10/6/2014. THE CUSTOMER JOURNEY ARTS Data Model Foundation for Customer Centric Retail Applications and Services

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1 THE CUSTOMER JOURNEY Data Foundation for Centric Retail Applications and Services Agenda Introduction centered retailing Defining customer Understanding the customer journey Retail context for a customer journey Operational Data V7 Support for customer journey future direction for supporting customer centric retail applications and services Centricity Merchandise Vs Centered Retailing Dimension Strategy People/Culture Key Metrics Organization Merchandise-centered centered Sell best stuff at the right price Sell to buy: Buyer central actor get the best deal Product GMROI period on period Buyer- product category silos Process Transaction oriented short tem Merchandising Push orientation retailer drives sales -centered Create best customer experience Buy to satisfy customer needs, wants & preferences - customer as central actor equity growth over customer lifetime Organize around customer segments Relationship oriented over long term Pull orientation customer drives sales as Core Component of Retail Enterprise Value Core - Concepts 1

2 Person Organization Aware of retailer Party is involved in Party Ty pecode may be a PartyRoleAssignment Walk in or land on page Conversion distinguishes role of may be is in a state defined by ConversionState defines condition for Stop/Hold Impression Select & Settle 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. Definition A is: An individual or organization (i.e. a Party) that assumes a role (PartyRoleAssignment) of a with respect to the retail enterprise Who purchases a product or service (exhibited behavior ConversionState) as a Super-type of 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 represents one of several consumer states that make up a consumer life cycle Sample Prospect Inactive Undifferentiated Ex-customer population Sample 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.) A or prospect that walks into a store or lands on a retailer s web site. A that stops and examines merchandise in a way that demonstrates a level of interest and potential purchase A that completes a purchase Inactive 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 may exist in one and only one state at any instant in time customer Generic Retail - Portfolio - Life Cycle Context Phase 3 Reviews, opinions, Influencers rumors, etc. Reviews, opinions, rumors, etc. Sentiment about retailer Population Prospect Acquisition & Retention Funnel Population Prospects Advertising, promotions, special events customer correspondence, ongoing customer services and other retailer directed conversations with consumers Phase 1 Inactive Retailer s Conversion Initiatives Attrition Attrition Reactivate & Recover Metrics Outcome: Lifetime Value Acquisition cost Retention and cultivation cost Net revenue Historical sales Forecast sales over anticipated tenure or retailer designated period Discounting model Ex s Inactive 2

3 Lifetime Value Basic Individual Lifetime Value Aggregated into Valuation Tiers Retailer s Equity is the aggregation of its customers lifetime values Retailer Equity managed as a portfolio portfolio organized into valuation tiers for investment decision making Portfolio Tiers Based on Valuation Organizing Retail Strategy Around Portfolio Allocation Of Marketing and Promotional Resources Retention Probability Hypothetical Unscaled Grading of CLV Segments for Demand Generation Investment 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 Data Support for ing & Analysis Profitability Crude Sample Allocation of Marketing & Promotional Resources AA 40% A 30% B 15% C 10% D 5% 3

4 is located at contains contains is credited with is place of is div ided into is mediated through / mediates defines has parent is desired outcome from influences occurence of is used by defines target of acts in iis referred by completes mediates execution of mediates is one of defines condition for defines success criteria for behav ior observ ed through defines how, when and where inc ludes defines pre-condition of marks occurence of defines post condition of contains / is contained in triggers change in refers is a may be a describes state changed by may be a / is a may be is a party to is in a state defined by defines status of Where Plays Data Work Product Support for Portfolio Management Strategic: Corporate Net Worth Operational: Factors Affecting Contribution to Corporate Net Worth Portfolio Retail Enterprise Net Worth (Equity) Equity Lifetime Value Acquisition Investment Historical Contribution Margin Future Contribution Margin - Measurement & Characterization / KPIs Goal Question Metric Data Warehouse A Data Work Products Operational Data Chain Store Age Survey Retailer Defined Market Retailer Merchandise/Service Categories and Brands Retailer Advertising, Selling, Retailer Competition & its Fulfillment and Delivery Channels competitive position Retailer defined desired customer experience Retailer Supply Chain Design & Execution Data for - Analytics and Reporting - Based Information Dependent Variables that reflect the Independent Variables that influence Retailer results of customer behavior customer behavior initatives to Behavioral Segments increase net profitability Merchandise Category Demographic Segments Brands Demographic Channels (where, when media for shopping) Geographic Segments Geographic purchase Promotion/Price Behavior condition Psychographic Segments Occasion Psychographic Transaction volume, sizing and value Data Work Products & customer Support Operational Data Data Warehouse Goal Question Metric basis for defining KPI KPI s used as basis for customer Measurement & Characterization ing Method & Probability Distrution Assumptions -customer Measurement & Characterization Shopping frequency & recency Relative Value to the Retailer A ODM V7 Support for Entities, attributes and relationships to persist 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 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 BusinessUnit TypeCode Site BusinessUnitSite Location LocationLevel FunctionCode SellingLocation RetailStore Process TouchPoint (PointOfInteraction) RetailTransaction ConversionGoal Party ConversionBehaviorType ConversionInitiative ProcessChannel RelationshipStage ConversionState ConversionEvent PartyRoleAssignment Referral Retailer PartyRole Channel Key Conversion Event State Transition ConversionState 4

