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Database Marketing with Title theDtaPaper SAS System Joanna Crosse - Product Manager Linda Saul - Sales Support Consultant Agenda þ þ þ þ þ þ What is database marketing? Why do it? Where is it going? How does SAS fit in? ý Data Warehousing ý Data Analysis Who’s doing it? Conclusion S What is database marketing? Develop Develop Test Modify Measure Data Test Implement S Mass Customisation S Why do it? þ Database marketing is: ý Selective ý Personal ý Measurable ý Adaptable -Forsyth S Why do it? þ Database marketing is: ý Selective P communication focused P maximise returns P tailored to recipients needs ý Personal ý Measurable ý Adaptable S Why do it? þ Database marketing is: ý Selective ý Personal P personal details and needs P preferences P customer loyalty ý Measurable ý Adaptable S Why do it? þ Database marketing is: ý Selective ý Personal ý Measurable P cost, results P profitability of individuals ý Adaptable S Why do it? þ Database marketing is: ý Selective ý Personal ý Measurable ý Adaptable P Modify to reflect experience P Cycle of improvement S Where is it going? Evolution of Database Marketing þ Three generations of Database Marketing identified by Gartner: ý ý ý First Generation - “Enhanced List Selection Systems” Second Generation - “Customer Information Repositories” Third Generation - “Enterprise Relationship Management” S Evolution of Database Marketing þ First Generation - “Enhanced List Selection Systems” ý ý ý single database / bureau /flat files limited sharing & combining of data limited analysis S Evolution of Database Marketing þ Second Generation - “Customer Information Repositories” ý ý ý typified by Data Warehouse initiatives combine different sources of customer information manage relationship with customer S Evolution of Database Marketing þ Third Generation - “Enterprise Relationship Management” ý ý focus on customer relationships enterprise-wide migration towards “Customer centric” enterprise S How does SAS fit in? - Data Warehousing þ Database marketing uses a variety of data sources: ý Internal sources ý External sources S How does SAS fit in? - Data Warehousing þ Database marketing uses a variety of data sources: ý ý Internal sources - operational P Customer service records P Sales order processing P Campaign results P .... External sources S How does SAS fit in? - Data Warehousing þ Database marketing uses a variety of data sources: ý ý Internal sources External sources P Market Research P Government statistics P Geodemographic profiles P .... S Data Warehouse for Database Marketing Census/ Survey Sales Order Processing Database Marketing GIS Access Data Cleaning Campaign Response Competitive Information Integration Data Mining Information Database Summarisation Customer Service Records Data Visualisation Multi-dimensional Reporting Organisation Management Campaign Management Customer Profiling Behavioral Modeling Strategy Formulation Exploitation S Benefits of Data Warehouse for Database Marketing þ þ þ Integration of Information sources ý Maximum information available for decision making Fast, easy access to information ý Timely, appropriate decisions ý Rapid response to market changes Marketing analysts concentrate skills on exploiting market intelligence S How Does SAS fit in? - Data Analysis þ þ þ þ þ þ þ Response analysis Customer profiling Segmentation Forecasting Reporting Developing scoring models Identifying purchase patterns S Segmentation þ Identifying distinct groups of buyers Market Segmentation Identify segmentation variables and segment the market. Develop profiles of resulting segments. Market Targeting Evaluate the attractiveness of each segment. Select the target segment(s). “Small is beautiful. Less is more.” Product Positioning Identify possible positioning concepts for each target segment. Select, develop, and signal the chosen positioning concept. S SAS for Segmentation þ þ þ þ þ þ Principal Components analysis Cluster analysis Tree-based models Data visualisation Geographical Data Visualisation Cross-tabulations 110 100 i 90 80 -20 -10 0 10 S Identifying Purchase Patterns þ þ þ þ Spanish saying: To be a bullfighter you must first learn to be a bull analysis of existing customers to determine patterns, trends and associations Product association Customer behaviour S SAS for Identifying Patterns þ þ þ þ þ þ þ Perceptual mapping Correlation analysis Graphics Data visualisation Cross tabulations Correspondence analysis Neural networks S Who’s doing it? þ þ þ þ þ Ellos (Sweden) Marks & Spencer Financial Services (UK) Mondadori (Italy) Le Groupe Redoute (France) Swedish Telecom (Sweden) S Mondadori Group þ þ þ þ CDE - books by mail order ý 900,000 members ý 8 million books sold annually Mass customisation Data Warehouse Analyse customer behaviour ý recruitment ý respond to their needs S Marks and Spencer Financial Services þ þ þ Subsidiary of leading UK retailer Problem ý “data rich but information poor” Solution ý Marketing Information Management System (MIMS) S Application areas þ þ þ Campaign Management Customer Profiling Segmentation S Application areas þ þ þ Campaign Management ý response analysis ý