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10/6/2014
1
THE CUSTOMER JOURNEYARTS Data Model Foundation for Customer
Centric Retail Applications and Services
Agenda � Introduction
� Customer centered retailing
� Defining customer
� Understanding the customer journey
� Retail context for a customer journey
� ARTS Operational Data Model V7 Support for customer journey
� ARTS future direction for supporting customer centric retail applications and services
Customer
Centricity
Merchandise
Vs Customer
Centered
Retailing
DimensionDimensionDimensionDimension MerchandiseMerchandiseMerchandiseMerchandise----centeredcenteredcenteredcentered CustomerCustomerCustomerCustomer----centeredcenteredcenteredcentered
Strategy Sell “best” stuff at the
right price
Create best customer
experience
People/Culture Sell to buy: Buyer central
actor – get the best deal
Buy to satisfy customer needs,
wants & preferences -
customer as central actor
Key Metrics Product GMROI period on
period
Customer equity growth over
customer lifetime
Organization Buyer- product category
silos
Organize around customer
segments
Process Transaction oriented –
short tem
Relationship oriented over long
term
Merchandising Push orientation – retailer
drives sales
Pull orientation – customer
drives sales
Customer as
Core
Component
of Retail
Enterprise
Value
Core Consumer-Customer Lifecycle
Concepts
10/6/2014
2
Customer
Definition
is inv olv ed in
may be a
may be
distinguishes role of
is in a s tate defined by
defines condition for
Party Ty peCode
Party PartyRoleAssignment
Person
Organization Consumer
ConsumerConversionState
Customer
• A Customer is:
– An individual or organization (i.e. a Party)
– that assumes a role (PartyRoleAssignment) of a
Consumer with respect to the retail enterprise
– Who purchases a product or service (exhibited behavior –
ConsumerConversionState)
Consumer as
a Super-type
of Customer
• Consumer
� A PartyRoleAssignment (role) type that represents the
association between the retailer and an individual or
organization (Party) where the party is a potential,
current or ex-purchaser of goods and services from the
retailer.
� A Customer represents one of several consumer states
that make up a consumer life cycle
ARTS Sample
Customer
Lifecycle
ProspectProspectProspectProspect
VisitorVisitorVisitorVisitor
ShopperShopperShopperShopperCustomerCustomerCustomerCustomer
Inactive CustomerInactive CustomerInactive CustomerInactive Customer
ExExExEx----customercustomercustomercustomerUndifferentiatedUndifferentiatedUndifferentiatedUndifferentiated
populationpopulationpopulationpopulation
ARTS Sample
Consumer
Lifecycle
State
Definitions
� Prospect� A consumer that is a potential customer and may be reached through
advertising, referrals, or identified through acquired data (e.g. mailing list, prospect list, etc.)
� Visitor� A Consumer or prospect that walks into a store or lands on a retailer’s web site.
� Shopper� A Visitor that stops and examines merchandise in a way that demonstrates a
level of interest and potential purchase
� Customer� A Shopper that completes a purchase
� Inactive Customer� A customer that has been dormant for a retailer designated period of time
� Ex-customer� A customer who is inactive and, based on retailer defined criteria, will never
become active
A Consumer may exist in one and only one state at any instant in time
Consumer-
customer
Lifecycle
ModelVisitor Customer
Walk in
or land on
page
Aware of
retailer
Inactive
Customers
Prospect
Ex Customers
Attritio
n
Rea
ctiv
ate
& R
eco
ver
Population
Generic Retail Consumer-Customer Portfolio - Life Cycle Context
Model
The red arrows represent CONVERSION EVENTS and mark the state transition of individuals
and organizations as they progress from being part of an undifferenitated popoulation to being
CUSTOMERS.
The funnel graphically illustrates the notion of
CONVERSION YIELD.
Conversion
Influencers
Stop/Hold
ImpressionShopper
Select &
Settle
Attritio
n
Population
Prospects
Visitor
Shopper
Customer
Inactive
Customer Acquisition & Retention Funnel
Phase 3
Phase 1
Sentiment about retailer
Reviews, opinions,
rumors, etc. Reviews, opinions,
rumors, etc.
