What Happened to Strategic Segmentation?

Introduction and the Research Problem

Angus Jenkinson proposed a study titled “What happened to strategic segmentation?” published in the Journal of Direct, Data and Digital Market Practice in 2009. The study discusses segmentation performed from descriptive, predictive, and operational analysis presents the gold standard for a market analysis for marketers. The author highlights that 20 years ago, database marketers focused on segmentation to gain insight with the help of a combination of behavioral data and user attitudinal information. The primary goal was to organize the knowledge to develop marketing strategies by identifying customers’ communication and service behaviors information with the help of descriptive, predictive analysis, including operational target approaches that helped marketers understand the brand’s various customer segments. Such segmentation allowed brands to evaluate geodemographic and lifestyle characteristics and customer prospects to identify innovation requirements and the need for strategic, tactical, and operational interventions at individual customer segments. According to the author, the gold standard model relationships can be expressed in various forms and two versions, as depicted in Figure 1.

Figure 1. Two Models Representing the Brand/Segment/Individual Relationship

The above figure represents a three-dimensional model that differs from the “one-solution for all” models that communicate with customers simultaneously with mass customization and the assumption of individuals receiving individualized communications from the brand. These models could provide insights for the organization and drive its business and marketing strategies through various value propositions and individualized tactical operations. Therefore, this model became a gold standard among experienced database marketers implementing various techniques, including clustering, factor analysis, regression, data mining, and more.

However, there has been a paradigm shift towards simpler and limited modes. These trends minimize the uses of the previous approaches such as descriptive market segmentation and predictive analysis as real-time behavioral algorithms such as collaborative algorithm has been adopted in recent years. Furthermore, the author suggests that these trends for more straightforward approaches occurred due to the failure in techniques, processes, and lack of imagination, focusing on typical causes and simple effectiveness tests to self-audit for decision-making in an organization. On the other hand, the emergence of parallel statistical predictive techniques standards in online social networking sites has led to consultancies and clients being wary of implementing a full-scale segmentation approach while opting for lesser valuable models. Therefore, the study discusses the nature of the problem, a model that can elaborate six limited forms of segmentation, the potential of full-scale strategic segmentation approach, and the ‘test of current effectiveness’ audit checklist to guide executive decisions for the target market.

Research Problem Solution

First, the study focuses on why the gold standard of using descriptive and predictive analysis was abandoned. The author indicates the primary reason was project failures and tremendous advances in statistical, data mining, and rule-based customer management techniques. Moreover, it was reported that 31% of the companies considered segmentation studies as complex and required plenty of experience to perform them well. Thus, the rising concern among marketers about the difficulty of performing segmentation studies has consistently led to the fall in its adoption.

The research discusses that companies should clearly distinguish market segmentation and customer segmentation with descriptive and predictive dimensions. Market segmentation is considered a straightforward descriptive account to identify the customer base, allowing companies to channel strategies to support operations and customer portfolio management. On the contrary, customer segmentation is a predictive and targeting technique that ensures campaign planning with the help of data mining and modeling techniques. Both the approaches are clearly distinguished in the model below (see Figure 2).

Figure 2. Segmentation Approaches and Dimensions

Next, the study highlights the behavioral segmentation model and pinpoints how the two-dimensional classification model can reveal interactions between six critical approaches. Finally, although the performance falls short of the gold standard, the author discusses the structure of the model comprising of two axes, namely behavioral analytics for predictive capabilities and market description (see Figure.3).

Figure 3. Descriptive and Predictive Dimensions

One of the critical aspects of predictive analysis is campaign analytics, which provides insights for direct marketing teams to report financial gains. In contrast, an advanced approach common in top companies like Amazon is community-related collaborative filtering and behavioral affinities. In addition, these statistical measures allow algorithms to make recommendations based on similarities in customer behavior or brand behavior. The study reveals that some of the industry practitioners consider this approach as a superior technique in comparison with the traditional segmentation techniques.

Moreover, the author discusses affinity modeling, allowing selecting the following best appropriate action for customers based on past behaviors. Besides, both the campaign analytics and collaborative filtering can be combined to attain a significant difference with the right resources, as observed with Amazon’s successful use of this approach. For descriptive analytics, the author presents customer-led market differentiation as useful for generic segmentation markets and identifying end-user attitudes. In addition, product-led customer differentiation allows identifying a customer base as per the product lines. However, the author highlights that a significant problem of this approach is the enhancement of product expertise at the cost of reduced customer insights. Besides, the author discusses that the problem with most market segmentation is the lack of competitive insight and differentiation; therefore, a needs-based segmentation has become essential to provide the edge.

