CRM Roadmap - Sample

XYZ COMPANY

CRM Roadmap Project
Final Presentation

The Anglum Group LLC

Agenda

• Executive Summary
• Micros: Business Requirements
• Marketing: Direct Mail
• Marketing: Email
• Marketing: Credit Cards
• Marketing: Rewards
• Marketing: Multi-Channel Execution
• Marketing: Analysis & Research
• Real Estate
• Merchandising
• Micros: CRM Solutions
• Data Architecture
• Data Model
• Point of Sale & Retail Ops
• Next Steps

2 The Anglum Group LLC

Executive Summary
• XYZ business users have identified an ambitious set of CRM-related
requirements, especially Marketing and Merchandising
• Supporting the business requirements requires a set of major IT initiatives
that will require significant focus and resources
• IT also needs to establish and expand ongoing processes to support CRM
• Ideal timing of the identified CRM initiatives will require a significant near-
term effort by IT to support, and may require adding resources or revising
deliverable timing
• Ideal execution of CRM will require unique customer-centric skill sets and
experience (both in IT and in other business disciplines)
• The complexity and cross-functional nature of CRM calls for more defined
CRM leadership and decision-making processes, both from business and IT


XYZ business users have identified an ambitious set of CRM-
related requirements, especially Marketing and Merchandising
Direct Marketing – XYZ plans to extensively scale its direct marketing efforts (direct mail and email)
to drive sales and create deeper customer engagement; direct marketing will be
guided by customer-specific transaction data

XYZ Co-Branded – A co-branded XYZ credit card will be issued in 20XX, and will be used to drive
Credit Card sales and gain additional insights into customer behavior

Rewards Program – Developing a compelling customer value proposition is a critical linchpin for
ongoing CRM success (esp. customer data capture); XYZ is considering a
Rewards Program to provide such a value proposition, although the specific
program design is still to be determined

Multi-Channel – Providing customers with the ability to order/exchange product across both in-
Execution store and online channels is an important business requirement

Analysis & Research – Analysis using customer-specific profile and transaction information will be used to
gain deeper customer insights and measure CRM results, and will drive business
results across disciplines (Marketing, Merchandising, Real Estate, etc.)

Real Estate – New store site selection and store closings will leverage customer-specific profile
and transaction data, as well as analytical tools

Merchandising – Optimizing in-store product assortment at the individual store level, as well as
improving pricing and markdown management, will also leverage customer-
specific data, and is likely the largest potential CRM benefit


Supporting the business requirements requires a set of major
IT initiatives that will require significant focus and resources
Point of Sale - Enable XYZ POS system to efficiently handle complex data capture,
transmittal back to the Customer Hub, and return of customer-
specific information that improves customer engagement
Customer Data - Ongoing update process, reflecting new data added over time (e.g., credit
Model card application information) and data aggregations and metrics (e.g.,
direct marketing performance metrics)
- Data must be highly accurate because it drives business decisions; XYZ
must standardize its definitions and create organizational buy-in
Credit Card - Activities to support the introduction of the XYZ co-branded credit
card, and associated POS and data model implementation

Rewards Program - Activities to support the launch of a Rewards Program; many IT
implications are dependent on Rewards Program strategy & design
Multi-Channel - Enable visibility to available inventory across channels, specifically
Execution providing online access to available in-store inventory and providing
in-store ability to order from eCommerce inventory
Direct Marketing - Support increasingly large direct mail and email campaign
execution, capture of campaign results, and associated analysis
Real Estate - Enable more informed site selection and store closings by
incorporating customer-level profile and transaction data
Merchandising - Drive better store-level assortment using customer-level transaction
data

IT also needs to establish and expand ongoing processes to
support CRM
• Inputs and Extracts
• Process for integrating new data files and matching to the database
• Process for extracting data to support campaign management, sharing with 3rd-party
partners and sharing across departments
• Data Model Definition
• Process of revising and refining the data contained in the customer data warehouse
• Data Aggregation and Rollups
• Process of massaging and preparing data that best serves internal users and production applications
• Application Development
• Process of developing solutions that build upon and enhance the existing CRM capability (e.g.,
flagging specific customers based on geography or style/size preference, customized merchandising
analysis/applications, etc.)
• Tools
• Process to evaluate and implement best-of-breed tools that serve the business and extend the ability
for XYZ to efficiently meet internal and external customer needs
• Metrics
• Process for defining, creating and managing new data elements and derived data


Ideal timing of the identified CRM initiatives will require a
significant near-term effort by IT to support, and may require
adding resources or revising deliverable timing
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Data Feeds (DB2 & Customer Hub) Data Feeds Data Feeds Data Feeds Data Feeds D
Direct Mail

Data Model Mods Data Model Mods Data Model Mods Data Model Mods

Campaign Management tool implementation

Mail File Creation

Community Integration with
Website Dev. XYZ.com
payless.com

Establish Monitor capability of DB2 to meet
DB2 SLA’s SLA’s

Update POS and Customer Hub for email capture
E-Mail

Update customer data model for email data

Migrate email address tables from Cheetah Maill

Data f eeds to/from Cheetah and DB2 Data Feeds Data Feeds

Enable capture of coupon redemption date

Enable ability to capture redemptions of individual bar coded coupons

Develop POS process to collect customer applications and provide credit decisions
Credit Card

