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

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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

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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