The Customer Analytics in E-commerce Market size was valued at USD 11.78 billion in 2024 and is projected to reach USD 31.46 billion by 2032, growing at a CAGR of 13.1% from 2025 to 2032.
The customer analytics in e-commerce market is witnessing rapid growth over the last few years as businesses are now more focused on data-driven strategies to improve customer engagement, retention, and sales. Analytics tools allow e-commerce platforms and retailers to analyse consumer actions across the channel to assist with personalised marketing and optimal experiences. AI, machine learning big data are only amplifying these capabilities. Insights from websites, social media, feedback, and purchase history are leveraged to improve decision-making at organisations. Companies like Microsoft, Google, and Salesforce are pouring big money into the movement for omnichannel strategies and focusing on customer lifetime value.
According to research, e-commerce businesses using predictive analytics reduce cart abandonment by 25%, while personalised recommendations drive 31% of revenue. Over 60% of retailers see ROI in 12 months, and real-time analytics boost conversions by 21%.
The U.S Customer Analytics in E-commerce Market was valued at USD 3.17 billion in 2024 and is projected to reach USD 7.94 billion by 2032 with a CAGR of 13.99% during the forecast period of 2025-2032.
The superiority of the digital infrastructure, higher adoption of AI and big data, and tech giants such as Amazon, Google, and Microsoft have spawned a strong growth in the U.S. Innovation and early adoption of these analytics tools are the forces that have shifted the US market to customer-centric e-commerce strategies. The upswing of online retail, rising need for personalised shopping experiences, rapid cloud adoption across industries, and increasing investments in predictive and prescriptive analytics to enhance consumer engagement and business outcomes are some of the high-impact rendering factors that drive market growth.
Drivers:
Growing Demand for Personalised Customer Experiences Across E-commerce Channels Drives the Adoption of Data Analytics Solutions.
The need for hyper-personalised shopping experiences is the primary reason behind the mass adoption of customer analytics tools. E-commerce companies are using advanced analytics to analyse consumer behaviour, preferences, and purchase history to provide personalised product recommendations, promotions, and content. The personalised strategy goes a long way to make the consumers more content and improve conversion rates. Innovations such as the incorporation of real-time analytics and AI-based engines into e-commerce systems empower brands to serve dynamic content and product assortments in direct response to user activities.
Restraints:
Data Privacy Regulations and Consumer Concerns Over Personal Information Restrict Wider Adoption of Analytics Solutions
The major constraint restraining the market is rising concerns regarding data privacy and the high complexity of compliance with cross-border data protection regulations. Policies like GDPR in Europe and CCPA in California enforce complex rules about how customer data can be collected, stored, and processed. But these rules can restrict the breadth of data analytics, particularly for multi-jurisdictional companies. The same applies to consumers, who are now mindful of the issue of the usage of their personal data, and thus, the consent-driven data practices have become a top priority.
Opportunities:
Surging Integration of Artificial Intelligence and Machine Learning Enhances Predictive Capabilities in Customer Engagement Strategies
The more AI and ML technologies are embedded into existing analytics platforms, the greater the opportunity for e-commerce players. These technologies allow businesses to forecast customer actions using predictive modelling, automate targeting, and strategies to optimize marketing in the moment. This innovation with regards to customer engagement is pushing the e-commerce stage towards making quick, information-driven decisions and hence making AI a position in the analytics space as the next helping hand.
Challenges:
Lack of Skilled Data Professionals Limits the Effective Implementation of Advanced Analytics Solutions Across E-commerce Businesses.
The first hurdle to jump over when it comes to doing customer analytics is the scarcity of analytical data professionals who know how to read complex datasets and return actionable insights. Most e-commerce businesses, particularly the SMEs, have an difficult task in terms of hiring and retaining skilled data scientists, analysts, and engineers. The talent gap due to the potential of the different analytics tools is at a limited scale due to the hurdles in the integration of such tools. With the increasing requirement of real-time analytics, there lies the need for a technically sound workforce.