Enable POS to accept a new card type

Enable data feeds of customer records from credit card issuer

Enable matching of credit card customer records to Payless records

Support Pilot launch and data transfers between POS, Customer Hub and issuing bank

Data feeds to/from POS, Customer Hub, and issuing bank Data Feeds

= Planned = Key = Firm Estimate of Timing
= Potential Dependency 7 = Guestimate of Timing

Ideal timing of the identified CRM initiatives (continued)
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Rewards Program

Develop infrastructure that will handle rewards program accounting

Develop (and code in data model) business rules for defining ‘transactions’‘

Modify data model to support program-specific data needs

Enable matching of credit card customer records to Payless records

Launch Rewards Program
Multi-Channel

Develop the Product Information Database (PID)

Enable ‘near real-time’ visibility to store-level inventory

Integrate PID and real-time inventory view to eCommerce website

Integrate PID and real-time inventory view to POS system
Analysis & Research

Identify typical customer data extracts

Customer data extracts Data Extracts

Enable scaled & automated Finance analysis of marketing programs

Embed customer segments/clusters into multiple applications


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Implement 3rd-party tool to analyze customer-specific trading area data
Real Estate

Integrate current mapping tool

Enable data sharing of store trading area profile information

Create central repository for Real Estate tools & data

Enable secure virtual access via web to RE central repository
Merchandising

Optimize current Assortment Planning tool

Apply new customer clusters into existing merchandising applications

Revise merchandising tools to leverage customer-level transaction data
Point of Sale

Identify root cause of slow data transfer to capture customer phone number and confirm customer ID

Pilot RGS ability to handle more complicated data transfer

Finish conversion of RPSS stores to RGS


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Enrichment process NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA
Data Architecture

Build initial customer data Revise and extend customer data warehouse
warehouse

Feed customer and Integrate new sources of customer data
transaction data to DW

Optimize and tune DB2 data warehouse

Implement Unica Extend campaign management tool

Implement ETL tool

Extend MicroStrategy DSS capability

Implement data stewardship process

Creation of business rules to support data requirements

Unica software installation
Data Model

Create ETL environment; develop ETL process

Create campaign management templates

Integration of promotion and response data

Integration of external data to support business analytics and advanced campaign management

Support business analytics, reporting and ad-hoc query requests

Write e-commerce requirements to understand Accenture e-commerce

Design data and business requirements for Real Estate, Merchandising, Store Operations, Call Center


Ideal execution of CRM will require unique customer-centric skill
sets and experience (both in IT and in other business disciplines)
• Creative, Inventive CRM Thinkers (Business)
– People who have the ability to monetize customer database capabilities, create value for the
organization and develop ROI across all departments using customer data
• Data Experts (Business / IT)
– People who are designated (or understood to be) well-versed in what data exists, where it
exists, what it means, how it was created or calculated and are seen as the 'go to' people to
resolve issues and reassure the business users
• Guardians and Stewards (Business / IT)
– People with expertise and passion with regard to protecting and managing the newly formed
corporate asset – the customer database – focusing on issues such as data quality, data access,
compliance, data retention, privacy, security and appropriate use/access that is consistent with XYZ
values and brand promise
• Planning and Support Tacticians (IT)
– People who have prior expertise in the tactical aspects of managing, manipulating and
maintaining customer data whose foreword-thinking makes it possible to keep pace with ever-
changing business demands and industry evolution


Agenda

• Real Estate
• Merchandising
• Data Model
• Next Steps


Marketing – Direct Mail:
Business Requirements
Description:
Direct mail marketing is designed to ‘supplement and enhance’ the existing free-standing-insert (FSI) program,
with a long-term goal of using direct marketing as a more effective vehicle than FSI (if proven).

Key Facts: Current / Future Plans:
• Expectation is to send XXXK direct mail pieces • Expand # of customer names to XX-YY million (2007) to
in 2006 ZZ million (2008)
• Expected response rate is X% to Y% • Increase # of direct mail pieces sent to XX million (2007)
• Current customer information (name/address) and YY-ZZ million (2008)
limits XYZ to geographic targeting today • Handle data analysis and campaign design in the
• Marketing’s intent is to evolve to a far more Marketing department; IT to manage campaign
targeted and customized DM model leveraging management tool admin
customer-specific transaction information • Use MicroStrategy for DM analysis and Unica for DM
• Future DM model will also include multiple test execution
cells (varying offer, format, etc.) • Add aggregated transaction history to customer record
• Direct mail vehicles will likely include postcards, (includes variables such as department-level purchases,
self-mailers, and envelopes purchase recency, purchase frequency, and total spend)
• MicroStrategy will be used to access data for • Add third-party demographic appends to customer record
analysis and reporting (see also analytics to identify the right variables to append)
– (2007)
• Unica Affinium Campaign is used to manage
campaign selection process • Add additional profile data to customer record (e.g.,
channel preferences, style preferences, etc.) that is
• Future use of direct mail depends on ability to
collected over time (2007)
prove effectiveness
• Implement customer profile ‘community site’ in 2007; likely
• Funding not currently in place, but would likely
to be hosted off-site but linked to the XYZ.com website’s
come from elsewhere in the marketing budget
‘my account’ section


