Acquisition and retention · Empirical survey and structural model
Linking big data analytical intelligence to customer relationship management performance
Chubing Zhang, Xinchun Wang, Annie Peng Cui, Shenghao Han (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2020.10.012.
Plain-English take
Links big-data analytical intelligence capabilities to CRM performance and tests the proposed capability relationships. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
How much weight to give this
2.5/5 Evidence needs cautious interpretation
The 1–5 evidence-check score is a practical reading aid, not a formal peer review or a probability that a conclusion is true. It combines design strength (2 points), sample reporting (1), comparison/control clarity (1), and how much of the paper could be checked (1). Access affects how confidently this directory can assess a study, not the study's underlying quality. Blinding is reported separately because it is not relevant to every design.
Empirical survey and structural model
Firms using big-data analytical intelligence for CRM
Judged from the publisher-described design and comparison; inspect the paper for complete analysis.
Publisher page and abstract were retrieved directly; full text was not assumed open.
Double-blind? Blinding was not established from the retrieved publisher record.
Research question
What the authors investigated
How does big-data analytical intelligence contribute to CRM performance?
Design
Empirical survey and structural model
Sample and setting
Firms using big-data analytical intelligence for CRM
Reported result
What the study found
Links big-data analytical intelligence capabilities to CRM performance and tests the proposed capability relationships.
This is a paraphrase of the source. Check the original paper for definitions, estimates, statistical uncertainty and analysis details.
Scope and transfer
Limitations to keep in view
Survey/common-method and cross-sectional design limit causal and objective-outcome claims.
Possible founder application
Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Evidence access
Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Publisher DOI landing page identifies the exact title, authors, journal record and empirical-design sections/abstract: https://doi.org/10.1016/j.indmarman.2020.10.012 Direct publisher-page retrieval was recorded on 2026-10-09; this profile uses the publisher abstract/metadata and does not claim independent review of unavailable full text.
If only an abstract or metadata was available, the profile is limited to claims supported there. A paper's paywall does not change its evidentiary status, but it limits what we can substantiate from accessible text.