performance tracking P results fed into next campaign Customer Profiling Segmentation S Application areas þ þ þ Campaign Management Customer Profiling ý who are best customers for a mailing? ý cross-selling Segmentation S Application areas þ þ þ Campaign Management Customer Profiling Segmentation ý Ultimate objective - segmenting “on a one to one basis” ý “Our aim is to treat customers as individuals” S Benefits þ þ þ Single product for Market Research Do everything at desktop Help achieving “individually targeted customer relationships” S Conclusion Database Marketing þ þ þ Cost effective means of communicating with carefully selected segments and building profitable long-term relationships Advantages: ý selective, personal, measurable, adaptable Requirements satisfied by Data Warehouse strategy S Conclusion Benefits of Database Marketing þ þ þ þ þ þ Aids understanding of customer Helps identify profitable segments/ niche opportunities Focuses resources to achieve maximum return on investment Maximises control Aids test marketing Enables treatment of customers as individuals S Thank you for your attention DtaPaper Title The SAS® System for successful decision making Database Marketing with the SAS System Joanna Crosse and Linda Saul SAS Institute 1. Introduction In the current climate of increased customer expectations and fierce competition, oneto-one marketing is the vision of today's organisations, rather than the traditional mass marketing approach. Database marketing facilitates the development and implementation of such focused knowledge-based marketing campaigns. This results in a more effective use of limited marketing resources and hence a stronger, more competitive position in your marketplace. This paper will define database marketing and discuss the advantages of employing a database marketing strategy. The techniques and processes available in the SAS System that enable database marketing will be discussed. 2. The Concept of Database Marketing There are many definitions of database marketing, ranging from being equivalent to direct mailing to doing anything that involves a database! One accepted definition is given by David Shepard Associates as “an information-driven marketing process that enables marketers to develop, test, implement, measure and appropriately modify customised marketing programs and strategies”. This means that existing data, for example, customers’ detailed transaction records, are used to develop a marketing campaign, which may then be tested on a sub-population. This campaign can be implemented, with responses being measured and the campaign details being modified to improve response rates, resulting in a cycle of continuous improvement and feedback. Thus database marketing is often characterised by a cyclical information flow, i.e. using information to direct a marketing initiative that is designed to elicit a response, which in turn provides more information that can be added to the original database. This paper will adopt this broader definition of database marketing. The term database marketing originated from the direct mail and catalogue industry, but it is evolving into an increasingly sophisticated marketing activity, with the aim of building, maintaining and enhancing customer relationships using the full range of communications methods, including telephone, newspapers, personal contact and, of course, direct mail. The exact combination of marketing methods and customer contact methods is unique to the particular product or service and the market in which it is sold. The combination of control over the message purveyed and the degree of personalisation possible is a key consideration in any marketing activity, see Figure 1 (Forsyth, 1995). Different media are employed depending on the combination of control and personalisation required, subject, of course, to financial considerations. For example, TV adverts offer considerable control over message, but almost no scope for personalisation, whereas telesales can be personalised but the message may be diluted. One of the key strengths of database marketing is that it facilitates both control and personalisation. In addition, with decreasing hardware costs and the increased power of both hardware and software the necessary management, organisation and exploitation of the database can now be a reality, providing the route to mass customisation. This is key to the emerging concept of relationship marketing and long term customer retention. As Peppers & Rogers (1993) comment “instead of concentrating on one product at a time and trying to sell it to as many customers as possible during a fiscal period, tomorrow’s share of customer marketeer will concentrate on one customer at a time and try to sell that customer as many products as possible over the customer’s lifetime.” As an example, direct marketing techniques and relationship marketing are central to Club degli Editori, a subsidiary of the Italian Mondadori Group, selling books by mail order. Ivano Maestri, General Manager of CDE SpA, says that in order to succeed “we must increase the performance of our customers, that is to increase their propensity to buy from