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5 best practices for nailing the ecommerce virtual assistant user experience
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“Hello there, how can I help you today?”, asks the virtual shopping assistant in the lower right-hand corner of the website or mobile app.

As with familiar voice assistants such as Siri from Apple or Amazon Alexa, that user-friendly line is the first step in your shoppers getting an answer to their query from information in your knowledge base, enabling them to complete the checkout process with their shopping basket or get an issue resolved.

Virtual shopping assistants have emerged as powerful, human-like functionality that helps shoppers find products, get personalized product recommendations, and resolve customer queries through an efficient conversational experience. The chatbot market is estimated to reach $454.8 million by 2027.  

Conversational commerce, buoyed by conversational AI, is here to stay as a way to drive online sales. 

However, creating a smooth virtual-agent end-user experience for meeting customer needs isn’t necessarily easy for online retailers. With AI chatbot dialog, people want seamless interaction — natural conversation without the “ummms” and “errs”.

Here are five ways to ensure that your ecommerce store virtual assistant helps streamline shopper interactions, provides better personalized recommendations, and offers excellent real-time customer support, from a visitor’s first moments on a site to when they’ve filled their shopping cart.

1. Be transparent

In the online shopping conversational user experience, people expect nothing but unbiased product recommendations from ecommerce virtual assistants. You don’t need user research to know that people expect an ecommerce bot’s recommendations to be based on their needs, not your marketing goals. For example, if they’re trying to find hiking boots through your user interface, they don’t want to be pushed to the electronics section of your Black Friday sale by the “helpful” online conversational interface.

Being transparent about the factors that influence your virtual assistant’s recommendations enhances shoppers’ understanding of particular product suggestions. For example, you can visually disclose any partnerships and affiliations with brands. You can explain which  algorithms you use to generate recommendations when you use chatbots, and you can share your data sources.

This transparency can lead to:

Greater collaboration 

When shoppers know the recommendations they’re receiving are relevant to their preferences, they’re more likely to actively engage with the virtual assistant and start to use it more.

Personalized experiences

More collaboration between a shopper and a chatbot leads to better personalized experiences as the chatbot learns from the shopper’s feedback and refines its recommendations over time. So if the shopper is regularly searching for sportswear, for instance, the chatbot remembers that, along with what they usually purchase.

The Wayfair (UK) AI shopping assistant is an example of one that effectively learns from shopper interactions to improve the customer journey.

Trust and loyalty 

When you’re open and honest about your recommendation practices in your efforts to help customers, shoppers will feel more confident in your virtual-assistant services. This trust can lead to increased loyalty, upselling, and cross-selling, with shoppers becoming more likely to rely on guidance from your assistant for answering queries and helping them make purchases.

2. Generate diverse responses 

Accuracy is important, of course. But focusing solely on it can hinder your virtual assistant’s responses and cause a number of issues:

  • Lack of flexibility: Virtual assistants that are geared solely toward providing the most accurate responses may struggle to handle unconventional queries, such as when people ask for personal opinions.
  • Inability to handle ambiguity and complexity: Not every shopper on an ecommerce website knows what they want. With an overly accuracy based chatbot, queries such as “a sweater suitable for Alaska” aren’t going to hit the nail on the head.
  • Difficulty in learning and adapting: A purely accuracy-focused approach may hinder the assistant’s ability to learn and adapt to broad questions, limiting opportunities for future personalization.

Ensuring that your virtual assistant generates diverse responses for queries improves the experience for your shoppers. This doesn’t mean pushing irrelevant answers to queries; it’s about opening up new opportunities that are related to their queries. By doing this, you can: 

Enhance understanding

Through offering a range of recommendations and highlighting features, benefits, and use cases for products, a virtual assistant can provide shoppers with deeper knowledge of their options. Presenting alternative products and brands can introduce them to new ideas.

Optimize the user experience

By suggesting a variety of options, the virtual assistant creates a more engaging and dynamic online shopping experience as it leads a shopper to choose a specific product. Shoppers feel empowered; they can explore to find the solution that best suits their needs, a process that results in improved customer satisfaction.

3. Have a clear data-usage policy 

Poor data privacy practices that surface through virtual shopping assistants can have detrimental effects on the shopping experience and thereby hinder the adoption of this technology. 

If your target audience feels that their personal information is being used to serve them targeted ads and it isn’t being adequately protected, your reputation will take a hit. According to statistics, 63% of shoppers think organizations aren’t transparent about how they use data. The study also reveals that 48% have stopped purchasing from a company due to privacy concerns.

To address this issue, respect the fact that shoppers need assurances that their information won’t be shared without their consent.