By Type
Predictive analytics holds the leading position with a revenue share of 38.10% in 2024, due to its capacity to predict customer behaviour, resulting in better accuracy for marketers. This definition increasingly plays an important role in the anticipation of customer analytics in e-commerce market trends, personalised campaigns delivery, and the churn problem on e-channel stores. Salesforce, amongst others, has made its Einstein Analytics smarter to provide actionable consumer behaviour data, and Google’s AI tool looks set to deliver in the retail forecast part of the market.
Prescriptive analytics is expected to grow at the fastest CAGR rate of 16.07%, due to its ability to recommend real-time actions based on data insights. This allows e-commerce companies to automate the decisions they make about price, inventory, and promotions. Examples of the ongoing investment in this area can be seen in SAP's announcement on upgrades to its Business Technology Platform, and IBM on their Watson-based prescriptive tools.
By End-User
Retailers hold the largest customer analytics in e-commerce market share of 41.46% in 2024, as they are the largest adopters of analytics for customer acquisition, retention, and satisfaction. Profile gaze Adobes & Oracles & Big Data in futile attempts to add some industry-specific analytics solutions for retail, focused on improved personalisation and supply chain synergy offerings. The ultimate driver is the increasing standard for retailers of all sizes to engage differently with consumers and to run their operations smarter in a crowded market.
E-commerce platforms are expanding rapidly at a 15.84% CAGR, owing to their amalgamated nature and increasing number of users. Shopify and BigCommerce have also rolled out more sophisticated analytics dashboards to merchants so they can get a clearer picture of what their consumers are doing and how they are spending. The segment growth is also attributed to Adobe Commerce Cloud's AI-driven tools. The key driver is the increase of small-to-medium sellers using full-stack platforms that include customer analytics tools to increase performance and enhance the end-user experience.
By Deployment Model
Cloud-based deployment leads with a 60.17% market share, driven by its scalability, minimal infrastructure expenditure, flexibility, and ease of access to data insights on multiple devices. Leading platforms such as Microsoft Azure, Google Cloud, and AWS have unveiled customized solutions to address the e-commerce analytics requirements. For instance, Google Cloud, which is transforming the way we provide customer insight with its retail AI solutions, or Salesforce's cloud-native analytics solution, which is building superior, seamless experiences.
According to research, approximately 90% of large e-commerce enterprises prefer cloud-based analytics platforms, whereas only 58% of small to medium-sized businesses have adopted the same approach.
The hybrid model is growing at 16.16 % CAGR, which gives a combination of security and flexibility of the cloud. IBM and Oracle have developed hybrid cloud analytics solutions that are intended to cater to the needs of enterprises with customer data that they consider sensitive. Customer analytics in e-commerce market companies like IBM Hybrid Cloud with AI and Oracle Analytics Cloud are key developments in this area. Demand for this trend is driven by the manageability or portability of data and the desire to maintain control with customizable data governance and compliance, while utilizing cloud-based AI/ML capabilities for advanced analytics and customer engagement.
By Data Source
Website analytics hold the largest market share of 36.39%, the basic component to analyze customer activities, bounce rates, and conversions. This category includes tools at the heart of your marketing and engagement strategy, such as Google Analytics 4 and Adobe Experience Platform. The need is the driver to collect precise customer journey data and insights from that data to convert it into USABLE insights to improve the website performance and sales.
The social media analytics segment is expanding at a 16.15% CAGR. E-commerce companies are gaining insights about campaign effectiveness and sentiment analysis through platforms like Sprout Social, Hootsuite, and Meta's Business Suite. Another recent development is the integration of Salesforce with large social platforms. This highlights the rising relevance of social media as a near real-time, high-resolution, high-value e-commerce customer touch point and voice of the customer analytics channel.
