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Development Trends of Facial Recognition in the Smart Retail Industry

  • ID: 4418489
  • Report
  • October 2017
  • Region: Global
  • 20 pages
  • Market Intelligence & Consulting Institute (MIC)
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  • Alibaba
  • Amazon
  • Criteo
  • Facebook
  • Google
  • IBM
  • MORE

The progress in innovation and technology is redefining the retail industry which has undergone continuous transformation. In 2016, Alibaba founder Jack Ma advocated the “New Retail” concept at the Yunqi Computing Conference. Given Alibaba's leading position in the retail industry, this concept has soon made a splash. According to AliResearch, New Retail is a data-driven, consumer-centric pan-retail business model. This means that data will play a key role in reshaping retailers' competitiveness. As an important measure to capture consumer identity and attribute information, facial recognition is expected to thrive in the retail industry. Smart retail will become a critical market for facial recognition companies.

List of Topics:

  • Development of facial recognition technology now and then
  • Analysis of the application of facial recognition in the retail industry, touching on how the technology can bring new opportunities by building a consumer-centric retail model and implementing the retargeting strategy
  • Analysis of how physical retailers can better compete with e-commerce retailors by reinforcing advantages and reducing disadvantages with facial recognition, touching on the concerns over the technology
Note: Product cover images may vary from those shown
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  • Alibaba
  • Amazon
  • Criteo
  • Facebook
  • Google
  • IBM
  • MORE

1. Introduction to Facial Recognition Technology

2. The Retail Industry has Strong Demand for Data while Transforming
2.1 Transformation Underway
2.2 Customer-centric Retail Model Needs Data Support

3. Demand for Data Brings New Opportunities for Facial Recognition
3.1 Winning Back Lost Consumers as the Start Point of a Consumer-centric Retail Model
3.1.1 Execution is the Key
3.2 Online Retailers Seize Potential Consumers with Retargeting (Data Analytics)
3.2.1 Big Data Analytics as a Major Means for Retailers to Retarget Consumers
3.3 Physical Retailers to Better Compete with E-commerce Retailors with More Precise Data via Facial Recognition
3.3.1 Facial Recognition Makes Consumer Identity and Attribute Information Available

4. Facial Recognition Reinforces Physical Retailers' Advantages and Reduce Disadvantages
4.1 Facial Recognition Enhances In-store Shopping Experience
4.1.1 Turn Shopping into an Experience
4.1.2 Facial Recognition Helps Collect Consumer Behavior Data and Optimize Shopping Experience
4.2 Facial Recognition Helps Physical Retailers Reduce Operation Costs
4.2.1 Data and Automation as Key to Effective Allocation and Reduction of Human Resources
4.2.2 Data and Logistics as Key to Warehouse Inventory Management

5. Mature Software and Hardware Systems Reduce Introduction Cost
5.1 Enhancement in PC Performance and Maturity of Recognition Technology Drives Facial Recognition towards Commercialization

6. Privacy a Stumbling Block to Facial Recognition
6.1 Winning the Hearts of Consumers with Practical Benefits to Reduce Objection to Facial Image Collection
6.2 Retailers' Brand Image Influences Consumers' Acceptance of Facial Recognition

7. Conclusion
7.1 Facial Recognition Helps Retailers Undergoing Transformation Create Competitiveness with Data
7.2 Competitiveness Comes from Solutions Capable of Reducing Consumers' Objection and Enhancing Data Effectiveness


Note: Product cover images may vary from those shown
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  • Alibaba
  • Amazon
  • Criteo
  • Facebook
  • Google
  • IBM
  • Konica Minolta
  • McKinsey
  • Polyvore
  • Shopzilla
Note: Product cover images may vary from those shown
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Primary research with a holistic, cross-domain approach

The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.

Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:


  • Component supplier interviews
  • System supplier interviews
  • User interviews
  • Channel interviews
  • IPO interviews
  • Focus groups
  • Consumer surveys
  • Production databases
  • Financial data
  • Custom databases


  • Technology forecasting and assessment
  • Product assessment and selection
  • Product life cycles
  • Added value analysis
  • Market trends
  • Scenario analysis
  • Competitor analysis