Published: 14 October, 2024 · 9 mins read
Customer experience (CX) is fundamental to long-term product growth and customer loyalty. It’s especially true for AI solutions that have yet to earn wider trust. Market leaders, like Grammarly and Canva, demonstrate how CX drives AI acceptance. You can follow their lead by implementing CX best practices for your AI solution, including responsive design, robust app performance, cross-platform accessibility, timely error handling, personalized interactions, and embedded self-service.
Every other startup fails because it
doesn’t get funding
or runs out of money, and AI investments have
gone up and down
in recent years. With investors getting more picky, the front-end for AI takes the front seat.
Clean design and intuitive navigation enhance customer experience in AI. But they can also
boost your chances of getting funded.
Exoft has assisted several startups in developing front-end for AI solutions, and we’re happy
to share our insights. For more info on the development outcomes, feel free to check our
case study library.
Four out of five business owners prioritized customer experience (CX) and support in 2023. CX trends provide a competitive advantage, remaining a driving force in achieving business goals in 2024 and the near future.
Non-user-friendly products cause 9% of startup failures. This rate is higher than the lack of demand and market competition, both 7%. Still, you need to understand that users strive for a perfect customer experience. CX management techniques like A/B testing work wonders for adjusting user interface, navigation, and customer support. Implementing these insights in front-end development for AI will herald other business benefits.
Zendesk CX Trend Report claims that 60% of customers choose vendors based on the services they expect to receive—moreover, 73% switch to competition after several negative experiences. Although early adopters often disregard CX shortcomings, expanding the user base requires enhanced customer experience for AI solutions. That’s especially true considering the cutthroat competition in the booming market.
According to McKinsey, improving customer experience affects the bottom line. Sales revenue increases by 2% to 7%, while profitability sees a modest 1% to 2% rise. At the same time, shareholder return can increase by up to 10%. The latter is a convincing argument for potential investors interested in your AI software.
Many generative AI solutions look alike. However, small things can make or break the customer experience. Here, we’ll cover several excellent examples of leveraging the front-end for AI.
Magic Edit in Canva enables users to add to or alter existing images. The interface includes a toggle between the Brush and Click options. Moreover, it’s possible to navigate the feature with pop-up tips. Also, a disclaimer reminds users that Magic Edit doesn’t work for faces, legs, or hands. Choosing among four generated results further enhances customer experience in AI editing.
Gemini is another prime example of successful front-end development for AI solutions. Thanks to its intuitive multimodal interface, users generate text, images, coding fragments, and more using simple prompts. Text-to-speech and speech recognition capabilities enhance accessibility. Meanwhile, instant feedback prompts user engagement and improves generative capabilities.
Grammarly has built generative AI functionality into the preexisting software.
AI-suggested improvements are neatly relegated to a separate tab. The app relies on its
tried-and-true approach of underlining problematic words and phrases and highlighting
suggested changes.
Like Grammarly’s standard suggestions, users can accept or dismiss AI-powered recommendations.
They can also report problems and turn off specific types of suggestions. Grammarly uses established
navigation and control patterns to make users comfortable with the new functionality — and
it works.
The “form before function” principle only looks good in designer portfolios. On the other
hand, the function can also suffer without putting much thought into form. Users who don’t
understand how to use and navigate your AI project will eventually get frustrated and
abandon it. In the worst-case scenario, they’ll also leave bad reviews and create a bad
reputation for your app.
In contrast, users rarely value attractive and intuitive interfaces because their experiences
are smooth and hassle-free.
An intuitive customer experience for AI solutions should focus on the following:
The front end is responsible for the user’s side of an AI solution. So, it’s the ultimate tool for improving customer experience. These are standard tasks front-end development for AI solutions can address:
Even if you don’t need to develop a solution from scratch, consider hiring an experienced front-end team. Skilled specialists will augment the user-facing element of your AI application and boost customer experience, providing the benefits discussed above.
Can you do anything else to improve your AI solution and front-end design? In addition to
a mobile-optimized and user-friendly interface, try these strategies to enhance customer
experience.
If you let Exoft implement these effective tactics, you won’t need to manage a development
team. Our front-end developers will deliver the outcomes you want.
Customer experience depends on how comfortable users are with your technology. Still,
intuitive interfaces aren’t always as clear and easy to navigate for customers as they are
for designers. For improvements in onboarding, include a clear tutorial. Cover basic
features, potential issues, and other hands-on tips. The tutorial should be short and
sweet, with an option to skip it and return to it whenever convenient.
