1 e-journal of Practical Business Research Digital transformation of CRM systems in the automotive industry Leveraging big data for customer profiling Pascal Schniepp Erschienen im e-journal of Practical Business Research unter: Automotive markets in general, and those in Europe and North America in particular, are becoming more and more saturated and competitive, which is why the concept of Customer Relationship Management (CRM) keeps on increasing in relevance. Recent advancements in the generation, storage, transfer and analysis of large amounts of data (big data) with the help of information technology (IT) make way for new possibilities in quantitative and qualitative analysis of customer data. The aim of this paper is to identify strategic potentials for the three main CRM stakeholders, namely OEMs, retailers and customers, made possible through incar IT applications and smartphone integration. Zitation: Schniepp, Pascal (2013): Digital transformation of CRM systems in the automotive industry Leveraging big data for customer profiling In: e-journal of Practical Business Research, Ausgabe 14 (12/2013), DOI: /
2 2 P a g e Table of Contents 1. INTRODUCTION/ BACKGROUND GOAL METHODOLOGY INTRODUCTION TO CRM Definitions and goals of CRM CRM architecture Customer profiling as a prerequisite for successful CRM Role of IT in CRM systems Customer relationship analytics and data mining STATUS QUO ANALYSIS OF THE GERMAN AUTOMOTIVE MARKET AND CRM PRACTICES Key figures of the worldwide automotive market Key figures of the German automotive market Current structure of the automotive market in Germany Current structure of the vertical sales organization and retail structure in Germany Status quo of key processes in CRM Customer profiling Campaign management Data mining Retailer integration CRM affecting trends in the automotive industry Connected cars Changes in consumer behavior and expectations Big data processing and cloud computing Changes in the automotive ecosystem Summary of the status quo analysis needs for action and potentials... 33
3 3 P a g e 6. CONCEPTUALIZATION OF A DIGITALLY TRANSFORMED CRM SYSTEM FOR THE AUTOMOTIVE INDUSTRY Targets of a digitally transformed CRM system Concept of a revised relationship and data flow model Benefits for the OEM Benefits for the retailer Benefits for the customer Definition of car-related data variables and their specific relevance in marketing Exemplified process model for campaign management CRITICAL SUCCESS FACTORS OF A DIGITALLY TRANSFORMED CRM SYSTEM Retailer integration Customer acceptance Technological feasibility and data protection Organizational change and multi-channel management Trust and brand strength Costing and partnering SMARTPHONE APP AS A MEANS FOR CRM EXTENSION AND CUSTOMER EXPERIENCE MANAGEMENT OUTLOOK/ SCENARIO ANALYSIS REFERENCES... 58
4 4 P a g e List of Figures Fig. 1: Functional chain of customer retention... 9 Fig. 2: Determinants of total customer value Fig. 3: CRM architecture Fig. 4: Potential contents of customer profiles Fig. 5: Classification of data analysis types Fig. 6: Most important passenger car markets worldwide in Fig. 7: Passenger car sales in Germany by OEM brands Fig. 8: Passenger car sales in Germany by segment in Fig. 9: Ecosystem of the automotive industry Fig. 10: Advantages and disadvantages of OEM own branches vs. authorized retailers Fig. 11: Dealership and service networks in Germany by brand Fig. 12: Profit pool of the European automotive industry in Fig. 13: Schematic process model of the CRISP-DM Fig. 14: Connected vehicles impact on ownership experience Fig. 15: Fulfilment of customer expectations Fig. 16: Status quo framework of the VSO Fig. 17: Revised framework of the VSO Fig. 18: Process model for campaign management Fig. 19: Smartphone app function categories and respective sub-goals Fig. 20: Scenario funnel for future CRM development in the German automotive industry... 57
5 5 P a g e List of Abbreviations acrm CAS CAN CSF CRISP-DM CRM CTP EIU ERP ICT IT LTE MCM ocrm OEM PA SFA SCM TCV VSO Analytical CRM Computer Aided Selling Controller Area Network Critical Success Factor CROSS-Industry Standard Process for Data Mining Customer Relationship Management Customer Touch Point Economist Intelligence Unit Enterprise Resource Planning Information and Communication Technology Information Technology Long Term Evolution Multi-Channel Management Operational CRM Original Equipment Manufacturer Predictive Analysis Sales Force Automation Supply Chain Management Total Customer Value Vertical Sales Organization
6 6 P a g e 1. Introduction/ Background Automotive markets in general, and those in Europe and North America in particular, are becoming more and more saturated and competitive, which is why the concept of Customer Relationship Management (CRM) keeps on increasing in relevance. Today, automotive CRM data is stored in decentralized databases at retailers with only very limited or no information exchange with the car manufacturers. This situation makes it nearly impossible to maintain a one-faceto-the-customer-approach. In opposition to this situation, customer profiles and demands in the automotive industry are becoming ever more complex. As a result, Original Equipment Manufacturers (OEMs) struggle to effectively address their target groups with the right message at the right time through the right channel. Recent advancements in the generation, storage, transfer and analysis of large amounts of data (big data) with the help of information technology (IT) make way for new possibilities in quantitative and qualitative analysis of customer data. New technology might provide a wide range