Findings of the Research

The author discusses that segmentation should be performed to identify ethnographically various groups of people with shared meaning regarding attitudes, emotional resonance, behaviors, and the possibility of an actual or potential relationship with the brand or an organization. On the other hand, the study identified that developing organizational strategy is difficult for customer segment-of-one. Therefore, it is suggested to realistically plan and operationalize for differentiated value propositions for customer communities. Understanding these customer-oriented communities is essential as it requires an intersection of needs, attitudinal, behavioral, and financial differences. Furthermore, this typology can be translated into business strategies and marketing behaviors, with operationalization being conducted with automated rule-based and statistical algorithms at execution time.

Furthermore, the author presents the conceptual and system-oriented architecture for using segmentation comprising layers for strategy, management, typology management, action planning, and operational execution capability management (see Figure 4).

Figure 4. Conceptual and Systems Architecture

The author suggests that developing strategy and customer segmentation typology is essential; therefore, it must be an iterative process. These two components provide the parameters for controlling the personal or automated statistical and rule-based algorithms to support marketing and service activities at the “right action” layer. Besides, operationalizing specific campaigns or service transactions is simpler when considering customers’ financial worth using highest-order insights for strategic organization.

In addition, community-based segmentation is controllable in all the analytical and behavioral algorithms with two potential benefits. First, the author discusses the benefits of acquiring more customer knowledge as the segmentation is based on more sophisticated stakeholder research to categorize the needs, attitudes, requirements, and emotional values associated with different customer communities.

On the other hand, this approach ensures a more customer-focused organization that holds practical insights with informed value solutions for these communities offered for individual customers with real-time modeling techniques. As a result, the businesses can operate with integrated capabilities of a shared platform focused on differentiated customer value.

The author also suggests a few advantages acquired from personal experience of the industry, which includes:

  1. There is a possibility of managing the business intelligently by combining performance management to ensure more productivity in marketing with lesser conflicts.
  2. There is a potential for identifying relevant value propositions to direct the company to become more customer-focused.
  3. An enhanced understanding of the brand is possible as segmentation develops a more affluent and a more layered understanding of the essential value of the brand itself.
  4. There are reduced costs of operations due to better targeting of customer groups.
  5. Finally, dealing with real people allows employee satisfaction, including enhancing customer experience. As a result, the brand and customer equity increases with more relevant and granular solutions.

The author indicates specific barriers to why companies across industries have not successfully implemented an appropriate segmentation model. The critical problem is the lack of mapping the attitude-based segmentation based on research onto a database as the methodology and approaches are misguided. Some of the barriers also occur due to lack of experience and expertise, whereas some occur due to the following factors highlighted in the research:

  1. One of the primary factors was the new generation marketers being busy with multiple issues.
  2. Lack of direction for financial investment areas.
  3. Single message to promote the brand and its services.
  4. Independent tasks being carried out for marketing and advertising.
  5. Business promotions relying on the product line solely.
  6. Lack of directions in developing market segmentation as strategies are either too complex or simplistic.
  7. Partial gains arising from data mining and market segmentation being considered as significant profit for the business.
  8. Gaps in the hierarchy among the strategists and the board members.
  9. Internal division-based segmentation being perform within the company.
  10. Project designs being considered without a multidisciplinary approach.
  11. Relying only on complex statistical methods to solve the issues in segmentation.
  12. Developing segmentation based on pure statistical reports without any room for assumptions or hypotheses.
  13. Consideration of only the Gaussian population.
  14. Lack of involvement of the top-level executives in decision making for segmentation.
  15. Ineffective approaches to select particular customer targets to the appropriate segment.
  16. Communication gap within the company.

Future Work Suggestions and Implications for Practitioners

The study proposes a discussion about the possible art and the effect on business success due to segmentation methods. Furthermore, the study provides a self-audit checklist of capabilities intended to summarize segmentation objectives and guide businesses and practitioners. In addition, audit questions and a checklist have been provided to build a pathway for future researchers.