CRM: Modelling Customer Relationships


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CRM: Modelling Customer Relationships

  1. 1. Modeling Customer Relationships A Flexible, Integrated Architecture Enables Customer-Centric Marketing s
  2. 2. A b s t r a c t This white paper draws upon the lessons learned by Sequent Computer Systems in implementing large-scale technology platforms to support Customer Relationship Management (CRM) strategies within major organizations worldwide. It is assumed that the reader is familiar with the strategic direction of most customer-focused organizations and understands the cyclical and repeatable nature of a technology-driven marketing strategy. Interested readers are likely to be concerned with how they might model customer relationships in a way which will support the transition from present product- focused views of marketing intelligence to more useful (and prof- itable) customer-focused views. This paper provides the data architect or modeler with a generic template for modeling customer data. This approach is not product or technology specific but does provide a flexible data architecture for integrating the various technology components that use data to drive marketing. This paper also highlights commonly faced problems that occur when modeling customer data. Editor: David Puckey Sequent Computer Systems UK Professional Services 1
  3. 3. Introduction used to ensure that the core data struc- The strategic importance of managing tures in the CRM technology layer customer relationships both drives support the integration of the various and is driven by technology. In par- components of the marketing process ticular, this applies to data and the and reduce the time required to design increasingly sophisticated and useful and execute a campaign. This approach ways in which data is used to model enables the creation of a complete model relationships and to drive contact of customer relationships over time. strategies. At the core of any technology enabler for CRM is the customer The Evolution of the database. The customer database rep- Customer Database resents the data hub that integrates Current approaches to the design of the the various statistical modeling, cam- customer database fall broadly into two paign management, contact history camps. The first—the flat earth view of and response tracking components of the world—hails from the glory days of the marketing campaign lifecycle. target marketing in the late 1980s. This is true whether the database is This approach, which is popular with used for the execution of marketing list providers and bureau operations, strategies (e.g., generates mailing lists), utilizes the concept of the customer or whether it exists purely as an file or list. Such a list tends to offer a analysis engine that passes contact current snapshot of the customer or strategies and information to a separate prospect base and is often the product customer interaction platform for of much tortuous cleansing, de-duplication execution (e.g., customer call centers). and point-in-time segmentation. This approach makes it difficult to analyze the The technology layer and its integration ups-and-downs of an organization’s with emerging business processes is relationship with a customer over time therefore key to the successful imple- due to its current snapshot view of the mentation of a data-driven Customer customer and prospect base. Further, it Relationship Management strategy. This typically lacks the transaction-level detail paper describes, in a generic way, an and promotional history needed to model approach that Sequent has successfully customer behavior. Evolution of Customer Database Campaign Customer Relationship Management Management s contact horizon s one-shot s sequence s output s offer s information s systems s mail/phone s touchpoint s execution s manual s scheduled s departments s marketing s front-office s data types s purchases s contacts s update interval s monthly s daily s reaction time s billing cycle s transaction s goal s reduce waste s add revenue Source: Raab and Associates Figure 1: Customer databases evolve with integration of technology and business processes 1
  4. 4. The second approach has evolved from approach incorporates the maximum the data warehousing movement and degree of analytical flexibility for the Sequent’s experience in helping hundreds marketer and marketing analyst with of organizations design and implement the efficient scoring, segmentation and data warehouses. Sequent has developed extraction of data to execute marketing a mature methodology for delivering campaigns or contact strategies. It also rapid business benefit by integrating places the customer or prospect at the sophisticated analytical tools with center of the model and seeks to model subject-oriented and time-consistent all facets of a relationship with that central databases. Such systems customer over the known lifetime of typically concentrate on the delivery the relationship. Sequent’s approach to of business intelligence and are generally the design of customer databases is not not designed to plug directly into an list based and is not designed to simply organization’s day-to-day operations. support ad-hoc, point-in-time marketing However, the modeling techniques