小売業向け予測アナリティクス市場：提供サービス、データ種類、用途、最終用途、用途別-2025年～2030年の世界予測

小売業向け予測アナリティクス市場は、2024年に14億7,000万米ドルと評価され、2025年には17億2,000万米ドル、CAGR 17.90%で成長し、2030年には39億5,000万米ドルに達すると予測されています。

進化する小売業の状況は、予測分析の重要性の高まりによって再構築されつつあります。このテクノロジーは業務効率を一変させるだけでなく、小売業界全体の戦略的意思決定プロセスの進化を促しています。洗練されたアルゴリズム、データに基づく洞察、機械学習モデルを活用することで、小売企業は予測分析によって、かつてない精度で顧客の行動を予測し、在庫を最適化し、マーケティング戦略を微調整することができます。ここ数年、このアプローチは、既存の市場リーダーと新興のイノベーター両方の競争戦略の中心となっています。

小売企業は、複雑な消費者パターンの理解に予測分析がもたらす価値をますます認識するようになっています。高度な手法により、過去のデータを分析して将来の動向を予測することで、需要予測を改善し、顧客体験をパーソナライズし、価格戦略を洗練させることができます。単に対応するだけでなく、プロアクティブに計画を立てることができるこのリアルタイム機能は、小売業の業務に敏捷性と効率性の重要なレイヤーを追加しています。

さらに、予測アナリティクスを統合することで、デジタルと物理的なチャネルにわたる顧客とのやり取りをより深く理解できるようになります。伝統的な小売業の慣行と革新的なデータサイエンス技術の橋渡しをすることで、企業はサプライチェーンの合理化、店舗レイアウトの強化、マーチャンダイジングアプローチの改良に有利な立場に立つことができます。こうしたテクノロジーの採用により、アナリティクスと小売業務の融合が成功の礎となる未来への舞台が整いつつあります。

小売予測アナリティクスの展望における変革的シフトの分析

近年、データサイエンスとオペレーション戦略の融合により、小売業における変革的シフトに拍車がかかっています。豊富なデータの流入に後押しされ、小売業界では直感に基づく意思決定から分析的な先見性へのパラダイムシフトが起きています。このシフトは、小売企業が在庫を管理し、価格戦略を構築し、デジタル化が進む世界で顧客と関わる方法を大きく変えつつあります。

テクノロジーの進歩と高度なツールの急速な導入により、小売企業は反応的な戦略から先を見越した予測手法へと軸足を移すことができるようになりました。テクノロジーが日常業務とシームレスに統合されたことで、企業はこれまで利用されていなかった広範なデータセットを活用できるようになり、消費者行動をより詳細に理解できるようになりました。その結果、企業はリソースをより効率的に配分し、サプライチェーンを最適化し、ターゲットとする消費者層に響くようカスタマイズされたマーケティング・キャンペーンを実施できるようになりました。

小売企業は現在、迅速な適応が優位性だけでなく必要不可欠となる競合環境に直面しています。人工知能と機械学習が継続的に進化する中、これらのテクノロジーと予測分析の統合がイノベーションを促進し、より正確な予測と戦略的プランニングにつながっています。この変革は、不正検知メカニズムの改善や、データインサイトに基づく店舗レイアウト設計の強化にも表れています。その結果、市場の変化に対応し、顧客の期待により合致した小売環境が実現します。

リテールアナリティクスを形成する主要なセグメンテーションインサイトの詳細

小売業における予測分析を検討する上で、主要なセグメンテーションの洞察は、多様な市場力学をマッピングする上で極めて重要な役割を果たします。サービス提供に基づくセグメンテーションを考慮すると、市場はサービスとソリューションという2つのレンズを通して調査され、それぞれが顧客の需要に対応する上で独自の価値提案に貢献します。同様に重要なのは、データの種類に基づくセグメンテーションです。ここでは、構造化データと非構造化データの両方を通じて市場を深く分析し、従来の情報とニュアンスの異なる洞察の両方を活用した包括的なビューを提供します。

