AI in product testing: How you can use AI to create innovative products

Did you know companies release roughly 30,000 new products a year? And according to Harvard Business School Professor Clay Christensen, 95% of them fail. 

Yet, McKinsey reports, “One-third of CPG executives call innovation the number-one lever for growth.”

Brands have to produce innovative products to stay competitive, but how can you improve on this staggering failure rate?

The answer is more effective market research. While you could hire focus groups and go talk to the customers buying the product in person, that’s a time-consuming process. Fortunately, AI in product testing offers a collaborative online approach so marketers can create more premium products and expand markets at the speed of consumers.

The result? More winning campaigns, and you look like a rockstar. 

In this article, I’ll dive into how and why you can use AI in product testing, as well as share some examples and tools to help you get started. 

You’ve heard of machine learning. It trains computers to “learn” from data and improve insights over time. AI uses machine learning to make recommendations, and allows you to build an ongoing learning loop for better results. Generative AI for product testing picks up on these patterns, learns from past behavior and delivers data to help you make smarter decisions continuously. 

Today’s generative AI tools, like Zappi’s AI Concept Creation Agents, let brands test consumer feedback and iterate at the speed of commerce. 

However fast you can pull data, AI can do it faster. What’s more, AI tools spot patterns in seconds, meaning the more you use them, the easier it is to spot useful insights. 

You don’t need to be an engineer to recognize the benefits of fast and accurate product testing and iterations. The faster you market products that match consumers' tastes, the more winners you’ll likely have.

Ultimately, AI speeds up your time to market.

Besides creating winning products faster, AI product testing can test software for fewer glitches. The right AI tools can also ensure your campaigns look consistent across platforms. 

While generative AI for product testing is still in its early stages, it offers unprecedented flexibility and feedback. However, there are some limitations to be on the lookout for, as with any software. 

1. Data quality 

AI needs large amounts of high-quality data to make accurate predictions. New products may provide limited data, but AI tools offering continuous feedback and adjustments can produce more accurate results.

2. “Black Box” results

Some AI tools provide results without a “reason why.” This makes it tough for you to make the best recommendations and gain support from leadership. 

3. Privacy concerns 

Every marketer knows it’s essential to strike the right balance between data and privacy. Between governmental regulations and attempts at relevancy and personalization, you need AI tools built with these in mind. 

4. Limited creativity 

By now, you’ve tried AI tools in some capacity. Whether trying out ChatGPT for ad copy, blog content or headlines, you know they don’t churn out polished material, which is great! You’re needed! But while AI can still be very creative, it still needs that human nuance. 

You can use good data to test your ideas and help shape a breakthrough. 

This is where it gets fun. When you have the right AI product testing tools, you collaborate with the machines and get immediate feedback. This helps you iterate based on real-life data and is more effective and efficient. 

1. Concept creation 

AI Concept Creation Agents give brands the tools to create and refine product concepts in hours instead of weeks. When you speed up the feedback loop, you can make faster iterations to test more ideas faster to find a winner. 

2. Testing and optimization 

Every product requires revisions, but the old way is time-consuming. The AI Concept Optimizer creates a real-time feedback loop so your consumer feedback guides the product refinements. Brands have seen a 4-6x higher success rate with this built-in agility; iterations happen in as little as four hours. 

AI tools can automate boring, repetitive tasks and improve productivity by as much as 80%. 

3. Improve customer experiences

AI product testing research analyzes consumer behavior to detect friction. When you spot stumbling points, you can simplify them to create a more intuitive customer experience. AI product testing tools run large-scale simulations to test breaking points before the big campaign launches. Afterward, it can create the most relevant customer pathways for each customer. 

4. Improve quality experience 

AI-driven product testing can analyze past data while gauging the quality of the product and detecting any potential problems. Continuous improvement using AI product testing can also enhance product positioning. 

5. A/B test at scale 

Find winning combinations of names, colors, logos or campaigns at scale with generative AI product testing faster than you ever thought possible so you can get to market faster. 

