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Analysis how IT enabled CRM can add value and save costs at Knapen Trailers BV


Knapen Trailers BV, located in the Netherlands, is a limited company which develops and manufactures lorry trailers for a wide segment of the road transport and distribution industries throughout Europe. The client assumes that their customers favour trailers that offer the highest quality. As Jandrik Knapen, sales manager, puts it: "The market is Europe and the aim is the highest quality." Quality reflects itself in durability, which puts the price of a Knapen Trailer above competition, while keeping long-term operating costs below its rivals. During 2008, the client built and sold 613 trailers (Knapen Trailers, 2009). However, 2009 was a tougher year for the European road transport business. According to the economics office of the ING bank (2009), the average long-term profitability of the road transport sector has dropped to a deficit of 2.1 percent during 2009, which is even less than the recession of 1982. This is the reality trailer manufactures like Knapen Trailers BV are facing. In 2008, still 50 percent of the investments in trucks and trailers where due to expanding road transport businesses (TLN, 2009). Compared to 2008, 2009 was quite a different year, as ING bank (2009) predicted a European wide reduction in trailer registrations of 55 percent. Sales revenue of the client decreased with 39 percent (Knapen Trailers, 2009).

The client recently applies - consciously or unconsciously - a traditional marketing approach, using the 4Ps of product, price, promotion, and place. At least in the Netherlands, it has been argued that businesses should move toward a retention focused marketing approach (Curry et al, 2003; Van Bel, 2007; Van Leeuwen, 2003). This approach is often referred to as CRM and this is also seen in the following definition: "CRM is a business strategy that combines skilled customer facing personal, optimal process and enabling technologies to balance enterprise revenues and profits with maximum customer satisfaction." (Gartner Research, no date supplied, cited by Van Leeuwen, 2005).

Treacy and Wiersema (1995) recognise three different value disciplines in which a company can compete. These are Operational Excellence, Product Leadership, and Customer Intimacy. They argue that any company that wants to be leading in its market, must excel in one of the three disciplines, while keeping the other two at market level. However, Verhoef and Langerak (2002) do recognise, not only Customer Intimacy, but also Operational Excellence as a value discipline for strategic CRM, because this discipline aims on a more efficient targeting and on a more effective use of marketing tools.

Product Leadership is the value discipline in which the client excels the most, because the most competing characteristic of the client is the quality of it's products, which reflects itself mostly in durability. The client is satisfied with this strategy. Although, the Operational Excellence and Customer Intimacy value disciplines offer opportunities for development. IT enabled CRM can be an approach to do so. However, CRM can also be helpful to a product leadership strategy, by signalling market trends and needs, which is the basis to generate ideas for new innovative products (Van Leeuwen, 2007).

An analysis of how IT enabled CRM can add value and save costs, inherently starts with a customer analysis (Van Leeuwen, 2007). Therefore the starting point of my research is the customer pyramid (Curry et al, 2003), which will be drafted by the accountancy department of the client organisation, before the start of the research (see article 6 of the cooperation agreement in appendix B). Curry claims that a lot of companies do not have an integrated view of their customers. At the client organisation, only 56 percent of sales are sold to known[1] customers. This percentage includes sales by the owned dealers (40 percent), sales agents (12 percent) and direct sales (4 percent). Of the other 44 percent of sales, the customers (i.e. end-users) are unknown, due to the fact that the transactions are made via independent dealers. The distribution chain of the client is depicted in diagram 2 of appendix A. The dotted square shows all known customers and the accountancy department will draft a customer pyramid only from this known customers.

The idea of the research is to identify the client's most important customer processes - based on customer retention. After the research, the client's management team can decide to apply the recommendations to the owned dealers, as a CRM pilot. After an extensive piloting period, the CRM practice can then be applied to the independent dealers, because implementing IT enabled CRM to the entire sales organisation at once would be very expensive, and above all a risky activity.

The client organisation and the student signed a cooperation agreement (see appendix B for a copy). This agreement states that the client organisation will provide the student with full access to cost calculations, financial statements and all other information that is necessary to complete the research.

