Customer relationship management (CRM) is a broad term that covers concepts used by companies to manage their relationships with customers, including the capture, storage and analysis of customer information.
• 1 Aspects of CRM
o 1.1 Operational CRM
o 1.2 Collaborative CRM
o 1.3 Analytical CRM
o 1.4 Strategy
o 1.5 Technology Considerations
o 1.6 Successes
• 2 Privacy and Data Security
• 3 Customer relationship management software
4 See also
Aspects of CRM
There are three aspects of CRM which can each be implemented in isolation from one another: 1-Operational CRM- automation or support of customer processes that include a company’s sales or service representative 2-Collaborative CRM- direct communication with customers that does not include a company’s sales or service representative (“self service”) 3- Analytical CRM- analysis of customer data for a broad range of purposes
META Group (acquired by Gartner in April 2005) developed this conceptual architecture in the late-1990s, and dubbed it the “CRM Ecosystem.”
Operational CRM provides support to "front office" business processes, including sales, marketing and service. Each interaction with a customer is generally added to a customer's contact history, and staff can retrieve information on customers from the database as necessary.
One of the main benefits of this contact history is that customers can interact with different people or different contact “channels” in a company over time without having to repeat the history of their interaction each time.
Consequently, many call centers use some kind of CRM software to support their call centre agents.
Collaborative CRM covers the direct interaction with customers. This can include a variety of channels, such as internet, email, automated phone/ Interactive Voice Response (IVR). It can generally be equated with “self service”.
The objectives of Collaborative CRM can be broad, including cost reduction and service improvements. Driven by authors from the Harvard Business School (Kracklauer/Mills/Seifert), Collaborative CRM seems to be the new paradigm to succeed the leading Efficient Consumer Response and Category Management concept in the industry/trade relationship. Many organizations are searching for new ways to achieve and retain a competitive advantage via customer intimacy. Collaborative CRM gives a 360 degree feedback of the customer, industry and trade are pooling their respective customer data in the different sales and communication channels and therefore generate better customer insight.
Analytical CRM analyzes customer data for a variety of purposes including....
• design and execution of targeted marketing campaigns to optimize marketing effectiveness
• design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention
• analysis of customer behavior to aid product and service decision making (eg pricing, new product development, etc)
• management decisions, e.g. financial forecasting and customer profitability analysis
• risk assessment and fraud detection, in particular for credit card transactions
Analytical CRM generally makes heavy use of Predictive analytics.
Customer Satisfaction Myths
Several commercial CRM software packages are available which vary in their approach to CRM. However, CRM is not just a technology, but rather a holistic approach to an organisation's philosophy in dealing with its customers. This includes policies and processes, front-of-house customer service, employee training, marketing, systems and information management. Hence, it is important that any CRM implementation considers not only technology, but furthermore the broader organisational requirements.
The objectives of CRM strategy must consider a company’s specific situation and its customers' needs and expectations..
The technology requirements of a CRM strategy are very complex and far reaching. The basic building blocks include
• A database to store customer information. This can be a CRM specific database or an Enterprise Data warehouse. There are many vendors in this space including IBM, ORACLE, Teradata etc.
• Operational CRM requires customer agent support software such as Siebel Systems etc
• Collaborative CRM requires customer interaction systems, eg an interactive website, automated phone systems etc
• Analytical CRM requires statistical analysis software such as Excel, SAS, etc, as well as software that manages any specific marketing campaigns such as Teradata Relationship Optimizer, Unica, etc
Each of these can be implemented in a very basic manner or in a high end complex installation.
While there are numerous reports of "failed" implementations of various types of CRM projects, these are often the result of unrealistic high expectations and exaggerated claims by CRM vendors.
In contrast there are a growing number of successes. One example is the National Australia Bank (NAB) which has pursued a CRM strategy for over ten years and has won numerous awards for its efforts.  
Privacy and Data Security
The data gathered as part of CRM must consider customer privacy and data security. Customers want the assurance that their data is not shared with 3rd parties without their consent and not accessed illegally by 3rd parties.
Customers also want their data used by companies to provide a benefit for them. For instance, an increase in unsolicited telemarketing calls is generally resented by customers while a small number of relevant offers is generally appreciated by customers and consumers.
Customer relationship management software
Customer relationship management software is defined as business management and automation of the front-office divisions of an organization. CRM software is essentially meant to address the needs of Marketing, Sales & Distribution and Customer Service and Support divisions within an organization and allow the three to share data on prospects, customers, partners, competitors and employees. The purpose of CRM software is to manage the customer through the entire lifecycle, i.e. from prospect to qualified opportunity to order.
CRM software automates many of the needs of Marketing, Sales and Support users, such as Telephony, or the ability to conduct phone calls and manage call data, and tools to capture, share and manage automated alerts on lead data as it passes through the sales pipeline. CRM software provides a standard framework for pushing leads through a sales pipeline and managing it amongst many stakeholders in real time, in order to provide better customer relations and grow revenues by creating more sales, and losing fewer customers.