さらに深く掘り下げると、アプリケーションに基づくセグメンテーションでは、顧客のセグメンテーションとターゲティング、需要予測、不正行為の検出と防止、在庫管理、パーソナライズされたマーケティング、価格設定の最適化、売上と収益の予測、革新的な店舗レイアウトとマーチャンダイジングなど、小売の機能性に関する詳細な物語が展開されます。各アプリケーションは、オペレーション戦術を洗練させるだけでなく、データ分析と戦術実行の橋渡しをする触媒としても機能します。アパレル・ファッション、エレクトロニクス・消費財、食料品・スーパーマーケット、健康・美容、家庭用品・家具、高級品などの小売市場が、同じ深さと精度で分析されています。

最後に、利用状況に基づくセグメンテーションの検討により、eコマースやオンライン小売業者が利用するプラットフォームと、オフライン小売業者が利用するプラットフォームが区別され、各チャネルに固有の課題と機会が浮き彫りになります。セグメンテーションへのこの統合的アプローチは、企業が多様な顧客ベースの微妙なニーズに対応するテーラーメイドの戦略を策定することを可能にする豊かな洞察をもたらします。小売業の意思決定者は、商品、データタイプ、用途、最終用途、利用方法など、さまざまな側面を理解することで、全体的かつ細かくセグメント化された戦略を考案することができ、急速に進化する市場で持続的な競争優位性を確保することができます。

目次

第1章 序文

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 市場の概要

第5章 市場洞察

• 市場力学
  • 促進要因
    • eコマースプラットフォームとモバイルショッピングアプリケーションの急速な成長
    • パーソナライズされた顧客中心のショッピング体験に対する需要の高まり
  • 抑制要因
    • 高度な分析ツールの高コストと技術的な複雑さ
  • 機会
    • 消費者データの入手可能性が高まることで、小売業における予測分析の導入が促進される
    • 正確な需要予測のための人工知能と機械学習の進歩
  • 課題
    • 予測分析に関連するデータプライバシーと規制遵守に関する懸念
• 市場セグメンテーション分析
  • 提供内容：カスタマイズの可能性と、市場の変化に即座に適応する能力により、サービスの利用が増加
  • 最終用途：最適な在庫レベルを維持し、廃棄物を削減し、プロモーションをパーソナライズするために、食料品店やスーパーマーケットで広く採用されています。
• ポーターのファイブフォース分析
• PESTEL分析
  • 政治的
  • 経済
  • 社会
  • 技術的
  • 法律上
  • 環境

第6章 小売業向け予測アナリティクス市場：提供別

• サービス
• ソリューション

第7章 小売業向け予測アナリティクス市場データタイプ別

• 構造化データ
• 非構造化データ

第8章 小売業向け予測アナリティクス市場：用途別

• 顧客セグメンテーションとターゲティング
• 需要予測
• 不正行為の検出と防止
• 在庫管理
• パーソナライズされたマーケティング
• 価格最適化
• 売上と収益の予測
• 店舗レイアウトとマーチャンダイジング
• サプライチェーンの最適化

第9章 小売業向け予測アナリティクス市場：最終用途別

• アパレル・ファッション
• 電子機器および消費財
• 食料品店とスーパーマーケット
• 健康と美容
• 家庭用品・家具
• 高級品

第10章 小売業向け予測アナリティクス市場用途別

• eコマースおよびオンライン小売業者
• オフライン小売業者

第11章 南北アメリカの小売業向け予測アナリティクス市場

• アルゼンチン
• ブラジル
• カナダ
• メキシコ
• 米国

第12章 アジア太平洋地域の小売業向け予測アナリティクス市場

• オーストラリア
• 中国
• インド
• インドネシア
• 日本
• マレーシア
• フィリピン
• シンガポール
• 韓国
• 台湾
• タイ
• ベトナム

第13章 欧州・中東・アフリカの小売業向け予測アナリティクス市場

• デンマーク
• エジプト
• フィンランド
• フランス
• ドイツ
• イスラエル
• イタリア
• オランダ
• ナイジェリア
• ノルウェー
• ポーランド
• カタール
• ロシア
• サウジアラビア
• 南アフリカ
• スペイン
• スウェーデン
• スイス
• トルコ
• アラブ首長国連邦
• 英国

