Customer Relationship Research: How to Spot, Understand and Respond to Marketplace Change Signals 1,2 Dr. Charles Stiffler1 DQG'U9HVQD/XåDU6WLIIOHU2 CAIR Research Center and 2University Computing Centre, Zagreb, Croatia  “TQM comprises more than statistics; but, without statistics, it is often 'smoke and mirrors'." (Harry V. Roberts, University of Chicago) Abstract: The primary goal in most customer relationship research situations is to make choices..., i.e., to measure, monitor (over time), and take optimum action using "fresh" periodic customer feedback data (i.e., primary data). Using live feedback combined with legacy data, expert opinion, and a variety of decision support tools allowed CAIR Center researchers to apply strategic marketing concepts (market segmentation, buyer/user targeting and product/service positioning concepts, e.g.) so as to forecast potential future demand opportunities at the industry, organizational, and product/service levels. (Usually, the forecasts are a result of “data mining” operations using: data on what people say, what they do, and what they have done to help reveal key signals to imminent marketplace change.) Multivariate statistics, data warehousing software and large data base "mining" tools (e.g., SAS®1) are vital to this "discovery" process. The global procedure is no mystery and can be found in any number of secondary sources (Deming (1987), Juran (1964), Kotler(1996), Walton(1990), Porter(1985)). The challenge, of course, is finding time to study and then synthesize these various sources. For the case study presented below, CAIR Research Center personnel used Porter’s and Kotler’s ideas to move our client from the “watch and wonder” stage into the “making it happen” stage. This is known also as adopting a "proactive" vs. a "reactive" attitude. Regardless of the type of business (consumer or industrial, product or service, profit or non-profit, government or private) the strategic planning and research stages are the same: After a portfolio analysis and corporate level mission statement decision, SBUs are identified, “new business goals” (e.g., growth, share, ROI) are stipulated, an SBU mission statement is articulated, a “SWOT” analysis (strengths/weakness - (internal), and opportunity/threat - (external) ) is performed, followed by the creation of specific, measurable, achievable, relevant, time-related goals (i.e., whereby we decide where the growth will come from and how best to position the SBU's offers). A brief portfolio analysis revealed three SBUs (Strategic Business Units) with impressive growth potential, five SBUs with “cash cow” characteristics (i.e., below average growth but huge market shares), two “dogs” (slow growth and weak share positions) and three SBU “question marks” (high growth potential and small market shares). Research efforts then focused on a high growth FMCG properties in the portfolio and in particular on 500+ small retail outlets (square meters range = 60 to 350 m2 ). Once these goals are established and agreed upon (sometimes a long and difficult ordeal) strategy (i.e., target market(s) with appropriate market mixes (price, product/brand, service offers, promotional decisions, and distribution arrangements) are developed for implementation and measurement, monitoring and control/improvement/adjustment by line personnel. (Note: Without total operational/ staff commitment, even the best strategies fail.) The following paragraphs give a “bird's eye” overview of how these procedures were applied to increase sales (over one year) at a rate twice that of the industry average. 1 SAS is a registered trademark of SAS Institute, Cary, NC, USA 1 Introduction Why bother with ISO 9000? It’s expensive, it’s time-consuming, and it’s complicated: 1.written statements (quality policy manual) of mission, of what will be done, 2.procedural statements/policies (quality procedures manual) of how it will be done, 3.complete documentation/proof (quality records) of what was done, 4.operational instructions/records (work instructions) on how procedures were carried out. We do it to prove to the customers/clients (and anyone else, including ourselves) that we are listening to our customers and that we have an effective, efficient quality management system in place (e.g., Huyink D.S., C. Westover (1994)). But what if after this proof, we find that a) our quality defects rate per million is too high, and b) that our prices are also too high? Is the answer simply to "satisfy the customer"? No. Because many others can also bring satisfaction to our customers ... we must bring "joy and elation" ... they must brag about us. Is the answer simply to have everyone doing their best? No. Because, first we must know what to do. And for this we require leadership, and leaders require decision making information which is efficient and effective ... it is about doing things right and doing the right things in any given circumstance. Finally, what would motivate workers to put forth the effort necessary for achieving constant quality improvement? The quality improvement movement took root back in the 1930s with research at Bell Laboratories (USA) by Dr. Shewhart and with his student Dr. W. Edward Deming. Research on telephone switching systems indicated that improved quality followed automatically after undesirable variation was removed from any process. Notably, costs also decreased. PLAN NING S QUALITY CONTROL (DURING OPERATION) original new zone IMPROV EMENT Figure 1. Juran’s Quality Improvement Diagram Quality can not be improved by inspection. Inspection is like scraping burnt toast or other burnt food. Quality can not be "inspected into" a product. The Deming "chain reaction" says we must improve quality in the process which in turn decreases waste, rework, mistakes, delays, etc. This allows for higher productivity at lower costs and increased quality. This enables the company to increase market share with lower price, make more profits, hire and train more people, and to stay in business. But only if we can identify a sustainable competitive advantage. Management’s job is to lead us to the right markets, with the right products/services, at the right times, with the right service levels, at the right places, and at the right price. 