CRM software helps organizations achieve the goal of excellent customer relations by measuring key performance indicators collected by the CRM software about customer lifecycle behaviour to isolate those marketing campaigns that drove the most and best quality leads, by allowing people to manage more business with less effort, never losing data on customers to eliminate deals slipping through the cracks and by providing good customer support to maintain the relationship with the customer for years to come.123
• Business intelligence
• Customer experience
• Customer experience management
• Customer Intelligence
• Customer Reference Management
• Database marketing
• Predictive analytics
• Predictive dialer
• Sales force management system
• Customer Service
Business intelligence (BI) is a business management term which refers to applications and technologies which are used to gather, provide access to, and analyze data and information about their company operations. Business intelligence systems can help companies to have a more comprehensive knowledge of the factors affecting their business, such as metrics on sales, production, internal operations, and they can help companies to make better business decisions. Business Intelligence should not be confused with competitive intelligence, which is a separate management concept.
• 1 Rationale for using BI
• 2 BI Technologies
• 3 History
• 4 The Future of Business Intelligence
o 4.1 Key Intelligence Topics
o 4.2 Designing and implementing a business intelligence program
• 5 See also
Rationale for using BI
Business Intelligence applications and technologies can enable organizations to make more informed business decisions, and they may give a company a competitive advantage. For example, a company could use business intelligence applications or technologies to extrapolate information from indicators in the external environment and forecast the future trends in their sector. Business intelligence is used to improve the timeliness and quality of information, and enable managers to be able to better understand the position of their firm as in comparison to its competitors.
Business intelligence applications and technologies can help companies to analyze changing trends in market share; changes in customer behaviour and spending patterns; customers' preferences; company capabilities; and market conditions. Business intelligence can be used to help analysts and managers determine which adjustments are most likely to respond to changing trends.
Using BI systems can help companies to develop a more consistent, data-based decision making process for business decisions, which can produce better results than making business decisions by "guesswork." As well, business intelligence applications can enhance communication among departments, coordinate activities, and enable companies to respond more quickly to changes (e.g., in financial conditions, customer preferences, supply chain operations, etc.). When a BI system is well-designed and properly integrated into a company's processes and decision-making process, it may be able to improve a company's performance. Having access to timely and accurate information is an important resource for a company, which can expedite decision-making and improve customers' experience.
In the competitive customer-service sector, companies need to have accurate, up-to-date information on customer preferences, so that the company can quickly adapt to their changing demands. Business Intelligence enables companies to gather information on the trends in the marketplace and come up with innovative products or services in anticipation of customer's changing demands. Business Intelligence applications can also help managers to be better informed about actions that a company's competitors are taking. As well, BI can help companies to share selected strategic information with business partners. For example, some businesses use BI systems to share information with their suppliers (e.g., inventory levels, performance metrics, and other supply chain data).
BI systems can also be designed to provide managers with information on the state of economic trends or marketplace factors, or to provide managers with in depth knowledge about the internal operations of a business.
For the BI technology system to work effectively, companies address the need to have a secure computer system which can specify different levels of user access to the data 'warehouse', depending on whether the user is a junior staffer, manager, or executive. As well, a BI system needs to have sufficient data capacity, a plan for how long data will be stored (data retention). Analysts also need to set benchmark and performance targets for the system.
Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured data, such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI vendor typically develops Business Intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.).
Business intelligence software and applications includes a range of tools. Some BI applications are used to analyze performance, projects, or internal operations, such as AQL - Associative Query Logic; Scorecarding; Business activity monitoring; Business Performance Management and Performance Measurement; Business Planning; Business Process Re-engineering; Competitive Analysis; User/End-user Query and Reporting;Enterprise Management systems; Executive Information Systems (EIS); Supply Chain Management/Demand Chain Management; and Finance and Budgeting tools.
Other BI applications are used to store and analyze data, such as Data mining (DM), Data Farming, and Data warehouses;Decision Support Systems (DSS) and Forecasting; Document warehouses and Document Management;Knowledge Management; Mapping, Information visualization, and Dashboarding; Management Information Systems (MIS);Geographic Information Systems (GIS); Trend Analysis; Software as a service (SaaS) Business Intelligence offerings (On Demand)- similar to traditional BI solutions but software is hosted for customers by a provider. ;Online Analytical Processing (OLAP) and multidimensional analysis; sometimes called "Analytics" (based on the "hypercube" or "cube"); Real time business intelligence;Statistics and Technical Data Analysis; Web Mining, Text mining and Systems intelligence.
Other BI applications are used to analyze or manage the "human" side of businesses, such as Customer Relationship Management (CRM) and Marketing tools and Human Resources applications.Web Personalization For examples of implemented Business Intelligence systems, see the BI screenshot collection at The Dashboard Spy.
Sun Tzu's The Art of War highlighted the importance of collecting and analyzing information. Sun Tzu claimed that to succeed in war, a general should have full knowledge of their own strengths and weaknesses and full knowledge of the enemy's strengths and weaknesses. Lack of either one might result in defeat.
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources to properly analyze the data, and as a result, companies often made business decisions primarily on the basis of intuition.
As businesses started automating more and more systems, more and more data became available. However, collection remained a challenge due to a lack of infrastructure for data exchange or to incompatibilities between systems. Analysis of the data that was gathered and reports on the data sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making continued to rely on intuition.
In modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have set up repositories to store this data. Improved Extract, transform, load (ETL) and even recently Enterprise Application Integration tools have increased the speedy collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sifting through large amounts of data, extracting pertinent information, and turning that information into knowledge upon which actions can be taken.
Business intelligence software incorporates the ability to mine data, analyze, and report. Some modern BI software allow users to cross-analyze and perform deep data research rapidly for better analysis of sales or performance on an individual, department, or company level. In modern applications of business intelligence software, managers are able to quickly compile reports from data for forecasting, analysis, and business decision making.
In 1989 Howard Dresner, a Research Fellow at Gartner Group popularized "BI" as an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems. Dresner left Gartner in 2005 and joined Hyperion Solutions as its Chief Strategy Officer.
The Future of Business Intelligence
In this rapidly changing world consumers are now demanding quicker more efficient service from businesses. To stay competitive, companies must meet or exceed the expectations of consumers. Companies will have to rely more heavily on their business intelligence systems to stay ahead of trends and future events. Business intelligence users are beginning to demand [Real time Business Intelligence] or near real time analysis relating to their business, particularly in front line operations. They will come to expect up to date and fresh information in the same fashion as they monitor stock quotes online. Monthly and even weekly analysis will not suffice. "Business users don't want to wait for information. Information needs to be always on and never out of date. This is the way we live our lives today. Why should Business Intelligence be any different?" Charles Nicholls, CEO of SeeWhy, a Software company, Windsor UK.
In the not too distant future companies will become dependent on real time business information in much the same fashion as people come to expect to get information on the internet in just one or two clicks. "This instant "Internet experience" will create the new framework for business intelligence, but business processes will have to change to accommodate and exploit the real-time flows of business data." -- Nigel Stokes, CEO, DataMirror Corp., Toronto
"BI 2.0" is the recently-coined term which is part of the continually developing Business Intelligence industry and heralds the next step for BI. "BI 2.0" is used to describe the acquisition, provision and analysis of "real time" data, the implication being that earlier Business Intelligence and Data Mining products (BI 1.0?) have not been capable of providing the kind of timely, current data end-users are now clamoring to have. Realizing that hype has historically outpaced reality as Business Intelligence software companies compete for marketshare, it would be wise to keep in mind the observation of veteran analyst Andy Hayler as they now begin to describe their products in terms of the "real time" and "BI 2.0" nomenclature.
Hayler recently wrote the following in an article titled, "Real Time BI - Get Real":"I permitted myself a wry smile when I first heard the hype about 'real time' business intelligence". Hayler then goes on to explain, "The mismatch between fantasy and reality is driven by two factors. The first is that business rules and structures (general ledgers, product classification, asset hierarchies, etc.) are not in fact uniform, but are spread out among many disparate transaction system implementations...The second problem is that the landscape of business structures is itself in constant flux, as groups reorganize, subsidiaries are sold or new companies acquired".
As long as Business Intelligence relies upon some kind of data warehouse structure (including web-based virtual data "warehouses"), data will have to be converted into what Hayler calls "a lowest common denominator consistent set." When it comes to dealing with multiple, disparate data sources and the constantly changing, often volatile, business environment which requires tweaking and restructuring of IT systems, getting BI data in a genuinely true, "real time" format remains, again according to Hayler, "a pipe dream...As long as people design data models and databases the traditional way, you can forget about true 'real-time' business intelligence across an enterprise: the real world gets in the way".
So, does this mean that "BI 2.0" is unattainable? Notice that, in Hayler's opinion, the caveat here has to do with data models and databases. If the design continues to remain essentially the same, the possibility of "real time" Business Intelligence is remote, so far as he can determine. However, rather than focusing on databases and their resistance to having any kind of change in structure, what if there was a way to bypass the database architecture and directly capture the data? This "outside the box" approach would allow real-time access to data. This is essentially what the new MSSO Technology has done. With MSSO, "real time" BI 2.0 is now not only within reach, it has become a reality.
Also in the near future business information will become more democratized where end users from throughout the organization will be able to view information on their particular segment to see how it's performing. In the future, the capability requirements of business intelligence will increase in the same way that consumer expectations increase. It is therefore imperative that companies increase at the same pace or even faster to stay competitive.
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Key Intelligence Topics
Business intelligence often uses Key performance indicators (KPIs) to assess the present state of business and to prescribe a course of action. In the 2000s, companies are tending to make data of various types available more promptly. Prior to the widespread adoption of computer and web applications, when information had to be manually inputted and calculated, performance data was often not available for weeks or months. Recently, banks have tried to make data available at shorter intervals and have reduced delays. The KPI methodology was further expanded with the Chief Performance Officer methodology which incorporated KPIs and root cause analysis into a single methodology.
Businesses that face higher operational/credit risk loading, such as credit card companies and "wealth management" services often make KPI-related data available weekly. In some cases, companies may even offer a daily analysis of data. This fast pace requires analysts to use IT systems to process this large volume of data.
Designing and implementing a business intelligence program
When implementing a BI programme one might like to pose a number of questions and take a number of resultant decisions, such as:
• Goal Alignment queries: The first step determines the short and medium-term purposes of the programme. What strategic goal(s) of the organization will the programme address? What organizational mission/vision does it relate to? A crafted hypothesis needs to detail how this initiative will eventually improve results / performance (i.e. a strategy map).