第14章 競合情勢

• 市場シェア分析, 2024
• FPNVポジショニングマトリックス, 2024
• 競合シナリオ分析
• 戦略分析と提言

企業一覧

• Alteryx, Inc.
• Amazon.com, Inc.
• C3.ai, Inc.
• Cloudera, Inc.
• Databricks, Inc.
• Endava
• Epic Systems Corporation
• Hitachi Solutions
• Honeywell International Inc.
• IBM Corporation
• Intel Corporation
• KPMG International Limited
• Manthan Systems Private Limited
• Mastech InfoTrellis, Inc.
• Microsoft Corporation
• NVIDIA Corporation
• Oracle Corporation
• QlikTech International AB
• Salesforce.com, Inc.
• SAP SE
• SAS Institute Inc.
• Teradata Corporation
• ThoughtSpot Inc.
• TIBCO Software Inc.
• Wipro Limited

LIST OF FIGURES

• FIGURE 1. PREDICTIVE ANALYTICS FOR RETAIL MARKET MULTI-CURRENCY
• FIGURE 2. PREDICTIVE ANALYTICS FOR RETAIL MARKET MULTI-LANGUAGE
• FIGURE 3. PREDICTIVE ANALYTICS FOR RETAIL MARKET RESEARCH PROCESS
• FIGURE 4. PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, 2024 VS 2030
• FIGURE 5. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, 2018-2030 (USD MILLION)
• FIGURE 6. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY REGION, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 7. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 8. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2024 VS 2030 (%)
• FIGURE 9. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 10. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2024 VS 2030 (%)
• FIGURE 11. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 12. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2024 VS 2030 (%)
• FIGURE 13. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 14. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2024 VS 2030 (%)
• FIGURE 15. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 16. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2024 VS 2030 (%)
• FIGURE 17. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 18. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
• FIGURE 19. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 20. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY STATE, 2024 VS 2030 (%)
• FIGURE 21. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY STATE, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 22. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
• FIGURE 23. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 24. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2030 (%)
• FIGURE 25. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2030 (USD MILLION)
• FIGURE 26. PREDICTIVE ANALYTICS FOR RETAIL MARKET SHARE, BY KEY PLAYER, 2024
• FIGURE 27. PREDICTIVE ANALYTICS FOR RETAIL MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