of opportunities to systematically manage data, allowing for an enhanced picture of consumer behavior in the car. New applications being added to the mobility ecosystem, such as smartphone integration, offer additional potential customer touch points. Also the processing of big data and data mining could both play a more significant role in respect to the refinement of customer profiles resulting in the convergence of marketing and IT. Clearly, there are certain factors opposing the rapid implementation of such applications, including customer acceptance, retailer integration, technological feasibility, costs of implementation and organizational change, to name but a few. In addition, the issue of data protection needs to be addressed. However, when looking at other industries and applications, it has been shown that with tangible added value for the customer, this hurdle seems to be conquerable. 2. Goal The aim of this thesis is to identify strategic potentials for the three main CRM stakeholders, namely OEMs, retailers and customers, made possible through in-
7 7 P a g e car IT applications and smartphone integration. In particular, the bottleneck of information transfer between retailers and OEMs shall be illustrated. The basis of the proposed strategy is the generation and collection of data within the car and the later usage of this data for marketing and re-engineering purposes. The data in focus includes driving style, car usage, car status and safety, infotainment, environment, smartphone integration and navigation data. This will be accompanied by data retrieved from mobile devices such as smartphones, which are expected to be increasingly used in the car. Each of the proposed data sets will be allocated specific usages for marketing or engineering purposes. Possible applications range from refined customer profiles as a pre-requisite for successful CRM operations to quantitative data mining analyses for better customer insights which are to be used throughout the entire marketing organization. Customers and drivers should alsoprofit from such data, e.g. in the form of interactive smartphone apps and diverse so-called connected services. This thesis aims to demonstate how this strategic CRM transformation could be made feasible in respect to the factors opposing the implementation. The thesis also aims to describe how existing technologies, which might already be used in nonmarketing domains, can leverage marketing conduct. 3. Methodology This thesis will be founded on the theoretic basis of the CRM concepts proposed by Hippner et al (2006) and Neckel/ Knobloch (2005), who specifically addressed CRM architecture and data analytics. The current working situation and upcoming trends related to CRM will be identified in a status quo analysis concluding with needs for action and business potentials. This analysis is based on available studies and publications as well as qualitative interviews with experts of their respective fields (interviewees list in appendix 1; interview goals and guidelines in appendix 10). Similar sources have been used for the identification of upcoming trends which demonstrate potential to positively transform CRM practices. In the conceptualization section, the findings of the status quo analysis are merged into a relationship framework between OEMs, retailers and customers. This concept is then theoretically extended by the newly introduced customer touch point (CTP) in the car, new technological capabilities of in-car telematics
8 8 P a g e and revised relationship approaches between OEMs, retailers and customers. An extensive list of car-related data variables including respective relevance in marketing, business potential, and possible utilization for a smartphone app will be presented. Afterwards, the concept will be contrasted with its critical success factors. A mobile app will then be modeled with the aim of demonstrating the practical implementation of theoretical recommendations. This thesis is concluded with a scenario analysis on potential developments in the analyzed field. The underlying time frame assumed necessary for realizing the proposed concept ranges from three to five years. This paper argues from a German premium OEM s perspective, as for them the revised CRM framework is assumed to be of highest relevance. 4. Introduction to CRM This chapter first defines the core principles of CRM. Then, the integral goal of increasing customer retention is described as a functional chain. Next, the concept of total customer value is introduced in order to show how customers contribute in different ways to the overall economic success of a company. This section is followed by a schematic architecture of CRM systems, including analytical and operational CRM components. The chapter is concluded by presenting customer relationship analytics methods including data mining as a way to generate knowledge and behavioral schemes based on data pattern recognition. In general, quantitative methods aiming at optimizing customer profiles and other CRM practices will be highlighted. 4.1 Definitions and goals of CRM During the last decade, the concept of CRM received widespread attention and was increasingly established in management practice. Especially in saturated markets like the German car market where customers are well-informed and switching costs are low, the aim of stabilizing customer relationships has become ever more important (Helmke/ Uebel 2013, p. 5). In order for this paper to clearly explain the concept of CRM and its two distinctive dimensions of business orientation and IT, a formal definition of these concepts is presented first.
