English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Growing competitiveness and increasing availability of data is generatingtremendous interest in data-driven analytics across industries. In the retailsector, stores need targeted guidance to improve both the efficiency andeffectiveness of individual stores based on their specific locations,demographics, and environment. We propose an effective data-driven frameworkfor internal benchmarking that can lead to targeted guidance for individualstores. In particular, we propose an objective method for segmenting storesusing a model-based clustering technique that accounts for similarity in storeperformance dynamics. The proposed method relies on an effective Finite Mixtureof Regressions technique based on competitive learning for carrying out themodel-based clustering with `must-link' constraints and modeling storeperformance. We also propose an optimization framework to derive tailoredrecommendations for individual stores within store clusters that jointlyimproves profitability for the store while also improving sales to satisfyfranchiser requirements. We validate the methods using synthetic experiments aswell as a real-world automotive dealership network study for a leading globalautomotive manufacturer.

Data-Driven Analytics for Benchmarking and Optimizing Retail Store Performance