Why is customer intimacy important? CUSTOMER RELATIONSHIP MANAGEMENT: CONCEPTS AND TOOLS Chapter 4 Developing, managing and using customer-related databases Customer data is needed for 1 Operational purposes to deliver better experience to customers at the sales, marketing and service interfaces 1 Analytical purposes to make sense out of customer behavior classifying, clustering, predicting 1 Management purposes To help construct the overall CRM strategy customers, propositions, channels Database structure 7 steps to building a customer database 1. Define the database functions Files (tables) hold information on a single topic such as customers, products and transactions Each file (table) contains a number of records (rows). 1 In the customer database, each record (row) is a unique customer. Each record (row) contains a number of elements of data 1 E.g. customer s name, address, gender, date-of-birth and telephone number. These elements are arranged in common set of fields (columns) across the table. A modern customer database therefore resembles a spreadsheet. 2. Define the information requirements ements 3. Identify the information sources 4. Select the database technology and hardware platform 5. Build or buy applications to access and process information 6. Populate the database 7. Maintain i the database
Database functions How customer data are often stored Operational 1 A telecoms customer service representative needs to access a customer record when she receives es a telephone query 1 A hotel receptionist needs access to a guest s s history so that she can reserve the preferred type of room smoking or non-smoking, standard or de-luxe. 1 A sales rep needs to check a customer s payment history to find out whether the account has reached the maximum credit limit Analytical 1 The telecoms company wants to know which customers are signalling an intention to switch to a different supplier 1 The hotel company wants to promote a weekend break to customers who have indicated their complete delight in previous customer satisfaction surveys 1 The sales rep wants to compute his customer s profitability, given the level of service that is being provided OLTP 1 Operational data resides in an OLTP (online transaction processing) database OLAP 1 Analytical data resides in an OLAP (online analytical processing) database. 1 Information in the OLAP database is normally a summarised extract of the OLTP database, enough to perform the analytical tasks. 1 The OLAP database might also draw in data from other internal sources, such as billing data. Defining the information needed Common customer information fields The information needed depends on 1 The operational processes to be performed Sales, marketing, service 1 The analytical decisions to be made Propensity to buy, potential to churn, credit risk Distinguish between need-to-know and liketo-know information Contact data Contact history Transactional history Intentions Needs Benefits Expectations Preferences Benchmarks
To understand needs, understand motivations Benefits vary across segments Because of motivations are linked to some prefigurative force. 1 motivation to buy or consume is driven by some pre-existing condition. a company buys spare parts for its equipment because of a history of down-time in operations. In order to motivations have a future perspective. 1 motivation to buy or consume is driven by the desire to achieve some future condition a private individual might buy a second home in order to enjoy the tranquillity of its rural location. Customers buy products to experience the benefits they create. 1 Customer A buys consistent product quality, which enables them to run their manufacturing processes with fewer disruptions 1 Customer B buys the same product because of its variety of applications, thereby eliminating the requirement to maintain and manage complex inventory. Miller s expectations taxonomy Oliver s customer expectations hierarchy 1. The ideal level. What can be 2. The predicted level. What will be 3. The minimum tolerable level. What must be 4. The deserved level. What should be What the customer wants Ideal Excellent Desired Deserved Tolerance zone Zone of indifference Needed Adequate Minimum tolerable What the What the customer predicts Intolerable
Two expectations zones Why are expectations important? The zone of tolerance 1 this ranges from what must be (minimum tolerable) to what can be (desired level). The zone of indifference 1 this ranges around the customer s judgement of what is a reasonable expectation of the supplier Matching offers to the expectations, whether 1 Ideal, desired, deserved, adequate Expectations change over time 1 Ideal expectations decay into normality Not all attributes are subject to customer expectations. Customers usually have expectations of a number of attributes. 1 Not all of these attributes are equally important. Expectations ti act as the basis for satisfaction judgements. Suppliers need to understand the limits to each customer s tolerance zone and zone of indifference. Preferences for. Desirable data attributes: STARTS Communication medium? 1 mail, telephone, email, etc? If email, is plain text or html preferred? Salutation? 1 Miss, Ms, Mrs, first name, family name? Contact time and location? 1 phone anytime for urgent product recall? 1 mail to work for invoicing? 1 face-to-face at branch for news about new products? Shareable Transportable Accurate Relevant Timely Secure The attributes are enabled by the architecture The attributes are enabled by the architecture of the CRM system
Identify information sources Compiled list data for a dancewear company Internal data 1 sales, marketing, service, finance data External data 1 compiled data 1 census data 1 modelled data Secondary and primary data memberships of dance schools student enrolments on dance courses at school and college recent purchasers of dance equipment life-style l questionnaire i respondents who cite dance as an interest subscribers to dance magazines purchasers of tickets for dance and musical theatre USA geo-demographic census data Individual-level data median income average household size average home value average monthly mortgage percentage ethnic breakdown marital status percentage college educated Individual-level level data are better predictors of behavior than geo-demographic data 1 in the absence of individual-level data census data 1 in the absence of individual level data census data may be the only option for enhancing internal data can use census data about median income and average household size to predict who might be prospects for a car reseller s promotion.