Retailer
Conversion
Initiatives
Advertising, promotions, special events customer correspondence,
ongoing customer services and other retailer directed conversations
with consumers
Consumer
Lifecycle
Metrics
� Customer Outcome: Lifetime Value
� Acquisition cost
� Retention and cultivation cost
� Net revenue
� Historical sales
� Forecast sales over anticipated tenure or retailer
designated period
� Discounting model
10/6/2014
3
Customer
Lifetime
Value – Basic
Model
Individual
Customer
Lifetime
Value
Aggregated
into Valuation
Tiers
� Retailer’s Customer Equity is the aggregation of its
customers’ lifetime values
� Retailer Customer Equity managed as a portfolio
� Customer portfolio organized into valuation tiers for
investment decision making
Customer
Portfolio Tiers
Based on
Customer
Valuation
Organizing
Retail
Strategy
Around
Customer
Portfolio
Allocation Of
Marketing
and
Promotional
Resources
Lead Iron Copper Silver Gold Platinum
NONE D C B A AA Platinum
NONE D C B A AA Gold
NONE D C B A A Silver
NONE D C B A A Copper
NONE NONE D D B B Iron
NONE NONE D D C C Lead
Crude Sample Allocation of Marketing & Promotional Resources
AA 40%
A 30%
B 15%
C 10%
D 5%
Hypothetical Unscaled Grading of CLV Segments for
Demand Generation Investment
Re
ten
tio
n
Pro
ba
bili
ty
Profitability
ARTS Data Model Support for Customer
Lifecycle Modeling & Analysis
10/6/2014
4
Where ARTS
Plays
ARTSARTSARTSARTS
ARTSARTSARTSARTS
ARTSARTSARTSARTS
ARTSARTSARTSARTS
ARTSARTSARTSARTS
Chain Store Age Survey
ARTS Data
Model Work
Product
Support for
Customer
Portfolio
Management
Customer Portfolio
Customer Lifetime Value
Customer Equity
Retail Enterprise Net Worth (Equity)
Consumer-Customer Lifecycle Measurement
& Characterization
Customer Acquisition Investment Customer Historical Contribution Margin Future Customer Contribution Margin
Goal Question Metric
Retailer Defined Market
Retailer Merchandise/Service
Categories and Brands
Retailer Advertising, Selling,
Fulfillment and Delivery Channels
Retailer defineddesired customer experience
Retailer Competition & its
competitive positionRetailer Supply Chain Design &
Execution
ARTS Consumer/Customer KPIs
ARTS Operational Data Model
ARTS Data Warehouse Model
Strategic: Corporate Net Worth
Operational: Factors
Affecting Customer
Contribution to
Corporate Net Worth
A
ARTS
Data Model
Work Products
Data for
Consumer-
Customer
Lifecycle
Analytics and
Reporting
Customer
Behavior
Merchandise Category
Brands
Channels (where, whenmedia for shopping)
Customer purchase Promotion/Pricecondition
Occasion
Relative Customer
Value to the Retailer
Customer DemographicCharacteristics
Customer GeographicCharacteristics
Customer PsychographicCharacteristics
Independent
Variables that influence
customer behavior
Dependent
Variables that reflect the
results of customer behavior
Consumer-customer Lifecycle Measurement
& Characterization
Retailer initatives to
increase net profitability
Demographic Segments
Geographic Segments
Psychographic Segments
Behavioral Segments
Consumer-Customer Lifecycle Based Information Model
Transaction volume, sizingand value
Shopping frequency & recencyModeling Method &
Probability Distrution
Assumptions
A
ARTS Data
Model Work
Products &
Consumer-
customer
Lifecycle
Support
� Operational Data Model
� Data Warehouse Model
� Goal Question Metric basis for defining KPI
� Customer KPI’s used as basis for Consumer-customer Lifecycle Measurement &
Characterization
ARTS ODM
V7 Support
for Customer
Lifecycle
� Entities, attributes and relationships to persist
� Consumer identity and characteristics that describe a
consumer independent of their observed behavior and
retailer actions
� Named, classified consumer behaviors – which are
dependent on retailer actions
� Consumer states, state changes and specific events
(aka conversion events) that triggered state changes
� Unambiguous association between consumer state
changes (aka conversions) and retail transactions
Operational
Data Model is div ided into
defines
has parent
acts in
may be a
may be
contains
is loc ated at
contains
m ay be a /
is aZ
refers
is a party to
defines c ondition for
Z
is credited w ith
is place of
descr ibes
is aZ
defines how , w hen and w here