Suggested prompts (i.e., Gemini, ChatGPT) and prompt builders can be onboarding and training
elements. The same applies to tooltips and explainer pop-up windows, however annoying they
may be for some users.
Aside from disclaimers reminding users that artificial intelligence can make mistakes,
every AI developer should address handling errors. Instead of blanking out, your solution
should either admit its error or replace it with alternatives. Built-in reporting and
correction capabilities will also serve a double purpose. On the one hand, they will
appease the users. On the other hand, they will help log errors and inappropriate
algorithm output, which you can address on the backend.
You can also add response confidence indicators to foster trust. It’s particularly crucial
for predictions. AI apps can include these indicators verbally or provide calculations to support
their predictions.
Dashed expectations lead to an unsatisfactory customer experience. Users should understand
what your AI solution can and can’t do. You can explain it during the onboarding process
or throughout the first few user interactions. For example, include a disclaimer about
potential mistakes. Besides, users should know about the database limitations. So, if your
AI app can only access data within a certain period, inform users about it.
Our case study on an
app for collecting customer data and feedback
demonstrates how gathering user insights can drive continuous improvements and enhance customer
satisfaction.
Use badges such as “New” or “Beta” to highlight the latest releases and their status. Users
will realize mistakes are more likely in the early adoption days. Thus, you’ll receive feedback
and improve individual features without losing customer trust.
Considering artificial intelligence is at the forefront of CX personalization efforts across various markets, personalized interactions should also be among your customer experience strategies for AI. Analyze past actions, experiences, and feedback to create custom prompt suggestions and personalize AI output. Even something as simple as the user’s name in the Gemini’s interface will make customers feel valued.
Unlike retail, education, or
trucking app development, AI solutions immediately adopt customer feedback to improve outcomes. Therefore,
encouraging feedback is equally critical for customer experience. Offer users the
opportunity to rate the results and share additional feedback. Explaining how user
feedback affects the model and output will encourage customers to be more active with
their reviews.
Many generative AIs rely on thumbs-up and thumbs-down buttons to generate instant feedback.
ChatGPT comes with a reload button that creates a new output. Gemini, in contrast, requests
additional feedback and provides several standard options for negative feedback.
If you provide enough data-driven materials for customers, they solve any arising issues
independently. These include FAQ sections, blog articles, community forums, and email
newsletters. Chatbots are also valuable for enhancing customer experience in AI.
To make these self-service options helpful, ensure the search engine works properly. Also,
check whether the information provided is relevant. Outdated answers lead to a negative customer
experience and a decision to look for another AI solution.
Synchronizing customer experience across different devices is crucial. However, you should also ensure their support journey is seamless and frictionless. Avoid making users repeat their issues to various department managers. It will only aggravate them and encourage them to switch to your competitors. Omnichannel routing should be a part of the front end for AI solutions to direct tickets according to their priority and your support team’s availability.
Businesses prioritize customer experience and support as competitive advantages, and AI
solutions are no exception. Developing an effective and intuitive front-end design for AI
requires consistency, simplicity, and clarity. Some other strategies for improving CX for
AI include self-service options, omnichannel experience, and transparency. Personalized
interactions and feedback loops also improve customer retention and boost lifetime value.
Exoft has used these insights through front-end development for several AI solutions. You can
learn more about our
cooperation models
and experience with front-end AI development. We’d be happy to provide a more thorough consultation
on the topic. Contact our team, and we’ll schedule a call to discuss your CX needs and how
we can help you meet them.
A great customer experience for AI solutions starts with understanding your users’ needs. Next comes the UI and UX design stage, which should prioritize simplicity, clarity, and consistency. Optimizing user-facing elements of your solution is also critical, as is cross-platform functionality and responsive design. Reliable customer support is another vital aspect of great CX.
The key elements of a great customer experience for AI solutions include an intuitive user interface, cross-platform functionality with a responsive design, and proactive customer support.
The common challenges in creating a great customer experience for AI solutions include the lack of front-end development expertise, poor design choices, and unclear user needs. Budget and timeframe creep encourage startups to overlook AI solution front-end design and prioritize backend development.
If you leverage front-end development to improve the customer experience of your AI solution, you may notice an increase in the number of new and returning customers. Your overall brand reputation will improve, driving up sales and revenue.
