2018零售技术预览(英文版)-2mb
报告摘要
2018 Retail Technology Preview Summary
Core Content
The 2018 Retail TouchPoints Technology Preview presents insights from 17 retail executives on the most significant technology trends impacting the industry. The report categorizes these insights into six key areas: Data/Analytics, Digital Innovation, Store Experience, Inventory Management, Marketing, and Payment/POS/Security. The main focus is on personalization, location-based services, and predictive analytics as drivers of growth and customer engagement.
Main Predictions and Key Points
Data/Analytics
1. Cross-Channel Purchase Data Makes Personalization More Relevant
- Predictor: Silvia Lacayo, 1010data
- Key Insight: Personalization based on actual purchase behavior across channels will be a key differentiator in 2018.
- Why It Matters:
- Personalization initiatives can drive revenue growth of 6% to 10%.
- Combining CRM, transactional, promotional, and external data (e.g., weather, location) enables more sophisticated personalization models.
- Retailers need scalable analytics systems to process large volumes of data and act on insights quickly.
- Challenges:
- Data integration and fostering an enterprise-wide analytics culture are major hurdles.
- Cross-channel data expansion from 2017 (e.g., Amazon and Walmart's omnichannel moves) will help retailers refine their personalization strategies.
2. Relevance and Personalization Drive Location-Based Services Adoption
- Predictor: Michael Brewer, Aruba
- Key Insight: Location-based services will become critical in enhancing customer service and in-store experiences.
- Why It Matters:
- Indoor navigation and associate location sharing improve customer engagement and reduce frustration.
- Asset tracking for high-value items (e.g., carts, POS devices) allows for faster assistance and inventory management.
- Analytics from location-based services can help identify product shortages and optimize in-store experiences.
- Benefits:
- Enables real-time, personalized service.
- Supports "test and learn" strategies by providing actionable insights.
3. Data Science and Big Data Are Changing the Retail Space
- Predictor: Haowen Chan, Kibo
- Key Insight: Data is central to the evolution of retail experiences and strategies.
- Why It Matters:
- Modern systems are becoming more intelligent, allowing for more nuanced engagement.
- Retailers must integrate data from multiple sources (e.g., e-commerce, in-store, social media) to deliver personalized journeys.
- Data integration challenges include siloed systems and the need for a unified data source.
- Challenges:
- Smaller retailers may lack the infrastructure to collect and leverage complex data.
- Customization and flexibility of data tools are essential for deriving value from data.
Digital Innovation
4. Putting the Consumer [Data] Back in the Consumer Experience
- Predictor: Rob Garf, Salesforce Commerce Cloud
- Key Insight: Consumer data should be at the core of all retail strategies.
- Why It Matters:
- AI and machine learning can drive personalized marketing and improve conversion rates.
- A unified consumer profile allows teams to collaborate on a seamless experience.
- Example: Stonewall Kitchen used AI to boost customer purchases by 14%.
- Call to Action:
- Retailers must prioritize data collection, cleaning, and integration.
- Clean, actionable data is not a one-time effort but an ongoing process.
5. Accessible and Actionable Data Is a Retailer's Superpower
- Predictor: Rob Garf, Salesforce Commerce Cloud
- Key Insight: Retailers must use data to improve both consumer and employee experiences.
- Why It Matters:
- Data-driven insights enable better staffing and operational decisions.
- AI can help understand customer behavior and preferences.
- Call to Action:
- Retailers should invest in systems that allow for real-time data access and analysis.
- Collaboration across departments is essential for creating unified experiences.
Inventory Management
6. How Retailers Can Use Predictive Analytics to Grow the Bottom Line
- Predictor: Mike Zorn, Workjam
- Key Insight: Predictive analytics can improve workforce efficiency and sales performance.
- Why It Matters:
- Retailers can use predictive models to forecast staffing needs and optimize operations.
- Example: Predictive analytics can identify unreliable employees and suggest replacements for busy shifts.
- Benefits:
- Reduces costs and increases productivity.
- Helps retailers anticipate workplace needs and enhance customer service.
Marketing
7. How Retailers Can Use Predictive Analytics to Grow the Bottom Line
- Predictor: Mike Zorn, Workjam
- Key Insight: Predictive analytics can improve marketing and sales strategies.
- Why It Matters:
- Enables smarter, more agile business decisions.
- Supports the development of a "test and learn" culture.
- Benefits:
- Enhances customer experience and loyalty.
- Increases sales potential and business growth.
Payment/POS/Security
8. As Channels Blur, Omni-Device Will Be the Biggest Trend of 2018
- Predictor: Matt Rhodus, Oracle NetSuite
- Key Insight: Omni-device experiences will be the key to future retail success.
- Why It Matters:
- Consumers expect seamless, personalized shopping across all devices.
- Unified data sources (customer, inventory, order) are essential for delivering consistent experiences.
- Shared shopping cart functionality helps customers make informed decisions.
- Benefits:
- Enhances the role of physical stores as places of expertise and testing.
- Enables a more holistic approach to retail engagement.
Conclusion
The 2018 Retail Technology Preview highlights that personalization, location-based services, and predictive analytics are driving major changes in the retail industry. Retailers who invest in data integration, AI capabilities, and omni-device strategies will be best positioned to meet evolving consumer expectations and gain a competitive edge. The report underscores that data is the foundation of all these innovations, and that a unified, agile data strategy is essential for success in the coming year.
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