Staying ahead of the technology curve means strengthening your competitive advantage. That is why we give you data-driven innovation insights into the retail industry. This time, you get to discover 5 hand-picked machine learning solutions impacting retail.
Out of 1 634, the Global Startup Heat Map highlights 5 Top Machine Learning Solutions impacting Retail
The insights of this data-driven analysis are derived from the Big Data & Artificial Intelligence (AI)-powered StartUs Insights Discovery Platform, covering 2 093 000+ startups & scaleups globally. The platform gives you an exhaustive overview of emerging technologies & relevant startups within a specific field in just a few clicks.
The Global Startup Heat Map below reveals the distribution of the 1 634 exemplary startups & scaleups we analyzed for this research. Further, it highlights 5 retail startups that we hand-picked based on criteria such as founding year, location, funding raised, and more. You get to explore the solutions of these 5 startups & scaleups in this report. For insights on the other 1 629 machine learning solutions impacting retail, get in touch.
Cameralyze advances Store Monitoring
Founding Year: 2019
Location: Istanbul, Turkey
Partner for Smart Retail Cameras
Turkish startup Cameralyze develops smart cameras for supermarkets and groceries. The startup combines machine learning technology with computer vision to transform the existing camera into smart devices. A startup’s analytical platform uses intelligent algorithms to provide real-time and predictive insights from the scanned data. In addition to that, the startup offers people counting and queue optimization solutions for retail businesses. The solutions collect customer footfall data to optimize customer waiting time, provide real-time occupancy and visitor analysis. This enables businesses to increase revenue, improve customer satisfaction and make data-driven decisions.
Outloud automates Frontline Customer Interaction
Founding Year: 2020
Location: California, US
Reach out for Frontline Intelligence
US-based startup Outloud automates frontline customer interaction. Its frontline intelligence platform uses voice recognition algorithms to understand conversations between customers and company personnel. This allows offline retailers to capture business conversations and provides insights and smart recommendations to improve customer service. Moreover, it enables retailers to track service-related key performance indicators (KPIs). The startup’s solution improves store revenues by providing smart upsell recommendations and bridging operational gaps.
Trigo builds Smart Check-out Systems
Founding Year: 2018
Location: Tel Aviv, Israel
Collaborate for Grocery Shopping Automation
Israeli start-up Trigo builds smart check-out systems. The startup uses computer vision and high-density neural networks to automate the digital infrastructure of the retail industry. The customers scan the startup’s application to check in to the grocery store. Further, the algorithms understand customer shopping patterns with their hand movements and utilize the fed store data to update their shopping lists on the go. The application allows users to shop hands-free and checkout automatically without waiting in the queue. The startup’s solution enables retailers to streamline retail operations, prevent shoplifting, and engage more customers.
Allparel develops Fashion Search Engines
Founding Year: 2019
Location: Boston, US
Work with Allparel for Fashion Brand Visibility
US-based startup Allparel develops a fashion search engine. It uses image recognition algorithms to enable image-based searching for clothes. This allows users to search apparel based on their pictures or descriptions. It offers available options on the internet, even if the keyword is missing in the product description. For retailers, the startup offers a way to increase their brand visibility and have a better reach.
Increasingly enables Cross-Selling
Founding Year: 2016
Location: London, UK
Partner for Customer Analytics
UK-based startup Increasingly enables cross-selling across eCommerce websites. The startup uses pattern recognition and other machine learning models to understand customer behavior digitally and personalize its shopping feed. Increasingly algorithms enable dynamic pricing and optimized discounting to incentivize the customer for repeat purchases. The startup uses past purchase data and customer buying patterns to provide personalized product recommendations to the customer. The technology allows e-retailers to bundle the product combinations and suggest the most relevant product for purchase. This enables companies to cross-sell the products and increase basket revenue while improving the customer experience.
Discover more Retail Startups
Retail startups such as the examples highlighted in this report focus on inventory management, store analytics, and customer engagement. While all of these technologies play a major role in advancing the retail industry, they only represent the tip of the iceberg. To explore retail technologies in more detail, simply let us look into your areas of interest. For a more general overview, download our free Retail Innovation Report to save your time and improve strategic decision-making.
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