solutions. Rather, the objective is to give employed by Sequent for the delivery the marketer true insight into the vari- of successful data warehousing projects ability of his relationship with a customer represent a radical shift in emphasis or customer segment over time and to from both flat earth views of data and deliver seamless integration with the the microscopic views of data used in widest possible choice of campaign online transaction processing (OLTP) management and statistical modeling systems. Sequent’s dimensional view of tools available. data provides the optimum combination of analysis of facts over time and high Elements of a Customer system performance when dealing with Relationship Management large data volumes. Database There are a number of required features Sequent’s approach to successfully of a CRM database that the architect delivering large-scale technology must integrate in order to support the platforms to support CRM strategies marketing lifecycle. These are (in no uses the best attributes from both particular order): of the previous approaches. This PRODUCT HOLDINGS PRODUCT USAGE CONTACTS WITH CUSTOMERS EVENTS TIME Figure 2: Typical facets of a customer relationship that need to be tracked over time 2
  5. 5. CONTACTS EVENTS CUSTOMER s AGE s GENDER s ADDRESS s SEGMENT_ID s PROPENSITY SCORE s SUPPRESSIONS PRODUCT PRODUCT HOLDINGS USAGE Figure 3: Customer focus is key. Each facet of a relationship may be treated as an island of analysis, linked centrally to the customer s Customer or prospect focus Taking a Lifetime View s All relevant facets of the relationship of the Customer over time In order to fully realize a CRM strategy, s Integration of external prospect lists the marketer must have information s Integration of external data classifi- that enables him to take a lifetime view cations of the relationship. A relationship is s Integration of external data most usefully defined as the starting enrichment point at which the organization has an s Ability to directly score the database initial interaction with a prospect. This and segment the database many times relationship then needs to be tracked s Ability to evaluate different campaigns as the prospect is encouraged to climb and treatment strategies over time the loyalty ladder from prospect to and across millions of transactions customer and eventually to highly and customers valued customer. The marketer needs s Campaign management, prioritiza- to see and understand past events, tion, etc. contacts and purchase information in s Ability to predict future customer order to assess the current and future behavior based on past behavior profitability of the relationship. The commonly used marketing analysis of It is not possible to achieve all of the recency, frequency and monetary value above features using either a flat file of transactions indicates some of the approach or a standard data ware- facets of the relationship that should housing approach alone. be tracked. 3
  6. 6. In Sequent’s experience of facilitating at a given point in time. For example, client workshops to establish the busi- the marketer may take a point-in-time ness requirements for a CRM solution, view of the relationship, a view over four relationship facets appear common time or make prescient predictions for to most organizations. These facets are: the future. Information about these four facets of a customer relationship enable Product Holding–What products has a the marketer to answer questions such customer purchased and what products as: How many customers have bought do they currently hold ? product X? How many customers display a repeatable purchasing pattern? Product Usage–How has the customer How often have I contacted this customer used that product? For example, can an and when? Who are my most profitable increase in credit card usage be attributed customers? What events or contacts to some prior interaction with the cus- occurred prior to customer defection? tomer or some promotional activity? The approach taken by Sequent to sup- Contacts–What has the organization’s port this kind of questioning is to place interaction with the customer been over a customer table at the center of the time and what were the outcomes? model and to surround it with satellite dimensional schema (star schema) rep- Events–What other events have resenting each facet of the relationship occurred, either within the life of the to be modeled. Modeling the facets of customer (e.g., marriage) or externally the relationship dimensionally allows to the relationship (e.g., competitor who, what, when, where style analysis. activity)? For example: Which segment bought which products and what contacts Each of these facets may be treated by preceded which purchase? Where do the modeler as an island of analysis the contacts live, and how do they linked centrally to an individual customer like to be addressed? Customer Cancellation of Terminate Customer Initial Service part of policy Behavior Inquiry Call No Activity Purchase No Activity Re-initiate Customer Acquisition Mail Information Customer "Next to Buy" New Product Customer Valuation/ Winback Action Campaign Kit/Thank You Valuation model Solicitation Solicitation Sequent Campaign Query/ Query/ Campaign Campaign Data Mining Campaign Decision Management/ Reporting/ Reporting/ Management/ Management Application Management Advantage Call Center OLAP OLAP Call Center Application Application Application Figure 4: Example—The Customer-Centric Model at an Insurance Company 4
