The customer-portfolio allocation matrix
Select customers by value, relationship objective, resource need, and the capacity displaced by the choice.
| Synthetic option | Value boundary | Resource need | Relationship objective | Alternative use of capacity | Decision |
|---|---|---|---|---|---|
| A | High expected value after declared service cost | High | Develop | Delays three smaller retention reviews | Review fit |
| B | Moderate value with low service burden | Medium | Retain | Preserves capacity for acquisition testing | Prioritize |
| C | Uncertain value with strong learning potential | Low | Learn | Replaces one routine account review | Test |
| D | Revenue only, cost boundary missing | Unknown | Unclear | Cannot compare | Stop |
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Reference & Evidence
Source: Author's synthetic decision framework grounded in Bhatnagar, Maryott and Bejou (2008), Malthouse and Blattberg (2005), and Gupta et al. (2004). Values and options are illustrative and do not describe an observed customer base.
Each line is a claim from the register this journal publishes against, resolved from the register at build time.
- A The article treats customer selection and prioritization as a resource-allocation problem aimed at maximizing customer value. Bhatnagar, Maryott & Bejou. (2008) ·
BMB08-C1 - A Customer acquisition, retention, relationship development, and customer lifetime value are presented as connected inputs to allocation decisions. Bhatnagar, Maryott & Bejou. (2008) ·
BMB08-C2 - A The framework implies that firms should differentiate customers and investment levels rather than apply uniform relationship-marketing spending across the customer base. Bhatnagar, Maryott & Bejou. (2008) ·
BMB08-C3 - A The question is feasibility, not desirability: relationship-marketing strategies "presume that a firm can accurately predict the future profitability of customers", and the paper is "a detailed empirical evaluation of how accurately the future profitability of customers" can be predicted across four industry data sets Malthouse & Blattberg. (2005) ·
MB05-C1 - A The 20-55 rule, as posited: of the actual best customers, "approximately 55% will be misclassified and not receive special treatment". The authors call these "two new empirical rules of thumb based on these results", so they are rules of thumb from four data sets, not constants. About 15% of the future bottom 80% may be misclassified and receive special treatment. Malthouse & Blattberg. (2005) ·
MB05-C2 - A "the feasibility of such strategies depends on the probabilities and costs of misclassifying customers", so it turns on prediction accuracy, forecast horizon, and the costs of misclassification. Malthouse & Blattberg. (2005) ·
MB05-C3 - A The sensitivity is theirs and it is asymmetric: "a 1% improvement in retention, margin, or acqui"sition cost "improves firm value by 5%, 1%, and .1%, respectively" Gupta, Lehmann & Stuart. (2004) ·
GLS04-C2 - A Five firms, public data: the method is demonstrated "by using publicly available data for five firms", and retention outweighs the discount rate, since "a 1% improvement in retention has almost five times greater impact on firm value than a 1% change in discount rate or cost of capital" Gupta, Lehmann & Stuart. (2004) ·
GLS04-C3
Grades: A, verified against the printed page of the primary source · B, primary source, text layer only · C, authoritative secondary · D, reported.
Related exhibits
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The customer-prioritization misclassification card
From the essay The 20-55 rule: customer prioritization misclassifies the portfolio
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The customer lifetime value boundary card
From the essay What is customer lifetime value?
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The multi-outcome customer model card
From the essay One customer model cannot predict every outcome