marks occurence of
is used by
Z
behav ior observ ed through
defines succ ess criteria for
mediates ex ec ution of m ediates is one of
Z
defines pre-condition of
defines post condition of
defines status of
defines target of
inc ludes
contains /
is contained in
triggers change in
is in a state defined by
influences occ urence of
completes
state changed by
is desired outcome from
iis referred by
is mediated through /
mediates
FunctionCode
TypeCode
Location
PartyBusinessUnitSite
BusinessUnit
KeyCustomer
Customer
Site
SellingLocation
LocationLevel
PartyRole
PartyRoleAssignment
RetailStore
RetailTransaction
TouchPoint (PointOfInteraction)
ConversionBehaviorType
Channel
Visitor
ConversionState
ConsumerConversionState
ConversionEvent
ConversionGoal
RelationshipStage
Consumer
ConversionInitiative
CustomerReferral
Process
ProcessChannel
Consumer
Retailer Consumer
Lifecycle Model
Conversion Event
State Transition
10/6/2014
5
Customer
Independent
Variables
Demographic Controlled Vocabulary
Psychographic Controlled Vocabulary
Geographi c Controll ed Vocabul ary
Contact Information
Health & Diet Controlled Vocabulary
Activity/Interest Controlled Vocabulary
Z
Z
Z
PartyT ypeCode
Customer
KeyCustomer
PartyPartyContactMethod
PartyAffiliationType
PartyAffiliation
PartyRole
PartyRoleAssignment
PartyType
Consumer
ConsumerConversionStateAddress
EmailAddress
Telephone
SocialNetworkHandle
SocialNetworkService
SocialNetworkType
WebSite
Person
ReligionType
RaceType
LifeStageType
LifestyleTypeCode
MaritalStatus
AnnualIncomeRange
EducationLevel
OccupationType
EthnicType
EmploymentStatusType
Language
PersonalityType
PersonalValueType
ValueAttitudeLifestyleType
PersonActivityInterest
GeographicSegment
ActivityInterest
CompositeDemographicSegment
CompositePsychographicSegment
KeyIndividualCustomerCompositeSegment
KeyCustomerGeographicSegment
CustomerPlaceUsageType
ContactMethodType
ContactPurposeType
DietaryHabitType
DisabilityImpairmentType
CompositeHealthSegment
ARTS Data
Warehouse
Model V3
Analytic
Directions
Chain Store Age Survey
Decomposing
“Analytics”
Demand Stewardship
New Customer
Acquisition
Customer
Retention & Cultivation
Customer
Recovery
Retailer Strategy Planning & Execution
Product &
ServicesPricing Promotion Place
Customer
Relationship
Customer
Attrition
Customer Needs, Wants,
Preferences
Customer
Behavior
Customer
Innate
Characteristics
Retailer-Customer Interaction
REVENUE
$
infers
demonstrates
defines
parameters
for
defines
primary
drivers
of
Acts out
retailers
role in
Demographic
Psychographic
Geographic
Interests & Activities
�
�
�
�
Independent
Customer
Attributes
Behavior
Dependent
Customer
Attributes
Retail
Transactions
Customer
Orders
Product/Service
Reviews,
Surveys,etc
Observed Unobserved
Models
Net Sales
Customer
KPIs and
Performance
Measures
� Customer Behavioral Metrics
� Timeliness
� Credit risk
� Purchase behavior patterns
� Products (by customer segment and as a way to defined customer segments)
� Pricing (demand elasticity/sensitivity)
� Affinity analysis
� Market Basket Analysis
� Cross sell/upsell
� Cannibalization
� Propensity analysis
� Channel preferences
� Shopping time and venue preferences
� Responsiveness to conversion initiatives
10/6/2014
6
Future
Direction for
ARTS Data
Model
Customer
Subject Areas
� Address consumer-customer privacy issues
� Extend ODM to capture non-transactional customer-retailer interactions
� Add subject areas support to define, describe and quantify promotion initiatives
� Extend data model support for capturing social media conversations about retailer
� Develop more complete sample customer analytics to support forecasting and modeling
Contact Information
Tom Sterling
tster9306@verizon.net
Technical Detail Slides
Sample KPI’s
like RFM
Provide
Concepts and
Sample
Implementation
-------------------------------------------------------------------------------- -- Create View VW_DW3_RFM_BEHAVIORAL_SEGMENT -- --------------------------------------------------------------------------------
-- This sample view presents a way to segment customers based on the recency -- -- frequency and monetary value of their behavior. The data source for this -- -- query is the stored summary table DW3_STRD_SMRY_CT_RP_TRN. The view uses --
