The article discusses using Customer Lifetime Value (CLV) as a basis for market segmentation strategy, and the associated issues. Using this model in an ideal world, firms could modify their goods to particular customers, and choose optimized marketing activities that exploit customer response. However, in the real world there are particular challenges associated with this approach.
Segmentation is fundamental in marketing and incorporating customer profitability has the potential to improve the effectiveness of marketing efforts. In addition, companies are experiencing pressure to conform to marketing strategies that focus on each individual customer (Kolko and Gazala 2005).
There is a shift in customer expectations resulting in firms needing to understand the new demands of the customer. CLV segmentation divides customers into groups based on factors driving purchase decisions (Vishwanath and Krawiec, 2011).
Marketers can use a number of variables for segmentation; however CLV segmentation may be the most effective method (Libai, Narayandas and Humby 2002). While there are difficulties with this, firms are replacing traditional segmentation variables with customer profitability. There is a 4 step process to develop CLV-based segmentation. This allows the reader to understand how this strategy can be incorporated.
While there are many ways to segment a market, the objective of segmentation is to provide customer insights to managers and to enable managers to reach the market. Integrating CLV alters these objectives to focus on improving the effective use of resources.
CLV is the sum of the revenues gained from customers over the lifetime of transactions after deducting the total cost of the customers, accounting for the time value of money (Hwang, Taesoo and Suh, 2004). CLV segmentation is based upon the current and potential profitability of existing customers and their categorization based upon CLV. This demonstrates the shift in objectives from targeting consumers to managing expenditures and allocation of resources towards the ‘’right’’ customers.
There is a renewed focus on customer segmentation due to several improvements in our ability to model it. This paves the way for a fresh approach to segmentation; relating it to customer profitability and CLV.
How Does CLV Complicate Segmentation Strategies?
Lemon and Mark (2006) use Wedel and Kamakura’s (1998) six criteria for effective segmentation to identify if CLV segmentation can be successful.
They indicate that using CLV can make it difficult to identify homogenous groups of consumers. CLV is generally based on the individual customer level, and this seems to contradict the substantiality criteria. However they mention that some markets require CLV to be calculated in a broader sense, and in these cases segments may be large enough to justify allocating resources. They assert that CLV can improve the success of communications, and can be improved further in conjunction with segmentation. Dynamic CLV segmentation models will be necessary, as segments are constantly changing, static models can become quickly outdated. CLV can help determine marketing strategies, but does not necessarily improve the effectiveness of any given campaign. Finally, regarding responsiveness, they advise describing market segments by a combination of high-low responsiveness and profitability, and that can guide strategic decision-making.
They cleverly use Reinhartz et al (2005) to support the accessibility and actionability criteria. They found that the acquisition, retention and profitability of customers are dependent on how much resources are invested, and in what way. They assert that segmentation decisions should be made using a responsiveness-responsibility matrix; however they provide little evidence to back this up. This framework can be useful for simple decisions, but doesn’t recognise the intricacies of complex decisions. Kim et al (2006) propose CLV based on past contribution, potential value and churn probability. This could enhance segmentation strategies; however it is not mentioned in the paper.
The authors use a balanced approach, while this gives an unbiased view; it results in a lack of decisiveness and clarity of argument. Overall, the authors achieve their goal of evaluating CLV as a basis for segmentation.
The Future of CLV Segmentation
1. Smaller Micro-Segments
This suggests incorporating more levels or smaller segments into segmentation models. The most important micro-segment is the profitability of individual consumers. Rather than building a profile of the consumers buying behaviour, the profitability of that consumer should be incorporated also, and other scholars agree (Jang, Morrison & O’Leary, 2002) and (Mark, Niraj & Dawar, 2012). Throughout the development of CRM it became evident that traditional segmentation was becoming increasingly ineffective (Egan 2011). From our own research it appears to be the consensus that markets should be divided into smaller segments and additional areas like profitability must be included.
2. Improve Efficiency of Marketing Programs
There is a focus on how the efficiency of marketing can be enhanced. (Richards & Jones, 2008). This article believes more research is necessary in this area. However, CLV and marketing efficiency has been the focus of much research in recent years. The findings state that focusing on CLV when conducting a marketing campaign improves its overall profitability (Egan 2011, and Lee-Kelley, Gilbert & Mannicom 2003). It is our understanding that this has been studied and needn’t be a focus for future research.
3. Dynamic Segmentation Models
This article mentions this as critical for future research. Dynamic segmentation models concentrate on developing and changing as consumer behaviors evolve. Consumers can sway between varying levels of profitability for an organization and their buying patterns will change as their lifestyle changes. Many scholars are in agreement that dynamic segmentation models should be a focus for future research. (Kumar, Lemon & Parasuraman, 2006 and Reutterer, Mild, Natter & Taudes, 2006).
4. Models for New Products and Customers
They suggest that research should be conducted on whether CLV can inform of future market potential. CLV could be beneficial if focusing on current customers behaviour and could provide information that help firms attract customers. This is an interesting point and not something that has been studied in detail before. CRM critics suggest that focusing on customer retention may result in firms not attracting enough new customers (Verhoef & Langerak, 2002). This is a valuable area for research as loyal customers will provide insights that help organizations attract new customers.
5. Facing Implementation Challenges
This article identifies areas where CLV segmentation approaches come into difficulty. Methods to overcome challenges include collecting appropriate data, developing critical analysis tools and company strategies that utilize this newfound information and incorporating it into marketing campaigns. The challenge is utilizing customer insights to their potential (Meyer, & Schwager, 2007). Insights are an important aspect of CRM and could prove beneficial when used correctly (Xu & Walton, 2005). Further study to overcome challenges that hinder implementation of CLV segmentation should be conducted.
6. Drivers of CLV Throughout Decision Process
The article suggests using CLV segmentation during the marketing campaign to help consumers through the purchasing process. Using the knowledge a firm has gained of consumer insights during each stage of the purchasing funnel, they could determine which are likely to become valued consumers. Consumers follow different paths through this process and this is another basis for market segmentation. The article acknowledges that it’s a complex task but the benefits are substantial. It’s our opinion that this is difficult research to conduct and other research suggestions should be prioritised.
We found this article interesting because it integrates a key CRM concept into basic marketing strategy. This article analyses the issues and challenges associated with CLV becoming a basis for market segmentation, and we wanted to learn more about this thought-provoking idea. Instead of trying to persuade the reader that CLV based segmentation is effective, the authors give a balanced evaluation of the concept and this unbiased viewpoint was appealing. The future research suggestions provide us with literature gaps, and could provide the basis for our academic studies in the future.
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