us” (SAS Communications, 1996). INCREASING CONTROL OF CONTENT Direct sales staff Telephone contact Franchise operations Letters/ mail The Customer Agents/ intermediaries Bills/ statements Distributors Inserts Influencers PR TV INCREASING PERSONAL ELEMENTS Delivery staff Service staff Retail /service outlets Press Figure 1: The many ways in which any organisation can contact and influence its customers (Forsyth, 1995) From the consumers viewpoint the move towards services, products and marketing tailored to each individual is the ultimate goal. However, organisations must balance the benefits against operating costs and often adopt the intermediate strategy of developing the right segmentation and analytical tools that will support mass customisation. This can be illustrated by Levi Strauss who have pioneered a service of offering custom sized jeans to women, thus increasing repeat business from an appearance conscious segment of the market. Similarly, CDE SpA have adopted a mass customisation strategy so that rather than producing a single catalogue for 900,000 members, they produce 900,000 catalogues (SAS Communications, 1996). 3. The Advantages of Database Marketing The objectives of database marketing are, of course, similar to those of any method designed to promote goods and services. These may include attracting new customers, retaining existing customers and increasing demand. However, the advantages that database marketing has over other marketing methods, as described by Forsyth (1995) are that it is: Selective: focusing communication on specific groups. For example, credit card companies can send material to individuals with a specific profile - they may send details of premium business travel services to gold card holders in a specific age band whose spending record shows many foreign transactions. Focusing communication in this way reduces promotional costs whilst maintaining or increasing sales volumes. In addition, well targeted campaigns reduce the irritation caused by inappropriate junk mail. Personal: allowing the customer not only to be addressed by their preferred name and title, but also to offer services and products relevant to their known lifestyle and preferences. For example, respecting people’s preferences not to be telephoned at home, or sending insurance details only when policies are due for renewal. Measurable: linking responses to actions enables campaign effectiveness and the resultant financial return to be measured. Adaptable: the flexibility and individuality underpinning database marketing enables the format and scale of customer contact to be tested and modified to maximize returns, leading to the cycle of information flow already discussed. For example, a loyalty card may be launched using a general campaign to all customers. Following analysis, a need for customised campaigns for certain segments such as pensioners and students can be identified and acted on. These advantages have made database marketing an application employed within a wide range of organisations, including retail, banking, financial services, insurance, health care, telecommunications, charities and mail order companies. It is a costeffective means of systematically communicating with many smaller, carefully selected segments of people, rather than using a mass marketing, shot gun approach. 4. The Evolution of Database Marketing Database marketing techniques require data from a variety of sources, both operational and external. This data needs to be accessed, managed, organised and exploited and is ideally suited to a data warehousing solution. Such a solution provides a means of gathering together data from a variety of sources, analysing it and presenting it to the business user in a consistent and accessible manner. The analysis, or exploitation, for database marketing includes some specialised analytical techniques, such as data mining, identification of segments and customer profiling. Both data warehousing and data analysis will be discussed later in this paper. In a recent report (Peak Performance, March 1996), The Gartner Group reviewed the current and future prospects for database marketing. They found that enterprises are now facing increased pressure to respond to global competition, rising customer expectations and new market opportunities, so database marketing is becoming critical. Thus posing the question “How will database marketing evolve to meet changing user requirements and to reflect the changing marketing process?” The resulting discussion defines the three generations of database marketing as: The First Generation: Enhanced list-selection Systems These are characterised by a proprietary database engine optimised for performance. First-generation strategies typically opt for an external service bureau to manage customer and marketing information. Whilst often containing a robust set of direct marketing functions, these systems limit data access to proprietary tools and they often lack the scalability required to incorporate growing volumes of detailed customer data. The Second Generation: Customer Information Repositories These are typified by data warehousing initiatives as the