Learn how your company can create applications to automate tasks and generate further efficiencies through low-code/no-code tools on November 9 at the virtual Low-Code/No-Code Summit. Register here.
Whether they know it or not, marketers are at a crossroads with how far they can take new technologies to enhance their brand. As we saw in Merkle’s 2022 Consumer Experience Sentiment Report, 47% of consumers feel that personalization online is invasive — up from 44% in 2021. This means that consumers don’t necessarily feel more comfortable with technology the longer it exists in our daily lives, which is especially true with AI-powered services.
AI has become a powerful tool for commerce experiences such as chatbots, product recommendations, voice search and other forms of personalization. But many consumers don’t trust that AI services are using and storing their data responsibly, and it often leads to a distrust of AI tools that are meant to enhance the user experience.
The most common obstacles to gaining trust with a brand’s AI-powered experiences include:
- A lack of transparency about how consumer data was used to provide recommendations
- Not setting appropriate expectations for what limitations the technology has
- Not allowing users to customize their AI experiences along the way
To overcome these obstacles for your AI-powered services, here are the top three ways to build trust with consumers:
Join today’s leading executives at the Low-Code/No-Code Summit virtually on November 9. Register for your free pass today.
1. Validate your recommendation with a “why”
As noted above, it can seem creepy to consumers if an AI-powered service recommends products on an ecommerce site without any explanation. If the rationale behind a particular AI prediction is unknown or too complicated, it creates a barrier to building trust. Instead, focus on only explaining the aspects that impact the user and their decision-making.
This can usually be done by:
- Articulating the data source — tell the website user exactly how you obtained their data. For example, if the user previously created an account on the ecommerce website, you have the email and preferences they shared when signing up.
- Tying the explanation to the user’s previous behavior — Once the user knows how you are able to gather and store their information, tell them what behaviors led to the AI-powered recommendation(s), such as previous purchases or saved products.
- Accounting for situational stakes — Set expectations for how accurate or inaccurate a prediction might be. For example, if your ecommerce website is predicting when an item would arrive at the customer’s door, you can share what variables could change the outcome.
In the next section, we’ll talk more about how to set expectations for website users.
2. Provide an onboarding experience for users
If users don’t understand how AI-powered services can support their experience, they often don’t understand the limitations of the AI they are interacting with. An onboarding experience is a way to break down how your brand’s AI provides support to users, and what obstacles it can help the user overcome. That way the user isn’t expecting something more than what the AI is capable of. An onboarding experience could include pop-up welcome slides when users start the service for the first time, or inline tool-tips or videos when users are using the AI-powered service. It provides users with guidance on how to best utilize the AI-supported service without them feeling overwhelmed by a new technology.
Within your onboarding experience, you should explain everything we talked about in the first section. Build an understanding of how data is collected from website users, and explain what the AI can and can’t do with that data.
Because there are some aspects of AI that are unpredictable in nature, brands should embrace interactive, multi-step guidance rather than the information-heavy onboarding that we’re seeing right now. Allow for users to go through an entire series if they want, or get answers to a simple one-off question. And this leads to our last point, about giving the user control over their AI-powered experience.
3. Allow ongoing user customization
AI-powered experiences should be user-centered. After all, experiences are built for the user’s benefit. One of the best ways to prioritize users’ preferences and needs is to let the user have the power to shape their experience with the AI-powered services. When you give users a sense of control in the AI-powered experience, it builds trust between the individual and your brand.
Ongoing user customization means that you allow users to change the course of their experience or provide feedback through their journey with the website. For example. you can offer “like” or “dislike” buttons when your AI recommends a product on your ecommerce website. When users provide feedback, the AI can learn users’ needs and be “smarter” so that it can tailor future recommendations to the specific user. Bonus points if you can collect information about why the user likes or dislikes the recommendation.
This intersection of AI transparency and user control is the sweet spot for AI-powered services to build trust with consumers. The more trust you have, the more likely consumers are to engage with the AI-powered experiences on your site that provide a helpful and streamlined shopping experience — and better shopping experiences engender the customer loyalty all marketers are looking to build.
Alvin Jin is Senior UX Designer at Merkle.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!