IT Implications
Work Effort Likely Risk
Major IT Implications Required Timing Near-Term Action Steps Level
• ETL to move data in/out of DB2 and/or  2006/Q4 – See Data Architecture Micro 
customer hub (esp. 3rd-party data,
community website)
• Customer data model modifications (esp.  2006/Q4 – See Data Model Micro 
direct mail-related aggregated variables)
• Implement campaign management tool  2006/Q4 – Install software, training, backups, 
support/maintenance

• Implement mail file creation processes  2006/Q4 – Create secure method to transfer 
data to 3rd-party mailers
• Support 3rd-party development of  2007/Q2 – Identify integration 
community website, including integration requirements, use content
with XYZ.com website developed by Marketing
• Need 1.0-2.0 FTEs to handle basic BI  2007/Q1 – Develop CRM staffing plan 
admin support
• Need .2 FTE to handle Unica/campaign  2006/Q4 – Develop CRM staffing plan 
management support
• Monitor capacity of DB2 to meet SLA’s for  2006/Q4 – Establish SLA's 
Marketing campaign management

IT Requirements by Type
 Data Warehouse / Storage Customer Hub (XXX)  Campaign Management
 Data Feeds  ETL Rules  Business Intelligence Tools
 Data Model  Business Rules Development  eCommerce
 POS System Mapping Software Other Application


Timeline / Planning Framework
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Data Feeds (DB2 & Customer Hub) Data Feeds Data Feeds Data Feeds Data Feeds Data Feeds

Data Model Mods Data Model Mods Data Model Mods Data Model Mods

Campaign Management tool implementation

Mail File Creation

Community Integration with
Website Dev. XYZ.com

Establish Monitor capability of DB2 to meet
DB2 SLA’s SLA’s

= Recommended Dependency 15 = Guestimate of Timing

Marketing – E-Mail:
Description:
Email marketing is a multi-channel strategy used to increase sales by (1) driving customers to the XYZ.com website, and
(2) driving people in-store; CRM will enable greater capture of email addresses and improved targeting.

• Less than XX million email addresses (not all • Increase # of available email address to XX million (2007)
active or good) and YY million (2008)
• About XXXK addresses are mailed today (about • Capture email addresses at POS (likely on the second
ZZ% of ‘available’ addresses customer visit); expectation is emails will be captured for
• Email response rate averages .X%; email costs XX% of total customer names (2007)
are 25X to 50X less than direct mail • Use DB2 analytical database as ‘source of truth’ for email
• Emails follow the XYZ promotional calendar, addresses, and use VVV as operational source (for
with X promos per season (XX--YY per year) execution (2007)
• Email addresses are primarily captured via the • Improve coupon redemption reporting, including date
website today parameters, also infer which customers are redeeming (by
• VVV is used for marketing email deployment; mapping redemption dates with transaction dates)
VVV handles campaign setup; email database • Develop ability to use POS system to confirm that coupons
management; opt-in/opt-out; performance stats; haven’t expired, also to prevent coupon ‘stacking’
and other services; XYZ handles campaign • Explore ability to capture customer-specific email coupon
parameters (queries and list selections) redemptions (2008?)
• Current email coupon redemption reporting is
limited (# of coupons redeemed, total sales
associated with redeemed coupons, total
markdowns)
• No system-driven enforcement of coupon
expiration dates


IT Implications
• Enable POS and Customer Hub to  2007/Q2 – Determine capacity / ability to capture 
efficiently capture email addresses email addresses

• Update customer data model to hold  2007/Q2 – Identify data fields 
email address information
• Conduct ongoing email address  2007/Q2-3 – Implement a process to perform 
maintenance address hygiene on email addresses
• Migrate email address tables from VVV  2007/Q3 – See Data Model micro 
to customer data model as ‘single
source of truth’

• Enable data feeds to/from VVV and  2007/Q3 – Build ETL processes 
DB2 for email addresses (when DB2
becomes ‘source of truth’
• Enable capture of coupon redemption  2007Q3 – Identify scope of work 
date @individual customer level

• Enable ability to capture redemptions  2008/Q2 – Identify scope of work 
of individual customer bar coded
coupons at POS

 Data Warehouse / Storage  Customer Hub (XXX) Campaign Management
 Data Feeds  ETL Rules Business Intelligence Tools
 Data Model Business Rules Development eCommerce
 POS System Mapping Software  Other Application

2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Update POS and Customer Hub for email capture

Update customer data model for email data

Migrate email address tables from VVV

Data feeds to/from Cheetah and DB2 Data Feeds Data Feeds

Conduct ongoing email address maintenance

Enable capture of coupon redemption date

Enable ability to capture redemptions of individual bar coded coupons


Marketing – Credit Card:
Description:
Co-branded XYZ credit card to be used to drive sales and gain additional insights into XYZ customer behavior.

