. • The Gartner Quadrant identifies no leaders in this category • Components include web analytics, campaign management, marketing resource management, lead management, event-driven marketing, predictive modeling and more. The marketing staff can get burdened with operational and manual activities and not focus enough on strategic activities. Reduce time-to-market by segmenting your customers iteratively and regularly, and not just when the next campaign’s targeting criteria are solidified. Lessons Learned & Best Practices The two most complex activities are: • Business process integration – adoption of new technologies and roles • Data integration – single view of the customer, sales attribution and response identification The holistic view of the customer must include a detailed understanding of customer “touches” - marketing deliverables can result from different departments and reduce the effectiveness of the combined message . You may have many campaign management initiatives but you are targeting individual customers. KPIs need to be identified that reflect the effectiveness of overall marketing strategies; there is a tendency to look at operational metrics at a very granular level, on a per campaign basis, that can be influenced by too many external factors. Best-of-Breed vs. Single (Integrated) Vendor • Vendor alternatives • Breadth and depth of requirements – today and in the future • Internal skill set Segmentation requires an intuitive interface and workflow as well as sophisticated analytics. Picking the right technical solution for each can be a challenge. An Introduction to Lunexa Business Processes and Technology Solutions Related Lunexa Case Studies Best Practices Methodology Technical Overview BI/DW Design Issues What Customers Want! The end user needs reporting capabilities with acceptable performance that delivers results as per their business requirements. Major factors that can directly influence the success of a BI/DW design and implementation ETL performance • Disparate legacy systems • Source system impact • Data volume growth . Report query performance • Complex queries • Database optimization • Data Model Data Quality • • • • Multiple data sources Business rules Error-free ETL Non-standardized business terminology Best Practices Methodology System Requirement Study: Gap analysis of the existing processes in order to provide concrete recommendations and set expectations on what can and cannot be met given the constraints Impact Analysis: Understand the clients’ business requirements and the potential ripple effect on the data model Integrated Requirements Gathering: To understand client’s business growth model and to optimize the long term reporting requirements, often beyond initial request Real Time Case study: RFM Customer Segmentation for Retail Business Requirement : Ability to look at unique customers from inception through to present time selected by Recency, Frequency, & Monetary value Recency : Elapsed time since last order Frequency : Lifetime number of orders Monetary Value : Lifetime order value • Level(s): Store, Product Category, Region, Customer Demographics • Date Range: Current snapshot of lifetime customer segmentation values Report Requirement Mockup Customer RFM Frequency/ Recency $1-$50 Cust 1 Time 01 - 03 Months 04 - 06 Months 07 - 09 Months 10 - 12 Months 13 - 24 Months 25+ Months 2 Times 01 - 03 Months 04 - 06 Months 07 - 09 Months 10 - 12 Months 13 - 24 Months 25+ Months 3+ Times 01 - 03 Months 04 - 06 Months 07 - 09 Months 10 - 12 Months 13 - 24 Months 25+ Months TOTAL 01 - 03 Months 04 - 06 Months 07 - 09 Months 10 - 12 Months 13 - 24 Months 25+ Months 942,181 $51-$100 Dollars Cust $27,279,507 230,621 Dollars $101-$200 Cust Dollars $201+ Cust Dollars $16,715,188 72,503 $10,013,495 17,902 $7,389,574 38,313 $1,064,176 10,597 $714,762 2,744 $376,994 1,112 $389,105 47,418 $1,465,983 14,481 $1,039,488 4,840 $658,907 1,369 $453,835 33,032 $1,059,866 7,281 $528,309 2,365 $324,535 913 $318,476 31,933 $954,637 6,676 $479,060 2,125 $290,236 907 $312,312 127,801 $3,746,943 31,471 $2,217,834 11,180 $1,561,224 3,234 $1,118,366 663,684 51,380 $4,549,660 160,115 $2,189,874 85,474 $2,518,046 $6,345,349 49,249 55,700 $2,104,126 $7,820,613 10,367 19,029 $2,654,590 $6,691,027 2,130 $90,688 4,710 $344,142 2,900 $401,392 1,241 $457,234 2,052 $89,533 5,106 $380,427 3,518 $493,378 1,379 $462,393 1,667 $71,066 3,239 $240,620 1,989 $276,349 761 $261,978 2,149 $87,618 3,039 $224,008 2,077 $288,254 789 $273,767 7,644 $315,336 12,943 $956,934 8,693 $1,212,356 3,380 $1,127,691 35,738 1,084 $338,715 $47,223 56,437 24,797 $1,020,566 $1,994,245 36,523 48,879 $1,301,676 $7,223,658 11,479 63,448 $1,585,450 $33,112,218 78 $3,353 1,497 $122,285 3,811 $565,193 7,694 $5,100,212 50 $2,149 1,266 $103,631 3,085 $454,813 5,779 $3,290,891 60 $2,563 984 $80,027 2,373 $351,401 3,793 $2,035,164 93 $3,873 1,013 $80,824 2,121 $315,263 3,424 $1,802,145 268 $11,030 3,611 $292,996 8,318 $1,229,118 11,841 $5,663,716 $1,257,971 30,917 $25,057,766 100,379 $7,640,962 $47,192,820 535 994,645 $7,044 16,426 $29,516,603 340,892 $315,495 29,171 $25,054,782 177,082 40,521 $1,158,217 16,804 $1,181,189 9,455 $1,343,579 10,047 $5,946,551 49,520 $1,557,665 20,853 $1,523,546 11,443 $1,607,098 8,527 $4,207,118 34,759 $1,133,494 11,504 $848,956 6,727 $952,285 5,467 $2,615,619 34,175 $1,046,129 10,728 $783,892 6,323 $893,753 5,120 $2,388,223 135,713 $4,073,309 48,025 $3,467,764 28,191 $4,002,698 18,455 $7,909,773 699,957 $4,895,418 232,978 $3,854,106 114,943 $4,663,773 52,763 $11,881,002 Technical Challenges Design Approach Primary: Fulfill reporting functionality of providing customer segmentation at multiple levels with acceptable levels of database query performance Secondary: Basic level of flexibility in changing segmentation buckets Tradeoff: Lifetime calculation limits reporting flexibility The Solution : Step 1 Create lookup tables for each RFM segment that will allow between joins Segmentation Attribute: Order Frequency The Order Frequency lookup table categorizes the number of orders made by a customer into data buckets. NA (No Orders) 1 Order 2 Orders 3+ Orders The Solution : Step 1 Create lookup tables for each RFM segment that will allow between joins Segmentation Attribute Order Recency The Order Recency lookup table categorizes into buckets the time elapsed since the last order made by a Customer. The buckets are defined to be: NA (No Orders) NTF (New To File, First lifetime Order this Month) 1-3 Months 4-6 Months 7-9 Months 10-12 Months 13-24 Months 25+ Months The Solution : Step 1 Create lookup tables for each RFM segment that will allow between joins Segmentation Attribute: Order Value Lookup table listing Pre-definded buckets based on the Order value in dollars. The buckets are defined to be: $ 1 – 10 $ 11 - 20 $ 21 - 30 $ 31 - 40 $ 41 - 50 $ 51 - 60 $ 61 - 70 $ 71 - 80 $ 81 - 90 $ 91 - 100 $ 101+ The Solution : Step 2 Summary level tables for each segmentation level (Region, Store, Product Category, Customer) Each table includes the data required for segmentation, like lifetime order value and order count Nightly ETL loads recalculate these metrics for each customer who made an order that day and updates the summary level tables Order Order Date Customer Region Order Value Product Store Region Customer tl_cust_orderrec Max(Order Date) Sum(Order Value) tl_cust_orderval Count(Order) tl_cust_orderfreq The Solution : Step 3 Views on top of the summary tables that do between joins up to your segmentation tables thus ensuring report performance: Single pass query Covers entire history of transactions Low query time select CASE WHEN a11.cust_num_orders = 1 THEN 1 WHEN a11.cust_num_orders = 2 THEN 2 WHEN a11.cust_num_orders > 2 THEN 3 ELSE 0 END custacct_num_orders, max(CASE WHEN a11.cust_num_orders = 1 THEN '1 Time' WHEN a11.cust_num_orders = 2 THEN '2 Times' WHEN a11.cust_num_orders > 2 THEN '3+ Times' ELSE 'No Orders' END) order_freq_desc, a11.cust_orderrec_id cust_orderrec_id, max(a11.custd_orderrec_desc) cust_orderrec_desc, a13.cust_net_orderlbl_id cust_orderval_id, max(a13.cust_net_orderlbl_desc) cust_net_orderlbl_desc, count(distinct a11.customer_id) WJXBFS1, sum(a11.cust_net_sales) WJXBFS2 from vl_cust_orderrec_seg a11 join vl_cust_net_orderval_seg a12 on (a11.customer_id = a12.customer_id) join tl_cust_net_orderval a13 on (a12.net_orderval_id = a13.cust_net_orderval_id) group by CASE WHEN a11.cust_num_orders = 1 THEN 1 WHEN a11.cust_num_orders = 2 THEN 2 WHEN a11.cust_num_orders > 2 THEN 3 ELSE 0 END, a11.cust_orderrec_id, a13.cust_net_orderlbl_id Deliverable Results Final RFM Report(Across All Stores) Actual Dashboard, Export to Excel