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Global In-store Analytics Market Forecast to 2023 - Tracking Engagement Metrics and Monitoring Customer Behavior in Real Time
DUBLIN, April 26, 2019 /PRNewswire/ -- The "In-store Analytics Market by Application, Component, Deployment, Organization Size, and Region - Global Forecast to 2023" report has been added to ResearchAndMarkets.com's offering.
The global in-store analytics market to grow from USD 1.1 billion in 2018 to USD 3.2 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 23.5% during the forecast period.
The in-store analytics market is growing rapidly with the increasing competition of brick-and-mortar retailers from eCommerce players, growing need for better customer service and enhanced shopping experience, and rising data volume around in-store operations. However, the reluctance of traditional retailers to adopt newer technologies would limit the growth of the market.
"Merchandising analysis segment is expected to grow at the highest CAGR during the forecast period"
Brick-and-mortar retailers have witnessed intense competition from various eCommerce websites, which has led to a decline in their growth. Merchandising analysis software provides analytical insights for building a localized strategy on the basis of strong and weaker-performing stores. It also explores incremental revenue opportunities with flexible ad-hoc analysis. The adoption of such applications helps improve the operational efficiency of the enterprise by meeting changing conditions for each selling season.
"In-store analytics market in Asia Pacific is projected to grow at the highest CAGR during the forecast period"
The high growth of the market in Asia Pacific (APAC) is attributed to the high growth potential, growing retail market, and increasing digitalization in the region with the rising need of businesses to remain globally competitive. Furthermore, the inclination of countries across the region toward emerging technologies such as Artificial Intelligence (AI) and advanced analytics, is also expected to fuel the growth of the in-store analytics market.
However, the lack of technological awareness, privacy issues, and limited technical expertise in advanced technologies remains the biggest hurdle in the in-store analytics adoption across the region. The cloud-based in-store analytics software presents an optimal solution for these countries by minimizing integration complexities, and installation costs.
"Large enterprises to hold the largest market share during the forecast period in the in-store analytics market"
Organizations have been gradually recognizing the importance of in-store analytics software, and have started deploying them, as per their needs and available resources. The adoption of in-store analytics software and services among large enterprises is high due to the voluminous data generation due to the widespread customer base. Large retailers need to correlate voluminous data with customer behavioral informaton exhibited across the stores to gain meaningful insights and help support revenue generation.
Key Topics Covered:
1 Introduction
2 Research Methodology
3 Executive Summary
4 Premium Insights
4.1 Attractive Market Opportunities in the In-Store Analytics Market
4.2 Market By Application (2018-2023)
4.3 Market By Organization Size (2018-2023)
4.4 Market Share Across Regions
5 Market Overview and Industry Trends
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers
5.2.1.1 Increased Competition From Ecommerce Players
5.2.1.2 Need for Better Customer Service and Enhanced Shopping Experience
5.2.1.3 Rising Data Volume Around In-Store Operations
5.2.2 Restraints
5.2.2.1 Data Security and Privacy Concerns Over New Advanced Technologies
5.2.2.2 Lack of Skilled Personnel
5.2.3 Opportunities
5.2.3.1 Advent of Cloud-Based Analytics
5.2.3.2 High Growth Potential in Emerging Economies
5.2.4 Challenges
5.2.4.1 Reluctance of Retailers
5.3 Industry Trends