• XYZ is currently identifying a bank partner for a • Begin credit card implementation readiness, including in-
co-branded card with XYZ name store acquisition process, capture of application at POS,
• Bank partner selection is expected in October ability to accept new card type, and development of
2006 supporting marketing materials (2007 Q1)
• Bank partner would handle all credit-related • Launch credit card pilot in May 2007
processing, including card issuance, customer • Identify ‘rewards layer’ of credit card offering (to encourage
statements, customer service, and collections sign up and utilization)
• Customer would be offered an incentive for card • Identify XYZ approach / policy for using credit card
activation (e.g., save $5) personally identifiable information from XYZ credit card
• Customer-specific data will be transferred from magnetic stripe
bank partner and added to XYZ customer • Expand credit card rollout (2007 Q3)
database • Extend call center role to handle co-branded credit card
• XYZ will also leverage customer behavior outbound calling (2008?)
information where applicable


IT Implications
• Develop POS process to collect  2007/Q1 – Verify address verification process; 
customer applications and render data security in store; verify
near real-time credit decisions communications links

• Enable POS to accept a new card  2007/Q1 – Verify compatibility with modules and 
type stress test

• Enable data feeds of customer  2007/Q1 – Identify what information to collect from 
records from credit card issuer credit card issuer; set up ETL
processes; identify fair/legal data use;
data model changes; enhance security
of data transmission

• Enable matching of credit card  2007/Q2 – Understand legal, FCRA, SOX, and 
customer records with existing issuing bank rules/regulations
customer records
• Support pilot launch and ensure timely  2007/Q2 – Stress test and ensure data 
data transfer between POS, customer security/privacy
hub, and issuing bank


 Data Model  Business Rules Development eCommerce


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Develop POS process to collect customer applications and provide credit decisions

Enable POS to accept a new card type

Enable data feeds of customer records from credit card issuer

Enable matching of credit card customer records to XYZ records

Support Pilot launch and data transfers between POS, Customer Hub and issuing bank

Data feeds to/from POS, Customer Hub, and issuing bank Data Feeds


Marketing – Rewards Program:
Description:
Rewards or loyalty program that provides XYZ customers with a compelling value proposition to identify
themselves at point-of-sale or at XYZ.com

• Prior rewards program (XYZ Points) tested in • Identify potential rewards program alternatives
20XX-20ZZ was not successful and was • Evaluate alternatives, including mechanics, brand fit,
terminated due to insufficient sales lift rough economics, exit strategy, and pilot strategy
• Marketing has identified the need to provide a • Build high-level blue print, including key processes, high-
compelling customer value proposition to level technology requirements, delivery process, and
encourage self-identification and engagement communications components
• Marketing is currently beginning to explore • Develop a budget and payback analysis
alternative rewards program approaches • Secure corporate commitment
• Marketing anticipates launching a pilot in late • Develop specific program execution strategy, supporting
2007 infrastructure, creative, and other details
• Any rewards program needs to sync with XYZ • Launch pilot in 20XX (Q4) or 20ZZ (Q1)
credit card customer offer(s)


IT Implications
Note: The IT implications for a Rewards Program will be very dependent on the actual
program design, but the following outlines some likely high-level needs.
• Develop infrastructure that will handle  2007/Q1 – Identify scope of work; determine 
rewards program accounting (e.g., whether to use internal systems or 3rd-
point or ‘credit’ accruals, rewards parties to support
issuance, etc.)

• Determine (and code in data model)  2007/Q2 – Include as part of rewards program 
business rules for defining design
‘transactions’, including handling of
returns
• Modify customer data model to  2007/Q2-3 – See Data Model micro 
support program-specific data needs

• Enable POS to handle reward  2007/Q3-4 – Identify scope of work (hardware, 
program requirements, including processes)
testing



2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Develop infrastructure that will handle rewards program accounting

Develop (and code in data model) business rules for defining ‘transactions’‘

Modify data model to support program-specific data needs

Enable matching of credit card customer records to XYZ records

Launch Rewards Program


Marketing – Multi-Channel Execution:
Description:
Ability for XYZ and its customers to interact seamlessly across in-store and online channels, with full visibility to
inventory availability whether in-store or online, and the ability to easily exchange merchandise in either channel.

• XYZ doesn’t currently enable cross-channel • Develop the Product Information Database (PID) to
shopping supplement / replace the Lot Master
• Customers can shop online and have the • Develop business case for ‘near real-time’ inventory
product shipped to a store reporting capabilities (e.g., ability to properly allocate
• Customers can buy online and return product to product and identify replenishment levels, ability to de-risk
a store the shopping trip for customers, ability to push emails
• Customers can’t go online to confirm that a based on in-store inventory levels)
particular style (and size) is available in the • Enable ‘near real-time’ inventory reporting capabilities
inventory of a nearby store; Famous Footwear • Modify eCommerce website to show local store inventory
customers can do this availability
• Customers can’t shop in-store and have the
product shipped from the eCommerce inventory
• One of the limiting factors is the Lot Master,
which doesn’t track individual styles and sizes
with the granularity necessary for managing
inventory across channels


IT Implications

• Develop the Product Information  2007/Q1-4 – Needs corporate approval to proceed 
Database (PID)

• Enable ‘near real-time’ visibility to  2008/Q2 – Identify scope of work 
store-level inventory

