Retail and ecommerce businesses across the globe are increasingly investing in virtual shopping assistants, conversational AI solutions designed to provide personalized product recommendations and real-time support to customers. This is reflected by the current and projected state of the global virtual shopping assistant market, which is expected to grow at a CAGR of 25.8% from 2025 to 2033 and reach $18.77 billion by the end of the forecast period, according to Grand View Research.
In this article, experts from Itransition, a company with 5+ years of experience in AI development, highlight three practices to help you successfully implement an AI virtual shopping assistant to enhance customer experience and achieve productivity improvements.
Conduct thorough response accuracy assessments
If a virtual shopping assistant frequently provides users with inaccurate answers and recommends products that don’t meet customers’ needs or are out of stock, the user experience will decrease, which in turn can negatively affect the company’s conversion rate. To ensure that virtual assistants provide relevant responses and recommendations, companies need to thoroughly test virtual assistants’ accuracy before the launch and closely monitor their performance afterward. To do so, they can take the following steps:
Define testing metrics
First, the company should clearly define which metrics it will use to assess the accuracy of a virtual shopping assistant. A company can develop its own custom metrics based on its unique needs or use common ones, such as:
- Intent recognition rate: AI assistant’s effectiveness in correctly identifying and understanding users’ main intent and interpreting their message.
- Fallback rate: The percentage of interactions in which the AI assistant failed to accurately understand the user and provided a generic answer or redirected the request to an operator.
- Self-service rate: The overall percentage of user sessions that were fully completed by the AI assistant without the need for human intervention.
Prepare testing data
Having defined testing metrics, the company needs to compile a set of test data used for the AI assistant evaluation. To create it, the company needs to collect a selection of user queries, including more common questions and specific and niche ones. The company can collect this data from existing sales and customer support logs, public conversational data sets, and other sources.
Develop and execute tests
The next step is creating a wide range of test scenarios that simulate real customer interactions to ensure that the assistant can effectively handle diverse situations. Some of them can focus solely on intent recognition to ensure that the assistant can correctly identify user intent. Others can involve checking whether the assistant can remember previous stages in the conversation and correctly use this information.
After selecting specific scenarios and conducting tests, the company needs to evaluate the results based on predefined metrics to assess the assistant’s accuracy. If the quality of the responses is below satisfactory, the company can add more instructions to AI assistants, configure the hyperparameters of the LLM, or use other techniques to increase it. After making improvements, the tests should be repeated to ensure that the assistant’s performance has improved.
Implement a human handoff workflow
Even an efficient and accurate AI assistant should be able to reroute a user’s query to a human agent in situations when the question is too complex or the user specifically requests a human agent’s involvement. To establish a reliable automated handoff workflow, the company can take the following algorithm.
Define handoff triggers
To start with, the company needs to determine triggers, specific events or conditions that initiate the transfer of a conversation from an AI shopping assistant to its human peer. Common triggers include explicit requests (the buyer asks to speak to a human), repetition (the customer asks the same question several times, and the AI assistant fails to provide the correct answer), and sentiment analysis cues (customer messages indicate that they are experiencing frustration).
Create conversation transfer protocols
Next, the company needs to define the process of transferring the conversation from the AI assistant to a human agent. The company needs to ensure that the virtual assistant transfers the entire chat history, including data already provided by the customers, such as their name, problem description, order number, and so on, so that they don’t have to repeat this information.
The AI assistant should also make it clear that the conversation is transferred to a human operator and provide an approximate wait time. Amongst other things, the company should set up an alert system to notify human agents in a timely manner when a transfer of the dialogue is needed, for example, via email or the customer support system control panel.
Establish a feedback loop
Сompanies also need to complement human handoff workflows with reliable customer feedback loops. For example, after a human agent resolves a customer’s problem, the latter can be asked to provide feedback on their customer service experience. This way, companies can not only analyze the obtained information to calculate CSAT and understand how buyers are satisfied with the customer service but also identify bottlenecks in the human handoff process and get insights on how to improve it.
Review data protection policies and mechanisms
While many customers nowadays are comfortable chatting with AI assistants, some remain wary of them, mainly due to concerns about the safety and ethical use of personal data. Since a single data leak would be enough to undermine customers’ trust, ensuring strong data security and the privacy of customer data when it’s handled by AI assistants is vital for companies implementing this technology.
To be more specific, a company must review its corporate data policy and ensure it includes principles and rules governing how customer data should be collected and processed by AI assistants, as well as how it should be stored later on. Developers can also make sure to design and configure a virtual assistant in such a way that it collects only the data strictly necessary for its defined purpose. It’s also important to specify data retention requirements, dictating how long customer data must be stored and how it should be securely disposed afterward.
Data policies should also include measures to proactively inform buyers about how the AI assistant processes and uses their data, and how users can opt out or manage data usage settings. 27% of respondents to Walmart’s Retail Rewired Report 2025 say they want clear transparency around how AI tools use and process their data.
However, robust data policy won’t help prevent data leaks if the AI assistant solution lacks strong security mechanisms. Thus, companies need to ensure that their AI assistants are equipped with measures such as end-to-end encryption, multifactor authentication, and user activity monitoring to safeguard user data.
Final thoughts
More and more companies are investing in the development and implementation of virtual shopping assistants to provide buyers with dynamic 24/7 support. If you plan to build and deploy one, following the recommendations listed in the article will help you ensure a smoother virtual shopping assistant implementation that brings higher ROI in the long run.
Also, if you find that your in-house team doesn’t have the expertise required to implement a virtual assistant, keep in mind that you don’t have to postpone the project and instead can turn to professional AI developers. They can identify your business and end-users’ needs, help define functional and non-functional requirements for the solution, and develop a virtual shopping assistant tailored to your unique requirements.