9 9 P a g e CRM is a customer-oriented business strategy that tries to establish profitable long-term relationships with customers with the help of modern information- and communication technologies. Those relationships are created and stabilized by holistic and individual marketing-, sales- and service concepts. (Hippner et al 2006, p. 18) Deriving from the definition above, CRM aims at establishing profitable customer relationships, thereby increasing a company s business success or its market value, respectively. (Matzler et al 2002, p. 7-10). Hippner s definition is chosen in this paper over others, e.g. Kreutzer (2009, p. 20) or Homburg/ Sieben (2000, p. 7) because it specifically highlights the importance of technology as an enabler for establishing profitable long-term relationships. In the course of this thesis, the aspect of retaining and further penetrating existing customers will be prioritized, whereas lead and churn management will be treated only peripherally. To demonstrate how customer satisfaction and eventually retention impacts companies long-term profitability, customer retention can be modeled as a functional chain divided into 5 phases. By progressing through the phases, customers gain trust, an important asset which will be discussed in more depth later (see fig. 1): Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Primary contact Customer satisfaction Customer loyalty Customer retention Purchase Usage of a service Evaluation Confirmation/ Disconfirmation paradigm Acceptance Positive attitude Rebuying Cross-buying Recommendation Economic success Fig. 1: Functional chain of customer retention (cf. Bruhn/ Homburg 2005, p. 10) Trust level of customer Every customer has an individual set of determinants of his total customer value (TCV). The TCV measures the overall perceived economic significance of an individual customer, a customer segment or the entirety of customers from a supplier s point of view (Neckel/ Knobloch 2005, p. 17). The determinants of TCV can be divided into two groups: transaction potential and relation potential (Fig. 2).
10 10 P a g e Total Customer Value (TCV) Transaction potential Relation potential Base volume Growth potential Cost Saving potential Recommendation potential Information potential Co-operation potential Penetration potential Cross-selling potential Up-selling potential Decreased price elasticity Determinants Fig. 2: Determinants of total customer value (Hippner 2006, p. 27) Without describing each determinant, it is still important to highlight which dimensions can be positively influenced by customer profiling, as it is the main target of the proposed CRM framework. The most relevant dimension for customer profiling is information potential. In theory, a more complete picture about the customer might lead to an optimization of individual product and service offerings, increasing their relevance for customers (Homburg/ Schnurr 1999, p. 5). While these customer insights can be neglected by cost leaders who rely on standardization, premium OEMs could draw competitive advantage by reinforcing their customer centricity in that way. On the transaction side, advanced customer profiling might increase the growth potential as more customer needs are identified, which in turn increases the penetration, crossselling and up-selling potential at lower marketing wastage. Applied to the automotive industry, this means that advanced customer and driver profiles could be extended by parameters on driving behavior and usage of in-car applications. When combined with the already existing attributes of automotive customers, this data is expected to increase the information potential. The new insights can then be used to tailor and orchestrate marketing activities aiming at a wider set of CTPs (especially in-car), selling of after-market car equipment, and might deliver tangible arguments for up-selling when the next car purchase is planned. Also, new service dimensions can be tackled, such as pro-active complaint management or advanced safety diagnostics, leading to potential overfulfillment of customer expectations. Further insights could be collected through smartphone apps, representing another potential CTP which enables a two-way communication between OEM and customer.