• TABLE 1. PREDICTIVE ANALYTICS FOR RETAIL MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
• TABLE 3. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, 2018-2030 (USD MILLION)
• TABLE 4. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 5. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 6. PREDICTIVE ANALYTICS FOR RETAIL MARKET DYNAMICS
• TABLE 7. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 8. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY SERVICES, BY REGION, 2018-2030 (USD MILLION)
• TABLE 9. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY SOLUTION, BY REGION, 2018-2030 (USD MILLION)
• TABLE 10. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 11. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY STRUCTURED DATA, BY REGION, 2018-2030 (USD MILLION)
• TABLE 12. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY UNSTRUCTURED DATA, BY REGION, 2018-2030 (USD MILLION)
• TABLE 13. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 14. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY CUSTOMER SEGMENTATION & TARGETING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 15. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DEMAND FORECASTING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 16. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY FRAUD DETECTION & PREVENTION, BY REGION, 2018-2030 (USD MILLION)
• TABLE 17. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY INVENTORY MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
• TABLE 18. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY PERSONALIZED MARKETING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 19. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY PRICING OPTIMIZATION, BY REGION, 2018-2030 (USD MILLION)
• TABLE 20. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY SALES & REVENUE FORECASTING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 21. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY STORE LAYOUT & MERCHANDISING, BY REGION, 2018-2030 (USD MILLION)
• TABLE 22. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY SUPPLY CHAIN OPTIMIZATION, BY REGION, 2018-2030 (USD MILLION)
• TABLE 23. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 24. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPAREL & FASHION, BY REGION, 2018-2030 (USD MILLION)
• TABLE 25. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY ELECTRONICS & CONSUMER GOODS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 26. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY GROCERIES & SUPERMARKETS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 27. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY HEALTH & BEAUTY, BY REGION, 2018-2030 (USD MILLION)
• TABLE 28. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY HOME GOODS & FURNITURE, BY REGION, 2018-2030 (USD MILLION)
• TABLE 29. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY LUXURY GOODS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 30. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 31. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY E-COMMERCE & ONLINE RETAILERS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 32. GLOBAL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFLINE RETAILERS, BY REGION, 2018-2030 (USD MILLION)
• TABLE 33. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 34. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 35. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 36. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 37. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 38. AMERICAS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 39. ARGENTINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 40. ARGENTINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 41. ARGENTINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 42. ARGENTINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 43. ARGENTINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 44. BRAZIL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 45. BRAZIL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 46. BRAZIL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 47. BRAZIL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 48. BRAZIL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 49. CANADA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 50. CANADA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 51. CANADA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 52. CANADA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 53. CANADA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 54. MEXICO PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 55. MEXICO PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 56. MEXICO PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 57. MEXICO PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 58. MEXICO PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 59. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 60. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 61. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 62. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 63. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 64. UNITED STATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
• TABLE 65. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 66. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 67. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 68. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 69. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 70. ASIA-PACIFIC PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 71. AUSTRALIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 72. AUSTRALIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 73. AUSTRALIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 74. AUSTRALIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 75. AUSTRALIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 76. CHINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 77. CHINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 78. CHINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 79. CHINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 80. CHINA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 81. INDIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 82. INDIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 83. INDIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 84. INDIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 85. INDIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 86. INDONESIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 87. INDONESIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 88. INDONESIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 89. INDONESIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 90. INDONESIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 91. JAPAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 92. JAPAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 93. JAPAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 94. JAPAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 95. JAPAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 96. MALAYSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 97. MALAYSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 98. MALAYSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 99. MALAYSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 100. MALAYSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 101. PHILIPPINES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 102. PHILIPPINES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 103. PHILIPPINES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 104. PHILIPPINES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 105. PHILIPPINES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 106. SINGAPORE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 107. SINGAPORE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 108. SINGAPORE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 109. SINGAPORE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 110. SINGAPORE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 111. SOUTH KOREA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 112. SOUTH KOREA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 113. SOUTH KOREA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 114. SOUTH KOREA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 115. SOUTH KOREA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 116. TAIWAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 117. TAIWAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 118. TAIWAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 119. TAIWAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 120. TAIWAN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 121. THAILAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 122. THAILAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 123. THAILAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 124. THAILAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 125. THAILAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 126. VIETNAM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 127. VIETNAM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 128. VIETNAM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 129. VIETNAM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 130. VIETNAM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 131. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 132. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 133. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 134. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 135. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 136. EUROPE, MIDDLE EAST & AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
• TABLE 137. DENMARK PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 138. DENMARK PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 139. DENMARK PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 140. DENMARK PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 141. DENMARK PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 142. EGYPT PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 143. EGYPT PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 144. EGYPT PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 145. EGYPT PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 146. EGYPT PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 147. FINLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 148. FINLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 149. FINLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 150. FINLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 151. FINLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 152. FRANCE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 153. FRANCE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 154. FRANCE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 155. FRANCE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 156. FRANCE PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 157. GERMANY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 158. GERMANY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 159. GERMANY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 160. GERMANY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 161. GERMANY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 162. ISRAEL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 163. ISRAEL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 164. ISRAEL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 165. ISRAEL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 166. ISRAEL PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 167. ITALY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 168. ITALY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 169. ITALY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 170. ITALY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 171. ITALY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 172. NETHERLANDS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 173. NETHERLANDS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 174. NETHERLANDS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 175. NETHERLANDS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 176. NETHERLANDS PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 177. NIGERIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 178. NIGERIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 179. NIGERIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 180. NIGERIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 181. NIGERIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 182. NORWAY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 183. NORWAY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 184. NORWAY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 185. NORWAY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 186. NORWAY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 187. POLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 188. POLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 189. POLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 190. POLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 191. POLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 192. QATAR PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 193. QATAR PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 194. QATAR PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 195. QATAR PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 196. QATAR PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 197. RUSSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 198. RUSSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 199. RUSSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 200. RUSSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 201. RUSSIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 202. SAUDI ARABIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 203. SAUDI ARABIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 204. SAUDI ARABIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 205. SAUDI ARABIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 206. SAUDI ARABIA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 207. SOUTH AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 208. SOUTH AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 209. SOUTH AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 210. SOUTH AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 211. SOUTH AFRICA PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 212. SPAIN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 213. SPAIN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 214. SPAIN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 215. SPAIN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 216. SPAIN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 217. SWEDEN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 218. SWEDEN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 219. SWEDEN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 220. SWEDEN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 221. SWEDEN PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 222. SWITZERLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 223. SWITZERLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 224. SWITZERLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 225. SWITZERLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 226. SWITZERLAND PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 227. TURKEY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 228. TURKEY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 229. TURKEY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 230. TURKEY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 231. TURKEY PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 232. UNITED ARAB EMIRATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 233. UNITED ARAB EMIRATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 234. UNITED ARAB EMIRATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 235. UNITED ARAB EMIRATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 236. UNITED ARAB EMIRATES PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 237. UNITED KINGDOM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY OFFERING, 2018-2030 (USD MILLION)
• TABLE 238. UNITED KINGDOM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY DATA TYPE, 2018-2030 (USD MILLION)
• TABLE 239. UNITED KINGDOM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
• TABLE 240. UNITED KINGDOM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY END-USE, 2018-2030 (USD MILLION)
• TABLE 241. UNITED KINGDOM PREDICTIVE ANALYTICS FOR RETAIL MARKET SIZE, BY USAGE, 2018-2030 (USD MILLION)
• TABLE 242. PREDICTIVE ANALYTICS FOR RETAIL MARKET SHARE, BY KEY PLAYER, 2024
• TABLE 243. PREDICTIVE ANALYTICS FOR RETAIL MARKET, FPNV POSITIONING MATRIX, 2024