• Integrate PID and real-time inventory  2008/Q4 – Identify scope of work 
view to eCommerce website (to
enable online lookup of available in-
store inventory)
• Integrate PID and real-time inventory  2009/Q2 – Identify scope of work 
view to POS system (to enable
ordering of eCommerce inventory by
store associates)

 Data Warehouse / Storage Customer Hub (XXX) Campaign Management
Data Model Business Rules Development  eCommerce
 POS System Mapping Software  Other Application


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Develop the Product Information Database (PID)

Enable ‘near real-time’ visibility to store-level inventory

Integrate PID and real-time inventory view to eCommerce website

Integrate PID and real-time inventory view to POS system


Marketing – Analysis & Research:
Description:
Analysis & research involves using the customer database for research project recruitment, leveraging customer-specific
profile and transaction history to gain deeper insights into customers, and measuring CRM results. Customer insights
are leveraged for marketing, merchandising, buying, and real estate site selection (among others).

• Currently Marketing uses primary research • Use customer database to recruit respondents for primary
vehicles (e.g., A&U studies) and CSAT data for research (to avoid expensive recruiting costs)
customer analysis and research • Identify customer segments / clusters using customer
• Finance supports Marketing with ROI analysis profile and transaction history, and apply to Real Estate,
and direct response analysis (including NPV, Merchandising, Marketing, Media Buys, FSI Distribution,
potential program scenarios, investment etc.
analysis, market test, radio test, and store • Understand market/store trading area demand using
concept design test analyses) customer transaction history
• Leverage customer-specific data to evaluate CRM
success, including metrics such as size/growth of
database, ratio of incremental sales to names mailed,
return on direct expense, segment/customer lifetime value
• Develop predictive models to identify which specific
demographic / psychographic variables need to be
purchased (to support Marketing and Real Estate)
• Utilize internal campaign analyst (on Marketing team) for
basic analytics (queries, some modeling)
• Utilize Finance team to help estimate campaign
profitability, also measure results; will likely require more
than one Finance analyst when direct marketing scales
• Utilize third-party analytic shops to handle more
complicated analysis

IT Implications
• Pull customer database extracts to  2007/Q1+ – Identify typical extracts and build into 
support customer analysis and work / staffing requirements
research
• Data model modifications to  2007/Q1 – See Data Model micro 
incorporate CRM metrics, third-party
demographic data

• Enable Finance analysis of direct  2007/Q1-2 – Develop more automated approach to 
marketing program (pre/post) at scale extracting relevant data from DB2 and
populating reports

• Embed identified customer segments /  2007/Q4 – Optimize Real Estate site selection 
clusters into multiple applications process and infrastructure
– Determine Merchandising approach
using customer clusters and implement

 Data Feeds ETL Rules  Business Intelligence Tools
 Data Model Business Rules Development eCommerce
POS System Mapping Software Other Application


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Identify typical customer data extracts

Customer data extracts Data Extracts

Enable scaled & automated Finance analysis of marketing programs

Embed customer segments/clusters into multiple applications


Real Estate:
Description:
Real Estate focuses on new store site selection and existing store closings, and is expected to use the customer-
specific profile and transaction data to optimize its site selection processes.

• Primary goal is to identify high potential sites • Use customer-specific data to support site selection (20XX
and optimize the store mix Q2?)
• Data used include store-level transaction data, • Incorporate mapping tools that graphically display data
Prizm cluster data, and some customer data (e.g., customer density in a trading area) (20XX?)
from CSAT research (with demo/psychographic • Incorporate analytical models that estimate customer value
data overlays) based on purchase history (20XX Q4?)
• Analytical models used include (a) new store • Develop the ability to click on a specific map location and
sales forecasting model (Nexus); (b) transfer automatically calculate predicted store sales and transfer
sales forecasting model and distance decay sales impact (20XX?)
model to estimate cannibalization of nearby • Locate Real Estate software tools / data in central
existing stores and the positive impact of store database with web browser access and wireless data
closings; and © total market analysis to optimize cards (20XX?)
store locations and # of stores
• Currently evaluating a new third-party tool that
will leverage customer-specific data
• Real Estate team members currently use
software / data installed on individual PC’s for
site selection analysis


Real Estate:
IT Implications
• Implement 3rd-party tool to analyze  2007/Q2 – Finalize vendor selection, define 
customer-specific data within trading requirements, implement software, train
areas users

• Integrate current mapping tool (used  2007/Q2-3 – Confirm license rights, implement 
by Marketing) into RE environment software, train users

• Enable data sharing of store trading  2007/Q3 – Establish process for store trading area 
area profile information (to be used by profiling, provide information to users,
RE, Marketing, and potentially upgrade regularly
Merchandising)
• Create central repository for RE tools /  2008/Q1-3 – Define data architecture requirements, 
data define business requirements, integrate
software, train users
• Enable secure virtual access via web  2008/Q3 – Purchase wireless data cards 
by DRO’s and RE managers

Data Feeds ETL Rules  Business Intelligence Tools
Data Model  Business Rules Development eCommerce
POS System  Mapping Software  Other Application


Real Estate:
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Implement 3rd-party tool to analyze customer-specific trading area data

Integrate current mapping tool

Enable data sharing of store trading area profile information

Create central repository for Real Estate tools & data

Enable secure virtual access via web to RE central repository


Merchandising:
Description:
Determining product mix in-store, including depth and breadth of assortment, price / markdown optimization,
inventory replenishment, and size management.

