Download Lunexa Consultants` Customer Experience

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:
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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:
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Centralized customer database with 50+ million cardholders
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Direct marketing engine
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Integrated workflow using Aprimo
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Post-campaign analytics
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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
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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
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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
$11,881,002
Technical Challenges
Design Approach
 Primary: Fulfill reporting functionality of providing customer
segmentation at multiple levels with acceptable levels of database
query performance
 Secondary: Basic level of flexibility in changing segmentation
buckets
 Tradeoff: Lifetime calculation limits reporting flexibility
The Solution : Step 1
Create lookup tables for each RFM segment that will allow between joins
Segmentation Attribute:
Order Frequency
The Order Frequency lookup table categorizes the number of orders made by a
customer into data buckets.
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NA (No Orders)
1 Order
2 Orders
3+ Orders
The Solution : Step 1
Create lookup tables for each RFM segment that will allow between joins
Segmentation Attribute
Order Recency
The Order Recency lookup table categorizes into buckets the time elapsed since
the last order made by a Customer. The buckets are defined to be:
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NA (No Orders)
NTF (New To File, First lifetime Order this Month)
1-3 Months
4-6 Months
7-9 Months
10-12 Months
13-24 Months
25+ Months
The Solution : Step 1
Create lookup tables for each RFM segment that will allow between joins
Segmentation Attribute:
Order Value
Lookup table listing Pre-definded buckets based on the Order value in dollars. The buckets are
defined to be:
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$ 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