Evidence map›Paper›PMID 40125521›Full record

SynthesisF1000Research2025

Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach.

Sebastián Cardona-Acevedo, Erica Agudelo-Ceballos, Diana Arango-Botero, Alejandro Valencia-Arias, Juana De La Cruz Ramírez Dávila, Jesus Alberto Jimenez Garcia, Carlos Flores Goycochea, Ezequiel Martínez Rojas

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in F1000Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sebastián Cardona-AcevedoCentro de Investigaciones, Institución Universitaria Escolme, Medellín, 50005, Colombia.
Erica Agudelo-CeballosDepartamento de Ciencias Administrativas, Instituto Tecnologico Metropolitano, Medellín, Antioquia, Colombia.
Diana Arango-BoteroDepartamento de Ciencias Administrativas, Instituto Tecnologico Metropolitano, Medellín, Antioquia, Colombia.
Alejandro Valencia-AriasEscuela de Ingeniería, Universidad Senor de Sipan, Chiclayo, Lambayeque, Peru.ORCID https://orcid.org/0000-0001-9434-6923
Juana De La Cruz Ramírez DávilaDepartamento de Estudios Generales, Universidad Senor de Sipan, Chiclayo, Lambayeque, Peru.ORCID https://orcid.org/0000-0003-4675-4070
Jesus Alberto Jimenez GarciaDirección de Planificación y Desarrollo Institucional, Universidad Senor de Sipan, Chiclayo, Lambayeque, Peru.ORCID https://orcid.org/0000-0002-0722-8484
Carlos Flores GoycocheaInstituto de Investigaciones y Estudios de la Mujer, Universidad Ricardo Palma, Santiago de Surco, Peru.
Ezequiel Martínez RojasVicerrectoría de Investigación e Innovación, Universidad Arturo Prat, Iquique, Tarapacá Region, Chile.ORCID https://orcid.org/0000-0002-0914-951X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, machine learning applications in marketing allow to optimize strategies, personalize experiences and improve decision making. However, there are still several research gaps, so the objective is to examine the research trends in the use of machine learning in marketing. A bibliometric analysis is proposed to assess the current scientific activity, following the parameters established by PRISMA-2020. Machine learning applications in marketing have experienced steady growth and increased attention in the academic community. Key references, such as Miklosik and Evans, and prominent journals, such as IEEE Access and Journal of Business Research, have been identified. A thematic evolution towards big data and digital marketing is observed, and thematic clusters such as "digital marketing", "interpretation", "prediction", and "healthcare" stand out. These findings demonstrate the continued importance and research potential of this evolving field.

Indexed as

BibliometricsMachine LearningMarketingHumansArtificial intelligenceDecision makingDigital marketingPersonalizationPRISMA-2020.

Identifiers

PMID40125521
PMCPMC11926528

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.