The global virtual shopping assistant market size was valued at USD 2.63 billion in 2024 and is projected to reach USD 18.77 billion by 2033, growing at a CAGR of 25.8% from 2025 to 2033. The virtual shopping assistant market is being propelled by the global shift toward mobile-first consumer behavior. The growing demand for personalized shopping experiences is driving the virtual shopping assistant market growth. Consumers expect retailers to understand their preferences, anticipate their needs, and deliver tailored product recommendations. Virtual shopping assistants use artificial intelligence, natural language processing, and machine learning to analyze user behavior, purchase history, and contextual data to offer curated suggestions and relevant offers. This high level of personalization not only improves the user experience but also increases the likelihood of conversion, making these tools valuable assets for retailers aiming to boost engagement and revenue.
The rise of omnichannel retail strategies is also fueling demand for virtual shopping assistants. As brands seek to provide a seamless experience across online and offline touchpoints, virtual shopping assistants serve as the bridge that connects the two. For example, customers can use their phones to scan products in-store for additional information, check inventory availability, or even complete purchases without waiting in line. This integration of digital capabilities into the physical shopping journey helps retailers streamline operations, reduce friction, and increase convenience, thereby improving customer retention and loyalty.
Advancements in voice recognition and conversational AI are further expanding the scope of virtual shopping assistants. Voice-enabled shopping assistants allow users to search for products, compare prices, and place orders through simple voice commands. This hands-free interaction is particularly appealing in scenarios such as driving, multitasking, or assisting visually impaired users. As voice technology continues to improve in accuracy and natural language understanding, it will drive broader adoption of virtual shopping assistants across different demographic groups and usage contexts.
Moreover, the growing adoption of mobile payment systems and digital wallets drives the virtual shopping assistant market growth. As payment technologies such as Apple Pay, Google Pay, and region-specific apps like Paytm or Alipay become widely accepted, virtual shopping assistants are increasingly integrated with these platforms to offer a seamless end-to-end shopping experience. This integration reduces the friction of switching between apps or entering payment information manually, which boosts user satisfaction and increases transaction completion rates. The convenience of having product discovery, decision support, and payment all within one mobile interface reinforces the value proposition of virtual shopping assistants.
Furthermore, the increasing role of data analytics and predictive intelligence in retail is also accelerating the adoption of virtual shopping assistants. These assistants are being designed not only to respond to customer queries but also to proactively suggest products based on browsing history, location, time of day, weather conditions, or seasonal demand. Retailers are leveraging these data-driven insights to anticipate customer needs and deliver highly contextual, timely interactions that can influence purchase decisions. As the volume of consumer data continues to grow, virtual shopping assistants are becoming more intelligent and intuitive, transforming from reactive tools into proactive, personalized shopping companions.
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Key Virtual Shopping Assistant Company Insights
Some of the key companies operating in the market include Amazon Rufus, and AliRadar among others are some of the leading players in the virtual shopping assistant market.
- Amazon Rufus is a generative AI-powered shopping assistant embedded directly into its mobile shopping app and desktop site. Designed as a conversational interface to help customers research, compare, and discover products, Rufus is trained on Amazon’s entire product catalog, customer reviews, Q&A entries, and select public web data. Its internal language model leverages retrieval-augmented generation (RAG), reinforcement learning from user feedback, and optimizations for low-latency responses to deliver contextual and assistive answers throughout the shopper’s experience.
Research Methodology
We employ a comprehensive and iterative research methodology focused on minimizing deviance in order to provide the most accurate estimates and forecasts possible. We utilize a combination of bottom-up and top-down approaches for segmenting and estimating quantitative aspects of the market. Data is continuously filtered to ensure that only validated and authenticated sources are considered. In addition, data is also mined from a host of reports in our repository, as well as a number of reputed paid databases. Our market estimates and forecasts are derived through simulation models. A unique model is created and customized for each study. Gathered information for market dynamics, technology landscape, application development, and pricing trends are fed into the model and analyzed simultaneously.
About Grand View Research
Grand View Research provides syndicated as well as customized research reports and consulting services on 46 industries across 25 major countries worldwide. This U.S. based market research and consulting company is registered in California and headquartered in San Francisco. Comprising over 425 analysts and consultants, the company adds 1200+ market research reports to its extensive database each year. Supported by an interactive market intelligence platform, the team at Grand View Research guides Fortune 500 companies and prominent academic institutes in comprehending the global and regional business environment and carefully identifying future opportunities.
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