Application of Customer Value Management in hotel industry

Customers play an important role in most of the businesses. Satisfying their needs is the key to achieve success in nowadays competitive business environment and it was enough to build long lasting relationship with customers. However, XXI century and therefore wide access to information changed the way customers perceive their obligations toward firms. It is said that in most of the branches customer loyalty doesn’t exist. This is only partially true as loyalty exists but it is not as easily achieved as it was before. Retaining customers require a lot of effort and investment. Therefore, the challenge that companies has is to assess whether retaining existing customer will not be more expensive than actually finding a new one. In order to do so, companies needs to analyze the value of different groups of customers and use this knowledge to devise marketing strategy. Such a process is usually called Customer Value Management.

One of the industries that were especially influenced by changing preferences of customers was hotel industry. Last ten years were especially harmful due to increase in number of websites where you can compare different hotels in certain places. It increased the competition and decreased the rates. Moreover, hotels lost a huge group of loyal customers without plan go to win them back. Websites like tripadvisor.com, hrc.com and many others enabled customer to discover hotels that they would have no idea about and what is more important those small and previously unknown hotels are often more appealing than well known chains in term of price to quality ratio. The only reason that big hotel chains are still profitable is fact that travel business is expanding so quickly, due to popularity of cheap flights and development of previously insecure countries. All those reasons made hotels consider implementing customer value management in order to create new strategies based on knowledge, not superstition.

Customer Value Management (CVM) term was mentioned few times already and this is the moment to explain what CVM actually is. CVM is based on the assumption that customers differ in value. It might sound rude but in fact some spend more money and some less. From the firm perspective, the higher value have those who spend more (without increasing costs). Therefore “Customer Value Management entails the optimization of the value of a company’s customer base” (Verhoef, et al. 2013, pp. 52). It means that information about individual customers are gathered to acquire new customers and to develop marketing strategy in order to drive customer behaviour in a way that will maximize the value of the whole customers’ base. The central part of CVM is customer lifetime value (CLV) that is defined as “discounted value of all future profits in a determined time period” (Bolton, et.al, 2004). Such a definition has an important implication, as customers are perceived as valuable asset of company, that should be cultivated. From managerial perspective, CVM might be seen as learning system, where strategies can be constantly improved based on customers satisfaction in order to increase value of customer base by one of three options:

  • attracting new customers,
  • increasing customer retention
  • creating customer expansion.

However, it is important to mention that the goal of CVM is not increasing all of those options, but maximizing value of customers’ base. The difference lays in fact that sometimes the cost of increasing customer retention might be higher than revenues from those customers and as result it will not be profitable. Moreover, even if the costs of retaining customers is not so high, it is important to keep a proper balance between those customers, because just focusing on retaining customers and neglecting customer acquisition might create some problems as customers’ base will became too old. Verhoef, et al. (2013) said that  “variety seeking behavior can weaken customer base”, however I don’t agree with this statement. Variety will enable to have elasticity if one dimension became harder to achieve or if it starts to have negative effect on value. Although, it is important that it is not about distributing resources in those three options based on some percentage division but to adapt and be elastic to changing environment. The last thing which is important to remember, is that those dimensions are not interdependent. Focusing on one dimension will have effect on others, e.g. giving significant discounts to obtain new customers will decrease customer retention. Therefore, customer acquisition and customer retention cannot be evaluated separately.

It was emphasized already that the base to devise customer strategy is customer analysis. Customer strategy is devised to influence customer acquisition and customer retention. Every strategy has to be evaluated with estimated costs and profits. Customer Lifetime Value is the last puzzle that should be a result of appropriate strategy, estimated costs and generated Revenue and should be directly correlated with company’s value (Gupta et. al, 2004). As we can see, the CVM is a complex concept and there are numerous of factors that influence it. In order to easier understand it, it is good to present it as a structured framework such as on figure below.

Screen Shot 2014-07-01 at 12.21.09

Figure 1. Source: Stremersch, et. al. (2007)

Based on this framework we can distinguish 6 dimensions that are crucial for appropriate implementation of Customer Value Management. Following list presents them together with areas that are important in each dimension and it will be described in the next chapter.