The North America region will continue to dominate the market with a share of 38.32% owing to the advanced digital infrastructure, increased adoption of artificial intelligence and big data technologies, and high Internet penetration in developed countries. It hosts analytics solution providers as well as global e-commerce giants, leading the continuous innovation of customer experience and personalization efforts.
The U.S. leads the regional market, which is dominated by tech giants such as Amazon, Google, and Salesforce, as well as wider adoption of e-commerce and investment in analytics technologies.
Europe experienced significant customer analytics in e-commerce market growth, which is underpinned by stringent regulations related to data protection that further drive the use of analytics in a responsible and transparent then use those in a responsible way. This has driven the adoption of analytics in e-commerce surroundings in this region, due to the omnichannel retailing growth and escalating need for a customised customer experience.
Germany is the leader in the region due to its solid retail customer analytics in e-commerce market industry, widespread IT infrastructure, and consistent adaptation of a digital transformation policy both in B2B and B2C e-commerce markets.
The Asia-Pacific is projected to increase by the fastest CAGR of 16.67% during the forecast period, owing to the rapid growth of the online consumer base, strong mobile-first economies, and an increased focus on digital transformation initiatives in many emerging markets. Everyone from Governments to businesses is pouring money into AI, Cloud, and Analytics to understand consumer behaviour.
China dominates this region, driven by e-commerce behemoths such as Alibaba and JD. dot com movement, and a massive scale of e-commerce functionality sustained by an advanced data analytics ecosystem.
The growing demand in the Middle East & Africa and Latin America for digital space, mobile commerce, and rising e-commerce activity in the region coupled with the high adoption of customer analytics in sectors such as retail, fashion, and electronics to gain insights on changing consumer behaviour are some of the factors driving the adoption of customer analytics.
The major key players in the customer analytics in e-commerce market are Microsoft, Google, Listrak, Tableau, CleverTap, Amazon, Bluecore, IBM, Salesforce, Adobe, and others.
In April 2025, moving to agentic AI with "Agentforce" to automate customer service tasks & personalising experiences, doubling down on e-commerce AI leadership.
In April 2025, retail technology provider Bluecore acquired the AI shopping assistant Alby to bolster its conversational AI capabilities to enable users to drive deeper customer engagement across e-commerce platforms and deliver predictive shopping experiences for customers at scale.
| Report Attributes | Details |
|---|---|
| Market Size in 2024 | USD 11.78 Billion |
| Market Size by 2032 | USD 31.46 Billion |
| CAGR | CAGR of 13.1% From 2025 to 2032 |
| Base Year | 2024 |
| Forecast Period | 2025-2032 |
| Historical Data | 2021-2023 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | •By Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Diagnostic Analytics) •By End User (Retailers, Consumer Goods, Marketing Agencies, E-commerce Platforms) •By Deployment Model (Cloud-Based, On-Premises, Hybrid) •By Data Source (Website Analytics, Social Media Analytics, Customer Feedback, Sales Data) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Germany, France, UK, Italy, Spain, Poland, Turkey, Rest of Europe), Asia Pacific (China, India, Japan, South Korea, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (UAE, Saudi Arabia, Qatar, South Africa, Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America) |
| Company Profiles | Microsoft, Google, Listrak, Tableau, CleverTap, Amazon, Bluecore, IBM, Salesforce, Adobe, and others. |
Ans: North America dominated the market in 2024 with a 38.32% share due to advanced digital infrastructure and widespread AI and big data adoption.
Ans: Predictive analytics dominated the market with a 38.10% share in 2024, driven by its ability to forecast customer behaviour and enhance personalised marketing.
Ans: The major growth factor is the growing demand for personalised customer experiences across e-commerce channels, driving widespread adoption of analytics tools.
Ans: The market size of the Customer Analytics in E-commerce Market was valued at USD 11.78 billion in 2024.
