scholarly journals MEASURING CUSTOMER LIFETIME VALUE: APPLICATION OF ANALYTIC HIERARCHY PROCESS IN DETERMINING RELATIVE WEIGHTS OF ‘LRFM’

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Saurabh Pradhan ◽  
Gokulananda Patel ◽  
Pankaj Priya
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Customer Lifetime Value ◽  
Marketing Strategies ◽  
Analytic Hierarchy ◽  
Customer Base ◽  
Monetary Value ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Hierarchy Process

ABSTRACT The limited availability of resources drives retailers to tailor their resources to identified profitable customers. In the present scenario, when the ROI of marketing is being questioned, the satisfaction of the profitable customers is of utmost importance as it drives their loyalty towards the retailer and the retailer’s brand. This research has considered Length of association with customers (L), apart from variables like Recency (R), Frequency (F) and Monetary-value of the purchase (M) in measuring customers’ relative-worth based on the calculation of Customer Lifetime-value (CLV). The contribution of this article lies in calculating weights of these variables – L, R, F, and M and demonstrating the calculation of CLV using weighted LRFM based on data collected from a leading apparel retailer in India. The obtained results for the customer base using the proposed approach is more reliable when compared with traditional non-weighted approaches of RFM based CLV. This methodology will provide a new and better option to retailers for measuring CLV of their customers, thus aiding their decision making about customer-friendly profitable marketing strategies and attaining optimum returns on their investments.

On Moderate Fuzzy Analytic Hierarchy Process Pairwise Comparison Model With Subcriteria

2016 ◽  
Vol 2 (3) ◽  
pp. 54
Author(s):  
G. Marimuthu ◽  
G. Ramesh
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Fuzzy Ahp ◽  
Pairwise Comparison ◽  
Decision Models ◽  
Fuzzy Analytic Hierarchy Process ◽  
Analysis Method ◽  
Analytic Hierarchy ◽  
Comparison Model ◽  
Hierarchy Process

Decisions usually involve the getting the best solution, selecting the suitable experiments, most appropriate judgments, taking the quality results etc., using some techniques.  Every decision making can be considered as the choice from the set of alternatives based on a set of criteria.  The fuzzy analytic hierarchy process is a multi-criteria decision making and is dealing with decision making problems through pairwise comparisons mode [10].  The weight vectors from this comparison model are obtained by using extent analysis method.  This paper concern with an alternate method of finding the weight vectors from the original fuzzy AHP decision model (moderate fuzzy AHP model), that has the same rank as obtained in original fuzzy AHP and ideal fuzzy AHP decision models.

Study of Influence of Road Construction on Grassland in Inner Mongolia

2013 ◽  
Vol 807-809 ◽  
pp. 1881-1885
Author(s):  
Chun Mei Zhang ◽  
Min Zhao ◽  
Xue Lv
Keyword(s):  
Decision Making ◽  
Sustainable Development ◽  
Analytic Hierarchy Process ◽  
Inner Mongolia ◽  
Road Construction ◽  
Environment Protection ◽  
Analytic Hierarchy ◽  
The Sustainable Development ◽  
Inner Mongolia Grassland ◽  
Hierarchy Process

In this paper, the indexes that are used to assess the influence of road construction on Inner Mongolia grassland have been proposed based on the environment protection perspective. The Analytic hierarchy process was employed to evaluate the importance of different indexes regarding to influence. These indexes would be used to provide information for decision making about road construction in order to achieve the sustainable development of grassland.

JUDGMENT NUMBER REDUCTION: AN ISSUE IN THE ANALYTIC HIERARCHY PROCESS

2010 ◽  
Vol 09 (01) ◽  
pp. 175-189 ◽  
Author(s):  
LONG-TING WU ◽  
XIA CUI ◽  
RU-WEI DAI
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Pairwise Comparison ◽  
Random Variable ◽  
Analytic Hierarchy ◽  
Priority Vector ◽  
Judgment Error ◽  
Scale Error ◽  
The Analytic Hierarchy Process ◽  
Hierarchy Process

The Analytic Hierarchy Process (AHP) uses pairwise comparison to evaluate alternatives' advantages to a certain criterion. For decision-making problem with many different criteria and alternatives, pairwise comparison causes a prolonged decision-making period and rises fatigue in decision-makers' mentality. A question of practical value is if there exists a way to reduce judgment number and what influence the reduction will have on the overall evaluation of alternative ratings. To answer this question, we introduce scale error and judgment error into AHP judgment matrix. By expanding the scales defined in the AHP, scale error is eliminated. Taking judgment error as random variable, a new estimator to calculate priority vector is presented. In the end, an example is proved to show lowering judgment number will increase the probability of larger errors appearing in priority vector computation.

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