  • Customer Analysis: Methods and Technical Issues
  • Customer Acquisition: Methods
  • Customer Retention and Customer Expansion: Determinants
  • Customer Lifetime Value: Correlation with Firm Value
  • Customer Value Management: Channels
  • Customer Value Management: Implementation

 

Six dimensions of CVM

1 Customer Analysis

First area of customer analysis is associated with models (methods) of predicting customer behaviours. Those models are mostly built on econometric techniques such as logit models (where depended variable has value of 0 or 1; and it describes e.g. response to a mailing) or decision trees (which explain which factors increase the probability of responding to mailing). Those to class of the models were chosen by Lemmens and Croux (2006) as the best predictors of customer behaviour.

The second area describes technical problems with analysing customer behaviour such as data fusion, sample selection problems, endogeneity and modelling rare events. This area is very important as without taking those problems into consideration the results derived using prediction models will follow the rule: “garbage in, garbage out”. Therefore I will spend a bit more time explaining what actually are those issues.

Data fusion occurs when researcher takes information from few data sources that don’t match perfectly. A good example would be analyst who is trying to match the satisfaction survey results (that are made based on one group of customers) with media exposure data (that is made on another group). Those results cannot be joined without using specific statistical techniques. Sample selection problems occurs when a certain observation is made on non-random sample and without standardization methods, the results are generalized for the whole population. Endogeneity is a difficult issue that has to separate when analysts use econometric models. The example might be studying the influence of loyalty programs on purchase intentions, when customers are decided to purchase certain product/service and afterwards chose a loyalty program to obtain incentives. In such situation the influence of loyalty program on purchase behaviour will be overestimated. Such an effect is called self-selection and Gensler et al. (2007) found that hybrid models are the best way to deal such effects. The last problem called rare events describes group of customers is small enough that might be oversampled in the study. However, sometimes oversampling rare event might mean oversampling one of the predictors (e.g. age), model might be biased an inefficient. Donkers et al. (2003) suggests Monte – Carlo simulation as a method of solving this problem for logit models.

 

2 Customer Acquisition

Customer acquisition is usually more effective when it is directed at selected group of people. For such a purpose firms uses following methods that enable them to conduct such as selection. One of the first type of models was built by Bult and Wansbeek (1995) and was based on logit model with a profit function as set of independent variables (usually they use behavioural data such as: recency, frequency and monetary value). Those models might be expanded by purchase and promotion history. Moreover as Von Wagenheim and Bayon (2007) emphasized a huge factor that influence customer acquisition is word-of-month marketing. On the other hand, there is a huge disproportion in between methods that are used in practice (simple heuristic and simple econometric models) and in scientific literature (e.g. logit models).

 

3 Customer Retention and Customer Expansion

The determinants of customer behaviour has been a topic that researchers and practitioners from different field such as psychology, marketing and statistics wanted to explain. However, there are two main obstacles that are on their way to perfect knowledge. Firstly, customers’ preferences changes over time and in some cases pretty rapidly. Secondly, there is a discrepancy between what customers say and what actually they do. Fortunately, with development of technology there is also easier access to databases and tools to analyze them. Therefore, researchers have more information about observed retention, cross-buying or such concepts as upgrading behaviour. Those information might be compared/merged with customer perceptions from surveys (remembering about data fusion problem) and as result give marketers more comprehend knowledge about customer behaviour (including their retention and expansion).

The determinants of retention are strongly correlated with customer relationship perception, which concern such areas as: a) satisfaction and quality; b) commitment and trust; c) price perceptions and payment equity. While satisfaction and price perception involves cognitive equity and backward- looking evaluation; commitment and trust are more affective andforward-looking. Retention is also determined by marketing instruments, which in the most general sense might be divided into: a) below-the-line-advertising (e.g. mailings, viral campaigns); b) above-the-line-marketing (e.g. mass marketing as tv commercials); c) loyalty and relationship programs (Verhoef, 2013)

Customer expansion occurs when company is able to sell more products or services to a customer, upgrading customers to higher class, increasing the usage of service or by adaptation of newly developed products or services (Bolton, at al. 2007). Hence, there are five determinants that describe customer expansion: a) cross-buying; b) service-usage; c) upgrading; d) new products/service adoption; e) customer share1/share of wallet2. What is important, marketing instruments have slight effect on customer expansion. However, as Verhof (2013, pp. 57) stated: “it is difficult to generalize the findings on the determinants of customer expansion, as customer expansion ismulti-dimensional and the number of studies per dimension is limited”.

