Designed to improve retail experience with intelligent Retail Analytics

Enhance your retail operations with SCIKIQ's intelligent retail analytics for strategic insights, optimize omnichannel strategy, and refine inventory management using real-time data. With AI-based fraud detection and supply chain optimization, ensure a seamless and secure shopping experience for your customers.

What is retail analytics?

Retail analytics is the practice of leveraging data analysis and technology to gain insights into retail operations, customer behavior, and market trends. It combines business strategies and technological expertise to collect, process, and analyze large volumes of data from various sources.

By applying advanced analytics techniques, such as predictive modeling, Data Discovery, real-time Analytics and more, Retail Analytics enables informed decision-making, optimization of processes, and improved customer experiences. It empowers both business and technology professionals to uncover actionable insights, enhance operational efficiency, drive innovation, and achieve business objectives.

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Top challenges faced by
the retail industry

As technology continues to evolve, consumer behavior does too — and retailers need to stay ahead of the curve. And using data is one way to make sure you stay ahead of trends and give customers products that solve their problems.

Multichannel Buying Experiences

Consumers expect a seamless experience across all channels, requiring retailers to have a strong online presence and a user-friendly e-commerce platform.


Retailers must compete with other players in a crowded market, both online and offline.

Changing Consumer Preferences

Retailers must constantly adapt to changing consumer preferences and trends

Supply Chain Management

Retail businesses rely on complex, global supply chains to source and distribute products.

Data Management

Retail businesses generate and process huge amounts of data from various sources, requiring advanced data management and analysis tools and skills to make sense of it.

Use Cases

Retail Analytics for Improved Operations & profitability

"Retail businesses today generate and process huge amounts of data from various sources, including point-of-sale systems, customer interactions, and supply chain operations. Managing and making sense of this data can be a daunting task, but it is essential for driving business growth and staying competitive. That's where SCIKIQ's Intelligent Data Management for Retail comes in.

  • Customer Analytics

    Focuses on understanding customer behavior and profitability through a 360-degree view, cohort analysis, and demand planning.

  • Sales Analytics

    Tracks the order lifecycle, SKU lifecycle, payment analytics, and manages return/cancellation, all crucial for optimizing sales processes.

  • Inventory Management

    Involves optimization, assortment planning, and seller analytics, aiming to maintain the right stock levels and ensure efficient warehouse operations.

  • Product

    Includes profitability, promotion effectiveness, and market basket analysis, important for devising effective marketing strategies and understanding product performance

  • Operational Intelligence

    Retailers can use data analytics to monitor and optimize key performance indicators, such as sales, customer satisfaction, and efficiency. This can help retailers make more informed, data-driven decisions about their operations.

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