  7. 7. The customer-centric nature of the Mr. Jones does not respond to the model also lends itself well to the receipt of the information pack, and prudent de-normalization of often- after three months the marketer plans a used facts, such as disposable income campaign targeted at Mr. Jones and all estimates, onto the customer table and the other Mr. Joneses who have inter- helps facilitate the efficient extraction acted with the organization but not of contact lists and integration with purchased any products in the last statistical modeling tools, such as SAS three months. or Unica. The customer-centric model also supports very well the iterative In this case, a query can be run against nature of the marketer’s questioning, the database asking, Who has contacted such as: How many customers hold us in the last three months with a con- product Y? Which of those customers tact type of inquiry? This query will are profitable? Which of those customers generate a list of keys into the customer did I contact last week and which of or prospect table, which, without further them complained about the contact? It refinement, could be used to generate a is also possible to assess what behavioral contact list. However, it is more likely changes are exhibited as a result of that the marketer’s questioning will identifiable interactions with the cus- continue further—How many of these tomer. Once the marketer has exhausted customers or prospects were sent an his questioning, which helps refine the information pack? The result set from contact list names, addresses and saluta- this query will be matched against the tions may be simply extracted from the result set from the last query to further customer table using the relevant keys. refine the list of keys. This process may Current suppression indicators and be further refined by asking, How many propensity scores may also be stored people in this list do not have a product against the central customer record, holding? Once the marketer has com- allowing the possible automation of pleted his refinement of the list, it is a standard hygiene filtering. simple, and highly performant, exercise to take the resulting list of keys and The Customer-Centric Model extract the name, address, salutation at an Insurance Company data, etc. from the central customer To see how this model might work, or prospect table and perform further take the example of an insurance filtering based on suppressions on the business. The firm’s relationship with customer table or assigning customers Mr. Jones begins when he makes an to campaign cells for different treatments initial inquiry about health insurance based on segmentation keys on the cus- via the organization’s call center. This tomer record. Once the contact list is initial inquiry is the result of a press finalized, the customer keys are used to advertising campaign that reached populate the contact table and to record Mr. Jones; this fact is recorded. the fact of the outbound contact. By storing all of this data in a centralized In response to his interest in the company’s relational database management system health insurance offering, the insurance (RDBMS), it is a relatively simple matter business sends Mr. Jones an information to make this data available to sophisti- pack. This step is also captured and cated campaign management tools and recorded in the database. At this point, statistical modeling tools. These tools Mr. Jones does not have a product hold- interface easily with an open RDBMS, ing, but his name and address and con- such as Oracle, and almost without tact records exist within the database. exception, such tools feature native connectivity options. 5
  8. 8. CAMPAIGN MANAGEMENT REPORTING BUSINESS CUSTOMER INTELLIGENCE SEGMENTS DATABASE SCORING MODEL CONTACT LISTS EXTRACTION TOOL DATA MINING Figure 5: An integrated architecture reduces the marketing cycle Those readers familiar with the pro- marketing analysis or campaign man- cessing dynamics of most RDBMS will agement) and to temporarily satisfy immediately spot a major dependency parochial needs, it has left a troublesome of this model—the various software legacy for the integrator of the technol- components deployed to support the ogy layer who seeks to accelerate the marketing lifecycle must allow the marketing cycle, empower the marketer generation of interim result sets. This and reduce the marketing department’s is absolutely crucial in order to support dependency on highly skilled and the marketer’s analytical processes as he expensive (and often obstructive) constantly shrinks and expands potential database experts. Such function-focused target lists, possibly to generate the solutions have ensured that the walls required list size to match a budget that block the implementation of a allocation. Already, a number of tools virtuous circle of continuous improve- vendors are acutely in-tune with the ment in the marketing process remain mindset and thought processes of the solid. The proliferation of file formats, modern marketer. APIs and unnecessary processing layers needed to integrate these elements have Integrated Infrastructure delivered a full employment charter for Supports Marketing Process those who wrangle with the complexity In the past, database marketing solutions of the technology layer at the expense often focused on individual user com- of marketing responsiveness and creativity. munities participating in the overall marketing process. While this focus Sequent’s solution to such technical has managed to hit the sweet spots of anarchy is to focus firmly on a techno- these often isolated communities (e.g., logical infrastructure that supports and 6