 

4 Customer Lifetime Value

Customer Lifetime Value is a very good indicator of expected long-term profitability that is very important for CVM. There are three main issues connected with CLV:

  • Modelling and prediction of CLV
  • Optimizing customer strategies that will maximize CLV
  • Correlation between CLV and firm performance

The first issue concerns models that forecast CLV using different types of information. Some models might calculate the expected value of total customer base using simply all available data on aggregate: retention rates, acquisition costs and margins of few firms. The additional information that can be taken into consideration could be: development of current customers and the acquisition of new ones. The higher class of models are those which can predict customer behaviour not on aggregated level but individual. The highest class of models areNBD-type models that predict the next purchase and purchase quantity of customers (summarized by: Verhof, 2013). The next important issue is CVL optimization, which means that based on data, algorithms allocate resources between acquisition and retention in order to maximize customer profitability. The last issue, studied by Gupta, et al. (2004) shows the link between CLV and firm performance. In their research, customer based valuation was a good proxy for firm’s value in 3 out of 5 cases (the two cases that were unsuccessful was from internet industry, which is more turbulent and harder to evaluate).

 

5 Channels in CVM

More and more companies use multi – channelling strategy in order to reach bigger number of customers. However, as this concept is growing in professional usage, the relationship between multi- channelling and CVN in scientific sources is still scarce. However, the existing researches shows alarming correlations. Apparently, multi-channelling is positively correlated with customer expansion but negatively correlated with customer loyalty (Ansari, et al., 2005) and positively correlated with customer profitability (Gensler et al., 2007).

 

6. Implementation of CVM

Appropriate implementation of CVM is probably the most important dimension because it is dependent from all those dimensions described above. However, there is once again not many scientific articles, just because companies which did it successfully keep this knowledge inside the company. Most of the existing studies take only Customer Relationship Management (CRM) perspective as both implementing CRM and CVM has a lot of common obstacles and challenges. However, I believe that this dimension is very important and it would be impossible to analyze the hotel industry in terms of potential application of CVM without some recommendations regarding implementation. Therefore, the next chapter will be exclusively about key practical advices regarding CVM created by Vorhef and Lemmon (2013)

 

 

Key practical advices and emerging trends in CVM

As it was stated before, CVM is not only academic concept that is developed only by academics. Its purpose is to help companies in managing their relationship with customers. Therefore following five advices are managerial implications of CVM with practical examples of usage in business.

 

Advice 1: use CVM to improve business performance.

CVM can improve performance by:

  • Improving competitive advantage – customer database and customer relationship are significant market-based resources, that are difficult to develop and copy. Moreover, formal systems that identify and manage high potential customers directly influence firms’ performance, therefore CVM build sustainable competitive advantage
  • Improving customer-centric orientation – CMV requires strong focus on customers and provides companies with extensive customer knowledge.
  • Having more accountable marketing – analytical approach to marketing expenses is more responsible and decrease risks as it is more responsible and more focus on return on investment. As a consequence marketing is more effective which improve bottom line result.

Studies conducted mostly by Kumar (2008) reported a successful implementation of CVM in two high-tech companies (one proving B2B services and the other B2C). Both companies estimated individual customer lifetime value using: a) next purchase probability; b) contribution margin; c) predicted individual customer-level marketing costs. Implementation of CVM strategy included: a) reallocation of resources among customer value segments; b) selective customer acquisition; c) channel proposition for specific customer value segments; d) selectivecross-selling. The best proof that those two companies significantly improved their performance after the implementation is increase in their stock price. B2B firm stock price increased by 32,8% (and outperformed S&P index by 2 times) and B2C firm increased by 57,6% (outperforming S&P index by 3,6 times). This example clearly shows that increase in customer value can lead to increase in shareholder value, what is greate indicator of successful implementation.

 

Advice 2: ensure that CVM is more customer driven than IT driven.

This point is very important as very often companies that want to improve their performance by new concepts like CVM, focus too much on advance technology and forget that CVM is about building customer oriented environment using technology and not the other way. If company is driven by trying to obtain sophisticated CVM system and invest their resources on technology solutions, software and consulting agencies; the implementation is likely to fail. Nethertheless, IT is an important part of CVM, but investments in systems should be in a way that will benefit customer – centric processes within organisation.

