Marketing Analytics with SQL Server

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    Marketing Analytics with SQL Server - Presentation Transcript

    1. Speaker: Sandeep Giri Presentation for San Francisco SQL Server User Group Copyright (c) 2007, Responsys, Inc.
    2. 2
    3. Agenda Introductions Part One: Marketing Analytics Framework 4 Problem Statement 4 Solution Framework Break Part Two: SQL Server & Analysis Services Deep Dive 4 Marketing Data Mart Schema 4 Marketing Cubes 4 Interactive Analysis Summary/Q&A 3
    4. Speaker & Company Bio Sandeep Giri • Senior Director, Responsys, Inc.. • 15 years in software product development • Focus: 4 Marketing databases 4 Business intelligence Responsys, Inc. recently acquired Loyalty Matrix, Inc. • Recently acquired San Francisco based; founded in 2001 • On-demand marketing analytics to optimize direct marketing • Clients include Apple, 24 Hour Fitness, Chicago Sun-Times • Sponsors openi.org – : open source BI app • http://www.responsys.com Copyright (c) 2007, Responsys, Inc. 4
    5. Part One Marketing Analytics Framework 5
    6. Direct Marketing: An industry that does cartwheels on “achieving” 99% failure Common questions NOT being answered effectively “How do I know which customers and prospects are most likely to respond to my marketing programs?“ “Who are my most valuable customers?” “How much should I spend on marketing in each segment?” “I’d like to know in advance exactly which customers I’m at risk of losing so I can at least do something about it in time” 6 Copyright (c) 2007, Responsys, Inc.
    7. Black Box of Marketing: Identify Optimal Segments & Tactics Leverage data Identify most relevant segments… Segment A Segment B Segment C Customer/ Behavioral Prospect (Usage, Black Box List Txn’s) of Program 1 Campaign 3rd Party Marketing Program Program & Response Appends Analytics 2 3 Program Program 4 5 … and the most optimal tactics for each segment Copyright (c) 2007, Responsys, Inc. 7
    8. What Happens Inside the Black Box? Build Phase I. Define Marketing WHAT is going on? KPI’s Data Mart Response Segments Find Phase II. LTV Predictive Define WHY is this occurring? Lists Triggers Test Learn Retention Tactical Segments Execute Phase III. Contact Analyze Past HOW do you improve? Strategy Campaigns Test Plans Tactics 8
    9. Segments: What Separates the Best from Worst? Before you segment Customers • What you are segmenting for? & Prospects • Identify events you want to control: response, attrition Define Key Performance Indicators (KPI’s) • Response: response rate, volume Segment A Segment B Segment C • Value: annual sales, LTV, profit • Propensity: 4 To attrite 4 To purchase (up-sell/cross-sell) Find predictive triggers for each KPI • Consolidate all data attributes related to the event/customer • Find strongest predictors of the event (predictive analytics) • Define segments based on the strongest predictors Copyright (c) 2007, Responsys, Inc. 9
    10. Example: Segmentation Candidate Attributes Copyright (c) 2007, Responsys, Inc. 10
    11. Example: Model Identifying Strongest Predictors Among the Candidates Copyright (c) 2007, Responsys, Inc. 11
    12. Tactics: What Works the Best for Each Segment? Marketing Right offer to the right person at the Programs List Frequency right time with the right media Media Creative • Segmentation answers “who” (person) Offer • Next up is “how” (offer, time, media, etc.) “History repeats itself, it is a good thing” Program • Consolidate past campaign data 1 Program Program • Overlay segment definitions 2 3 • Apply predictive analytics to Program 4 Program 5 identify optimal mixes of tactics • Create tactical segments Contact Strategy & Test Plan based on tactical segments Copyright (c) 2007, Responsys, Inc. 12
    13. Example: Response Rates on Multiple Contacts for Different Tactical Segments … Copyright (c) 2007, Responsys, Inc. 13
    14. … Results into Executable Direct Marketing Plan Segment A Segment B Segment C Segment D Former Weekend Former Weekday Former Weekend Former Weekday Former Weekend Former Weekday Former Weekend Former Weekday (Segment A3) (Segment A7) (Segment B3) (Segment B7) (Segment C3) (Segment C7) (Segment D3) (Segment D7) Month 1 Weekly 3D WB1 Weekly 7D WB1 Weekly 3D WB1 Weekly 7D WB1 Weekly 3D WB1 Weekly 7D WB1 Weekly 3D WB1 Weekly 7D WB1 Month 2 Month 3 Weekly 3D WB2 Weekly 3D WB2 Weekly 3D WB2 Weekly 3D WB2 Weekly 3D WB2 Weekly 3D WB2 Month 4 Month 5 Weekly 3D Weekly 7D Weekly 3D Weekly 7D Weekly 3D Weekly 7D Month 6 Annual 3D Annual 7D Annual 3D Annual 7D Annual 3D Annual 7D Month 7 Weekly 3D Weekly 3D Weekly 3D Weekly 3D Weekly 3D Weekly 3D Month 8 Annual 3D Annual 3D Annual 3D Annual 3D Annual 3D Annual 3D Month 9 Annual + 3D Annual + 7D Annual + 3D Annual + 7D Annual + 3D Annual + 7D Month 10 Weekly 3D Weekly 7D Weekly 3D Weekly 7D Month 11 Annual 3D Annual 7D Annual 3D Annual 7D Month 12 Annual + 3D Annual + 3D Annual + 3D Annual + 3D Ongoing 3 Month Ongoing Ongoing 6 Month cycle To segment 5 after To segment 5 after To Segment 5 After To Segment 5 After To segment 5 After To segment 5 After Cycle as in Weeks 7- Rule as in Weeks 7-12 month 12 month 12 Month 9 Month 9 month 1 month 1 9 and 10-12 Copyright (c) 2007, Responsys, Inc. 14
    15. Test-Learn-Execute-Repeat How do you determine the effectiveness of your marketing analytics? Test Learn Test Execute • Create test plans for each recommendation • Models available to calculate sizes for test and control groups Learn • Actual lift versus predicted lift • Champion models Execute, and test again! Copyright (c) 2007, Responsys, Inc. 15
    16. Break Warriors up by 10 3:26 to go in 2nd 16
    17. Part Two SQL Server & Analysis Services Deep Dive 17
    18. 18
    19. Design and Build Marketing Data Mart • ETL: Extract, Transform, and Load • Star Schema (versus Snowflake): Denormalization • Facts = events • Dimensions = slicer attributes OLAP • Cubes – Facts, Dimensions, and Measures • Careful of the joins Visualization: Reporting Services, open source options such as OpenI Walkthrough Copyright (c) 2007, Responsys, Inc. 19
    20. Summary Marketing Analytics = Segmentation & tactical optimization based on marketing data (customer/prospects, commerce, campaign) Microsoft SQL Server and Analysis Services provide: • ETL: DTS, SSIS • Marketing data mart: star schema in SQL database • OLAP: Analysis Services cubes organized by marketing facts and dimensions (Campaign, Sales, etc.) As always, setting analytics objectives as important (if not more) as designing/building the solution Copyright (c) 2007, Responsys, Inc. 20
    21. Questions/Comments? Now would be the time Feel free to email me: sgiri@responsys.com This presentation will be downloadable at baadd.org This conversation will continue at http://sandeep-giri.blogspot.com Copyright (c) 2007, Responsys, Inc. 21

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