A clear, comprehensive data-usage policy plays a crucial role in earning trust. By providing an accessible policy that underlines the measures you’ve taken to protect your shopper data, you can demonstrate your commitment to data privacy. 

In your policy, it’s a good idea to specify:

  • The parties involved in data sharing, e.g. third-party vendors and advertising networks
  • The specific purposes for which the data may be used

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4. Seamlessly escalate to a human 

Virtual assistants can’t handle everything for ecommerce businesses, and, at certain times, they can’t be much help at all. Recognizing the scenarios in which a handover to a human representative is warranted will help you maintain the trust of your shoppers. 

According to one study, 77% of shoppers said having the option to escalate to a human agent is one of the most important things companies get right about chatbots.

Situations that may demand access to the human touch include:

  • Complex queries: AI may struggle with intricate or highly specific inquiries, especially those that require a nuanced understanding beyond its programmed capabilities, such as questions about opinions and comparisons.
  • Shipping disruptions: Issues such as delayed shipments and lost packages often need human discretion and decision-making skills for resolution.
  • Personal issues: If a shopper is dealing with a unique or sensitive challenging situation, AI might not provide the empathy or conversational style that’s needed.
  • Miscommunication: At times, AI may misinterpret user needs and queries, leading to shopper frustration; human intervention can resolve the misunderstandings.

The transition from an AI assistant to a human representative should also be smooth and frustration free for the shopper; no one wants delays in getting their questions answered. To achieve this, you’ll want to ensure:

Smooth information transfer

Design your AI system to pass all relevant conversation history and shopper information gleaned from an AI shopping assistant to the human representative in customer support. This approach will prevent shoppers from having to repeat themselves during the human conversation, thereby reducing frustration and saving time.

Clear communication

When transitioning a query to a human representative, the AI should inform the shopper about the hand-over process, conveying the fact that intervention is necessary and providing an estimated wait time for human customer support. For example: “I’m afraid I’m not able to assist you with this. Please wait 2 minutes to speak with one of our customer-service team members.”

High-quality training and integration 

Human representatives should be well trained in picking up where the AI left off to help a customer determine the right product or get answers. They should also have access to the same information and tools as the AI in order to provide an informed, consistent response.

A feedback loop

Implement a system in which feedback from human-assisted interactions is used to improve your AI algorithms. This continual learning process may reduce the frequency of handovers over time.

5. Deliver brand-specific experiences from data 

When virtual assistants are trained on universal datasets, it may be tough to provide a personalized and brand-specific experience that offers broad understanding of language and shopper interactions. To truly resonate with shoppers, an AI-powered assistant must reflect the unique aspects of your brand, factoring in your company values and product specifics, and using just the right conversational tone.

Creating a more engaging and relevant shopping experience requires training a virtual assistant using company-specific data, such as:

  • Merchandise catalogs: Utilizing detailed product catalogs helps the ecommerce chatbot understand the nuances of your offerings in terms of features, variations, and unique selling points
  • Usage events: Analyzing shopper interactions such as purchases, browsing patterns, and feedback allows the AI to develop a deeper understanding of shopper preferences and behaviors specific to your brand

Integrating brand-specific data in AI training helps provide:

Tailored interactions

Virtual assistants that understand your brand nuances can provide recommendations and support that are more aligned with individual shopper needs and preferences, making the shopping experience more engaging and relevant.

A consistent brand voice 

Whether they’re interacting with artificial intelligence or talking to live agents, new customers and returning buyers should feel that the customer engagement experience is seamless in terms of brand. Brand-specific data ensures that your brand voice remains consistent across all channels.

Sephora is a good example of an online store effectively incorporating branding in its chatbot and AI technology. Its virtual shopping assistant is efficient, and instead of sounding like just a machine, its conversation flows make it sound like it’s part of the ecommerce retailer’s brand-specific machinery as it suggests relevant products, answers a FAQ, or relays an order status.

Level up your search and discovery

You’ve now got some data points on why delivering an online shopping customer experience that includes an optimized virtual assistant for shoppers is key to staying ahead in the digital era.

Along with offering up the best ecommerce chatbot solution possible, another key to success for online retailers is providing an efficient search and discovery experience across the ecommerce platform. 

Algolia’s site-search API uses advanced machine-learning techniques to enhance natural language understanding (NLU) for ecommerce companies. That means your online shoppers can enjoy a personalized shopping experience and find what they need, whether they do it through the help of your pro chatbot or exploring your site on their own.

Discover how we can help you transform your customers’ search experiences and improve your conversion rates. Contact us or book a demo today.

About the authorVincent Caruana

Vincent Caruana

Senior Digital Marketing Manager, SEO

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