Research aim and objectives

Research aim: The aim is to help a client from an organisation without Customer Relationship Management (CRM) initiatives, to become an organisation that exhibits customer-centric capabilities by considering CRM initiatives. This is graphically depicted in Gartner Research's CRM maturity model (2001), which can be found in diagram 1 of appendix A.

Specific objectives:
  • Create awareness for CRM within the client organisation;
  • systematically, map customer processes;
  • designing and testing an in-depth semi-structured interview;
  • selecting and interviewing eight customers and two dealers;
  • calculating three future scenarios based on the client's customer pyramid;
  • ranking customer processes, based on retention;
  • recommending how IT enabled CRM can add value and save costs.


To give a clear view of the research, every phase has been given a number. Please, notice that the timescale of each phases are covered in section 1.5.

Mazurencu Marinescu et al (2007) state: "The improvement of customer retention consists of the following 3 steps. (1) Customer retention measurement, (2) identification of root causes of defection and related key service issues; and (3) the development of corrective action to improve retention." Step 1 can be seen in the analysis that is done by the accountancy department of the client and in phase 4. Step 2 is mainly covered in phase 1 until 3 and in phase 5. Step 3 is mainly covered in phase 6.

Phase 1; Mapping customer processes: Gartner Research (2001) assumes processes as one of the characteristics of CRM winners. Most processes are executed by more than one department and therefore departments working together (organisational collaboration) to deliver the customer what is promised and sufficient information is needed to achieve this. I will map customer processes one by one, starting with one department and then moving on to the next.

Phase 2; Designing and testing an in-depth interview: Based on the mapped customer processes, it is necessary to know which ones are most important in a customer point of view. To get this information, I am going to design an in-depth semi-structured interview. I am using this technique, because it is of utmost importance that I have the ability to adjust the questions to the situation's context (Saunders et al, 2009). I think that, in this situation, for instance a mail survey, will not provide me the proper underlying reasons of customer defection. Therefore a personal conversation and certain flexibility are necessary. When the interview is designed, I am going to test it by interviewing a customer. After the testing, I am going to review and redesign the interview until I am happy with it.

Phase 3; Selecting and interviewing eight customers and two dealers: I will conduct ten in-depth interviews in total. It is very unlikely that everyone is willing to cooperate. Finding ten recipients, probably means that I will have to contact more than twice this amount. To conduct the interviews, I will visit the recipients at their place. Notice that the interviewee is speaking to an unknown person in his/her own, save environment. In this situation it is likely that the interviewee will be more forthcoming, at least, this is what I have experienced during my bachelor thesis. The recipients will be selected by letting the department managers each name fifty customers and then select the recipients at random out of this list. As can be seen in the cooperation agreement in appendix B, the client will compensate the travelling costs.

Phase 4; Calculating future scenarios: Because it will not be suitable to conduct interviews fulltime, during the interview phase, I will calculate three future scenarios using the customer pyramid and recent cost calculations provided by the accountancy department of the client organisation.

  • A prudent scenario where 1 percent of the customers is developed one pyramid level;
  • a realistic scenario where 3 percent of the customers is developed one pyramid level;
  • an optimistic scenario where 10 percent of the customers is developed one pyramid level.

When calculating the scenarios, I will take both fixed costs and variable costs into account.

Phase 5; Ranking customer processes: I will rank customer processes using the obtained information from the earlier conducted interviews. The ranking criteria will be retention, which means that the process most affecting customer retention is most important and therefore appears on top of the ranking list. It is also possible that the client has customer processes in which it is over-delivering on customer requirements and, thus, can make cost savings without affecting retention (Gartner Research, 2001).

Phase 6; Recommending how IT enabled CRM can add value and save costs:

For the five most important customer processes I will write recommendations. I will concentrate on both value adding as well as cost saving recommendations. As Gartner Research (2001) puts it: "Enterprises have to establish the key elements of their value proposition and service delivery that really drive customer loyalty - as the customer sees it."

