marketing analytics with sql server
DESCRIPTION
Marketing Analytics: Leveraging SQL Server and Analysis Services. Presentation to the San Francisco SQL Server Group by Sandeep Giri of Responsys.TRANSCRIPT
Copyright (c) 2007, Responsys, Inc.
Speaker: Sandeep Giri
Presentation forSan Francisco SQL Server User Group
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Agenda
� Introductions
� Part One: Marketing Analytics Framework
4Problem Statement
4Solution Framework
� Break
� Part Two: SQL Server & Analysis Services Deep Dive
4Marketing Data Mart Schema
4Marketing Cubes
4 Interactive Analysis
� Summary/Q&A
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Speaker & Company Bio
� Sandeep Giri
• Senior Director, Responsys, Inc..
• 15 years in software product development
• Focus:
4Marketing databases
4Business 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.
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Part One
Marketing Analytics Framework
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Direct Marketing: An industry that does cartwheels on
“achieving” 99% failure
� “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”
Common questions NOT being answered effectively
Copyright (c) 2007, Responsys, Inc.
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Black Box of Marketing:
Identify Optimal Segments & Tactics
Identify most relevant segments…
… and the most optimal
tactics for each segment
Black Box
of
Marketing
AnalyticsCampaign
& Response
3rd Party
Appends
Behavioral
(Usage,
Txn’s)
Customer/
Prospect
List
Leverage data
Segment A Segment B Segment C
Program
4
Program
1
Program
2Program
3
Program
5
Copyright (c) 2007, Responsys, Inc.
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Build
Marketing
Data Mart
Response
LTV
Retention
Find
Predictive
Triggers
Define
Lists
Analyze Past
Campaigns
Tactical
Segments
Contact
Strategy
Test
Plans
Test
Execute
Learn
Segments
Tactics
Phase I.
WHAT is going on?
Phase II.
WHY is this occurring?
Phase III.
HOW do you improve?
Define
KPI’s
What Happens Inside the Black Box?
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Segments: What Separates the Best from Worst?
� Before you segment
• What you are segmenting for?
• Identify events you want to control: response, attrition
� Define Key Performance Indicators (KPI’s)
• Response: response rate, volume
• Value: annual sales, LTV, profit
• Propensity:
4To attrite
4To 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
Customers
& Prospects
Segment A Segment B Segment C
Copyright (c) 2007, Responsys, Inc.
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Example: Segmentation Candidate Attributes
Copyright (c) 2007, Responsys, Inc.
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Example: Model Identifying Strongest Predictors
Among the Candidates
Copyright (c) 2007, Responsys, Inc.
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Tactics: What Works the Best for Each Segment?
� Right offer to the right person at the
right time with the right media
• Segmentation answers “who” (person)
• Next up is “how” (offer, time, media, etc.)
� “History repeats itself, it is a good thing”
• Consolidate past campaign data
• Overlay segment definitions
• Apply predictive analytics to
identify optimal mixes of tactics
• Create tactical segments
� Contact Strategy & Test Plan based on tactical segments
Media
Offer
Creative
FrequencyList
Marketing
Programs
Program
4
Program
1
Program
2Program
3
Program
5
Copyright (c) 2007, Responsys, Inc.
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Example: Response Rates on Multiple Contacts for
Different Tactical Segments …
Copyright (c) 2007, Responsys, Inc.
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… Results into Executable Direct Marketing Plan
Former Weekend
(Segment A3)
Former Weekday
(Segment A7)
Former Weekend
(Segment B3)
Former Weekday
(Segment B7)
Former Weekend
(Segment C3)
Former Weekday
(Segment C7)
Former Weekend
(Segment D3)
Former Weekday
(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
Rule
Ongoing 3 Month
Cycle as in Weeks 7-
9 and 10-12
Ongoing 6 Month cycle
as in Weeks 7-12
To segment 5 after
month 12
To segment 5 after
month 12
To Segment 5 After
Month 9
To Segment 5 After
Month 9
To segment 5 After
month 1
To segment 5 After
month 1
Segment A Segment B Segment C Segment D
Copyright (c) 2007, Responsys, Inc.
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Test-Learn-Execute-Repeat
� How do you determine the effectiveness of your marketing analytics?
� Test
• 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!
Test
Execute
Learn
Copyright (c) 2007, Responsys, Inc.
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Break
Warriors up by 10
3:26 to go in 2nd
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Part Two
SQL Server & Analysis Services Deep Dive
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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.
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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.
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Questions/Comments?
� Now would be the time
� Feel free to email me: [email protected]
� This presentation will be downloadable at baadd.org
� This conversation will continue at
http://sandeep-giri.blogspot.com
Copyright (c) 2007, Responsys, Inc.