Modelled data: PRIZM analysis of TW9 1UU, UK Secondary and primary data young professionals rented accommodation above average car ownership take foreign holidays read the quality press assigned to PRIZM code A101 Lifestyle: A (A-D) Income quintile: 1 (1-5) Cluster type: 1 (1-72) 0.34% of GB households Income rank: 5 (1-72) Age rank: 28 (1-72) Secondary 1 Secondary data are data that have already been collected, perhaps for a purpose that is very different from the CRM requirement. Primary 1 Primary data are data that are collected for the first time, either for CRM or other purposes. Primary data collection schemes for CRM programs Database technology and hardware platform 1. Competition entries. 1. Customers supply personal data on the entry forms. 2. Subscriptions. 1. Customers subscribe to a newsletter or magazine, again surrendering personal details 3. Registrations. 1. Customers register their purchase. This may be so that they can be advised on product updates 4. Loyalty programs. 1. New members compete application forms, providing personal, demographic and even lifestyle information Relational databases are the standard architecture for CRM databases. 1 Relational databases store data in 2-dimensional tables comprised of rows and columns. 1 In a customer database, each row is a unique customer and each column contains some attribute of that customer. 1 Each customer is given a unique identifying i number.
Customer unique identification number Criteria influencing choice of hardware platform Allows linkages to be made between several customer-related databases (e.g. transactional, product and service databases) Customer records can be linked in 3 ways 1 One-to-one. Each record in one database can be linked to one other record in another database. 1 One-to-many. Each record in one database can be linked to many records in another database 1 Many-to-many. Each record in one database can be linked to many records in another database, and each record in that database can, in turn, be linked to many records in the first. Size of the the database. 1 Even standard desktop PCs are capable of storing huge amounts of customer data. Existing technology. 1 Most companies will already have technology that lends itself to database applications. Number and location of users. 1 Many applications are quite simple, but the hardware might need to enable a geographically dispersed, multi-lingual, user group to access data for both analytical and operational purposes. CRM applications 1 CRM applications 2 1. Marketing applications 2. Sales applications 1. market and customer 1. managing the sales segmentation pipeline 2. campaign management 2. lead management 3. direct marketing 3. opportunity 4. event-based marketing management 5. multi-channel marketing 4. contact management 3. sales management 4. service applications applications 1. contact centre 1. salesperson management performance 2. customer management communications 2. workload allocation 3. enquiry handling 3. salesperson appraisal 4. helpdesk management 5. complaints management
Selecting the correct analytical applications Populating the database 1. how many variables need to be analysed at the same time? Univariate, bi-variate, multi-variate 2. do you want to describe a set of data or to draw inferences about a population? 3. what types of data are you analysing? Nominal, ordinal, interval, ratio Four methods for creating appropriately accurate customer records 1 verify the data Double-keying 1 validate the data Range validation Check for values that are missing (empty cells) Check against external sources. 1 de-duplicate the data 1 merge and purge the data Maintaining the database Single view of the customer 1. Enter data from all new transactions, campaigns and communications immediately 2. Regularly de-duplicate the database 3. Audit a subset of the files every year 4. Purge customers who have been inactive for a certain period of time 5. Drip-feed the database 6. Get customers to update their own records 7. Remove customers records on request 8. Insert decoy records, if the database is managed by an external agency Retail store Party plan Catalogue store Web-site Home shopping Integrated customer database Analytical and operational applications CRM Strategy developmentelopment and implementation External data