6. Predict performance 

How will your product perform with Gen Z or in a new-to-you market? Generative AI product testing research analyzes past and current data to deliver valuable insights. Plus, it tests platforms before they’re rolled out to make sure they function properly. 

7. Reporting and insights 

AI reporting tools deliver comprehensive, up-to-the-minute data and reporting so teams can make smarter, data-backed decisions. Imagine less time making decks and more time testing campaigns. 

There are so many valid use cases for using AI in product testing. Speed and accuracy are chief among them, ensuring the software works seamlessly before launching a new initiative. For example, marketers appreciate the ability to increase the number of product concepts they can test exponentially. 

Speed to winning concepts 

What would it mean to your team if you could generate new ideas and refine them with customer-backed data within four hours? That’s what’s possible with Zappi’s AI Concept Content Creation Agents. 

"The AI solutions I've seen coming up from Zappi are immediately relevant to my business. We're writing concepts and claims the whole day, Zappi is helping with that immediately. Compared to some of the other vendors out there, Zappi has a deep understanding of what it feels like to be a client-side researcher and what needs to get done on a frequent basis."

- Jacci Weber, Team Lead of Pet Parent Insights at Mars

Optimize product testing

AI can help automate software performance testing so it’s always current and doesn’t break. On the marketing side, you can use Zappi’s Concept Optimizer to optimize for winning features, pricing and designs.

Mariline Alsuar-Dean, Global Insights Director for Intimate Wellness at Reckitt, commented on Zappi's AI Concept Optimizer, saying:

"Zappi’s AI optimizer helps us determine in a matter of seconds how to strengthen our innovation concepts and maximize their chances of success at launch. It also helps us understand how to flex our messaging across the different audiences we want to appeal to. Zappi's AI optimizer gives us the speed and agility that we need in a space like sexual well-being that is constantly disrupted by new trends in culture, politics, and technology.”

Traditionally, engineers have manually handled stress tests and bug fixes. It’s not very exciting work but someone had to do it. Now, AI tools create environments where the code can fix itself, repair bugs and check for consistency across devices. 

The marketer’s version trades time-consuming focus groups for continuous consumer feedback that shapes new products. They can test concepts, packaging, pricing and more. 

Here’s a few examples:

1. Meta’s SapFix

This feature from Meta uses AI tools to test zap bugs in Meta’s code, so it runs smoothly. The AI searches for historical code patterns to predict and fix problems so developers can focus on other projects. 

2. Netflix's Chaos Monkey

Netflix named their AI tool “Chaos Monkey,” which stress-tests the system and finds the weak points so they can keep the streaming service running without a hitch. 

3. Uber and Tesla

Uber and Tesla use AI to test autonomous vehicles for safety and reliability. 

4. Nike

Nike uses generative AI to develop new shoe models based on athlete’s preferences. 

5. McDonald's

Finally, McDonalds also uses AI for an iterative “test and learn” approach, which you can learn more about below. 

 Now that you probably have a list of ideas, here are some of the available tools.

Now, we marketers have AI product testing tools to test our phases of the development cycle. Here are a few examples:

1. Zappi's AI Concept Creation Agents

Zappi’s AI Concept Creation Agents integrates consumer feedback into product development in a continuous cycle. Brands can use the innovative system to create concepts, test, optimize and release winning products in a fraction of the time. 

Learn more about the AI Agents here.

2. TestGrid 

Automates the software testing process from creation to performance and maintenance to ensure uptimes and reliability. 

3. Applitools 

Specializes in visual testing and UI validation to keep visual appearances the same across devices and browsers. Applitools Eyes keeps logos, layouts, and styles the same, no matter where you log in. 

4. Testim 

Offers AI-powered, automated software testing using no-code tools to simplify maintenance and maintain reliability through continuous testing. 

5. UserZoom

UX research and testing show how people interact with the product and help marketers gather useful data on consumer preferences. 

AI in product testing is a competitive advantage. Brands can use it to build in continuous testing loops,stay current with software performance as well as understand consumer’s reaction to production innovations. And with up-to-the-minute information, marketers can make data-backed decisions faster to produce more winning innovations.