• Baseline queries: Current information-gathering competency needs assessing. Does the organization have the capability of monitoring important sources of information? What data does the organization collect and how does it store that data? What are the statistical parameters of this data, e.g. how much random variation does it contain? Does the organization measure this?
• Cost and risk queries: The financial consequences of a new BI initiative should be estimated. It is necessary to assess the cost of the present operations and the increase in costs associated with the BI initiative? What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.
• Customer and Stakeholder queries: Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers/stakeholders will benefit directly from this initiative? Who will benefit indirectly? What are the quantitative / qualitative benefits? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customers' benefits be monitored? What about employees,... shareholders,... distribution channel members?
• Metrics-related queries: These information requirements must be operationalized into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can be used to track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
• Measurement Methodology-related queries: One should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will the organization collect data? Do industry standards exist for this? Is this the best way to do the measurements? How do we know that?
• Results-related queries: Someone should monitor the BI programme to ensure that objectives are being met. Adjustments in the programme may be necessary. The programme should be tested for accuracy, reliability, and validity. How can one demonstrate that the BI initiative (rather than other factors) contributed to a change in results? How much of the change was probably random?.
• Business intelligence tools
• Digital dashboard
• Economic Espionage Act of 1996
• Environmental scanning
• Intelligent document
• List of management topics
• OODA Loop
• Pivot table
• Predictive analytics
• Reverse engineering
1. ^ Industry Analyst Think Strategies & it's SaaS Showplace
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Customer experience management
Customer experience management (CEM) desrcibes the processes and methods used to design and manage a customer's entire experience with a product or a company.
Marketing research is said to have shown that about 70 to 80% of all products are perceived as commodities, that is, seen as being more-or-less the same as competing products. Marketers have responded to this in a variety of ways, including: branding, product differentiation, market segmentation, and relationship marketing.
• 1 A CEM Framework
• 2 Organization of CEM
• 3 References
4 See also
A CEM Framework
CEM stresses four aspects of marketing management :
• CEM focuses on all sorts of customer-related issues
• CEM combines the analytical and the creative
• CEM considers both, strategy and implementation
• CEM operates internally and externally
The holistic customer experience is the key to these.
Schmitt's book "Customer Experience Management" offers the following five step framework that should help managers understand and manage the "customer experience":
Step 1: Analyzing the Experiential world of the customer
• analyze sociocultural context of the customer (needs/wants/lifestyle)
• analyze business concept (requirements/solutions)
Step 2: Building the Experiential platform
• connection between strategy and implementation
• specifies the value that the customer can expect from the product (EVP = experiential value promise)
Whereas steps 1 (Analysis) and 2 (Strategy) form the basis for CEM, steps 3, 4, and 5 are focusing on Implementation.
Step 3: Designing the Brand experience
• experiential features, product aesthetics, “look and feel”, e.g. logos
Step 4: Structuring the Customer interface
• all sorts of dynamic exchanges and contract points with customers
• intangible elements (i.e. value, attitude, behaviour)
Step 5: Engaging in Continuous Experiential innovation
• anything that improves end customers' personal lives and business customers' working lives
Organization of CEM
Organizing for CEM includes three tasks:
• Financial planning of CEM in terms of customers - CEM's ultimate goal is a long-term business relationship between a company and its customers. Customers will reward the company financially by doing business with it. The value of the customer to the firm, referred to as customer equity, will increase.
• Allocation of organizational resources - Improving the customer experience, and thus increasing customer equity, requires internal resources. The company needs to ask what financial, structural, and personnel resources it needs to engage in CEM to deliver an ongoing desirable experience to customers. Resources must be allocated to the brand experience, the customer interface, and innovation.
• Enhancement of the employee experience - The concept of experience applies also to the internal customers, the company's employees. What all employees, across all levels, get from an experience-oriented organization is a more rewarding employee experience that includes a new form of professional and personal development. Employees of such an organization live a more experiential and thus more satisfying and productive life. They are also more motivated and capable of delivering a great experience to customers.
• Bliss, J. (2006) Chief Customer Officer, Jossey-Bass, San Francisco, 2006.
• Pine, J. and Gilmore, J. (1999) The Experience Economy, Harvard Business School Press, Boston, 1999.
• Schmitt, B. (2003) Customer Experience Management, The Free Press, New York, 2001.
• Schmitt, B. and Simonson, A. (1997) In Marketing Aesthetics:The strategic management of brands, identity, and image The Free Press, New York, 1997.
• Customer service
• Customer survey
• experience economy (Pine and Gilmore)
• list of marketing topics
• Customer relationship management
Retrieved from "http://en.wikipedia.org/wiki/Customer_experience_management"
Categories: Cleanup from November 2006 | All pages needing cleanup | Marketing strategies and paradigms | Customer experience management
Customer Intelligence is the process of gathering, analyzing and exploiting information of a company's customer base. Information can be obtained about customers' existing and future needs, how they reach decisions, about their behaviour as well as about the competition, conditions in the industry, and general trends.  To properly manage the relationship with the customer the business needs to collect the right information about its customers and organise that information for proper analysis and action. 