The Predictive Analytics for Retail Market was valued at USD 1.47 billion in 2024 and is projected to grow to USD 1.72 billion in 2025, with a CAGR of 17.90%, reaching USD 3.95 billion by 2030.

The evolving landscape of retail is being reshaped by the growing prominence of predictive analytics. This technology is not only transforming operational efficiencies, but it is also driving the evolution of strategic decision-making processes across the retail industry. Leveraging sophisticated algorithms, data-driven insights, and machine learning models, predictive analytics enables retailers to anticipate customer behaviors, optimize inventory, and fine-tune marketing strategies with unprecedented precision. Over the past few years, this approach has become central to the competitive strategies of both established market leaders and emerging innovators.

Retailers are increasingly recognizing the value that predictive analytics brings to understanding complex consumer patterns. Advanced methodologies allow for the analysis of historical data to forecast future trends, thereby improving demand forecasting, personalizing customer experiences, and refining pricing strategies. This real-time capability to not just react but also proactively plan is adding significant layers of agility and efficiency to retail operations.

Furthermore, the integration of predictive analytics catalyzes a deeper understanding of customer interactions across digital and physical channels. By bridging traditional retail practices with innovative data science techniques, businesses are better positioned to streamline their supply chain, enhance store layouts, and refine merchandising approaches. The adoption of these technologies is setting the stage for a future where the fusion of analytics and retail operations becomes the cornerstone of success.

Analyzing Transformative Shifts in Retail Predictive Analytics Landscape

Over recent years, transformative shifts in retail have been spurred by the convergence of data science and operational strategy. The retail sector, driven by an influx of rich data, has experienced a paradigm shift from intuition-based decision making to analytical foresight. This shift is profoundly changing the way retailers manage inventories, structure pricing strategies, and engage with customers in an increasingly digital world.

Technological advancements and the rapid adoption of advanced tools have allowed retailers to pivot from reactive strategies to proactive forecasting methods. Because of the seamless integration of technology with day-to-day operations, businesses have been able to harness extensive data sets that were previously untapped, enabling a more granular understanding of consumer behaviors. Consequently, organizations are able to allocate resources more efficiently, optimize supply chains, and implement customized marketing campaigns that resonate with targeted audience segments.

