 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21653
Article Published : 8541
Total Authors : 22459
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Data Driven Customer Service Optimization Through Behavioral Analytics for Reducing Support Contacts |
| Authors Name: | Peeyush Patel |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205446
|
| Published Paper Id: | IJRTI2507124 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i7.205446 |
| Abstract: | The behavioral analytics data-informed customer service optimization uses customer service data to predict contact frequencies and leverage proactive customer care actions to streamline contact without decreasing service outcomes by focusing on specific contact frequency. This is an adoption of multichannel data aggregation, preprocessing pipelines, sequential behavior modeling, decision engines on context-aware prompts, and continuous feedback loops. Recorded data on interactions between users chat, email, voice, and clickstream is combined together to create a holistic profile of customers. Such heterogeneous data is subject to data cleansing, normalization, and feature engineering to both batch training and real-time inference and be robust to noise and missing values. Sequential modeling methods can identify how action would be done to predict the requirements that will support it with a high probability. The predictive signals are then converted to specific actions via decision engines that offer personalised interventions, such as dynamic helpfully answered frequently asked questions, smart routing modification or contextual self-help messages which preempt the need to contact an agent. An iterative process of refinement is accomplished by a means of a feedback loop that evaluates the success of the interventions, giving performance measures to earlier preprocessing and modeling steps in order to continue improving. Empirical analyses show a big payoff in major service metrics. The average handle time reduced by around 20 percent and the rates of escalation prediction were over 80 percent. Self-service success was improved by close to 18 %, the number of inbound contacts decreased almost by 10 per cent, and forecasting accuracy was enhanced roughly by 25 per cent. |
| Keywords: | Behavioral analytics; customer service optimization; proactive intervention; predictive modeling; feedback loop |
| Cite Article: | "Data Driven Customer Service Optimization Through Behavioral Analytics for Reducing Support Contacts", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b154-b157, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507124.pdf |
| Downloads: | 000406 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2507124
Registration ID:205446
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i7.205446
Page No: b154-b157 Country: Hayward, California, United States Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507124 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507124 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||