Tools like Speech Analytics can be used to understand customer and prospect behavior. Some Customer Intelligence solutions analyze phone conversations taking place between companies and customers and then deliver insights to the desktops of senior executives and managers. Customer Intelligence enables senior level managers and executives responsible for the Customer Experience to:
• Define and measure the customer experience
• Understand the experience of their customers
• Identify the reasons why customers call
• Maximize loyalty and retention
• Gain Market and Competitive Intelligence
• Increase Sales Effectiveness
1. ^ http://www.crm2day.com/customer-intelligence/
2. ^ http://www.bestpricecomputers.co.uk/...-solutions.htm
• Speech Analytics
• Customer relationship management
• Customer Reference Management
Customer Reference Management
The purpose of Customer Reference Management is to improve and enhance the level of "advocacy" a set of customers displays related to a vendor's products & services.
Specifically, a vendor's objective is to gain referrals and positive "word of mouth" from this advocacy.
Methods employed include participation in a written case study, speaking on a telephone call with a potential customer or the media, or engaging in an event or seminar to share the story of a product or services success.Customer
Retrieved from "http://en.wikipedia.org/wiki/Customer_Reference_Management"
Categories: Orphaned articles from October 2006 | All orphaned articles | Marketing | Customer experience management
Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.
The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built.
The "database" is usually name, address, and transaction history details from internal sales or delivery systems, or a bought-in compiled "list" from another organization, which has captured that information from its customers. Typical sources of compiled lists are charity donation forms, application forms for any free product or contest, product warranty cards, subscription forms, and credit application forms.
The communications generated by database marketing may be described as junk mail or spam, if it is unwanted by the addressee. Direct and database marketing organizations, on the other hand, argue that a targeted letter or e-mail to a customer, who wants to be contacted about offerings that may interest the customer, benefits both the customer and the marketer.
Some countries and some organizations insist that individuals are able to prevent entry to or delete their name and address details from database marketing lists.
• 1 Sources of data
o 1.1 Consumer data
o 1.2 Business data
• 2 Analytics and modeling
• 3 Laws and regulations
• 4 Evolution
• 5 See also
• 6 References
7 External links
Sources of data
Although organizations of any size can employ database marketing, it is particularly well-suited to companies with large numbers of customers. This is because a large population provides greater opportunity to find segments of customers or prospects that can be communicated with in a customized manner. In smaller (and more homogeneous) databases, it will be difficult to justify on economic terms the investment required to differentiate messages. As a result, database marketing has flourished in sectors, such as financial services, telecommunications, and retail, all of which have the ability to generate significant amounts transaction data for millions of customers.
Database marketing applications can be divided logically between those marketing programs that reach existing customers and those that are aimed at prospective customers.
In general, database marketers seek to have as much data available about customers and prospects as possible.
For marketing to existing customers, more sophisticated marketers often build elaborate databases of customer information. These may include a variety of data, including name and address, history of shopping and purchases, demographics, and the history of past communications to and from customers. For larger companies with millions of customers, such data warehouses can often be multiple terabytes in size.
Marketing to prospects relies extensively on third-party sources of data. In most developed countries, there are a number of providers of such data. Such data is usually restricted to name, address, and telephone, along with demographics, some supplied by consumers, and others inferred by the data compiler. Companies may also acquire prospect data directly through the use of sweepstakes, contests, on-line registrations, and other lead generation activities.
For many business-to-business marketers, the number of customers and prospects will be smaller than that of comparable business-to-consumer (B2C) companies. Also, their relationships with customers will often rely on intermediaries, such as salespeople, agents, and dealers, and the number of transactions per customer may be small. As a result, business-to-business marketers may not have as much data at their disposal. One other complication is that they may have many contacts for a single organization, and determining which contact to communicate with through direct marketing may be difficult. On the other hand the database of business-to-business marketers often include data on the business activity of the respective client that can be used to segment markets, e.g. special software packages for transport companies, for lawyers etc. Customers in Business-to-business environments often tend to be loyal since they need after-sales-service for their products and appreciate information on product upgrades and service offerings.
Sources of customer data often come from the sales force employed by the company and from the service engineers. Increasingly, online interactions with customers are providing b-to-b marketers with a lower cost source of customer information.
For prospect data, businesses can purchase data from compilers of business data, as well as gather information from their direct sales efforts, on-line sites, and specialty publications.
Analytics and modeling
Companies with large databases of customer information risk being "data rich and information poor." As a result, a considerable amount of attention is paid to the analysis of data. For instance, companies often segment their customers based on the analysis of differences in behavior, needs, or attitudes of their customers. A common method of behavioral segmentation is RFM, in which customers are placed into subsegments based on the recency, frequency, and monetary value of past purchases. Van den Poel (2003) gives an overview of the predictive performance of a large class of variables typically used in database-marketing modeling.
They may also develop predictive models, which forecast the propensity of customers to behave in certain ways. For instance, marketers may build a model that rank orders customers on their likelihood to respond to a promotion. Commonly employed statistical techniques for such models include logistic regression and neural networks.