Retailers now face a competitive environment where quick adaptation is not just an advantage but a necessity. As artificial intelligence and machine learning continuously evolve, the integration of these technologies with predictive analytics is driving innovation, leading to more accurate forecasts and strategic planning. This transformation is also evident in improved fraud detection mechanisms and enhanced store layout designs that are informed by data insights. The result is a retail environment that is more responsive to market changes and better aligned with customer expectations.

In-Depth Key Segmentation Insights Shaping Retail Analytics

In exploring predictive analytics within the retail sector, key segmentation insights play a pivotal role in mapping diverse market dynamics. Considering the segmentation based on offering, the market is examined through the dual lenses of services and solutions, each contributing unique value propositions in addressing customer demands. Equally important is the segmentation based on data type, where the market is deeply analyzed through both structured data and unstructured data, providing a comprehensive view that leverages conventional information and nuanced insights alike.

Diving deeper, the segmentation based on application lays out a detailed narrative of retail functionalities such as customer segmentation and targeting, demand forecasting, fraud detection and prevention, inventory management, personalized marketing, pricing optimization, sales and revenue forecasting, and innovative store layout and merchandising. Each application not only refines the operational tactics but also acts as a catalyst in bridging data analytics with tactical execution. Alongside these applications comes the critical segmentation based on end-use, where retail markets such as apparel and fashion, electronics and consumer goods, groceries and supermarkets, health and beauty, home goods and furniture, and luxury goods are analyzed with equal depth and precision.

Finally, an examination of the segmentation based on usage distinguishes between platforms followed by e-commerce and online retailers versus offline retailers, thereby highlighting the unique challenges and opportunities inherent in each channel. This integrated approach to segmentation yields rich insights that enable businesses to formulate tailored strategies that cater to the nuanced needs of diverse customer bases. By understanding the various dimensions across offering, data type, application, end-use, and usage, retail decision-makers can devise strategies that are both holistic and finely segmented, ensuring sustained competitive advantage in a rapidly evolving market.

Based on Offering, market is studied across Services and Solution.

Based on Data Type, market is studied across Structured Data and Unstructured Data.

Based on Application, market is studied across Customer Segmentation & Targeting, Demand Forecasting, Fraud Detection & Prevention, Inventory Management, Personalized Marketing, Pricing Optimization, Sales & Revenue Forecasting, Store Layout & Merchandising, and Supply Chain Optimization.

Based on End-Use, market is studied across Apparel & Fashion, Electronics & Consumer Goods, Groceries & Supermarkets, Health & Beauty, Home Goods & Furniture, and Luxury Goods.

Based on Usage, market is studied across E-commerce & Online Retailers and Offline Retailers.

Regional Insights: Global Trends and Market Dynamics

Understanding the geographical contours of the market is essential for making informed strategic decisions in retail predictive analytics. The regional insights reveal that markets within the Americas are experiencing significant technological advancements driven by high consumer engagement and robust digital infrastructures. In parallel, regions covering Europe, the Middle East, and Africa are embracing digital transformation, with many retailers adopting predictive models to optimize operations in an increasingly competitive environment.

Additionally, the Asia-Pacific region stands out due to its rapid adoption of advanced analytics technologies, along with a booming e-commerce industry that continues to reshape traditional retail business models. This region is characterized by dynamic consumer behavior trends and a youthful demographic, which collectively drive the demand for innovative predictive solutions. As retailers in these regions seek to capitalize on their distinct market conditions, the regional insights provide a strategic roadmap for harnessing technology to drive growth and enhance operational efficiency. Each region, with its unique set of opportunities and challenges, contributes valuable lessons and benchmarks for the broader retail industry.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Leading Innovators: Key Company Insights in Predictive Analytics

The role of key industry players cannot be understated in the evolution of predictive analytics within the retail environment. Companies such as Alteryx, Inc. and Amazon.com, Inc. have been at the forefront, pioneering innovations that integrate data analytics into diverse retail operations. Their technological contributions complement the innovative strategies developed by industry frontrunners like C3.ai, Inc., Cloudera, Inc., and Databricks, Inc., who continue to set the benchmark for how analytics drive business intelligence.