central information repository. Accompanying the change in technology is an expanded use of customer information outside of the marketing function. The Third Generation: Enterprise Relationship Management These systems must focus on customer relationships enterprise wide, not be limited to a single function, product or channel view, and can be supported by an enterprise wide data warehouse. These systems and strategies allow multiple functions outside of the marketing function to share information and allocate enterprise resources to optimise long-term profitable customer relationships. Gartner’s conclusion is that “with rapid technological change, conflicting vendor claims and a confusing marketplace, enterprises must align their vision, strategies and goals with the appropriate systems, infrastructure and capabilities. Enterprises with first-generation database marketing implementations must begin to invest in scalable systems, developing new metrics, redesigning business processes surrounding the customer, and gaining senior managers support to enable the migration toward a customer-centric enterprise”. 5. Database Marketing with the SAS System The SAS System is the industry standard business intelligence environment. Its modular approach provides one of the most functionally rich and flexible solutions, fully supporting all generations of database marketing. Data warehousing and data analysis are key components of any successful database marketing implementation. 5.1 Data Warehousing SAS Institute is the leading supplier of data warehousing technology, with over 200 data warehouse implementations in Europe alone. Successful second generation database marketing applications are invariably based on a data warehousing architecture, see Figure 2. SAS Institute will continue to provide the platform that enables organisations to move forward to the third generation. The SAS Data Warehouse for Database Marketing Census/ Survey Sales Order Processing Campaign Response Competitive Information Data Cleaning GIS Data Mining Integration Information Database Data Visualisation Database Marketing Campaign Management Customer Profiling Consolidation Multi-dimensional Reporting Behavioural Modelling Summarisation Customer Service Records Strategy Formulation Management Organisation Exploitation Figure 2: The SAS Data Warehouse for Database Marketing The key requirement for a successful database marketing strategy is to have the data readily accessible in a useable form. As the database marketing strategy matures within an organisation, data may come from a variety of sources including sales order processing systems, market research, campaign response information, customer service records, mailing lists and product registrations. External data sources may include geodemographic profiles, government statistics, news feeds and stock market information. This is facilitated through the SAS System’s ability to access data from a vast number of different sources, ranging from flat files and spreadsheets to merchant databases. Gathering together data on customers from different sources available within the organisation is of paramount importance when investigating customer behaviour patterns, characteristics and preferences in order to identify target segments, customise messages and track responses. The trend is for the amount of data available to increase. For instance, many major retailers are introducing loyalty schemes and more sophisticated electronic point of sale (EPOS) systems enabling more detailed information such as the combination of purchases in a single transaction to be recorded and acted upon. Data from multiple sources must be combined and differences in coding methods must be addressed and anomalous data where possible identified and corrected. For example, male and female could be coded as 0 and 1 in one data source, and M and F in another. When performing queries against a SAS data warehouse, the end-user or marketing analyst need not be concerned with either the original data sources or the attendant data management issues, nor be dependent on an overworked IT department to generate the necessary report. They are able to concentrate their skills on exploiting data orientated to their subject of concern, which will include customers or products. Reports and analysis are available in a timely manner enabling effective decisions to be made. Rapid access to information enables fast reaction to changes in the organisation’s marketing environment such as unexpected threats or opportunities through external influences (e.g. competitive action or political change) in order to protect or improve the organisation’s competitive position within the marketplace. 