オムニチャネル小売における顧客忠誠度予測 購買行動,社会文化的要因,学習技術
Shima Roosta1, Seyed Jafar Sadjadi1, Ahmad Makui1
1Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
PloS one
|August 21, 2025
まとめ
この研究は,包括的なデータと高度なAIを使用して,オムニチャネル小売における顧客忠誠度を予測するためのインテリジェントフレームワークを導入します. 顧客関係を改善し 顧客を維持し 市場競争力を高めます
科学分野:
- ビジネス アナリティクス
- * 人工知能
- * 小売管理
背景:
- * 競争力のあるオムニチャネル小売において,顧客の忠誠心は極めて重要です.しかし,既存の研究は,影響する要因を孤立的に検討することがよくあります.
- * 従来の研究では,柔軟なオムニチャネル環境における忠誠心推進者の全体的な見方が欠けている.
- 様々な小売プラットフォームにおける顧客忠誠度に影響を与える要因の複雑な相互作用を理解する上でギャップが存在します.
研究 の 目的:
- * オムニチャネル小売環境における顧客忠誠度を予測するためのインテリジェントな分析枠組みを開発する.
- * 販売,社会経済,行動,感情のデータを含む包括的なデータセットを統合する.
- 企業情報システム (EIS) 内の顧客関係管理 (CRM) サブシステムを強化し,意思決定を改善する.
主な方法:
- * 感情分析のためのBERTと行動分析のための強化学習を組み合わせたハイブリッドAIアプローチを使用します.
- * グラフコンボリューションネットワーク (GCN) とトレンド分析と忠誠度予測のための適応学習を使用しています.
- * 個別レベルや地域レベルでの予測精度を高めるための微調整技術を含んでいます.
主要な成果:
- * この研究は,統合されたオムニチャネルデータを用いて顧客の忠誠度を予測するための新しい枠組みを提供します.
- * ハイブリッドAIモデルは,複雑な顧客行動や感情を分析する上で有効性を示しています.
- * フレームワークは,個々のレベルと近所のレベルの両方で予測を可能にし,細かい洞察を提供します.
結論:
- * 提案されたインテリジェント・フレームワークは,オムニチャネル小売における顧客忠誠度予測を大幅に改善します.
- このフレームワークを通じて最適化された CRM 戦略は,価格設定,在庫管理,パーソナライズされた広告を改善することができます.
- * この研究は,最終的に顧客維持率の向上と市場競争力の向上に貢献します.
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