Timescale Resources

The most precious resource will be the 12 weeks time that is planned for this research. Furthermore, the client has to pay a total fee of 1,400 Euro to the researcher (see the cooperation agreement in appendix B). In addition to the fee, there is a travel cost compensation applicable, which means that the researcher is compensated for travel costs. These costs will most likely occur by visiting the client's customers for interviews.


  • Close, W., Ferrara, C., Galvin, J., Hagemeyer, D., Eisenfeld, B., and Maoz, M. (2001) CRM at Work: Eight Characteristics of CRM Winners. Stamford: Gartner research, [online]. Available: [date accessed: 16 November 2009].
  • Curry, J., Wurtz, W., Thys, G., Zijlstra, C. (2003) De Customer Marketing Methode (English: The Customer Marketing Method). Amsterdam: MSP associates.
  • ING Bank (2009) Truck- en Trailermarkt uit de Wolken (English: Truck and Trailer Market out of the Clouds), [online]. Available: [date accessed: 4 December 2009].
  • Knapen Trailers (2009) Sales Report. Deurne: Knapen Trailers BV Mazurencu Marinescu, M., Mihaescu, C., and Niculescu-Aron, G. (2007) Why should SME adopt IT enabled CRM strategy? Bucharest: Academy of Economic Studies, [online]. Available: [date accessed: 16 December 2009].
  • Saunders, M., Lewis, P., and Thornhill, A. (2009) Research Methods for Business Students. Harlow: FT Prentice Hall
  • TLN (2009) Business Cycle Survey, [online]. Available: [date accessed: 23 December 2009].
  • Treacy, M. and Wiersema, F. (1995) De discipline van marktleiders (English: The discipline of market leaders). Schiedam: Scriptum Management.
  • Van Bel, E.J. (2007) Kloteklanten, De Klant als Noodzakelijk Kwaad (English: Bloody Customers, The Customer as Necessarily Evil). Amsterdam: Kluwer.
  • Van Leeuwen., S. (2007) CRM in de praktijk (English: CRM in Practice). The Hague: Sdu uitgevers.
  • Verhoef, P. and Langerak, F. (2002) Further Thoughts on CRM. Rotterdam: Erasmus University, [online]. Available: [date accessed: 18 December 2009].
Section II
  1. Quantitative data are all data that can be addressed in hard figures. Like the yearly amount of spending of a customer or the number of errors in a batch. However, for instance, the information that a customer is not satisfied can also be derived out of qualitative data. For example, the customer might have told this via the customer service line.
  2. Nominal data does not allow extensive statistical analysis and has no order. Whether we state a sequence like carpenter, salesman, pilot or a sequence like pilot, salesman, carpenter makes no sense. In ordinal data, sequence does make sense. When measuring students, it is meaningful to make a difference between undergraduates and postgraduates, because there is a logical order in their level of education. Hence, assigning codes (e.g. 1=undergraduate, 2=postgraduate) is not a reason to state that two undergraduate students together are the same as one postgraduate student or that 1.5 represents the average student. To allow calculations (at least) discrete data is necessary. This kind of data only exists of whole numbers, such as someone's age, expressed in a number of years. Only in continuous data, the scale can be subdivided into as many intervals as required. Continues data can exist of ratio-scale data and interval-scale data. The former has no defined zero, and therefore does only allow comparing intervals. An example of an interval scale is the intelligence quotient (IQ) of a person. A person's IQ of 130 is 65 points higher than a person's IQ of 65, but is certainly not twice as high, because the interval scale has no true starting point. A ratio scale - statistically the highest level of data - has a defined zero and can therefore be used in both interval and ratio calculations (e.g. the weight of a loaf of bread). A distinction between univariate and bivariate data can be made. Bivariate data contains two variables, while univariate data contains only one. Storing the IQ scores of people would be univariate data, because this is only about one variable. Bivariate data is about relationships. For instance, data collected about the IQ score in relation to the average income of a person.
  3. Doing research is not without ethics. Informed consent means that the researcher has the participant's consent, after fully informing him/her about all aspects of the research project. For example, when a researcher prepares to collect data form the high school, care must be taken to fully inform all participants (e.g. the pupils, their parents or guardians and the school's board). It is the task of the researcher to make sure the participant fully understands the research project, as well as providing the opportunity to ask questions, and informing about the participant's right not to cooperate at all.