• Merchandising leverages a number of analysis • One near-term priority is to use customer insight to group
and planning tools, including tools for case pack (cluster) stores, which will benefit most merchandising
optimization, price execution, size management applications (esp. assortment planning); goal is to assort
by store, markdown optimization, promotion individual stores based on their specific customer base
tracker, and store-level inventory management, • Assortment planning driven by customer-level data is
etc. another priority, and is perceived to drive a lot of future
• XYZ cycles XX% of its lots annually; with value by assisting in the ability to assort at the individual
seasonality and fashion trends, this complicates store level
the ability to assort • Customer-level transaction data will also help drive size
• One of the largest potential benefits of CRM is its inventory decisions (especially by learning the extent that
application to assortment plans a customer or customers may size up or down)
• Assortment planning is currently driven by store • Customer-level data will also improve the effectiveness of
clusters, store volume, and other historical data the PromoTracker tool (measuring impact of putting a Lot
• Clustering currently is a ‘mixed bag’, with on a particular promotional vehicle) vs. the current
clusters defined by ethnicity, geography, and ambiguity because of existing product aggregations
channel • Other ‘wish list’ items include individual store-level
• A new Assortment Planning tool is being customer reporting (who is buying, not just what is being
implemented; it uses a dedicated data mart and bought), tracking at the linear foot level, profitability by
i2 software fixture, etc.
• PromoTracker is used today for a lot of the • One key enabler is to “put (customer) data into a central
merchandising analysis, and is housed in an repository in a format suitable for analysis”
ACCESS database; MicroStrategy is also used
for merchandising analysis 34 The Anglum Group LLC

Merchandising:
IT Implications
• Optimize current Assortment  2006/Q4 – Identify what is required to reduce ‘wait 
Planning tool to increase analytical time’ between analysis requests
processing speed

• Apply new customer clusters /  2007/Q1 – See Marketing: Analysis & Research 
segments into existing merchandising
applications
 2007/Q3-4 – Identify scope of work 
• Revise merchandising tools (e.g.,
Assortment Planner) to leverage
customer-level transaction data
 2008 – Identify scope of work 
• Additional integration of customer-
level transaction data -- TBD

POS System Mapping Software  Other Application ___i2___


Merchandising:
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Optimize current Assortment Planning tool

Apply new customer clusters into existing merchandising applications

Revise merchandising tools to leverage customer-level transaction data


Agenda

• Real Estate
• Merchandising
• Data Model
• Next Steps


Data Architecture:
Description:
The manner in which data is stored, managed, and used in a system; establishing common guidelines for data
operations that make it possible to predict, model, gauge, and control the flow of data in the system.

• Customer data is captured and stored in the • XXX customer operational database will be the 'single
customer operational database housed on a version of the truth' and be augmented with external data
MySQL server in the XYZ environment • Operational database will be linked to POS and will feed
• Enrichment provider updates individual records customer data to the data warehouse
with name/address, provides NCOA and other • Plan to extend the existing data warehouse in DB2 to
address hygiene services integrate customer and transaction level data
• CSAT data is housed in the operational database • Expect to have XX million phone numbers and ZZ million
• Existing data warehouse maintained in a DB2 unique customers by the end of 2006 in the CDW
mainframe environment and does not include • Plan to store granular level data (sku/store/day) AND
customer level data operate marketing and merchandising support applications
• Data warehouse is configured for analytical in DB2 environment
processing (OLAP) and not structured to support • Replacing COBOL data integration process with the ETL
transaction-oriented applications (OLTP) tool from TIBCO
• Sales Audit system supplies transaction data • Unica's campaign management tool to be implemented in
• DB2 selected as platform for customer data the data warehouse by external vendor
warehouse (after analyzing several alternatives) • MicroStrategy DSS to be used as analytical tool for
• Business has worry for "pipes" to move data marketing as well as merchandising
among systems • Future data sources include e-commerce, data appends,
• Marketing has concerns about DB2 ability to meet call center data and promotion/response history
their needs but has deferred to IT expertise


Data Architecture:
IT Implications
• Build data warehouse for customer data  2006/Q4 – Develop logical and physical models 
• Enrichment process with Harte-Hanks  2006/Q4 – Build process to update customer hub 
• Feed customer/transaction data to DW  2006/Q4 – Create data model, ETL process, business rules 

• Optimize and tune DB2 data warehouse  2007/Q1 – Performance tuning based on SLA's 
• Implement ETL tool process (ETL)  2007/Q2 – Licensing and contract negotiations 
• Implement campaign management tool  2007/Q1 – Install, requirements, create dimensional data 
• Extend MicroStrategy DSS capability  2007/Q2 – Create dimensional data/metrics; system tuning 

• Integrate new sources of customer data  2007/Q2 – Requirements, business rules, mapping, metrics 

• Implement data stewardship process  2006/Q4 – Data access, quality, retention privacy standards 

• Rollout new XXX system for POS  2007/Q3 – Understand / resolve performance issues 

 Data Warehouse / Storage  Customer Hub (XXX)  Campaign Management


Data Architecture:
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Enrichment process NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA NCOA