The other problem that is reason of implementation failure is lack of customer strategy before implementing the CVM. As it may seem to be obvious, it often forgotten that CVM should be powered by devised, detail strategy. Moreover, in big companies that are build on many different departments or even businesses, it is extremely difficult to change people’s thinking and persuade new strategy. Therefore a formal leader of CVM should be appointed on director’s level in order to have enough power to sometimes even change the orientation of company. Such a situation is necessary when companies are too product-centric and IT- centric and want to became customer-oriented. Organisational change will meet following barriers: a) cultural; b) structural; c) financial; d) operational. The only way to overcome those obstacles is to let the employee be part of the change and use one of the change management methodologies (like Lewis’s (1968) three steps of organisational change: unfreezing, change, refreezing). The structural and operational barriers might be overcome by well defined responsibilities of each department. The financial barriers will be easy to overcome if a firm develop a detailed and reliable business plan. However, the biggest barrier might actually accepting that the customers are becoming the evaluators of our products. Employees have to take seriously customer profitability, customer value management and customer satisfaction. The

company needs to change its thinking from: “we think that…” to “customers think that…”. To sum up, “firms that focus on the customer first are successful in implementing CVM. They begin with small projects, identify an executive champion, understand data limitations, and actively manage expectations (Capon & Senn, 2010).

Advice 3: Adopt CVM as core metric

This is a huge challenge to change the metrics that companies usually use to evaluate its performance from product-centric like market share to customer-centric like customer lifetime value – which again can be described as “net present value of all future profits derived from customer over his/her lifetime with the firm” (Verhoef and Lemon, 2013, pp.4). The main advantage of CLV as firm’s performance indicator compare to market share or pure profit is fact that the rest two are usually achieved to fulfil short-term obligations by respectively discounted sales or decreased quality of products. As a result in long-term they can significantly harm the value and future of company. Additionally, sales discounts have one more huge disadvantage as they increase price sensitivity of customers and destroy customer loyalty. The other disadvantage of product-centric metrics is fact that they are usually focus on one product or one product category, while customer might buy from different categories. When this interdependence is not acknowledged, offering a discount on one product but increasing price of another one can decrease the customer satisfaction. The other consequence of big discounts of one product is that it is possible that certain customer will migrate from a high-price product to the newer lower priced products. Therefore, CLV is better indicator as it describes the customer as an complete, coherent entity.

The other advantage of CVL is that, it can be used in different ways to suit firm’s requirements. Potential managerial applications of CVL distinguished by Verhoef and Lemon (2013) are:

  • to steer firm’s culture to became more customer-oriented – when employees will acknowledge that the goal of company is to maximize CLV and focus strategies to achieve this goal, it will stimulate changes in organisational culture
  • to evaluate marketing campaigns and investments
  • as valuation tool of the customer base – evaluating customer equity is very important ofr overall firms valuation purposes. It emphasized that customers are important asset and they should be financially valued.
  • as a metric for customer segmentation and resource allocation – based on CLV calculations customers are divided into groups and more resources might allocated to segments with higher CVL. Moreover, such a segmentation will enable to examine is it more important to acquire new customers or retain existing ones, based on their CLV.
  • To evaluate effectiveness of managers – CLV might be also used in this way to assess employees work in creating CVM culture. And it should, because if incentives and bonuses are still calculated based on sales of products, managers will be reluctant to pursue CVM strategies.

 

Advice 4: Invest in strong analytical capabilities

Many times previously, we emphasized that core part of CVM is information. Therefore, strong IT (but not forgetting about advice 2) and human resources are very important in order to create many business processes tan will create more intelligence-based decision making. Strong and effective analytical capabilities might be alone a firm’s competitive advantage. Those capabilities will not only be used in CVM but also in human resources management, logistics, finance and marketing. The appropriate use of those capabilities will decrease the waste in all kinds of processes on optimize certain decisions. But the important part here is that those analytical capabilities cannot transform into unit that will generate hundreds of unnecessary reports. Therefore, the design of reporting process and information that are included in reports has be done very carefully.

Because of reasons above, each bigger company should have customer intelligence (CI) or business intelligence department that will collect and store customer data and then analyze all important information to acquire customer insights. Customer insights have to be constantlyup-to-date and relevant. Fields that should be evaluated by customer intelligence might be: a) identifying potential customers; b) predicting response behaviour of existing customers; c) calculating costs of maintaining a relationship; d) cross-selling predictions. In order to calculate such fields, employees should use: cross-tabulation, tree-based methods, logistic regression, boosting or analysis of variance. The other important thing is honest and good cooperation between marketing and customer intelligence departments, because marketers will make decisions based on CI calculations.