5.2 Data Analysis Techniques Within the SAS System, the data analysis techniques suitable for database marketing are constantly expanding in response to customer demand. For example, tree based models and neural networks have recently been included. Typical requirements might include customer profiling, forecasting, segmentation, reporting, developing scoring models and identifying purchase patterns. Customer profiling: identifying customer characteristics based on attributes stored in your data warehouse. For example, enabling you to answer questions such as who is your typical customer? who are your best or most profitable customers? did the respondents match your original target audience? Sabine Descamps, of the marketing department of La Redoute Catalogue, France, makes the point that “the most important selection criteria in direct mail are historical: who has had a catalogue in the past, how long they have been a customer, and what they have bought by value and volume” (Inform 7). Segmentation: dividing a market into distinct groups of buyers who might require separate products and/or marketing mixes, enabling you to target profitable segments. Organisations may use a range of analytical techniques to identify the characteristics of their best customers, or those most receptive to new products and/or services. This knowledge can then be used to target those most profitable market segments. Forecasting: forecasting and predicting future trends and patterns. For example, enabling you to predict response rates, forecast demand and profitability. As well as traditional techniques, for example discriminant analysis, data mining algorithms such as neural networks might be utilised to predict long term profitability of customers. Descamps of La Redoute Catalogue states that “we use the SAS System to analyse our customers’ past buying behaviour with discriminant analysis techniques. This reveals the combinations of criteria which correspond to a high order rate and helps us to predict the probable future behaviour of the client” (Inform 7). Reporting: producing management and exception reporting. For example enabling you to identify areas of opportunity or concern, where new products or services could be offered or how you compare with the competition. Developing scoring models: attributing a measure or score to individuals. For example assessment of credit worthiness of individuals allowing you to model the risk associated with providing services to particular customers; or using behavioural scores to segment your data. At Credit Lyonnais, a French bank, scoring card techniques are not only used to accept or refuse an applicant at branch level, but also to decide who will be offered a product and to identify who is a potential buyer or non-buyer in a direct marketing campaign ( Inform 5). Identifying purchase patterns: analysing behaviour of existing customers to determine patterns, trends or product associations. For example, retailers may perform basket analysis to look for buying patterns, such as a strong association between barbeque equipment and sausages. This is illustrated at Printemps, a leading retailer; Norbert Bachelot, director of computing studies, comments “We want to use the SAS System’s analysis facilities to identify the links across departments so that we can successfully gain new customers for, say, the perfumery department by promoting to customers who buy ladies’ hats” (Inform 7). Strategy formulation: determining the direction of the organisation based on information gathered about the market place and environment, for example competitors, demographic trends and technological advances. The SAS System is rich in depth and range of functionality available for database marketing, including: • ad-hoc querying • natural language querying • report generation • frequency counts • multi-dimensional reporting • correlation analysis • linear and non-linear regression • discriminant analysis • factor analysis • linear programming • data visualisation • geographical information systems • neural networks • tree based models • perceptual mapping • conjoint analysis • clustering techniques • econometric time series and forecasting • pareto analysis • control charts and, of course, data mining. A combination of these techniques and the relevant historical data enable you to predict how a customer, or customer segment will behave in the near future and over the long term. 6. Summary Database marketing is for targeting customer populations and profiles of interest. The advantages of database marketing is that it is selective, personal, measurable and adaptable. By their nature, database marketing strategies are constantly evolving. They must be supported by flexible business intelligence systems, implementing data warehousing and data analysis technology. The SAS System is ideally suited to this role. References David Shepard Associates, The New Direct Marketing: How to Implement a ProfitDriven Database Marketing Strategy, 1995 Forsyth, P, Database Marketing, The Financial Times Handbook of Management, 1995, pp623-631 Gartner Group, Database Marketing, Peak Performance, Q1 1996, pp1-10 Peppers D & Rogers M, The One to One Future, 1993 SAS Institute, Accurate Segmentation, Inform 7 ‘One-Stop Shopping’, pp14-15 SAS Institute, A Closely Guarded Secret, Inform 5 ‘Marketing for Profit’,pp14-15 SAS Institute, Delivering the Goods, Inform 7 ‘One-Stop Shopping’, pp16-17 SAS Institute, The Experience of the Mondadori Group, SAS Communications, Q1 1996, pp36-39 Further Reading Brown, M. & Bulkley, J., Database Marketing and Business Geographics, SUGI 21, 1996, pp830-835