  4. When reading newspapers, magazines, websites, and other media, lots of 'truths' are presented. However, there is a chance that these 'truths' are based on personal judgements or beliefs. Managers who have to engage in professional decision-making should first test these 'truths', before relying on them. The reason why statistical methods are needed to find and describe relationships ('truths') is to eliminate personal judgement.
  5. Most interviews are structured: the interviewer asks every interviewee the same questions in a predetermined sequence. When the interviewer is free to vary the sequence of the questions in the context of the answers given, it is called a semi-structured interview. In this kind of interview the researcher is also free to define new questions in the situation's context. For example when an interviewee expresses a certain opinion, the interviewer can ask why this opinion is expressed. When an interview is informally conducted and loosely structured, it is called an unstructured interview, mostly without any predetermined questions. The interviewer is completely guided by the perceptions of the interviewee. The more unstructured the interview, the more skills are needed to conduct it, but the better the data may fit to the research questions.
  6. Researchers engage in statistical hypothesis testing, because they want to check - on population level - whether there are more factors involved than only coincidence. Researchers assume that there is only coincidence (H0) until the opposite has been proven (H1). What researchers do is calculating the probability that a certain sample result could have occurred by chance. The result of this test is significant when the test statistic lies within the critical region, which indicates that it is unlikely that the sample result has occurred by chance. Therefore, researchers say that the model is also true for the underlying population. Significant means that there is probably something going on in a systematic way. A Chi-square test is used to test whether there is an association between categories, based on expected values. The Z-test and the t-test are used to calculate the probability that a test statistic could have occurred by chance. Both tests assume normality in the data. The Z-test is used for testing large samples and the t-test is used for testing smaller samples.
  7. There is always a (slight) probability that the null hypothesis (H0) is rejected, while H0 should be accepted, because it's true. The probability that this so-called type I error occurs equals the chosen level of significance. When decreasing the significance level the probability for a type I error decreasing, but also the critical region. This makes it harder to reject H0 which can lead to a situation where the test statistic lies outside the critical region, while H0 should be rejected, because it is false. This is called a type II error. The relationship between a type I error and a type II error is therefore inversely proportional.
  8. There is unreliability when repeated measurements on the same object deliver different values. For example, it is likely that the measurement of grades is more reliable than the measurement of how people rate their looks (romance dataset). Validity is concerned with measuring what is mend to be measured. For example, when people rate their partner quality higher then they actually think it is, there is an extend of invalidity. When a measurement is less reliable, the researcher is not fully able to measure what is mend to be measured, and therefore the measurement is less valid. In other words: reliability is a condition for validity. However, not every reliable measurement is also valid. For instance, if data about the number of nights people go out to a club is collected in the club itself, the sample is likely to be biased in favour of one particular group!
  9. The ANOVA table shows a calculated critical value in the F column (35.424) and the probability that the correlation is based on chance in the Sig. column. Because this probability is even smaller than 0.001, the model is significant. The ANOVA table also shows the amount of variance to be explained by the regression formula, depicted in the sum of squares. Together with the total amount of variance to be explained, a value for R and R square can be derived, as shown in the model summary table. However, because we have more than one predictor for risk, it is better to use the adjusted R square. This indicates that the model is very good in forecasting the risk of the independent variable, because only 15.5 percent of the differences in risk cannot be explained by a difference in the independent variables.

The effect each independent variable has on the dependent variable is shown in the B column of the coefficients table. From this information the straight line formula (independent variable = A+B*dependent variable) can be derived, where A represents the value which is states at constant and B represents which effect the age, blood pressure, and number of cigarettes have on the risk.