11 11 P a g e 4.2 CRM architecture In order to structure the rather broad topic of CRM into more business-applicable components, we can initially distinguish between analytical CRM (acrm) and operational CRM (ocrm). Fig. 3 gives an overview of the relations between the CTPs, acrm, ocrm and CRM service systems. Fig. 3: CRM architecture (cf. Neckel/ Knobloch 2005, p. 45) This model illustrates that information about customers is collected at different CTPs. This information is captured in front-offices of marketing, sales, and service facilities. From the various front-offices, the information is then transferred (ideally) to a centralized back-office where analytical CRM procedures take place. Here, raw data or information fragments can be upgraded to tangible customer knowledge and insights. These findings are then fed back to the frontoffices where they can help automate different marketing, sales and service processes supporting ocrm (Neckel/ Knobloch 2005, p. 45). Note that the CTP in-car is already integrated here in order to demonstrate its potential role in the CRM architecture, including its direct connection to the acrm database system. In the automotive industry, front-end office activities mainly take place at the retailers and service institution facilities. The retailer, who at the same time can function as service and after-market supplier, remains the most important touch
12 12 P a g e point for customers in the automotive industry, and also holds the generated customer transaction data (Marek 2013, p. 4.). 4.3 Customer profiling as a prerequisite for successful CRM As the sum of various descriptive information and evaluation fields about a customer, customer profiles allow for a holistic view of either an individual customer or customer groups or segments which are clustered according to equality or similarity in their characteristics (Neckel/ Knobloch 2005, p. 57). In this way, for instance, the most profitable customers can be grouped according to their shared characteristics. The input for customer profiles is generally collected through the CTPs. This implies that profiles should be retrieved consistently across all customer CTPs, so as to guarantee a comparable data basis. Aside from the OEM website, social media appearances and call-centers which fall under the responsibility of the OEMs, car producers depend on retailers, who occupy the most relevant CTPs when it comes to customer data (Interviewpartner - Strategy Consulting 2013). The contents of customer profiles can be categorized as follows: Customer profiles Description data Transaction data Basic data Derived data Activity data Reactivity data Customer ID Name, address Sales region Customer advisor Geographic attributes Socio-demographic attributes Psychographic attributes Potential data Product category specific demands Timing of concrete requirement situations Current demand Current service claims Customer value Classification (e.g. segment belongings) Behavior patterns (Confirmed) behavioral patterns Purchase behavior relevant attributes Types of actions initiated by the OEM towards customer (e.g. direct marketing) Duration of customer contact Frequency of customer contact/ action Timing of customer contact/ action Contents of customer contact/ action Sales volume and structure (e.g. by product, outlet, time) Timing of purchase Customer requests, complaints and claims Recommendations Duration of customer relationship Stage on loyalty ladder Fig. 4: Potential contents of customer profiles (cf. Neckel 2002 & Link/ Hildebrand 1993) On the one hand, description data includes basic data about characteristics that do not change over time and are collected independently from the purchased products. On the other hand, description data is also made up of derived data which carries validated information about the preferences and potentials of the
13 13 P a g e customers. Derived data is generated from data analyses of transactional data. Behavioral patterns can give hints to specific correlation factors between customers, and also allows for market basket analyses. Transaction data documents the dialog between the company and the customer. Also, actions initiated by the customer towards the company fall under this category. It is important to note that the collected information must be validated over time before tailored campaigns can be rolled-out with confidence (Neckel/ Knobloch 2005, p. 60/61). It is hereby assumed that all mass customized marketing campaigns as well as data mining operations are fed with the information stored in customer profiles, thus making consistently administered customer profiles an essential component for further, more sophisticated CRM-related data analyses. 4.4 Role of IT in CRM systems As mentioned before, IT systems are an integral part of the overall CRM concept. IT systems enable companies to maintain large databases digitally. The fact that CRM has various stakeholders within and without the company implies the need for sharing customer information. Given the amount of customer information in large organizations, the crucial role of IT in CRM becomes obvious (Hippner 2004, p. 15). Also, the core concept of this paper is, in fact, made possible by advancements in the performance, availability and ongoing cost reductions of suitable IT systems and CRM-related IT innovations, most of which are indexed on Gartner s hype cycle depicted in appendix 2. The technological commitment towards CRM has been reflected in the study results at The Economist Intelligence Unit (EIU) from 2008, in which customer service ranked the most frequent response (40 %) to the question: In which areas of operation will technology cause the greatest change in business practices over the next five years? (EIU 2008, p. 21; study design in appendix 3). With the growing number of channels through which a company communicates with the customer, it becomes increasingly important to actively manage the data provision at each channel. At this point, companies running integrated IT systems sharing information across channels can be expected to be more successful than companies operating a multiplicity of isolated applications (Hippner 2004, p. 33).