 

Advice 5: understand the key drivers of customer acquisition, retention and expansion.

The last advice is maybe the most important, as without knowledge of what drives customer acquisition, retention and expansion over time. Therefore, firms should examine the areas that directly influence customer acquisition and retention: value equity, brand equity and relationship equity. Value equity is customers objective assessment of what they expected and what they achieved.

Drivers that influence value equity are: a) features and benefits of product or service; b) overall quality; c) factors that influence purchase convenience; d) all elements of pricing. Brand value is customers’ subjective perception of firm. Key drivers that affect Brand equity are: a) marketing strategies that influence brand awareness and brand perception; b) corporate citizenship efforts.

Relationship equity is customer perception of customer’s – firm touching points. Key drivers that influence relationship equity are: a) customer loyalty programs; b) customer communities; c) website interactions. Understanding which of those three equities has the biggest influence of customer behaviour will became firm’s core capability.

It is also important to remember drivers of customer acquisition might be different than drivers of retention and expansion. Usually, when acquiring customers the most dominant are brand and value equity are the most important. When trying to retain high CVL customers and don’t let them to switch to competition, the most important are the value and relationship equity.

The other important issue concerning customers that is often forgotten by companies is fact that knowledge about existing customers and their shopping behaviour is not sufficient. Sometimes the knowledge why customers don’t buy firms’ products might be even more interesting. Therefore key drivers of customer behaviour should be derived both from customers as well asnon-customers.

 

 


 

References:

1.Ansari, A., Mela, C.F., Neslin, S.A. (2005): Customer Channel Migration, Working Paper, Teradata Center, Duke University, Durham, NC, Paper Series No.13.

2.Bolton, R.N., Lemon, K.N., Verhoef, P.C. (2004): The Theoretical Underpinnings of Customer Asset Management: A Framework and Propositions and for Future Research, in: Journal of the Academy of Marketing Science, Vol. 32, pp. 271–293.

3.Bolton, R.N., Lemon, K.N., Verhoef, P.C. (2007), Expanding Business- to-Business Customer

Relationships: Modelling the Customer’s Upgrade Decision, in: Journal of Marketing, forthcoming.

4.Bult, J.R.,Wansbeek, T. (1995): Optimal Selection for Direct Mail, in: Marketing Science, Vol. 14, pp. 378–394.

5.Capon, N., Senn, C. (2010). Global customer management programs: How to make them really work. California Management Review, 52, 32–55

6.Donkers, B., Franses, Ph.H.,Verhoef, P.C. (2003): Using Selective Sampling for Binary Choice Models to Reduce Survey Costs, in: Journal of Marketing Research, Vol. 40, pp. 492– 497.

7.Gensler, S., Boehm, M., Leeflang, P.S.H. ,Skiera, B. (2007), Effect of Chanel Use on Customer Profitability, Working Paper, Frankfurt University

8.Gupta, S., Lehmannn, D.R., Stuart, J.A. (2004): Valuing Customers, in: Journal of Marketing Research, Vol. 41, pp. 7–18.

9.Kumar, V. (2008). Managing customers for profit: Strategies to increase profits and build loyalty. Philadelphia: Wharton Publishing.

10.Lemmens, A., Croux, C. (2006): Bagging and Boosting Classification Trees to Predict Churn, in: Journal of Marketing Research, Vol. 43, pp. 276–286.

11.Stremersch, S., Verniers, I.,, Verhoef. P.C. (2007): The Quest for Citations: Drivers of Article Impact, in: Journal of Marketing, Vol. 71, pp. 171–193.

12.Verhoef, P.C., Neslin, S.A., Vroomen, B. (2007): Multichannel Customer Management: Understanding the Research Shopper Phenomenon, in: International Journal of Research in Marketing, Vol. 24, pp. 129–148.

13.Vorhef P.C., Lemmon (2013) Successful customer value management: key lessons and emerging trends; in European Management Journal, Vol 31, pp. 1-15

14.Wangenheim, F. von,Bayon, T. (2007): The Chain from Customer Satisfaction viaWord-of- Mouth Referrals to New Customer Acquisition, in: Journal of the Academy of Marketing Science, Vol. 35, pp. 233–249.

 

 


Author: Mateusz Konopelski

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