  9. 9. integrates the overall marketing process, s Identification of significant life and underpins the progressive develop- events (coming of age, birth, ment of a relationship management marriage, etc.) strategy. The use of a centralized s Analysis of geodemographic data relational database and open systems to by household manage customer data, contact history and relationship history allows the easy Multiple households can be problematic integration, at the data level, of the var- for both the marketer and the system ious technologies deployed at different designer. Individual customers may stages in the marketing process. Analysts’ have multiple addresses, each of which models may be stored alongside the is related to the customer via the product actual data, and scoring and segmentation holding. For example, Mr. Jones has a keys can be made directly available to main residence in the city and a weekend campaign management and campaign retreat by the coast. Mr. Jones has a scheduling software. The automation of household insurance policy for each routine communications is simplified address. An insurance marketer may and database triggers can be utilized to wish to sell Mr. Jones a life insurance make marketing more event driven. policy. However, for the modeler, a household is just a simple grouping of Typical Data Modeling individuals. Specific business questions Challenges must be answered in order to track This section details some of the data the household movements of individuals. modeling challenges, which, in The difficulty is in the actual identification Sequent’s experience, are common of a household—particularly in high- across a number of industries and density urban residential areas or areas organizations. with a highly transient population. Householding There are several approaches to handling The grouping of individuals by house- customer householding, de-duping and hold or relationship patterns is often geocoding challenges. These include: a difficult process in product-focused s Service Bureau operations legacy systems. These systems often s Integrating specialized software have great difficulty in even identifying the tools to perform this function on individual responsible for purchasing a a regular basis (this also requires given product. The benefits of groupings process integration for proper and for the relationship marketer are many: effective handling) s Avoidance of unnecessary duplicate contacts per household A number of marketing data processing s Understanding loyalty patterns bureau services perform household among relationship groups identification, based on, for example, s Identification of cross-sell and electoral register information, etc. up-sell opportunities (e.g., family However, such matching is never policies, etc.) 100 percent accurate. 7
  10. 10. Products Held by Groups grouping. In some cases, Sequent has of People allowed a “degree of confidence” value Certain types of products, for example to be assigned to the grouping record to joint bank accounts, introduce a many- provide the marketer with a coefficient to-many relationship between product that validates assumptions. The business holdings and persons. This fact, if rules for deriving this coefficient clearly modeled literally, can cause performance evolve over time, and can result in the problems in the database and confuse creation of specific profiling questions campaign management and extraction targeted to specific customers during tools seeking to identify a single interactions. prospect. This is particularly true in cases where organizations are transi- As with householding, some marketing tioning to a customer-focused marketing data providers can perform unique strategy yet still require the ability to person identification based on postal market in the interim period based on lists, real estate listings, electoral rolls, product holding attributes. This situation and other data. This identification is common in large businesses that cannot activity can be cumbersome as it possibly switch from a product to a involves exporting and re-importing customer focus overnight. The only data periodically. If the grouping of answer to this problem is a business one. seemingly multiple individuals into Identifying a primary marketing contact one is handled as a grouping table, for a product holding can simplify the the impact on, for example, referential problem in some cases. integrity within the database can be minimized. However, this kind of Person Matching group can also make the model more Another key challenge for the designer complex—with a possible impact on of a CRM database is the identification performance. of individuals. Often, seemingly