14 14 P a g e Appendix 4 gives an overview of the most prominent CRM suites currently available (Forrester 2012). 4.5 Customer relationship analytics and data mining In this paragraph, the functionality of the acrm component, particularly in regard to IT-supported analytics, is evaluated. To achieve the CRM goal of establishing long-lasting relationships with profitable clients, it is necessary for companies to possess knowledge about the structure, behavior, and demands of their customers (Hippner/ Wilde 2013, p. 181). Referring to fig. 5, two general types of data analyses can be run hypothesis verification and data pattern recognition. Data Analysis Subject of investigation Hypothesis verification Data pattern recognition Target of analysis Hypothesis driven methods (top-down) Reports KPIs (-systems) Database queries OLAP ABC-analysis Portfolio analysis Regression analysis Variance analysis Cluster analysis Etc. Data driven methods (bottom-up) Data Mining: Explorative data analysis/ visualization Association rule learning Decision trees Heuristics Cluster analysis Artificial neuronal networks Evolutionary algorithms Types of data analysis Fig. 5: Classification of data analysis types (cf. Neckel/ Knobloch 2005, p. 83/ 84) When confronted with the subject of investigation, a data analyst has to define whether the analysis should either have the target of hypothesis verification or data pattern recognition. In theory, hypothesis verification is used to answer questions such as which data fits a predefined pattern (e.g. how many customers who bought an Audi A5 series also ordered the optional high-end audio system in 2012?). Data pattern recognition, on the other hand, does not predefine any factors which are to be fulfilled and therefore generally asks questions such as which patterns can be identified in the underlying set of data (e.g. which customers should be addressed with a direct marketing campaign?). Consequently, different types of data analysis can then be chosen due to their suitability for the desired
15 15 P a g e investigation. In practice, however, there is seldom the case of a researcher who does not have at least a vague relation in mind he or she wants to analyze (Neckel/ Knobloch 2005, p ). 5. Status quo analysis of the German automotive market and CRM practices The goal of the status quo analysis is to first construct a clear understanding of the current developments in the worldwide and German car markets. Next, the current structure of the German automotive market including the vertical sales organization (VSO) is analyzed. The status quo of four CRM key processes, namely customer profiling, campaign management, data mining, and retailer integration will be evaluated. The presentation of CRM-related trends in the automotive industry will follow. This chapter concludes with a summary of needs for action and business potentials discovered in the status quo analysis. Selected regions Units sold in 2012 Change 12/11 Share within selected regions Change 11/10 Passenger cars per 1,000 people (as of 2010) Europe (EU27+EFTA)* ,8% 18,0% -1,4% n/a Western europe (EU15+EFTA) ,1% 16,9% -1,3% n/a New EU-countries (EU11)* ,8% 1,1% -2,9% n/a Germany ,9% 4,4% 8,6% 517,3 Russia** ,6% 4,2% 38,7% 233,2*** USA** ,4% 20,7% 10,2% 423,0 Japan ,7% 6,6% -16,3% 452,6 Brasil** ,1% 5,2% 2,9% 178,3*** India ,3% 4,0% 6,0% 11,8*** China ,4% 19,0% 8,4% 43,8 Sum ,56% 100,00% 4,10% n/a * without Malta ** Light Vehicles *** as of Key figures of the worldwide automotive market The automotive industry remains one of the vital drivers for worldwide economic value creation. In 2012, more than 69 million cars were sold in the most influential national markets as stated in fig. 6. Fig. 6: Most important passenger car markets worldwide in 2012 (Sources: Absolute numbers and changes: VDA 2013a; VDA 2012; Passenger car density: World Bank 2013) The table delivers multiple important findings. Firstly, Europe and especially Western Europe is facing a severe downturn in sales of new passenger cars. In
16 16 P a g e Germany, the largest European market, the situation has been more positive, slowing down Europe s overall slump. The USA as well as the BRIC countries spearheaded by China demonstrate consistent growth. Interestingly, during the last two years the passenger car market saw little correlation between the overall density of passenger cars and growth rates of units sold. Nevertheless, in this paper it is assumed that with increasing saturation of markets, the growth rates will eventually slow down, fostering competition among OEMs. 5.2 Key figures of the German automotive market Traditionally, German car brands enjoy a strong foothold in the German market. In 2012, German manufacturers and German automotive group brands combined (see list below fig. 7) held over 70% of the market share in the German passenger car market. The three largest premium car brands, Audi, BMW, and Mercedes, compete neck-and-neck, each holding approximately 9% market share each Passenger car sales in Germany in % % 22% % 9% 9% Volkswagen BMW Mercedes Audi Other German manufacturers Total sales in Germany: 3,082,504 *German group brands include: Ford, Opel, Porsche, Seat, Skoda and Smart and group brands* Foreign brands w/o German group brands Fig. 7: Passenger car sales in Germany by OEM brands (VDA 2013b) This situation creates the need for premium OEMs to turn over the existing client base of their direct competitors. Taking a look at fig. 8 below, it becomes clear why SUVs and compact cars are currently chosen more frequently as a means of diversification of German premium OEMs. SUVs enjoy increasing popularity, fueling growth rates, while compact cars have the overall highest market share as a segment. In addition to the entering of new premium segments by certain OEMs, a change in car buyers behavior, specifically with regard to intensified