Laws and regulations
As database marketing has grown, it has come under increased scrutiny from privacy advocates and government regulators. For instance, the European Commission has established a set of data protection rules that determine what uses can be made of customer data and how consumers can influence what data are retained. In the United States, there are a variety of state and federal laws, including the Fair Credit Reporting Act, or FCRA, (which regulates the gathering and use of credit data), the Health Insurance Portability and Accountability Act (HIPAA) (which regulates the gathering and use of consumer health data), and various programs that enable consumers to suppress their telephones numbers from telemarketing.
While the idea of storing customer data in electronic formats in order to use them for database-marketing purposes has been around for decades the computer systems available today make it possible to have the complete history of a client on-screen the moment he or she calls. Today´s Customer Relationship Management systems use the stored data not only for direct marketing purposes but to manage the complete relationship with this particular customer and to further develop the range of products and services offered.
• Customer Relationship Management
• Lifetime value
• Baesens Bart, Stijn Viaene, Dirk Van den Poel, Jan Vanthienen, and Guido Dedene (2002), “Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing”, European Journal of Operational Research, 138 (1), 191-211.
• Hughes, Arthur M. (2000), Strategic Database Marketing: The Masterplan for Starting and Managing a Profitable Customer-Based Marketing Program, 2nd edition, McGraw-Hill, New York.
• David Shepard Associates (1999), The New Direct Marketing: How to Implement A Profit-Driven Database Marketing Strategy, 3rd edition, McGraw-Hill, New York.
• Hillstrom, Kevin (2006), Hillstrom's Database Marketing, Direct Academy
• Peppers, Don and Rogers, Martha (1996), The One to One Future (One to One), Current.
• Prinzie Anita, Dirk Van den Poel (2005), "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application", Expert Systems with Applications, 29 (3), 630-640.
• Tapp, Alan (1998), Principles of Direct and Database Marketing, Trans-Atlantic Publications.
• Van den Poel Dirk (2003), “Predicting Mail-Order Repeat Buying: Which Variables Matter?”, Tijdschrift voor Economie & Management, 48 (3), 371-403.
• Multichannel Merchant - Database marketing articles
• DIRECT Magazine - CRM / Database
• Federal Trade Commission
• Multichannel Marketing UK - Database marketing article
• Medill IMC at Northwestern University
• Master of Marketing Analysis at Ghent University
Predictive analytics encompasses a variety of techniques from statistics and data mining that process current and historical data in order to make “predictions” about future events. Such predictions rarely take the form of absolute statements, and are more likely to be expressed as values that correspond to the odds of a particular event or behavior taking place in the future.
In business, the models often process historical and transactional data to identify the risk or opportunity associated with a specific customer or transaction. These analyses weigh the relationship between many data elements to isolate each customer’s risk or potential, which guides the action on that customer.
Predictive analytics is widely used in making customer decisions. One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics are also used in insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
• 1 Types of predictive analytics
o 1.1 Predictive models
o 1.2 Descriptive models
o 1.3 Decision models
• 2 Predictive analytics
o 2.1 Definition
o 2.2 Current uses
2.2.1 Analytical Customer Relationship Management (CRM)
2.2.2 Direct marketing
2.2.4 Customer retention
2.2.6 Collection analytics
2.2.7 Fraud detection
2.2.8 Portfolio, product or economy level prediction
o 2.3 Statistical techniques
2.3.1 Regression Techniques
220.127.116.11 Linear Regression Model
2.3.2 Discrete choice models
18.104.22.168 Logistic regression
22.214.171.124 Multinomial logit regression
126.96.36.199 Probit regression
188.8.131.52 Logit vs. Probit
2.3.3 Time series models
2.3.4 Survival or duration analysis
2.3.5 Classification and regression trees
2.3.6 Multivariate regression splines
o 2.4 Machine learning techniques
2.4.1 Neural networks
2.4.2 Radial basis functions
2.4.3 Support vector machines
2.4.4 Naïve Bayes
2.4.5 k-nearest neighbours
• 3 Popular tools
• 4 Conclusion
• 5 References
• 6 See also
7 External links
Types of predictive analytics
Generally, predictive analytics is used to mean predictive modeling. However, people are increasingly using the term to describe related analytic disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary.
Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. This category also encompasses models that seek out subtle data patterns to answer questions about customer performance, such as fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision.
Descriptive models “describe” relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. But the descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Descriptive models are often used “offline,” for example, to categorize customers by their product preferences and life stage.
Decision models describe the relationship between all the elements of a decision — the known data (including results of predictive models), the decision and the forecast results of the decision — in order to predict the results of decisions involving many variables. These models can be used in optimization, a data-driven approach to improving decision logic that involves maximizing certain outcomes while minimizing others. Decision models are generally used offline, to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance.
Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes.
Although predictive analytics can be put to use in many applications, we outline a few examples where predictive analytics has shown positive impact in recent years.
Analytical Customer Relationship Management (CRM)
Analytical Customer Relationship Management is a frequent commercial application of Predictive Analysis. Methods of predictive analysis are applied to customer data to pursue CRM objectives.