5 PartyAffiliationType PartyAffiliation Party PartyT ypecode PartyType PartyRoleAssignment Demographic Controlled Vocabulary Psychographic Controlled Vocabulary Person PartyRole ConversionState RaceType ReligionType LifeStageType MaritalStatus EmploymentStatusType OccupationType LifestyleTypeCode EducationLevel AnnualIncomeRange EthnicType ValueAttitudeLifestyleType PersonalValueType Contact Information PersonalityType Language DietaryHabitType PartyContactMethod PersonActivityInterest WebSite Health & Diet Controlled Vocabulary Activity/Interest Controlled Vocabulary ActivityInterest SocialNetworkHandle Key CompositeDemographicSegment CompositePsychographicSegment DisabilityImpairmentType ContactMethodType ContactPurposeType Address SocialNetworkService Address Telephone Geographic Controlled Vocabulary SocialNetworkType GeographicSegment KeyGeographicSegment PlaceUsageType KeyIndividualCompositeSegment CompositeHealthSegment Independent Variables Data Warehouse V3 Analytic Directions Decomposing Analytics Chain Store Age Survey Independent Attributes Innate Demographic Psychographic Geographic Interests & Activities infers Needs, Wants, Preferences defines primary drivers of Acts out retailers role in defines parameters for New Acquisition Product & Services Retail Transactions Net Sales demonstrates Demand Stewardship Retention & Recovery Cultivation Retailer Strategy Planning & Execution Pricing Promotion Place Retailer- Interaction Observed Product/Service Reviews, Orders Surveys,etc Attrition Relationship Unobserved s Behavior Dependent Attributes Behavior KPIs and Performance Measures 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 REVENUE $ 5

6 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. -- where DC_DY_BSN < ' ' group by -- 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. --,MIN(DC_DY_BSN) as FIRST_PURCH_DATE,COUNT(ID_TRN) as TRANS_COUNT,FLOOR(DATEDIFF(dd,MIN(DC_DY_BSN), ' ') / COUNT(ID_TRN)) as FREQ from DW3_STRD_SMRY_CT_RP_TRN where DC_DY_BSN < ' ' group by ),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 -- -,AVG(TRN_NET_SLS) as AVG_SPEND from DW3_STRD_SMRY_CT_RP_TRN where DC_DY_BSN < ' ' group by CT_RECENCY. Future Direction for Data 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,DATEDIFF(dd,MAX(DC_DY_BSN),' ') as RECENCY from DW3_STRD_SMRY_CT_RP_TRN,CT_FREQ as ( Quintile Value Recency Frequency Monetary Sample RFM Classification 1 CURRENT FREQUENT HIGH SPENDER 2 RECENT STEADY ABOVE AVERAGE SPENDER 3 TIMELY NEEDS REMINDER AVERAGE SPENDER 4 LOSING STEAM LOSING INTEREST BELOW AVERAGE SPENDER 5 LAGGARD SLOTH THRIFT Recency (days) Freq (days) Monetary ($) Recency Bin (1-5) Freq Bin (1-5) Monetary Bin (1-5) RECENCY FREQ RECENCY_QUI AVG_SPEND FREQ_QUINTILE SPEND_QUINTILE NTILE $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Privacy Challegne Privacy metadata Assign privacy rules to consumer, customer, worker and other entities Rules at column/ion set level -customer contracts Data usage permissions Date bounded with renewal -customer data usage audit and tracking -customer right to be forgotten 6