Ans: The Customer Analytics in E-commerce Market is expected to grow at a CAGR of 13.1% from 2025 to 2032.
Table Of Content
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Approach
3.2 Bottom-up Approach
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 Customer Retention & Engagement Impact
5.2 Data Volume & Source Metrics
5.3 Consumer Behavior Insights
5.4 Talent & Workforce Data
6. Competitive Landscape
6.1 List of Major Companies By Region
6.2 Market Share Analysis By Region
6.3 Product Benchmarking
6.3.1 Product specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and Supply Chain Strategies
6.4.3 Expansion plans and new product launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. Customer Analytics in E-commerce Market Segmentation By Type
7.1 Chapter Overview
7.2 Descriptive Analytics
7.2.1 Descriptive Analytics Market Trends Analysis (2020-2032)
7.2.2 Descriptive Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
7.3 Predictive Analytics
7.3.1 Predictive Analytics Market Trends Analysis (2020-2032)
7.3.2 Predictive Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
7.4 Prescriptive Analytics
7.4.1 Prescriptive Analytics Market Trends Analysis (2020-2032)
7.4.2 Prescriptive Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
7.5 Diagnostic Analytics
7.5.1 Diagnostic Analytics Market Trends Analysis (2020-2032)
7.5.2 Diagnostic Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
8. Customer Analytics in E-commerce Market Segmentation By Data Source
8.1 Chapter Overview
8.2 Website Analytics
8.2.1 Website Analytics Market Trends Analysis (2020-2032)
8.2.2 Website Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
8.3 Social Media Analytics
8.3.1 Social Media Analytics Market Trends Analysis (2020-2032)
8.3.2 Social Media Analytics Market Size Estimates and Forecasts to 2032 (USD Billion)
8.4 Customer Feedback
8.4.1 Customer Feedback Market Trends Analysis (2020-2032)
8.4.2 Customer Feedback Market Size Estimates and Forecasts to 2032 (USD Billion)
8.5 Sales Data
8.5.1 Sales Data Market Trends Analysis (2020-2032)
8.5.2 Sales Data Market Size Estimates and Forecasts To 2032 (USD Billion)
9. Customer Analytics in E-commerce Market Segmentation By Deployment Model
9.1 Chapter Overview
9.2 Cloud-Based
9.2.1 Cloud-Based Market Trends Analysis (2020-2032)
9.2.2 Cloud-Based Market Size Estimates and Forecasts to 2032 (USD Billion)
9.3 On-Premises
9.3.1 On-Premises Market Trends Analysis (2020-2032)
9.3.2 On-Premises Market Size Estimates and Forecasts to 2032 (USD Billion)
9.3 Hybrid
9.3.1 Hybrid Market Trends Analysis (2020-2032)
9.3.2 Hybrid Market Size Estimates and Forecasts to 2032 (USD Billion)
10. Customer Analytics in E-commerce Market Segmentation By End User
10.1 Chapter Overview
10.2 Retailers
10.2.1 Retailers Market Trends Analysis (2020-2032)
10.2.2 Retailers Market Size Estimates and Forecasts to 2032 (USD Billion)
10.3 Consumer Goods
10.3.1 Consumer Goods Market Trend Analysis (2020-2032)
10.3.2 Consumer Goods Market Size Estimates and Forecasts to 2032 (USD Billion)
10.4 Marketing Agencies
10.4.1 Marketing Agencies Market Trends Analysis (2020-2032)
10.4.2 Marketing Agencies Market Size Estimates and Forecasts to 2032 (USD Billion)
10.5 E-commerce Platforms
10.5.1 E-commerce Platforms Market Trends Analysis (2020-2032)
10.5.2 E-commerce Platforms Market Size Estimates and Forecasts to 2032 (USD Billion)
11. Regional Analysis
11.1 Chapter Overview
11.2 North America
11.2.1 Trend Analysis
11.2.2 North America Customer Analytics in E-commerce Market Estimates and Forecasts by Country (2020-2032) (USD Billion)
11.2.3 North America Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.2.4 North America Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.2.5 North America Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.2.6 North America Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.2.7 USA
11.2.7.1 USA Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.2.7.2 USA Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.2.7.3 USA Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.2.7.4 USA Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.2.8 Canada
11.2.8.1 Canada Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.2.8.2 Canada Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.2.8.3 Canada Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.2.8.4 Canada Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.2.9 Mexico
11.2.9.1 Mexico Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.2.9.2 Mexico Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.2.9.3 Mexico Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.2.9.4 Mexico Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3 Europe
11.3.1 Trend Analysis
11.3.2 Europe Customer Analytics in E-commerce Market Estimates and Forecasts by Country (2020-2032) (USD Billion)
11.3.3 Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.4 Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.5 Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.6 Europe Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.7 Germany