Product marketing is constantly faced with the challenge of coping with the increasing number of competing products, different consumer preferences and the variety of methods (channels) available to interact with each consumer. Efficient marketing is a process of understanding the amount of variability and tailoring the marketing strategy for greater profitability. Predictive analytics can help identify consumers with a higher likelihood of responding to a particular marketing offer. Models can be built using data from consumers’ past purchasing history and past response rates for each channel. Additional information about the consumers demographic, geographic and other characteristics can be used to make more accurate predictions. Targeting only these consumers can lead to substantial increase in response rate which can lead to a significant reduction in cost per acquisition. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of products and marketing channels that should be used to target a given consumer.
Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) and exploiting hidden relationships in the data can provide a competitive advantage to the organization. For an organization that offers multiple products, an analysis of existing customer behavior can lead to efficient cross sell of products. This directly leads to higher profitability per customer and strengthening of the customer relationship. Predictive analytics can help analyze customers’ spending, usage and other behavior, and help cross-sell the right product at the right time.
With the amount of competing services available, businesses need to focus efforts on maintaining continuous consumer satisfaction. In such a competitive scenario, consumer loyalty needs to be rewarded and customer attrition needs to be minimized. Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. At this stage, the chance of changing the customer’s decision is almost impossible. Proper application of predictive analytics can lead to a more proactive retention strategy. By a frequent examination of a customer’s past service usage, service performance, spending and other behavior patterns, predictive models can determine the likelihood of a customer wanting to terminate service sometime in the near future. An intervention with lucrative offers can increase the chance of retaining the customer. Silent attrition is the behavior of a customer to slowly but steadily reduce usage and is another problem faced by many companies. Predictive analytics can also predict this behavior accurately and before it occurs, so that the company can take proper actions to increase customer activity.
Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk. For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver. A financial company needs to assess a borrower’s potential and ability to pay before granting a loan. For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future. Predictive analytics can help underwriting of these quantities by predicting the chances of illness, default, bankruptcy, etc. Predictive analytics can streamline the process of customer acquisition, by predicting the future risk behavior of a customer using application level data. Proper predictive analytics can lead to proper pricing decisions, which can help mitigate future risk of default.
Every portfolio has a set of delinquent customers who do not make their payments on time. The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs.
Fraud is a big problem for many businesses and can be of various types. Inaccurate credit applications, fraudulent transactions, identity thefts and false insurance claims are some examples of this problem. These problems plague firms all across the spectrum and some examples of likely victims are credit card issuers, Insurance companies, retail merchants, manufacturers, business to business suppliers and even services providers. This is an area where a predictive model is often used to help weed out the “bads” and reduce a businesses exposure to fraud.
Portfolio, product or economy level prediction
Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example a retailer might be interested in predicting store level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These type of problems can be addressed by predictive analytics using Time Series techniques (see below).
The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.
Regression models are the mainstay of predictive analytics. The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration. Depending on the situation, there is a wide variety of models that can be applied while performing predictive analytics. Some of them are briefly discussed below.
Linear Regression Model
The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions.
The goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. This is referred to as ordinary least squares (OLS) estimation and results in best linear unbiased estimates (BLUE) of the parameters.
Once the model has been estimated we would be interested to know if the predictor variables belong in the model – i.e. is the estimate of each variable’s contribution reliable? To do this we can check the statistical significance of the model’s coefficients which can be measured using the t-statistic. This amounts to testing whether the coefficient is significantly different from zero. How well the model predicts the dependent variable based on the value of the independent variables can be assessed by using the R2 statistic. It measures predictive power of the model i.e. the proportion of the total variation in the dependent variable that is “explained” (accounted for) by variation in the independent variables.
Discrete choice models
Multivariate regression (above) is generally used when the response variable is continuous with an unbounded range. Often the response variable may not be continuous but rather discrete. While mathematically it is feasible to apply multivariate regression to discrete ordered dependent variables, some of the assumptions behind the theory of multivariate linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models. Logistic regression and probit models are used when the dependent variable is binary.
In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model (See Allison’s Logistic Regression for more information on the theory of Logistic Regression).
The Wald and likelihood-ratio test are used to test the statistical significance of each coefficient b in the model (analogous to the t tests used in OLS regression; see above). A test assessing the goodness-of-fit of a classification model is the Hosmer and Lemeshow test.
Multinomial logit regression
An extension of the binary logit model to cases where the dependent variable has more than 2 categories is the multinomial logit model. In such cases collapsing the data into two categories might not make good sense or may lead to loss in the richness of the data. The multinomial logit model is the appropriate technique in these cases, especially when the dependent variable categories are not ordered (for examples colors like red, blue, green).
Probit models offer an alternative to logistic regression for modeling categorical dependent variables. Even though the outcomes tend to be similar, the underlying distributions are different. Probit models are popular in social sciences like economics.
A good way to understand the key difference between probit and logit models, is to assume that there is a latent variable z.
We do not observe z but instead observe y which takes the value 0 or 1. In the logit model we assume that follows a logistic distribution. In the probit model we assume that follows a standard normal distribution. Note that in social sciences (example economics), probit is often used to model situations where the observed variable y is continuous but takes values between 0 and 1.