17 17 P a g e brand orientation and polarization, explains the strong demand in the premium segments (Diez 2009, p. 42). Volkswagen s CEO Martin Winterkorn also confirmed the significance of the premium segment in 2013: No other segment is so profitable and few other segments demonstrate such stable growth rates (Winterkorn 2013, p. 13) Passenger car sales in Germany by segment in Fig. 8: Passenger car sales in Germany by segment in 2012 (KBA 2013, KBA 2012, KBA 2011) 5.3 Current structure of the automotive market in Germany Although there are significant regional differences in the way the automotive markets are structured, the most basic assumptions apply to all of them. Therefore, the analyzed German market is assumed to function as follows: The automotive industry follows a value creation process (see fig. 9). Along this value creation chain, the overall automotive market is further split into sub-markets with their specific rules and players. At this stage, the OEM does not and should not cover all transactions taking place along the value creation chain due to its specialized competencies. Still, the OEM plays the leading role in the integration of upstream activities, although more and more R&D processes are being shifted towards the supplier side (Diez 2009 p. 131). This also applies to the in-car telematics systems which are an integral subject later on in this paper. When it comes to downstream activities, the OEM has to share control of the value creation chain with a number of other players. The traditional and still
18 18 P a g e predominant automotive ecosystem can be described as closed, as it has clearly defined boundaries, structures and processes (Roland Berger 2012, p. 18). Value creation chain Product development Procurement Production New car sales Financing & Insurance Service & Parts Used car sales Fuels Scrapping & Recycling Uptream activities Downstream activities Sub-markets Engineering services market Components and parts market Assembly & superstructure market New car market Financial services market After market Used car market Fuel market Scrapping & used parts market Fig. 9: Ecosystem of the automotive industry (cf. Diez 2009, p. 18) 5.4 Current structure of the vertical sales organization and retail structure in Germany As for new car sales, OEMs today display different strategic approaches to manage their dealership network. Generally, the OEM can opt for a direct sales approach where cars are sold via the OEM s own central sales division, or more importantly, through the OEM s own subsidiary branches. The indirect sales approach foresees the orchestration of authorized retailers which are independent entrepreneurs selling one or more brands in their retail shops (Diez 2009, p. 270). The two generic retail approaches come with respective advantages and disadvantages, listed in figure 10:
19 19 P a g e Direct Sales (via central sales division or OEM own branches) Indirect Sales (via authorized retailers) Advantages Direct influence on the vertical distribution chain including all sales instruments Direct customer contact Securing brand-conform appearance of sales space (especially in the case of flagship stores) Usage of own retailers as field test for innovative sales and service techniques Avoidance of the retailer margin Brand presence in expensive yet strategically important locations No capital commitment in the sales organization Sales- and warehousing risk is largely shifted towards the authorized retailer (retailer as buffer between demand and production) Strong entrepreneurial motivation of independent authorized retailers (in contrast to a potential nine-tofive mentality in OEM own retailers) Disadvantages Additional capital commitment Adoption of the entire sales risk Potentially lower motivation in contrast to the entrepreneur oriented situation at authorized dealers Necessity of brand strength to acquire qualified retail companies Low degree of influence and control over the vertical distribution chain therefore higher coordination effort Danger of blind spots in the sales structure due to insolvencies in economically tough times Fig. 10: Advantages and disadvantages of OEM own branches vs. authorized retailers (Diez 2009, p. 275/ 286) Taking a look at the current dealership networks in Germany depicted in figure 11, several trends can be observed. Firstly, German premium OEMs and Volkswagen (formatted in bold) rely relatively often on the OEM s own branches. Mercedes, in particular, is following the strategy of direct sales with a total of 125 of its own branches in There is a decline of retail outlets during the last 4 years. This again attests to the high level of saturation in the German market, as well as the OEMs attempts to consolidate sales operations. A hypothesis, rejected within the bounds of this research, is that OEMs running networks with a high percentage of their own branches vs. authorized retailers have more direct control over the distribution system and more potential of sharing insights between the OEM and retailer level. Interviews suggest that personal philosophy of sales conduct and collaboration has a significant influence both positive and negative on the degree of direct control (Interviewpartner - Automotive IT-Consulting 2013).
20 20 P a g e Authorized retailers Pure service partners OEM own branches Brand Grow th '12 vs. ' Grow th '12 vs. ' Grow th '12 vs. '09 Audi % % % BMW % % % Ford % % Mercedes % % % Mini % % % Opel % % Porsche % % Renault % % % Seat % % % Skoda % % Toyota % % Volksw agen % % 13 60* 46** 46** 254% Sum Total % % % *Autohaus.de estimation ** OEM ow n retailers Fig. 11: Dealership and service networks in Germany by brand (Autohaus 2012; Autohaus 2010) When regarding the different downstream markets for new car sales, financial services, repair services and parts, as well as used car sales, further findings become obvious. Figure 12 illustrates the downstream market sizes as of 2010 with respect to revenues and contribution margins. The first striking finding from the analysis of the downstream market sizes in Germany is the difference between revenue volumes and profit margins of the segments. The comparably larger markets for new and used cars are characterized by low profit margins.
