Submission declined on 12 January 2026 by Netherzone (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. These tools are prone to specific issues that violate our policies: hallucinations: they often invent false information and cite non-existent references. unencyclopedic tone: they tend to be vague, promotional, or essay-like, rather than neutral and factual. copyright issues: they may closely paraphrase existing text, leading to copyright violations. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by Netherzone 4 months ago. Last edited by Netherzone 4 months ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

Submission declined on 9 December 2025 by Netherzone (talk). This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. These tools are prone to specific issues that violate our policies: hallucinations: they often invent false information and cite non-existent references. unencyclopedic tone: they tend to be vague, promotional, or essay-like, rather than neutral and factual. copyright issues: they may closely paraphrase existing text, leading to copyright violations. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. Declined by Netherzone 5 months ago.

• Comment: Checks out as 100% AI/LLM produced content. Also, it's a duplicate draft and either this one or the other one which is also an AI creation should be deleted. Please do not submit duplicate drafts. Duplicate draft located here: Draft:Customer Value Management (CVM) Netherzone (talk) 19:12, 12 January 2026 (UTC)

• Comment: This is a duplicate draft, there is another draft for this company at: Draft:Customer Value Management (CVM). Please do not submit duplicate drafts, one of these should be deleted. Netherzone (talk) 15:16, 9 January 2026 (UTC)

• Comment: Has many of the signs of being written by AI / LLM. Should be re-written in your own words based on the best reliable sources using human decisions, over machine-generated outcomes. Draft is somewhat ref-bombed. Netherzone (talk) 14:27, 9 December 2025 (UTC)

• Comment: Probably LLM-generated —pythoncoder (talk | contribs) 16:13, 1 December 2025 (UTC)

Customer value management (CVM) is a management approach that uses customer data and financial measures to evaluate how different customers contribute to business outcomes over time. In marketing research, the term is closely associated with customer lifetime value (CLV) and customer equity models, and with decisions about customer acquisition, retention, and relationship development.^[1][2] The idea that customers differ in profitability, risk, and growth potential is central to CVM, and is used to justify differentiated investments in customer relationships.^[3]

In business and practitioner writing, customer value management may also refer to cross-functional work that combines marketing, analytics, technology, and customer operations to manage a base of customers through the lifecycle.^[4][5]

Academic discussions of CVM are usually traced to earlier research in relationship marketing, customer satisfaction, and the development of CLV and customer equity metrics in the late 20th century. One early use of the term appears in Mark Stirling’s 2000 article, which discussed managing customers based on their economic value rather than treating the customer base as homogeneous.^[6]

Later work positioned CVM as an area that links marketing decisions to firm value through customer-level metrics. Verhoef and co-authors described research themes around retention, expansion, and the drivers of CLV, and discussed how customer metrics can be connected to business performance.^[2] Verhoef and Lemon later summarised lessons and trends in CVM research, including measurement and implementation issues in applied settings.^[7] Related work on customer equity and value management has also examined the intersection of marketing and finance for brand and customer assets.^[8]

CVM has also been discussed in practitioner and consulting literature, particularly in industries with recurring revenue and high churn risk. Strategy& (PwC) published a report focused on telecommunications that described CVM as an integrated approach to managing customer bases and reported performance improvements for some operators adopting such programmes.^[9]

There is no single, universally accepted definition of CVM. Academic and industry descriptions tend to share several recurring elements:

• an emphasis on the customer (or segment) as the unit of analysis;
• a long-term view over the customer lifecycle; and
• the use of data and models to guide resource allocation and customer-facing decisions.^[2][6][10]

Some practitioner sources describe CVM in broad, customer-centric terms, focusing on the value delivered to customers and how that value is measured and improved.^[5][11] In much of the academic literature, the emphasis is on customer-level economics and the connection between customer metrics and firm outcomes.^[3][2]

CLV (sometimes called LTV) is often treated as a core metric in CVM. It is typically defined as the net present value of expected future cash flows from a customer relationship, and is used to evaluate acquisition spending and retention investments.^[12] At a portfolio level, customer equity aggregates customer-level value to represent the value of the firm’s customer base as an asset.^[8][12]

Because CLV is model-based and can vary by method and assumptions, CVM implementations often combine CLV with operational and behavioural measures. In subscription and usage-based settings, common examples include churn rate, tenure, usage measures, and revenue-based indicators such as ARPU.^[9][7] In business practice, sources also discuss segment-level views of value and risk, where high-revenue customers may differ from high-margin customers, and short-term revenue can differ from long-term profitability.^[4][11]

CVM writing often distinguishes between (1) value to the firm and (2) value perceived by the customer. The first is typically measured using financial outcomes and CLV-related metrics, while the second is described in terms of benefits received relative to costs and alternatives.^[2][3] Some research frames CVM as a managerial effort to connect these perspectives, linking customer experience and relationship variables to customer retention and long-term value outcomes.^[7]

Scholarly work frequently describes CVM around three broad activities:

• Acquisition, with an emphasis on the expected value of acquired customers rather than volume alone;
• 'Retention, often discussed in relation to churn prevention and relationship stability; and
• 'Expansion, including cross-buying, up-selling, and increasing usage of products or services among existing customers.^[2][7][10]

CVM is related to customer relationship management (CRM) and customer lifecycle management (CLM). CRM is commonly described as the systems and processes used to record customer data and manage interactions, while CVM is described as using customer data to support decisions about investment levels, prioritisation, and targeting across the lifecycle.^[13][3] In academic writing, CVM is often treated as a model-driven subset of customer management that places greater emphasis on customer economics and customer-level metrics.^[2][14]

CVM implementations generally depend on integrating customer data from multiple sources such as transactions, product usage, service interactions, and digital behaviour. Research and practitioner writing discuss statistical and predictive modelling for outcomes such as churn risk, purchase propensity, and CLV, as well as practical challenges around data quality and operational use of models.^{[2][7][14][10]}

Industry-oriented sources describe technology stacks that may include data storage (data warehouses or data lakes), analytics environments, and tools for coordinating customer communications across channels. These descriptions are often tied to marketing automation and personalisation systems in large customer bases.^[9][15]

CVM is frequently described as a cross-functional activity spanning marketing, analytics, and customer operations. Academic discussions highlight coordination and incentives as recurring topics when customer value is managed over time rather than measured in isolated campaigns.^[3][7][10] Some practitioner sources describe CVM as a discipline with defined roles and workflows, but terminology varies between organisations and industries.^[11][5]

Telecommunications is commonly cited as an early adopter of CVM because of market saturation, recurring revenue, and large customer bases. Industry and consulting sources describe CVM programmes in telecoms as focusing on churn reduction, subscriber development, and the management of ARPU and related financial indicators.^[9][16]

In banking and insurance, CVM is discussed in relation to customer profitability, retention, and cross-selling of financial products, often based on transaction and product holding data.^[17] Practitioner writing also uses the term in contexts such as software-as-a-service, subscription media, and online services, where customer retention and expansion are key drivers of performance.^[18]

Some academic work has connected CVM to adjacent concepts such as brand equity and customer-perceived value. Bartosz (2017), for example, discussed the relationship between customer value management and brand equity development in an organisational context.^[19]

• Customer lifetime value
• Customer relationship management
• Relationship marketing
• Customer experience

• "Customer Value Management Body of Knowledge (CVMBoK)". Exacaster. 2025. Retrieved 27 November 2025.