Data warehouses and data marts Data transformation before warehousing A data warehouse is a repository of large amounts of operational, historical and customer data. 1 Data volume can reach terabyte levels, i.e. 2 40 bytes of data. 1 Attached to the front-end of the warehouse is a set of analytical procedures 1 Retailers, home shopping companies and banks have been early adopters of data warehouses. A data mart is a scaled down version of the data warehouse. 1 Data mart project costs are lower because the volume of data stored is reduced, and the number of users is capped 1 Technology requirements are less demanding. Data standardisation 1 Personal data: m/f, M/F, male/female 1 Units of measurement: metric/imperial 1 Field names: sales value, Sale$, $val 1 Dates: mm/dd/yy, /yy, dd/mm/yyyy, /yyyy, yyyy-mm-dd Data cleaning 1 De-duplication 1 Updating and purging 1 Identify misuse of data entry fields e.g. use of phone field to record email address Mining warehoused data SEMMA - the SAS data-mining model Mining warehoused data can find 1 Associations 1 Sequential patterns Mining warehoused data can establish 1 Classifications 1 Clusters Mining warehoused data can enable predictions to be made Sample: Explore: Modify: Model: Assess: Extract a portion of the dataset for data mining Search for trends and relationships Create, select, transform variables with the intention of building a model Specify relationships between variables to predict a specific outcome Evaluate the model
Responses to privacy concerns Scope of the OECD privacy principles, 1980 1. Self-regulation by companies and associations 1. companies may publish their privacy policies and make a commercial virtue out of their transparency 2. professional bodies in fields such as direct marketing, advertising and market research have adopted d codes of practice 2. Legislation 1. Purpose specification 2. Data collection processes 3. Limited application 4. Data quality 5. Use limitation Opt-out, opt-in 6. Openness 7. Access 8. Data security 9. Accountability Legislation guarantee these rights to EU citizens Obligations on data controllers Notification 1 Individuals are to be advised with without delay about what information is being collected, and the origins of that data, if not from the individual Explanation 1 Of the logic behind the results of automated decisions based on customer data. For example, why a credit application was rejected. Correction/deleting/blocking 1 Of data that does not comply with legislation. Objection 1 Individuals can object to the way their data is processed (opt-out). Where the objection is justified, the data controller must no longer process the information Only collect and process data for legitimate and explicit purposes Only collect personal data when individual consent has been granted, or is required to enter into or fulfil a contract, or is required by law Ensure the data is accurate and up-to-date At the point of data collection, to advise the individual of the identity of the collector, the reason for data collection, the recipients of the data, and the individual s rights in respect of data access, correction and deletion Ensure that the data is kept secure and safe from unauthorized access and disclosure.
W3C s approach to internet privacy (P3P) contains 3 elements (1) W3C s approach to internet privacy (P3P) contains 3 elements (2) 1. A personal profile 1. Each Internet user creates a file consisting of personal data and privacy rules for use of that data. 2. Personal data might include demographic, life-style, preference and click-stream data. 3. Privacy rules are the rules that the user prescribes for use of the data, e.g. opt-in or opt-out rules, and disclosure to third parties. 4. The profile is stored in encrypted form on the user s hard drive, can be updated at any time by the users, and is administered by the user s Web browser. 2. A profile of web-site privacy practices. 1. Each Web-site discloses what information has been accessed from the user s personal profile and how it has been used. 3. Automated protocols for accessing and using the user s data. 1. This allows either the user or the user s agent (perhaps the Web browser) automatically to ensure that the personal profile and the privacy rules are observed. 2. If compliance is assured, then users can enter Web- sites and transact without problem.