21 21 P a g e Fig. 12: Profit pool of the European automotive industry in 2010 (Bain & Company 2010, p. 12) Clearly, the sweet spots in terms of profitability are financial services and the parts, accessories, and services business. A scenario analysis for 2015 conducted by Mercer Management Consulting (later Oliver Wyman) in 2005 foresaw approximately 30 % of the overall profit in the analyzed market segments being up for disposal by 2015 (Oliver Wyman 2007, p. 11). This implies a strong need for OEMs to reconsider their business models along the vertical sales organization (VSO) in order to compete with upcoming competition from specialized players in their respective segments. A collaborative strategy with retailers might be the key to success when it comes to tackling those challenges. 5.5 Status quo of key processes in CRM It is now time to take a look at how CRM is dealt with in practice at German OEMs and retailers, respectively. Despite the recent increase in awareness concerning CRM as a management practice, it cannot be stated with confidence that CRM arrived permanently on the C-level agenda of all German OEMs (Interviewpartner - Automotive IT-Consulting 2013). Interestingly, consultants report their most successful clients also allocate high priority to CRM, placing it on the same level as marketing and sales practices and therefore qualify CRM as a
22 22 P a g e top management focus (Interviewpartner - Automotive IT-Consulting 2013). Volkswagen, for instance, sees specific aspects of CRM (in this case customer retention) as an executive level responsibility (Interviewee X). Some OEMs pursue marketing strategies with low effort in CRM. The relative importance of the CRM practice can roughly be estimated according to the headcount of CRM professionals in ratio to overall employees (Interviewpartner - Automotive IT- Consulting 2013). Certainly, it is quite a complex task to consistently streamline CRM processes according to a customer-centric business strategy. There is a multitude of CRM instruments at hand for each of the customer lifecycle stages. This paper primarily discusses instruments aiming at stabilizing the customer relationship through e.g. campaign management and retailer integration, while lead and churn management instruments will be treated marginally. Nevertheless, lead management might also profit from the proposed concept for instance in the campaign management process presented in section 6.7. Within the domain of stabilizing and further penetrating customers the paramount aim is to increase loyalty and trust, enlarge the share of wallet, and condition customers considering a new car purchase for up-selling (cf. Dees 2012, p. 1-5). Therefore, four key processes in automotive CRM are assessed in regard to their current ability to fulfill the stated goals. These four key processes regarded with special attention in this paper are: 1. Customer profiling 2. Campaign management 3. Data mining 4. Retailer integration Each of them will now be analyzed individually. The status quo analysis of key CRM processes in the German automotive industries will be based on published research papers, studies, and expert interviews. Although some upside potential could be identified by personal observation and has been confirmed by qualitative interviews, it is understandable that OEMs and retailers alike are not keen on publicly admitting space for potential improvement within their organization. Statements listed in this chapter have been verified by at least two sources. Nevertheless, some interesting findings have been made.
23 23 P a g e Customer profiling As stated in section 4.3, customer profiles represent the innate basis for CRM operations and analytics. This paragraph will identify how customer data is collected and which attributes are stored in order to use them for marketing campaigns. The range of involvement concerning the collection and storage of customer profile data varies significantly. Almost every OEM, however, possesses a database with relevant sales data in regard to lead management and call-center data. In some cases, information from retailer systems is captured centrally, including information about which customer purchased which car. The most advanced OEMs in terms of CRM possess many more data variables and run a full vertical sales integration from the OEM through the wholesaler and retailer levels down to the customer. In these cases, data consistency is given, and information about hobbies and (household) income are integrated as well (Interviewpartner - Automotive IT-Consulting 2013). Our most dedicated client collects up to 200 attributes per customer. Of course, this allows for a very fine grained representation of the customer (Interviewpartner - Automotive IT- Consulting 2013). The abundance of transactional data might deliver valuable insights for desired vehicle specification, planned purchase date or potential service appointments (Interviewpartner - Automotive IT-Consulting 2013). Volkswagen reports that most data is collected by and is in the exclusive possession of retailers. Here, address and transaction data concerning service appointments, sales volumes for new car sales, and equipment purchases are recorded. Interviewee X, an employee of VW s dialog marketing department considers the following profile attributes to be of integral importance to targeted sales approaches: Sales volume data Socio-demographic data Vehicle data connected to buyer and driver Data concerning customers privately selling their cars Information about channel preferences and communication behavior Furthermore, Volkswagen states that they only receive information concerning desired product specifications for new car purchases and address data. This