Logit vs. Probit
The Probit model has been around longer than the logit model. They look identical, except that the logistic distribution tends to be a little flat tailed. In fact one of the reasons the logit model was formulated was that the probit model was extremely hard to compute because it involved taking the integrals. Modern computing however has made this computation fairly simple. The coefficients obtained from the logit and probit model are also fairly close. However the odds ratio makes the logit model easier to interpret.
For practical purposes the only reasons for choosing the probit model over the logistic model would be:
• There is a strong belief that the underlying distribution is normal
• The actual event is not a binary outcome (e.g. Bankrupt/not bankrupt) but a proportion (e.g. Proportion of population at different debt level.
Time series models
Time series models are used for predicting or forecasting the future behavior of variables. These models account for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. As a result standard regression techniques cannot be applied to time series data and methodology has been developed to decompose the trend, seasonal and cyclical component of the series. Modeling the dynamic path of a variable can improve forecasts since the predictable component of the series can be projected into the future.
Time series models estimate difference equations containing stochastic components. Two commonly used forms of these models are autoregressive models (AR) and moving average (MA) models. The Box-Jenkins methodology (1976) developed by George Box and G.M. Jenkins combines the AR and MA models to produce the ARMA (autoregressive moving average) model which is the cornerstone of stationary time series analysis. ARIMA (autoregressive integrated moving average models) on the other hand are used to describe non-stationary time series. Box and Jenkins suggest differencing a non stationary time series to obtain a stationary series to which an ARMA model can be applied. Non stationary time series have a pronounced trend and do not have a constant long-run mean or variance.
Box and Jenkins proposed a three stage methodology which includes: model identification, estimation and validation. The identification stage involves identifying if the series is stationary or not and the presence of seasonality by examining plots of the series, autocorrelation and partial autocorrelation functions. In the estimation stage, models are estimated using non-linear time series or maximum likelihood estimation procedures. Finally the validation stage involves diagnostic checking such as plotting the residuals to detect outliers and evidence of model fit.
In recent years time series models have become more sophisticated and attempt to model conditional heteroskedasticity with models such as ARCH (autoregressive conditional heteroskedasticity) and GARCH (generalized autoregressive conditional heteroskedasticity) models frequently used for financial time series. In addition time series models are also used to understand inter-relationships among economic variables represented by systems of equations using VAR (vector autoregression) and structural VAR models.
Survival or duration analysis
Survival analysis is another name for time to event analysis. These techniques were primarily developed in the medical and biological sciences, but they are also widely used in the social sciences like economics, as well as in engineering (reliability and failure time analysis).
Censoring and non-normality which are characteristic of survival data generate difficulty when trying to analyze the data using conventional statistical models such as multiple linear regression. The Normal distribution, being a symmetric distribution, takes positive as well as negative values, but duration by its very nature cannot be negative and therefore normality cannot be assumed when dealing with duration/survival data. Hence the normality assumption of regression models is violated.
A censored observation is defined as an observation with incomplete information. Censoring introduces distortions into traditional statistical methods and is essentially a defect of the sample data. The assumption is that if the data were not censored it would be representative of the population of interest. In survival analysis, censored observations arise whenever the dependent variable of interest represents the time to a terminal event, and the duration of the study is limited in time.
An important concept in survival analysis is the hazard rate. The hazard rate is defined as the probability that the event will occur at time t conditional on surviving until time t. Another concept related to the hazard rate is the survival function which can be defined as the probability of surviving to time t.
Most models try to model the hazard rate by choosing the underlying distribution depending on the shape of the hazard function. A distribution whose hazard function slopes upward is said to have positive duration dependence, a decreasing hazard shows negative duration dependence whereas constant hazard is a process with no memory usually characterized by the exponential distribution. Some of the distributional choices in survival models are: F, gamma, Weibull, log normal, inverse normal, exponential etc. All these distributions are for a non-negative random variable.
Duration models can be parametric, non-parametric or semi-parametric. Some of the models commonly used are Kaplan-Meier, Cox proportional hazard model (non parametric).
Classification and regression trees
Classification and regression trees (CART) is a non-parametric technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively.
Trees are formed by a collection of rules based on values of certain variables in the modeling data set
• Rules are selected based on how well splits based on variables’ values can differentiate observations based on the dependent variable
• Once a rule is selected and splits a node into two, the same logic is applied to each “child” node (i.e. it is a recursive procedure)
• Splitting stops when CART detects no further gain can be made, or some pre-set stopping rules are met
Each branch of the tree ends in a terminal node
• Each observation falls into one and exactly one terminal node
• Each terminal node is uniquely defined by a set of rules
Multivariate regression splines
Multivariate adaptive regression splines is a non-parametric technique that builds flexible models by fitting piecewise linear regressions.
An important concept associated with regression splines is that of a knot. Knot is where one local regression model gives way to another and thus is the point of intersection between two splines.
In multivariate and adaptive regression splines, basis functions are the tool used for generalizing the search for knots. Basis functions are a set of functions used to represent the information contained in one or more variables. Multivariate and Adaptive Regression Splines model almost always creates the basis functions in pairs.
Multivariate and adaptive regression spline approach deliberately overfits the model and then prunes to get to the optimal model. The algorithm is computationally very intensive and in practice we are required to specify an upper limit on the number of basis functions.
Machine learning techniques
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