Further, organizations including Endava, Epic Systems Corporation, and Hitachi Solutions are rapidly scaling their analytical capabilities, while global conglomerates such as Honeywell International Inc., IBM Corporation, and Intel Corporation bring extensive domain expertise to bear. Professional services firms like KPMG International Limited, along with dedicated technology providers such as Manthan Systems Private Limited and Mastech InfoTrellis, Inc., have also deepened market maturity by integrating high-value data solutions. In addition, the influence of major corporations such as Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, QlikTech International AB, Salesforce.com, Inc., SAP SE, SAS Institute Inc., Teradata Corporation, ThoughtSpot Inc., TIBCO Software Inc., and Wipro Limited is evident in the market. These players collectively harness innovation to refine predictive models that are vital for transforming retail strategies on a global scale.

The report delves into recent significant developments in the Predictive Analytics for Retail Market, highlighting leading vendors and their innovative profiles. These include Alteryx, Inc., Amazon.com, Inc., C3.ai, Inc., Cloudera, Inc., Databricks, Inc., Endava, Epic Systems Corporation, Hitachi Solutions, Honeywell International Inc., IBM Corporation, Intel Corporation, KPMG International Limited, Manthan Systems Private Limited, Mastech InfoTrellis, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, QlikTech International AB, Salesforce.com, Inc., SAP SE, SAS Institute Inc., Teradata Corporation, ThoughtSpot Inc., TIBCO Software Inc., and Wipro Limited. Actionable Recommendations for Retail Industry Leaders

Industry leaders are encouraged to leverage the insights from predictive analytics to create forward-thinking strategies that cultivate sustainable growth. Firstly, investing in advanced data management platforms is critical, as it enables a comprehensive approach to integrating structured and unstructured data from various sources. Such investments pave the way for more accurate forecasting and streamlined operations.

In addition, developing cross-functional teams that bridge technical expertise with strategic vision can propel an organization's ability to harness the full potential of analytics. Embracing agile methodologies and continuous learning will also ensure that teams remain at the cutting edge of technological advances. By fostering a culture of innovation, industry leaders can capitalize on emerging tools and techniques, thereby establishing a competitive edge in an evolving market.

Moreover, aligning technology initiatives with customer-centric strategies will help integrate predictive insights into the core of retail operations. This means targeting personalized marketing efforts, optimizing inventory management, and refining pricing strategies based on robust demand forecasting. Each initiative should be tailored to specific market segments, ensuring that every decision is data-driven. Those at the helm are advised to maintain a clear focus on both operational efficiency and customer engagement, empowering them to navigate complexities and maximize return on investment in a rapidly shifting landscape.

Conclusion: Summarizing the Strategic Roadmap for Retail Predictive Analytics

Bringing all the insights together, it becomes evident that predictive analytics is not merely an operational tool but a strategic imperative for the modern retail landscape. The integration of advanced segmentation, regional dynamics, and the innovation propelled by key industry players frames a comprehensive roadmap for retail success. By synthesizing these multifaceted aspects, companies are better positioned to negotiate global market challenges and capitalize on emerging opportunities.

In essence, the journey towards leveraging predictive analytics effectively is a continuous process of adaptation and refinement. Success depends on a relentless commitment to harnessing deep data insights, fostering innovation, and maintaining an agile approach to market changes. This strategic roadmap paves the way for retail entities to not only survive but thrive in the digital age.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Rapid growth in e-commerce platforms and mobile shopping applications
    • 5.1.1.2. Rising demand for personalized and customer-centric shopping experiences
  • 5.1.2. Restraints
    • 5.1.2.1. High costs and technical complexity of advanced analytics tools
  • 5.1.3. Opportunities
    • 5.1.3.1. Increasing availability of consumer data drives the adoption of predictive analytics in retail
    • 5.1.3.2. Advancements in artificial intelligence and machine learning for precise demand forecasting
  • 5.1.4. Challenges
    • 5.1.4.1. Concerns over data privacy and regulatory compliance associated with predictive analytics
• 5.2. Market Segmentation Analysis
  • 5.2.1. Offering: Customization potential and capacity to adjust to immediate market shifts increases usage of services
  • 5.2.2. End-Use: Widespread adoption in groceries & supermarkets to maintain optimal inventory levels, reduce waste, and personalize promotions
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Predictive Analytics for Retail Market, by Offering