24 24 P a g e information, however, is only to be used in the context of the ordering process (Interviewee X). Audi considers customer profiles as the product of a learning relationship in the form of a cycle, including customer dialog, understanding of customer needs, and implementation of according actions (Finsterwalder/ Lutz/ Packenius 2003, p. 8). Another target situation in CRM is that OEMs are able to provide a consistent and integrated view of the customer throughout the organization. This situation cannot be confirmed for some of the interview partners. The main reasons for inconsistent customer profiles are (Interviewee X 2013): Too many individually grown and established processes, structures and systems in place No centralized database for customer- and lead management Data compliance restrictions especially in regard to exclusive data usage right of the retailers The holistic view of the customer, including his or her vehicle(s), behavior, and interests, as well as retailer affiliation, remains an art in its own right (Interviewpartner - Automotive IT-Consulting 2013). The issue of maintaining customer profiles over long periods of time and especially after selling and switching cars on a private market, makes it extremely hard for OEMs to effectively track and match customers and their cars. This leads to extremely poor customer addressing with marketing messages being sent with the wrong message at the wrong time through the wrong channel Campaign management A campaign is understood as a target-oriented and time-limited communication measure with defined and coordinated content and the aim to approach a predefined target group. Therefore, campaign management is defined as the planning, execution, and coordination of all activities necessary to implement a campaign (Finsterwalder/ Lutz/ Packenius 2003, p. 2). In practice, campaign management is already well-advanced at some OEMs. A reason for this might be that campaign management can fall under the responsibility of dialog marketing departments, which, at times, are further developed than other CRM-related marketing activities. In this case, the efforts of dialog marketing departments can be more
25 25 P a g e flexible, but the approach also bears the risk of conflicting actions between temporary campaign-centered initiatives and more long-term oriented CRM activities. Audi is relying on campaign management within their (dialog) communication strategy. In 2003, this led to a 3-step teaser campaign for the new Audi A8 in Italy. The aim was to guide prospects from the pure awareness at the moment the car was unveiled to test drive appointments. The pre-defined target group was addressed differentially through mass customization. The prospects were addressed personally (Finsterwalder/ Lutz/ Packenius 2003, p. 7). As of 2011, BMW also allocates greater significance to dialog marketing and integrated campaigns. Robert Weiss, director of international sales communications and dialog marketing, states That [great significance of dialog marketing] is demonstrated by the fact that it is influenced by a multitude of planning processes (Weiss 2011). Also here it is acknowledged that the right informationneeds to be presented at the right time. BMW develops strategies which are to be rolled-out internationally and which can be adapted for regional markets with the help of a toolbox. Recently, the integration of diverse customer touch points has become especially important. A more recent challenge is the intertwining of online and offline channels in order to create a seamless transition to dialog marketing efforts. Traditional mailings remain a very important measure in dialog marketing at BMW, but new channels for campaigns are also used and evaluated, such as the connected drive system (an in-car communication platform) and social media (Weiss 2011). Volkswagen also collaborates with its retailers during campaigns. For this purpose, retailers use their own customer database and supplement it with scorings of external service providers in order to profile target segments (Interviewee X 2013) Data mining As of today, the existence of a profound data foundation, necessary to enable continuous data mining analyses, is not common practice for most OEMs. Those who can already draw on such a data foundation are currently exploring approaches such as next-best-offer or next-best-activity. This is achieved by analyzing individual customers behavior as well as evaluating the behavior of
26 26 P a g e other peer customers of the same segment (Interviewpartner - Automotive IT- Consulting 2013). At Porsche, data mining is used in the CRM context to optimize lead qualifications, customer segmentation and churn analyses (Marek 2008, p. 6). Mercedes has been running data mining applications in CRM at least since 2006, employing the CRISP-DM (CROSS-Industry Standard Process for Data Mining), which follows the model depicted in figure 13 (Arndt/ Roggon/ Wachter 2008, p. 688): Fig. 13: Schematic process model of the CRISP-DM (Chapman et al 2000, p. 13) It is mainly used for customer loyalty increasing measures and allows for a more individual communication approach. At Daimler, the complex yet decisively important measure of customer loyalty is derived from three components: repurchase intention, brand affinity, and recommendation behavior. To assess those aspects of customer loyalty, data mining instruments are employed (Arndt/ Roggon/ Wachter 2008, p. 700). BMW runs data mining as a part of their Top Drive CRM system (schematic representation of the Top Drive s system in appendix 5), where it is used to optimize contents for marketing approaches and target group compositions (Armbrecht/ Braekler/ Wortmann 2009, p. 329) Retailer integration From an OEM s point of view, retailers have been and will remain for the near future the single most important entity in CRM. Thus, their integration is vital to any functioning automotive CRM process. Retailers normally act as independent
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