BGS Customer Relationship Management Chapter 5 CRM and

Introduction • Data management is a key CRM enabler • Data integration is a series of steps • Critical path to create a single accurate view of the customer • Manage the customer interaction

Managing Customer Interactions • Customer perceptions driven by interaction experiences • Digital age has enabled the customer to expect more • Improper management undermines relationship between the organization and the customer • Proper management requires a centric view of the customer

Managing Customer Interactions • Successful management allows organizations to – – Create and sustain loyalty Differentiate itself from competition Grow relationships Increase favorable customer word-of-mouth • Attaining a centric view of the customer requires data integration across the enterprise

Customer Data Integration (CDI) Problem • • • Data capture and storage process variances Disparate databases Real-time customer interaction Data latency Lack of standards Data inaccuracy

CDI Definition and Requirements • CDI: A data management process where all customer and prospect data is consolidated to create a single accurate view of the customer • All organization points of customer interaction must have access to an accurate and current customer centric view • Data to be distributed accurately to points of interaction in a timely manner

CDI Definition and Requirements • CDI requires: – Enabling technology to manage initial and ongoing data integration efforts – Customer linkage capability – Organization-wide adoption of technology and customer linkage

House-Holding Concepts • Individuals living at the same address and having the same last name are considered to be in the same household • House-holding allows an organization to view a customer at several levels – Individual level – Household level • Mechanics for creating households are similar for consumers and businesses

House-Holding Concepts • Consumer house-holding considerations – Children have temporary address while attending college – People have secondary residences (e. g. , snowbirds) – Changing norms (e. g. , home office, nontraditional family) – Ethnic names

House-Holding Concepts • Business house-holding is more complex – Different addresses and different names but same organization – People within organization are new, promoted, transferred, and leave the organization – Virtual offices

CDI Steps Identify Touch Points • Any area that an interaction can occur between an organization and a customer or prospect • Interaction medium may vary – Human to human (e. g. , POS) – Human to human with technology as enabler (e. g. , customer communicating via Web chat session) – Human to technology (e. g. , customer interfacing with computer telephony) – Technology to technology (e. g. , Web transaction)

CDI Steps Identify Touch Points • Position in value chain can present challenges – – Mfg. not having access to retail transaction Retailer not having access to mfg. warranty transaction Outsourced Web hosting and data capture quality issues Retailer not having access to subcontractors for delivery and customer service – Organizations not capturing informal data from outsourced telemarketing firm • B 2 C business interface examples – POS, order processing, customer service, distribution, repair/maintenance, PR, survey, promotion response, unsolicited communication from consumer, noncountry of origin

CDI Steps Identify Touch Points • B 2 B business interface examples – procurement, accounts payable and receivable, sales, technical support, order processing, customer service, distribution, repair/maintenance, PR, survey, promotion response, investing community, value chain partners, unsolicited communication

CDI Steps Define How Data is Collected • Technology – Web forms, Web free form text, computer telephony, kiosks, self-service POS, fax • Human – Verbal, written, observation

CDI Steps Establish Data Collection Rules • Identify data variables to be collected (e. g. , demographic, psychographic, geographic, behavioral, transaction) • Define priority scheme for data variable capture based on source

Consumer Data Rule Construction Data Element Age Occupation Homeowner Touch Point B Touch Point C Touch Point D Data Used $65 K-$70 K $120 K N/A X $65 K 35 -40 37 37 X 37 Professional Other Unskilled X Professional N/A Income Touch Point A N/A Yes X Yes N/A 2(4 -8 yrs) 1 @ 4 yrs, 1 @ 3 yrs 2 (4 -8 yrs) Children

Business Data Rule Construction Data Element Industry Research Web Sites Sales Personnel Trade Publications Data Used Annual Revenue $10 MM $11 -13 MM $10 MM N/A $10 MM 4, 000 5, 500 3, 500 – 4, 000 3, 800 - 4, 200 4, 000 Plant Square Footage

CDI Steps Manage Input Process after Collection • Timing – Process step dependency – Data flow and scheduling • Security – Corrupted or lost – Unauthorized use or access • Inconsistency worse than inaccuracy

CDI Steps Place Data in Common Formats • Provides for: – Efficient data hygiene processing – Enhances matching logic for postal, linkage, and enhancement processing • Is required by some software processing • May not always be necessary depending on software capability in handling dynamic processing

CDI Steps Split Linkage Data • Split data into two categories: nonlinkage and linkage • Nonlinkage data can contain many variables and is not needed for data hygiene, postal, matching, and secondary data enhancement processing • Linkage data is required for the data hygiene, postal, matching, and enhancement steps • Splitting the data increases processing efficiency and reduces data management efforts

CDI Steps Standardize and Correct Linkage Data • Ensure address variables from all touch points are in a standard format for an optimal address correction process • Utilize commercial software to correct address components – Outsource or acquire software and process internally – Process in real-time or batch mode

CDI Steps Postal Processing • Varies by country • Mandatory for certain types of mailing in United States. • Usually an outsourced function • Required to ensure most current addresses are on file for customers and prospects, which supports the relationship build and sustain strategy

CDI Steps Postal Processing • 18 -20 percent of the population change addresses annually • LACS – Locatable Address Conversion System • NDI – National Deliverability Index • DSF 2 – Delivery Sequence File Second Generation

CDI Steps Customer Linkage Identification • Sometimes referred to as deduplication or merge/purge • Objective – identify each appearance of an individual or business and assign an identifier to each linkage record

CDI Steps Customer Linkage Identification • Record linkage categories include Manual, Deterministic, or Probabilistic – Manual is not feasible for large files – With deterministic, individuals or companies are said to be the same if there is a match on certain variables – Probabilistic uses weights and probability algorithms to determine if two or more records are the same person or company

CDI Steps Customer Linkage Identification • Phonemic name compression • Customer linkage approaches – – Tight versus loose matching Industry nuances Some industries prone to less accuracy Organizational structure influences business rule definitions – Changing linkage rules require extensive rework to database contents

CDI Steps Data Enhancement • • • Nonprimary sources of data Highly dependent upon linkage capability Can be costly Source credibility must be determined Determine if the process will be outsourced

CDI Steps Data Suppression • Purpose – Avoid contact with nonprofitable customer – Adhere to customer request for no interaction – Legal and ethical conformance (e. g. , deceased, young children, prisons, military, fraud detection) – Optimize marketing investments • Perform using internal information as basis for suppression (e. g. , opt-out, fraud detection, avoid nonprofitable customers) • Use external suppression files (Table 5. 10)

CDI Steps Consolidate Linkage and Non-Linkage Data • Match linkage to non-linkage data on sequence numbers • Consolidate and aggregate appropriate variables • Prepare data for update process to respective Database entities • Data is not actionable knowledge

CDI Ancillary Benefits • Fraud detection • Data anomalies • Identify data collection areas that need improvement or that present new opportunities • Identify business process areas that may need improvement, are unnecessary, or that are missing

Summary • CDI is a CRM key success factor • CDI is dynamic due to technology changes, business objective changes, new best • CDI can vary by industry or country • CDI has ancillary benefits