2 Reduction in variation (see Figure 1) is also a key topic for a contemporary of the late Dr. Deming - Dr. Joseph Juran. Both men used statistical analyses (vital for any quality improvement venture) to isolate "special cause" variation from "common cause" variation. Knowledge concerning which source of variation is causing errors in a process helps management to make the right decision on how best to improve the process. Variation is inevitable both in a company’s internal processes and in the external environment. Those interested in more detailed information on TQM are referred to the reference list at the end of this monograph. The following case focuses on variation in customer satisfaction so that management could help the organization "adjust course" based on early warning signals received from selected customer segments that indicated the beginning of marketplace change. Case Overview Objectives Case objectives were developed after a corporate review of all strategic business unit (SBU) performances as measured against key competitors (where possible) and included ROI (Return On Investment), market shares, and relative growth rate indicators. A model was developed (please see Figure 2) over a two year period of time which simulated the probable impact of various marketplace variable changes (some of which are under direct management control, others which are not) on sales, market share, and ROI. MKT,SALES QUAL,BRANDS HRM ROI lower costs higher quality KRAs TECHNO,HW/SW ECON Figure 2. Ishikawa diagram to identify what is driving key performance indicators The key research objectives were to: 1. Identify the variables most associated with quarterly sales growth, 2. Identify those variables under direct management control which lead to sales increases/decreases, 3. Identify sub-segments of the market having the most relative growth potential, 4. Identify the best media for reaching high growth segments, 5. Identify the best message(s) to present to each high growth segment, 6. Design advertising goals so as to permit the measurement of future promotional effectiveness and relative efficiency (i.e., market basket scanner data), and 7. Identify possible competitor responses to (our clients) marketing offensive and outline appropriate countermeasures. 3 The informed reader will recognize this scenario as being a basic "Segmentation, Targeting, and Positioning" study plus the general process of strategic marketing planning. Methodology A small data warehouse was developed by CAIR Center business system design and data mining experts. Data from two years’ worth of (judgment) sample data (n=2000+) were gathered from client customers and competitor customers by personal (intercept) interviews (20-25) over approximately a one week period each quarter. The data collection instrument was essentially identical each quarter and included just over 100 items including opinion measures, product and store preference data, shopping behavior and expenditure information, brand preference information, media behavior, product ownership and a battery of demographic and credit card usage data. Interviews (8-10 minutes) were held outside of twenty-five randomly selected competitor locations and inside of 55 (out of 500+) selected (by management judgment) client locations. Panel data was obtained from a list of 700+ volunteers who permitted the scanning of all client credit card purchases for a period of 15 weeks over 3 randomly selected quarters. Media expenditure, flight times, and copy platform data was supplied on EXCEL files by the client for a two year period. Data were inputted, manipulated, analyzed, and presented to management approximately one week after the close of each quarterly data collection period. Results Where did the growth come from? •Inflation accounted for about one-seventh of the increase in sales performance, •"Loyalty" (more frequent visits) explained another one-seventh of the total improvement, •Increases in Consumer Disposable Income explained two sevenths of the performance improvement, •And most interestingly, 3 sevenths of all the growth (i.e., over 40% of the improvement in sales performance) could be traced back to changes in the composition of the client’s target markets. Overview of the Key Findings Trends in Univariate Findings Individual outlets were monitored over time on various dimensions including price, assortment, appearance of store, service, brand availability, and loyalty, etc. The most striking changes (over the previous four quarters) were shifts in promotional tactics by different competitors, and a growing preference for "quality brands and assortment" as opposed to just "lower prices". Managers at individual locations were able to