• 6.1. Introduction
• 6.2. Services
• 6.3. Solution

7. Predictive Analytics for Retail Market, by Data Type

• 7.1. Introduction
• 7.2. Structured Data
• 7.3. Unstructured Data

8. Predictive Analytics for Retail Market, by Application

• 8.1. Introduction
• 8.2. Customer Segmentation & Targeting
• 8.3. Demand Forecasting
• 8.4. Fraud Detection & Prevention
• 8.5. Inventory Management
• 8.6. Personalized Marketing
• 8.7. Pricing Optimization
• 8.8. Sales & Revenue Forecasting
• 8.9. Store Layout & Merchandising
• 8.10. Supply Chain Optimization

9. Predictive Analytics for Retail Market, by End-Use

• 9.1. Introduction
• 9.2. Apparel & Fashion
• 9.3. Electronics & Consumer Goods
• 9.4. Groceries & Supermarkets
• 9.5. Health & Beauty
• 9.6. Home Goods & Furniture
• 9.7. Luxury Goods

10. Predictive Analytics for Retail Market, by Usage

• 10.1. Introduction
• 10.2. E-commerce & Online Retailers
• 10.3. Offline Retailers

11. Americas Predictive Analytics for Retail Market

• 11.1. Introduction
• 11.2. Argentina
• 11.3. Brazil
• 11.4. Canada
• 11.5. Mexico
• 11.6. United States

12. Asia-Pacific Predictive Analytics for Retail Market

• 12.1. Introduction
• 12.2. Australia
• 12.3. China
• 12.4. India
• 12.5. Indonesia
• 12.6. Japan
• 12.7. Malaysia
• 12.8. Philippines
• 12.9. Singapore
• 12.10. South Korea
• 12.11. Taiwan
• 12.12. Thailand
• 12.13. Vietnam

13. Europe, Middle East & Africa Predictive Analytics for Retail Market

• 13.1. Introduction
• 13.2. Denmark
• 13.3. Egypt
• 13.4. Finland
• 13.5. France
• 13.6. Germany
• 13.7. Israel
• 13.8. Italy
• 13.9. Netherlands
• 13.10. Nigeria
• 13.11. Norway
• 13.12. Poland
• 13.13. Qatar
• 13.14. Russia
• 13.15. Saudi Arabia
• 13.16. South Africa
• 13.17. Spain
• 13.18. Sweden
• 13.19. Switzerland
• 13.20. Turkey
• 13.21. United Arab Emirates
• 13.22. United Kingdom

14. Competitive Landscape

• 14.1. Market Share Analysis, 2024
• 14.2. FPNV Positioning Matrix, 2024
• 14.3. Competitive Scenario Analysis
  • 14.3.1. Mallcomm launches AI-powered platform for real-time sales and operational insights that drive enhanced retail asset management strategies
  • 14.3.2. Green Street launches Retail Analytics Pro
  • 14.3.3. Microsoft unveils new generative AI and data solutions across the shopper journey
• 14.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Alteryx, Inc.
• 2. Amazon.com, Inc.
• 3. C3.ai, Inc.
• 4. Cloudera, Inc.
• 5. Databricks, Inc.
• 6. Endava
• 7. Epic Systems Corporation
• 8. Hitachi Solutions
• 9. Honeywell International Inc.
• 10. IBM Corporation
• 11. Intel Corporation
• 12. KPMG International Limited
• 13. Manthan Systems Private Limited
• 14. Mastech InfoTrellis, Inc.
• 15. Microsoft Corporation
• 16. NVIDIA Corporation
• 17. Oracle Corporation
• 18. QlikTech International AB
• 19. Salesforce.com, Inc.
• 20. SAP SE
• 21. SAS Institute Inc.
• 22. Teradata Corporation
• 23. ThoughtSpot Inc.
• 24. TIBCO Software Inc.
• 25. Wipro Limited