-- common table expressions to create three subqueries to handle summarizing -- -- recency, frequency and monetary value (which we are populating with -- -- average net margin). Each subquery is documented. The main query uses --
-- the NTILE function to assign the returned customer summary values to a -- -- quintile. The quintile values (1-5) represent bins along three dimensions -- -- which provide the values used to assign customers to RFM behavioral --
-- segments. -- -------------------------------------------------------------------------------- --drop view VW_DW3_RFM_BEHAVIORAL_SEGMENT
Create VIEW VW_DW3_RFM_BEHAVIORAL_SEGMENT as with CT_RECENCY as (
------------------------------------------------------------------------- -- Recency is the number of days from the cutoff date since the last -- -- transaction was completed for the customer. --
------------------------------------------------------------------------- select ID_CT
,DATEDIFF(dd,MAX(DC_DY_BSN),'2013-07-01') as RECENCY from DW3_STRD_SMRY_CT_RP_TRN
where DC_DY_BSN < '2013-07-01' group by
ID_CT )
,CT_FREQ as ( -------------------------------------------------------------------------
-- Frequency is expressed as a transaction occurred every FREQ days -- -- We calculate it for each customer so it can be returned to the -- -- outer query for assignment to a quintile bucket for RFM behavioral --
-- classification. -- ------------------------------------------------------------------------- select
ID_CT ,MIN(DC_DY_BSN) as FIRST_PURCH_DATE ,COUNT(ID_TRN) as TRANS_COUNT
,FLOOR(DATEDIFF(dd,MIN(DC_DY_BSN), '2013-07-01') / COUNT(ID_TRN)) as FREQ from DW3_STRD_SMRY_CT_RP_TRN
where DC_DY_BSN < '2013-07-01' group by
ID_CT ) ,CT_MONETARY as
( -------------------------------------------------------------------------- -- The monetary value used in this sample is the NET MARGIN. This --
-- could be replaced by NET SALES. Our example, however is congruent -- -- with the earlier sample for customer liftime value. Note that we are-- -- using an AVERAGE TRANSACTION NET VALUE because simply summing the --
-- net values is too closely correlated with frequency and we want to -- -- draw a distinction. Average is a good indicator of customer spending-- -- magnitude over time and will distinguish between frequent convenience--
-- shoppers versus less frequent stock up shoppers -- -------------------------------------------------------------------------- select
ID_CT ,AVG(TRN_NET_SLS) as AVG_SPEND from
DW3_STRD_SMRY_CT_RP_TRN where DC_DY_BSN < '2013-07-01'
group by ID_CT )
select
CT_RECENCY.ID_CT
Sample RFM
Classification Customer Recency (days) Freq (days) Monetary ($) Recency Bin (1-5) Freq Bin (1-5) Monetary Bin (1-5)
ID_CT RECENCY FREQ AVG_SPENDRECENCY_QUI
NTILEFREQ_QUINTILE SPEND_QUINTILE
10048 13.00 42 309.27$ 2 1 1
10037 36.00 34 294.00$ 3 1 1
10021 122.00 49 293.31$ 5 3 1
10028 4.00 55 279.44$ 1 4 1
10082 68.00 46 270.92$ 4 2 1
10065 51.00 44 257.37$ 4 2 1
10005 29.00 47 255.19$ 3 3 1
10084 43.00 44 249.66$ 3 2 1
10041 91.00 47 243.68$ 5 3 1
10085 5.00 53 243.00$ 1 4 1
10056 7.00 57 241.47$ 1 5 1
10064 97.00 46 239.08$ 5 2 1
10019 54.00 61 235.90$ 4 5 1
10099 13.00 42 235.84$ 2 1 1
10092 15.00 44 235.70$ 2 2 1
10025 12.00 40 235.16$ 2 1 1
10010 40.00 50 234.89$ 3 3 1
10001 7.00 46 234.00$ 1 2 1
10015 49.00 56 233.81$ 4 4 1
Quintile Value Recency Frequency Monetary
1 CURRENT FREQUENT HI GH SPENDER
2 RECENT STEADY ABOVE AVERAGE SPENDER
3 TIMELY NEEDS REMI NDER AVERAGE SPENDER
4 LOSING STEAM LOSI NG INTEREST BELOW AVERAGE SPENDER
5 LAGGARD SLOTH THRIFT
Quintile Value Recency Frequency Monetary
1 CURRENT FREQUENT HI GH SPENDER
2 RECENT STEADY ABOVE AVERAGE SPENDER
3 TIMELY NEEDS REMI NDER AVERAGE SPENDER
4 LOSING STEAM LOSI NG INTEREST BELOW AVERAGE SPENDER
5 LAGGARD SLOTH THRIFT
Privacy
Challegne
� Privacy metadata
� Assign privacy rules to consumer, customer, worker and other entities
� Rules at column/selection set level
� Consumer-customer contracts
� Data usage permissions
� Date bounded with renewal
� Consumer-customer data usage audit and tracking
� Consumer-customer right to be forgotten
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