5.3.1 Use Cases
5.3.1.1 Use Case 1: Increasing Profits By Leveraging Store Inventories
5.3.1.2 Use Case 2: Understanding Customer Behavior to Enhance Revenue and Profitability
5.3.1.3 Use Case 3: Tracking Engagement Metrics and Monitoring Customer Behavior in Real Time
5.3.2 Impact of AI and ML on the In-Store Analytics Market
5.3.3 In-Store Analytics Process
6 In-Store Analytics Market By Component
6.1 Introduction
6.2 Software
6.2.1 Need for Leveraging Distinct Data to Enhance Customer Retention and Store Profitability
6.3 Services
6.3.1 Professional Services
6.3.1.1 Support and Maintenance Services
6.3.1.1.1 Complexity of Operations and the Need for Regular Assistance During the Software Lifecycle to Foster the Growth of Support and Maintenance Services
6.3.1.2 Consulting Services
6.3.1.2.1 Need for A Strategic Outlook Exploring New Avenues for Improving Business Performance to Drive the Growth of Consulting Services
6.3.2 Managed Services
6.3.2.1 Need for Monitoring and Maintaining Software Operations and Reducing Overhead Costs
7 In-Store Analytics Market By Application
7.1 Introduction
7.2 Customer Management
7.2.1 Customer Footfall Analysis
7.2.1.1 Monitoring and Measuring Footfalls to Identify Various Sales Opportunities
7.2.2 Customer Behavioral Analysis
7.2.2.1 Understanding Customer Behavior to Discover Pain Points Affecting Customer Behavior
7.2.3 Customer Service
7.2.3.1 Assisting Customers in Enhancing Customer Experience and Improving Customer Retention
7.3 Marketing Management
7.3.1 Campaign Management
7.3.1.1 Improving Customer Experience Through Customized Campaigns
7.3.2 Loyalty Management
7.3.2.1 Initiating Loyalty Programs to Target the Mass Market
7.3.3 Cross-Sell and Upsell and Point of Sale
7.3.3.1 Generating Additional Revenues and Increasing Customer Lifetime Value
7.3.4 Market Basket Analysis
7.3.4.1 Identifying Correlation Among Products to Provide Real-Time Recommendations
7.4 Merchandising Analysis
7.4.1 Space Planning and Optimization
7.4.1.1 Optimizing Spaces to Improve Operational Efficiency
7.4.2 Product Category Analysis
7.4.2.1 Categorizing Products of Similar Nature and Attributes
7.4.3 Store Layout Analysis
7.4.3.1 Optimizing Store Layout for Maximum Utilization of Floor Space
7.5 Store Operations Management
7.5.1 Workforce Optimization
7.5.1.1 Scheduling Tasks and Utilizing Workforce to Improve the Overall Efficiency
7.5.2 Top-Performing Categories and Product Identification
7.5.2.1 Predicting Customer Demands and Top Performing Categories to Drive Sales and Profitability
7.5.3 Inventory Management
7.5.3.1 Managing Inventory to Identify Non-Performing Products and Prevent Out-Of-Stock Situations
7.6 Risk and Compliance Management
7.6.1 Fraud Detection
7.6.1.1 Real-Time Recognition of Suspicious Activities to Safeguard Confidential Information
7.7 Others
8 In-Store Analytics Market By Deployment Model
8.1 Introduction
8.2 Cloud
8.2.1 Improved Flexibility and Scalability to Drive the Growth of Cloud-Based In-Store Analytics Software
8.3 On-Premises
8.3.1 Data Security and Privacy Requirements to Remain Factors Dominating On-Premises In-Store Analytics Solutions
9 In-Store Analytics Market By Organization Size
9.1 Introduction
9.2 Small and Medium-Sized Enterprises
9.2.1 Demand for Analytics Software With Low Operational Costs
9.3 Large Enterprises
9.3.1 Need for Leveraging Voluminous Data to Stay Competitive
10 In-Store Analytics Market By Region
11 Competitive Landscape
11.1 Microquadrant Overview
11.2 Competitive Benchmarking
11.3 Market Ranking
12 Company Profiles
12.1 Introduction
12.2 RetailNext
12.3 Mindtree
12.4 Thinkinside
12.5 Happiest Minds
12.6 SAP
12.7 Celect
12.8 Capillary Technologies
12.9 Inpixon
12.10 Scanalytics
12.11 Retail Solutions
12.12 Dor Technologies
12.13 SEMSEYE
12.14 InvenSense
12.15 Walkbase
12.16 Amoobi
For more information about this report visit https://www.researchandmarkets.com/r/58tmei
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