adjust brand assortment, store appearance and monitor competitive action on a more local basis. Trends in Macro Segment Composition and Expenditures Four macro market segments were identified based on household income and store expenditure patterns (etc.) and multivariate statistical analysis revealed significant shifts in the type of people the organization was beginning to attract more frequently ... and from which competitors they were gaining (or to whom they were losing) market share ... by sub-segment group. Further analysis allowed management to identify those product group offers and promotional efforts which were most apt to attract one or the other macro segments (please see Figures 3 and 4 for quarterly change pie chart data). Figure 5 shows how each segment 4 size (coded as spade, heart, club, diamond) changed over time ... mostly as a function of the organization’s statistically informed promotional decision making efforts (price, product, etc.). ♠ ♦ L/L ♥ ♠ H/H ♣ L/H L/L ♥ ♦ H/H H/L L/H L/L H/H L/H ♣ H/L How many? H/L How much DM? Figure 3. Macro segment distribution by frequency and value ♠ ♦ L/L ♥ L/H ♠ H/H H/H H/L L/H L/L ♣ H/L 1. qtr L/L ♥ L/H ♦ H/H ♣ H/L 2. qtr Figure 4. Change in macro segments between quarters Targeting the Best Macro/Micro Segments Micro segments (of about 400 people each) were analyzed on both behavioral dimensions and on the stage of family life cycle dimension. Significant differences were discovered in amounts spent at competitor location and on a variety of branded items/categories. Figure 5. Macro segments and sales change 1st-2nd quarter 5 Media Platform and Message Content Appeal Further multivariate data mining techniques revealed distinct media viewership, listenership, and readership preferences by four out of five key micro segments (please see Figure 6). Each sub group responded differentially to selected message themes. Management was able to "fine-tune" both media selection choices and to direct more closely the "creative strategy statements" of their advertising agencies’ efforts based on this new statistical media/message response effectiveness evidence. KGNU EN2 KDKA EN1 WLS other WLK WCKY FN2 FN1 KOGO YNC Figure 6. Macro segments and favorite radio stations Market Basket - Scanner Data Findings Figure 7 presents data from the top seven selected product categories by "stage of family life cycle" and "macro market" segment (high vs low income group). The purchase profiles indicate that low income customers (for example) restrict their purchases to fewer categories than do the higher income groups. Also, product category D is apparently related to "married with children" households. (Ticket analysis is not part of this presentation.) The market basket analysis procedure shown here is much simplified and was done at the "line item" level for the most part (i.e., each product on a given ticket). Much more complex analysis are possible using SAS Institute state-of-the-art data mining software. For example, primary and secondary data interface (i.e., client data + industry wide data) revealed the 10 top brands/products offered by local competition which were not even being carried by our client. This one finding eliminated 80% of all "brand availability" complaints. 6 young, no ch full nest I full nest II empty nest I empty nest II 70 L o w I n c o m e 60 P e r c e n t 50 40 30 20 10 0 70 H i g h I n c o m e 60 P e r c e n t 50 40 30 20 10 0 A B G F C E D A B G F C E D A B G F C E D A B G F C E D A B G F C E D Figure 7. Market Basket Analysis Summary Subsequent market share simulation trials indicated that, as a result of the above research, and management’s decisions to change target market emphasis, product assortment and promotional strategy, etc., sales performance improved each year by an average of 2.5 to 3.5 percentage points. This paid for the entire two year research effort many times over. (Note: The other four sevenths of total growth came from the three other areas identified in the Results section on page 4.) Most importantly, the client was able to avoid weak areas and focus on its organizational strengths. They were able to do the right things ... and to do things right. Now management is able to lead the company where it should go for the next few years ... into the growth areas of the market ... and CAIR Research Center was able "to introduce decision makers to the benefits of statistical analysis and to present useful research results in a right and simple way." References: Deming, W.Edwards: “Out of the Crisis”, 1987, MIT Press Huyink David S., C. Westover: "ISO 9000: Motivating the People, Mastering the Process, Achieving Registration!", 1994, IRWIN Juran, Joseph: “Management Breakthrough”, 1964, McGraw-Hill Juran, Joseph: "Juran's Quality Control Handbook", 4th ed., 1988, McGraw-Hill Kotler, Philip: “Market Management: Analysis, Planning, Implementation, and Control”, 1996, Prentice Hall Kuhfeld, Warren F.: "Marketing Research Methods in the SAS System", 1994, SAS Institute Inc. Luzar-Stiffler, Vesna and Charles Stiffler: "Seven Tools for Statistical Quality improvement", 1996, CAIR Center Course notes Porter, Michael. E.: Competitive Advantage: Creating and Sustaining Superior Performance”, 1985, Freepress publications SAS/QC Software: Usage and Reference, 1995, SAS Institute Inc. SAS/STAT User's Guide, Version 6, 1994, SAS Institute Inc. Walton, Mary: “Deming Management at Work” (Six great case studies for implementing TQM), 1990, Putnam’s Press 7