11.3.7.1 Germany Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.7.2 Germany Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.7.3 Germany Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.7.4 Germany Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.8 France
11.3.8.1 France Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.8.2 France Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.8.3 France Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.8.4 France Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.9 UK
11.3.9.1 UK Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.9.2 UK Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.9.3 UK Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.9.4 UK Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.10 Italy
11.3.10.1 ItalyCustomer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.10.2 Italy Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.10.3 Italy Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.10.4 Italy Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.11 Spain
11.3.11.1 Spain Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.11.2 Spain Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.11.3 Spain Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.11.4 Spain Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.12 Poland
11.3.12.1 Poland Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.12.2 Poland Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.12.3 Poland Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.12.4 Poland Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.13 Turkey
11.3.13.1 Turkey Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.13.2 Turkey Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.13.3 Turkey Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.13.4 Turkey Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.3.14 Rest of Europe
11.3.14.1 Rest of Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.3.14.2 Rest of Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.3.14.3 Rest of Europe Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.3.14.4 Rest of Europe Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4 Asia Pacific
11.4.1 Trend Analysis
11.4.2 Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts by Country (2020-2032) (USD Billion)
11.4.3 Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.4 Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.5 Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.6 Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.7 China
11.4.7.1 China Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.7.2 China Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.7.3 China Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.7.4 China Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.8 India
11.4.8.1 India Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.8.2 India Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.8.3 India Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.8.4 India Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.9 Japan
11.4.9.1 Japan Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.9.2 Japan Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.9.3 Japan Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.9.4 Japan Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.10 South Korea
11.4.10.1 South Korea Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.10.2 South Korea Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.10.3 South Korea Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.10.4 South Korea Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.11 Singapore
11.4.11.1 Singapore Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.11.2 Singapore Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.11.3 Singapore Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.11.4 Singapore Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.12 Australia
11.4.12.1 Australia Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.12.2 Australia Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.12.3 Australia Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.12.4 Australia Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.4.13 Rest of Asia Pacific
11.4.13.1 Rest of Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.4.13.2 Rest of Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.4.13.3 Rest of Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.4.13.4 Rest of Asia Pacific Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5 Middle East and Africa
11.5.1 Trend Analysis
11.5.2 Middle East and Africa Customer Analytics in E-commerce Market Estimates and Forecasts by Country (2020-2032) (USD Billion)
11.5.3 Middle East and Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.4 Middle East and Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.5 Middle East and Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.6 Middle East and Africa Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5.7 UAE
11.5.7.1 UAE Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.7.2 UAE Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.7.3 UAE Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.7.4 UAE Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5.8 Saudi Arabia
