International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2025) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Predictive Sales Analytics For Zepto: Unlocking Business Growth

Author(s) Ms. Aastha Soni, Prof. Nitin Kumar R Chaudhary, Prof. Devendra Kumar Pandey
Country India
Abstract Adoption of new technologies in the industry of quick commerce is fueled by imminent consumer delivery expectations and demand volatility with the rapid pace of change in delivery expectations and shifting consumer behaviors, the quick commerce sector faces intense challenges, increasing the urgency for firms to implement smart and automated decision systems. Zepto, a leader in the 10-minute delivery space, operates under very dynamic conditions where reasonable demand estimation and optimal resource utilization are vital for enduring growth. This business predictive analytics research aims to explore predictive sales analytics and operational efficiency enhancement at Zepto to improve its market reactivity. In collaboration with various influencing factors such as weather and local events, customer transaction data, historical sales data, and seasonal trends, multiple statistical and machine learning methods, including time series forecasting, ensemble methods, linear regression, and other techniques, were used to forecast both long- and short-term sales. These models also underwent validation processes to maintain a level of accuracy and reliability sufficient for achieving actionable results. Furthermore, this research incorporates real-time decision making through the data visualization capabilities of Power BI, providing interactive dashboards which enhance strategic planning. The findings indicate enhanced accuracy in forecasting, helping streamline inventory
Keywords Analytics for predictive sales, fast commerce, Zepto, analysis of customer behavior, forecasting demand, trend sales prediction, applied machine learning, driven strategy based on data, management of inventory, advancement of enterprise, analytics in real-time, e-grocery sector, Power BI dashboard, enhancement of sales, retail data analytics.
Field Business Administration
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-10
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com