Evidence map›Paper›PMID 41006349›Full record

ArticleScientific reports2025

iDigiChat: intelligent digital marketing service chatbot for providing efficient customer services using artificial intelligence.

Ji He, Yi Luo, Ting Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Ji HeSchool of Business Administration, Guizhou University of Finance and Economics, Guiyang, 550025, China. jihehej850478@gmail.com.
Yi LuoBusiness School, Guizhou University, Guiyang, 550025, China.
Ting WangInternational Academy, Guizhou City Vocational College, Guiyang, 550025, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the increasing demand for intelligent customer service in the financial sector, the use of AI-powered chatbots is becoming unavoidable. Existing chatbot systems lack sufficient knowledge of complex language patterns, which limits their ability to handle intricate customer queries effectively. This study introduces an advanced AI-based customer service framework called iDigiChat, which integrates a knowledge graph (KG) and artificial neural network (ANN) techniques for more precise and responsive interactions. The system converts customers' queries into a structured graph format using Text-to-Graphql. It then applies Knowledge Graph Completion models to extract relational insights, which are classified and refined through a multi-layered ANN framework (KG-ANN). The simulation was conducted using a large-scale dataset consisting of 34,089 chatbot logs and 317,438 customer service call logs from a leading Korean bank, collected over a three-year period (2018-2020). The results show that the system achieves 90% R

Indexed as

Artificial intelligenceChatbotCustomer servicesDigital marketingFinancial servicesNeural network

Identifiers

PMID41006349
PMCPMC12474934

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.