Build initial customer data Revise and extend customer data warehouse
warehouse

Feed customer and Integrate new sources of customer data
transaction data to DW

Optimize and tune DB2 data warehouse

Implement Unica Extend campaign management tool

Implement ETL tool

Extend MicroStrategy DSS capability

Implement data stewardship process


Data Architecture:
Issues and Concerns
ISSUE / CONCERN POINT OF VIEW / BEST PRACTICE
1. Mainframe environment may not be suited Most large retail customer database are managed in
for managing analytics customer database a server environment

1. Marketing analytics are resource-intensive, Data mart may be needed to support analytical
requiring distributed processing needs and provide needed response

1. Marketing and merchandising may require Separate data marts are often created to support
different data rollups distinct subject areas

1. Initial infrastructure design / development Need to identify / support broader and growing business
represents very basic CRM capabilities needs (i.e., with focused help of Business Analyst)

1. Quarterly NCOA processing will identify Create extensive and complex process to merge
customers who have moved, resulting in duplicate records into one record
duplicate records

1. Creation of personally identifiable data (PII) Data stewardship process is needed to mitigate risk
creates a risk management issue and protect value asset

(continued)


Data Architecture:
Issues and Concerns (continued)
1. Customer-facing applications demand a Anticipating and solving customer-facing problems
higher level of accuracy and requires high-level management involvement, issue
responsiveness resolution processes, defined SLA’s, and robust
infrastructure

1. Management of ‘persistent key’ is an Need to define XYZ point-of-view on persistent key
essential component of customer data methodology and ensure execution follows the
management methodology (vs. delegating to 3rd-party resource)

1. The need to demonstrate accuracy of Data audit / validation process needs to be created
customer data to business users will to identify issues proactively; IT needs to designate
increase as the use of CRM increases in a ‘data expert’
the organization


Data Model:
Description:
The Data Model includes how data are represented in the CRM database management system including
structure, integrity, manipulation and querying.

• Campaign management process is driving data • Install, customize and implement campaign management
requirements for Phase I tool (Unica – Affinium Campaign)
• Scope includes basic Recency, Frequency, • Develop business rules to support data requirements
Monetary (RFM) values along with store and lot • Create ETL process to integrate foundation data to
master data support basic campaign management
• CRM will never be "done", therefore customer • Develop campaign management templates to stream-line
data models will be revised on an iterative basis development process of future, advanced campaigns
• Currently under consideration is whether PSS • Integrate promotion and response data
should use the data warehouse to run analytics • Integrate external data to support business analytics and
or create a dedicated campaign management advanced campaign management
data mart
• Develop analytic metrics, derived data and aggregated
• Campaign/response subject areas are not in
data to support business requirements
scope for Phase 1, nor is granular level data
• Write a set of e-commerce requirements to aid XYZ in their
• E-commerce data is out of scope for Phase 1
interaction with Accenture
• Hardware has been purchased to support a • Integrate e-commerce, data appends, call center and
strategy to not require massive denormalization promotion/response history into the customer data
or aggregation warehouse
• Support for email marketing is out of scope for • Define data and business requirements for Real Estate,
Phase 1 Merchandising, Store Operations and Call Center
• Data and business requirements for Real
Estate, Merchandising, Store Operations and
Call Center not well defined


Data Model:
IT Implications
• Campaign management tool  2006/Q4 – Map campaigns/Affinium Campaign to 
implementation data model and install software
• Creation of business rules to support  2006/Q4 – Meet with users to gain consensus on 
data requirements business rules
• ETL process to support basic campaign  2006/Q4 – Create ETL environment, install software 
management or develop COBOL programs
• Support initial basic proof-of-concept  2006/Q4 – Create re-useable campaign templates 
customer campaigns in Affinium Campaign
• Integration of promotion/response data  2007/Q2 – Define data and business requirements 
• Integration of external data to support  2007/Q2 – Gather business requirements; review 
business analytics and advanced available data sources; analyze
campaign management Accenture e-commerce system and data
• Support business analytics, reporting  2007/Q2 – Document business requirements; 
and ad-hoc queries develop analytic metrics, create derived
data and aggregated data

 Data Warehouse / Storage Customer Hub (XXX)  Campaign Management
 Data Model  Business Rules Development  eCommerce
POS System Mapping Software  Other Application


Data Model:
2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Creation of business rules to
support data requirements

Unica software installation

Create ETL environment; develop ETL process

Create campaign management templates

Integration of promotion and response data

Integration of external data to support business analytics and advanced campaign management

Support business analytics, reporting and ad-hoc query requests

Write e-commerce requirements to understand Accenture e-commerce

Design data and business requirements for Real Estate, Merchandising, Store Operations, Call Center


Data Model:
Issues and Concerns
1. Customers and/or households may move Detailed worksession with XYZ and enrichment
over time partner needed to review scenarios and craft
solution
1. Data mart may be needed to support Industry best practice is to create a separate data
analytical needs mart to deliver response time

1. Marketing and merchandising may require Data requirements are different for each subject
different data rollups area and need to be supported accordingly

1. Creation of meta data and a data dictionary Documentation at a technical and business user
is vitally important level is needed, especially because CRM is new for
XYZ