11.5.8.1 Saudi Arabia Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.8.2 Saudi Arabia Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.8.3 Saudi Arabia Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.8.4 Saudi Arabia Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5.1.9 Qatar
11.5.9.1 Qatar Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.9.2 Qatar Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.9.3 Qatar Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.1.9.4 Qatar Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5.10 South Africa
11.5.10.1 South Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.10.2 South Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.10.3 South Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.10.4 South Africa Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.5.11 Rest of Middle East & Africa
11.5.11.1 Rest of Middle East & Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.5.11.2 Rest of Middle East & Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.5.11.3 Rest of Middle East & Africa Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.5.11.4 Rest of Middle East & Africa Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.6 Latin America
11.6.1 Trend Analysis
11.6.2 Latin America Customer Analytics in E-commerce Market Estimates and Forecasts by Country (2020-2032) (USD Billion)
11.6.3 Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.6.4 Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.6.5 Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.6.6 Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.6.7 Brazil
11.6.7.1 Brazil Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.6.7.2 Brazil Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.6.7.3 Brazil Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.6.7.4 Brazil Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.6.8 Argentina
11.6.8.1 Argentina Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.6.8.2 Argentina Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.6.8.3 Argentina Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.6.8.4 Argentina Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
11.6.9 Rest of Latin America
11.6.9.1 Rest of Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Type (2020-2032) (USD Billion)
11.6.9.2 Rest of Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Data Source (2020-2032) (USD Billion)
11.6.9.3 Rest of Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By Deployment Model (2020-2032) (USD Billion)
11.6.9.4 Rest of Latin America Customer Analytics in E-commerce Market Estimates and Forecasts By End User (2020-2032) (USD Billion)
12. Company Profiles
12.1 MicGoogle
12.1.1 Company Overview
12.1.2 Financial
12.1.3 Products/ Services Offered
12.1.4 SWOT Analysis
12.2 Listrak
12.2.1 Company Overview
12.2.2 Financial
12.2.3 Products/ Services Offered
12.2.4 SWOT Analysis
12.3 Tableau
12.3.1 Company Overview
12.3.2 Financial
12.3.3 Products/ Services Offered
12.3.4 SWOT Analysis
12.4 CleverTap
12.4.1 Company Overview
12.4.2 Financial
12.4.3 Products/ Services Offered
12.4.4 SWOT Analysis
12.5 Amazon
12.5.1 Company Overview
12.5.2 Financial
12.5.3 Products/ Services Offered
12.5.4 SWOT Analysis
12.6 Bluecore
12.6.1 Company Overview
12.6.2 Financial
12.6.3 Products/ Services Offered
12.6.4 SWOT Analysis
12.7 IBM
12.7.1 Company Overview
12.7.2 Financial
12.7.3 Products/ Services Offered
12.7.4 SWOT Analysis
12.8 Salesforce
12.8.1 Company Overview
12.8.2 Financial
12.8.3 Products/ Services Offered
12.8.4 SWOT Analysis
12.9 Adobe
12.9.1 Company Overview
12.9.2 Financial
12.9.3 Products/ Services Offered
12.9.4 SWOT Analysis
12.10 Oracle
12.10.1 Company Overview
12.10.2 Financial
12.10.3 Products/ Services Offered
12.10.4 SWOT Analysis
13. Use Cases and Best Practices
14. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
Key Segments:
By Type
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
Diagnostic Analytics
By End User
Retailers
Consumer Goods
Marketing Agencies
E-commerce Platforms
By Deployment Model
Cloud-Based
On-Premises
Hybrid
By Data Source
Website Analytics
Social Media Analytics
Customer Feedback
Sales Data
Request for Segment Customization as per your Business Requirement: Segment Customization Request
Regional Coverage:
North America
US
Canada
Mexico
Europe
Germany
France
UK
Italy
Spain
Poland
Turkey
Rest of Europe
Asia Pacific
China
India
Japan
South Korea
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
UAE
Saudi Arabia
Qatar
South Africa
Rest of Middle East & Africa
Latin America
Brazil
Argentina
Rest of Latin America
Request for Country Level Research Report: Country Level Customization Request
Available Customization
With the given market data, SNS Insider offers customization as per the company’s specific needs. The following customization options are available for the report:
Detailed Volume Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Competitive Product Benchmarking
Geographic Analysis
Additional countries in any of the regions
Customized Data Representation
Detailed analysis and profiling of additional market players
