Transparency is the foundation upon which trust in AI is built.
When shoppers interact with an AI-powered shopping assistant, they expect unbiased recommendations tailored to their needs. However, you have a responsibility to not just ensure that you are adhering to transparent practices, but prove this to your customers and shoppers as well. Without visual cues of decision-making transparency, you risk eroding trust by creating the perception that you’re recommending products based on your company’s interest (i.e. inventory, margin, etc.), rather than the shoppers’ needs.
No one likes a “busy sales rep,” even when that sales rep is an AI. Maybe even more so, when that sales rep is an AI. By explaining the reasoning or criteria behind a recommendation, shoppers can understand why a particular product is being suggested as a sincere effort to support their shopping journey.
Ultimately, visual communication of decision-making creates the transparency needed to foster a sense of collaboration between the shopper and the AI, leading to a more personalized and satisfactory experience.
2. Teach through recommended interaction responses
You strive to “get things right” for your customers and AI can do that in an incredibly effective way.
However, it’s possible to go too far with accuracy, particularly when it gets in the way of learning. Shoppers are still learning how to interact with AI, and virtual shopping assistants are no exception. Shoppers require learning to be baked into their every interaction. It's tempting to do this explicitly, through interactions such as wizards and help documentation. However, the most effective learning is subtle. A great example of this type of “subtle learning” is recommended responses.
Instead of solely focusing on delivering accurate responses, virtual assistants should aim to create teachable moments. For instance, when asked about a budget, the AI should provide various response options that reflect different mental models, such as:
- Precise monetary amounts: $40
- Ranges: $20-$50
- Directionality: less than $20
- Even an opt-out option: I don’t know
By presenting diverse options, the AI helps users understand different approaches and mental frameworks for budgeting. This approach not only empowers shoppers to make informed decisions but also showcases the AI's ability to communicate and understand a range of standards, setting it apart from static filters.
3. Honesty about data usage
Consider your data usage policy a critical portion of your messaging strategy
Unfortunately, we live in an era where targeted ads relentlessly follow shoppers across the internet. As a side effect, shoppers have pivotal concerns about data privacy and usage when it comes to generative AI and virtual shopping assistants. Teams risk spending time and energy investing in creating an AI-powered shopping assistant, only to see poor adoption due to privacy anxiety by consumers. Don’t fall into this pitfall! Consider your data usage policy a critical portion of your messaging strategy and place it front and center when shoppers try out your virtual shopping assistant.
In the initial days of ChatGPT 4.0, there were multiple articles about the risks of sharing proprietary data with ChatGPT. As a result, Open AI went out of their way to add warnings throughout the user experience about how the data that users share can (and can’t) be used by the company. Shoppers have similar expectations as well as anxieties about their own data usage. Consequently, companies need to assure shoppers that their information will not be shared without their consent. If sharing is necessary, companies should disclose with whom and for what purpose. By embracing candor assertively and pre-emptively addressing customer concerns, companies can avoid being perceived as intrusive and earn the trust of their customers.
4. Escalating to a (human) teammate
Customers expect a consistent experience when interacting with both AI and human representative
While AI-powered shopping assistants are designed to handle a wide range of customer queries, there will be instances where escalation to a human representative may become necessary. In such cases, the transition should be seamless, with the AI passing along all relevant information to the human agent.
Customers expect a consistent experience when interacting with both AI and human representatives, and repeating information already shared with the AI can be frustrating. By enabling smooth handovers and ensuring the AI acts as a true representative of your company, you can provide a more customer-centric experience that builds trust and loyalty.
5. Provide a brand-specific experience via data
To establish a unique and recognizable brand experience, virtual shopping assistants need to be trained on your company's specific data. AI models are often trained on universal datasets, which can make the initial out-of-the-box AI sound generic and fail to reflect your brand identity. By utilizing company-specific data, including merchandise catalogs and usage events, the AI can align with the unique aspects of your business.
This customization allows the AI to better understand the nuances of the products, services, and customer preferences specific to your brand. Implementing AI with quality input data ensures that it represents your brand right from the start, delivering a more tailored and engaging customer experience.
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