1. The need for subject matter expertise in IT needs to set up policies and procedures to
overall data stewardship, training, security, establish credibility and earn the right to be the
and privacy is needed; currently XYZ is keeper of the ‘one version of the truth’
very reliant on enrichment provider

1. Business is concerned with overall data Proactively create an audit process the proves data
quality and data integrity integrity

(continued)


Data Model:
Issues and Concerns (continued)
1. Rules for handling customer preferences Current industry best practice is to maintain
(e.g., opt-in / opt-out) aren’t fully defined customer preferences by channel; other rules need
to be developed for preference management

1. Significant customer-centric expertise does Need to establish expertise in each functional area
not exist in Real Estate, Retail Ops, Call to maximize usage of CRM and prioritize requests in
Center or Merchandising departments order to realize ROI

1. Access to phone number append data from Licensing and housing data internally enhances
enrichment provider capability and will drive down cost


Point of Sale & Retail Ops:
Description:
Executing CRM at retail, including leveraging the point-of-sale system to capture key customer information AND
providing summary information about customers back to the store-level associates to improve customer
interactions and engagement.

• XYZ move from its current ABC POS system to • Convert all stores from XXX to YYY by the end of 2007
a more sophisticated capability is partially • Solve current problem of slow data transfer (for capturing
complete with 9999 stores converted (XX% of customer phone numbers and validating customer ID)
sales volume) • XXX will be leveraged for more complex CRM applications
• ABC is a critical enabler of CRM because it in the future, including communicating a richer set of
allows a persistent connection to a supporting information to the store associate (e.g., which segment a
corporate customer name database customer belongs to, customer purchase history summary,
• The Customer Hub supports CRM data at POS etc.)
• The first POS initiative is to enable capture of • XXX will also be used in the future to capture customer
customer phone number and (where preference information (useful to direct marketing)
appropriate) return a customer name
• XYZ is experiencing challenges in timely
exchange of information (up to 25 second
delay), and has delayed the CRM phone number
capture rollout until this is resolved
• CRM also leverages the SMILES program,
which is a training initiative designed to improve
customer engagement at store level


IT Implications
• Identify root cause of slow data  2006/Q4 – Identify root cause, fix, and continue 
transfer to capture customer phone rollout of customer phone number
number and confirm customer ID capture
• Pilot XXX ability to handle more  2007/Q2 – Design stress test of system 
complicated data transfer (to support
more complex CRM in-store
interactions)

• Finish conversion of YYY stores to  2007/Q4 – Continue ongoing conversion 
XXX

Data Warehouse / Storage  Customer Hub (XXX) Campaign Management
Data Feeds ETL Rules Business Intelligence Tools
Data Model Business Rules Development eCommerce


2006 2007 2008 2009
Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Identify root cause of slow data transfer to capture customer phone number and confirm customer ID

Pilot XXX ability to handle more complicated data transfer

Finish conversion of YYY stores to XXX


Agenda

• Real Estate
• Merchandising
• Data Model
• Next Steps


Next Steps
• Confirm timing and priority of business requirements
• Conduct additional planning to define scope of work required where needed
• Specifically this includes:
– Ability to capture email address at POS
– Ability to capture individual customer coupon redemption date
– Ability to handle customer bar coded coupons
– POS process to collected customer credit card applications and render near-real-time credit decisions
– POS ability to process new credit card type
– Data transfers to support credit card launch, ongoing execution
– Rewards Program accounting infrastructure
– Ability to handle Reward Program requirements at POS
– Near real-time visibility to store-level inventory
– Integration of PID and real-time inventory to eCommerce website and POS
– Merchandising initiatives
• Revise IT support plans as needed based on revised timing, priority of
requirements, and defined scope of work
• Identify executional taskforces for near-term high priority initiatives
• Point-of-Sale -- Ability to Handle Complex Data Capture & Transfers
• Customer Data Model – Modifications to Support Direct Marketing, Other Business Initiatives
• Credit Card – Preparation and Launch
• Rewards Program – Strategy Design, Preparation, and Launch
• Multi-Channel Execution -- Inventory Visibility Across Channels
• Direct Marketing -- Program Analysis
• Real Estate – Incorporation of Customer Data and Creation of Central Tools/Data Repository
• Merchandising – Incorporation of Customer Data
• Define CRM decision making model and processes


How We Can Help Tactically in the Near Term
• Persistent Key Process – Review persistent key process and logic with Harte Hanks

• Data Stewardship – Review existing data stewardship and recommend policies
and procedures

• Data Audit and Validation – Identify checks and balances in data process to ensure
accuracy and quality of the data

• Merge Rules and – Planning tactical approach for merging duplicate records
Householding Rules

• Reporting Requirements – Develop comprehensive set of requirements that can translate
into report design to measure overall CRM effectiveness, direct
marketing effectiveness, CRM system performance, etc.

• eCommerce – Develop documentation of Accenture eCommerce
Documentation capabilities, policies, and data management


CRM Roadmap - Sample

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à CRM Roadmap - Sample

Similaire à CRM Roadmap - Sample (20)

Dernier

Dernier (20)

CRM Roadmap - Sample

Notes de l'éditeur