multiple individuals on the database are in fact Unfortunately, there are no magic cures the same person, albeit at a different for the problem of person matching, point-in-time, or with a different product and the database modeler should be holding, or at a different address. wary of the purveyors of such cures. Organizations with multiple operational systems serving multiple customer touch Classing and Banding points often find that the non-uniformity A number of marketing database designs of input validation across these systems use fields such as “date of birth” or leads to situations where Mr. John Jones, “age” on the customer record. Though Mr. J. Jones and Mr. J. B. Jones at the there is a clear use for such fields, mar- same address could perhaps be one, keters rarely wish to contact people who two or three actual people. This problem are, for example, 51 or 23 years of age. is further exacerbated when external Usually, the marketer wants to target prospect lists are brought into the people aged between 25 and 35 or database. Once again, the modeler can those who are past retirement age. incorporate a simple grouping of people Such targeting calls for some sort of within the database design but the banding of customers to reduce wasted problem is identifying the actual processing and simplify the process for the marketer. 8
  11. 11. Age is not the only candidate attribute approach will, on its own, support the for banding. The modeler should seek management of customer relationships to understand other candidates and over time. Likewise, neither will integrate include these in the model. all components of the marketing process in the most efficient way. Regularly Used Measures Initially, and over time, the modeler of The template presented in this paper the customer database should seek to may form the basis of the data architect identify those frequently asked market- or analyst’s initial attempts to define ing questions, such as: Who earns more data structures, which will support both than $20,000? Who has made more of the above objectives. This template than four insurance claims in the last reflects the work Sequent has done with period, etc.? It makes little sense to a number of major organizations to have multiple marketing campaign support their database marketing activities designers all scanning the product usage and to drive the strategic implementation table over and over again. This can be of Customer Relationship Management avoided by denormalizing regularly at both the business and the systems used measures directly onto the cus- levels. tomer or prospect record. CRM is an emerging strategy and as Suppressions such requires a fresh approach to sys- Most organizations are able to identify tems design, along with the flexibility to a number of standard reasons for sup- accommodate unexpected change. pressing marketing communications. Many piecemeal or point solutions in Suppressions can range from blanket the market fail to take an integrated “do not communicate at all” indicators view of the entire marketing lifecycle to “do not market a specific product” and focus only on data structures to to this individual. These suppressions support their own specific components should be held directly on the customer of that lifecycle. As CRM matures as an or prospect record to enable swift and operational reality, it is imperative that easy filtering of targets. organizations have an integrated view of business processes and data. Failure Summary to take an integrated view of requirements will lead to significant effort and cost While both flat file and standard data reengineering the organization’s market- warehousing approaches to the customer ing databases—sometimes comprising database will allow analysis of customers many terabytes of data. and the selection of target lists, neither 9
  12. 12. Corporate headquarters: American headquarters: Sequent Computer Systems, Inc. 15450 SW Koll Parkway Beaverton, Oregon 97006-6063 (503) 626-5700 or (800) 257-9044 European headquarters: Sequent Computer Systems, Ltd. Sequent House Unit 3, Weybridge Business Park Addlestone Road Weybridge, Surrey KT15 2UF England +44 (0) 1932 851111 Asia/Pacific headquarters: Sequent Computer Systems (Singapore) Pte Ltd. 80 Robinson Road, #18-03 Singapore 068898 +65 223 5455 With offices in: Australia, Austria, Czech Republic, France, Germany, Hong Kong, India, Indonesia, Italy, Japan, Korea, Malaysia, The Netherlands, New Zealand, Philippines, Poland, Russia, Singapore, United Kingdom, and United States. With distribution partners in: Bahrain, Brunei, Croatia, Czech Republic, Egypt, Greece, Hong Kong, Indonesia, Japan, Korea, Kuwait, Malaysia, Mexico, Oman, People’s Republic of China, Philippines, Russia, Saudi Arabia, Slovenia, South Africa, Sri Lanka, Taiwan, Thailand, United Arab Emirates, and Yugoslavia/Serbia. Sequent is a registered trademark of Sequent Computer Systems, Inc. All other trademarks and registered trademarks are the property of their respective owner. Copyright ©1998 Sequent Computer Systems, Inc. All rights reserved. This document may not be copied in any form without written permission from Sequent Computer Systems, Inc. Information in this document is subject to change without notice. Printed in U.S.A. CP-1340 12/98 s