More than 30,000 new products are launched every year, and 28,500 of them (or 95%) fail. This staggering failure rate underscores the importance of gathering customer feedback early and often. Without understanding the true needs and pain points of your target audience, it’s nearly impossible to develop a successful product that will resonate in the market. Seeking customer feedback is not just a common practice – it’s a vital step that can make the difference between success and failure in the highly competitive world of product development.
There’s an old saying, “test before you invest.” Whether you’re a SaaS founder or creating consumer goods at a Fortune 100 company, you’d be crazy not to ask your customers for their feedback on your product. It sounds cliché, but it’s actually deeply rooted in human behavior to seek feedback.
Humans thrive on feedback; we create ideas, we test ideas, and we improve ideas. In fact, we’ve built large industries, like market research, designed to acquire feedback. We’ve become obsessed with data as a proof point to help us make decisions. Even more, we’ve adapted our means of gathering data to become faster and more economical.
Now, none of this is revelatory, but there is a purpose in bringing it up. In about 20 years, market research has gone from phone and mail surveys that take weeks to digital data acquisition with live face and eye tracking that takes just hours. These developments mean that we’ve become very efficient at gathering data, but we are incredibly inefficient at learning from it.
For the first time in nearly a decade, the way we learn from feedback is changing. Artificial intelligence has stormed on the scene to change our view of data and how we can use it to enhance innovation. It doesn’t just process information; it understands it, collating and translating our disparate feedback into meaningful consumer understanding.
If we can get our data right, there’s endless potential to create new ideas, optimize them and localize them to the right audiences.
Co-founder & CEO at Zappi.
Accessing the value of data with AI
Where humans thrive on feedback, AI thrives on data. While human intuition can be a valuable tool with some data, too much data makes it categorically impossible for a human to understand its nuances. Details begin to fall through the cracks and disconnected data sets only tell part of the story.
The question remains for brands: how do I apply AI to data to truly leverage what we have learned beyond just project based insights.
It’s important to think about how you access value and what tools you want to use. Ultimately, you should start with your generative AI of choice, be it OpenAI, Microsoft Azure, Bard or any of the other tools rapidly coming to market. While businesses are notoriously skeptical of sharing data with AI platforms, they are becoming more secure as new platforms emerge for businesses.
On top of this generative AI should sit data which is specifically designed to train your AI and give it the data-rich context to deepen its understanding of the customers for which it is creating. But it requires thinking about data in a more connected way. We can’t just funnel individual project data into AI and hope that it fills in the gaps. That’s how you get hallucinations and, well, bad ideas. Instead, we need to leverage everything we know through a cohesive, connected data asset. There’s a framework for how to do this, but if you truly want to leverage AI, an organized, single-source of truth with data is non-negotiable.
Once you’ve established a foundation of AI informed by a well-connected data asset, then you can utilize applications that make innovating faster and better, so you can democratize access to a super-trained AI that can create, optimize and localize ideas.
Data-led and creativity enhanced
Shifting our collective understanding of how we use, connect and mobilize data will be the next evolution in how we think of ideas. We’ll move from a human-centric process of creating ideas, workshopping, testing and improving them. Our new process will be data-led and creativity enhanced to look like this;
Create:
By leveraging AI’s data processing and pattern recognition capabilities, businesses can channel everything they’ve learned to create an initial product concept from scratch.
Rather than sitting in a brainstorming meeting and bouncing disparate ideas off the wall until something sticks, you can look into a cohesive data set about your target audience and what they connect with, why, what the think about their category and any unmet needs and let the AI start to create. This accelerates the process and you immediately develop minimum viable ideas based on consumer insights that lay the foundation of our human intuition to refine and align with our business strategy and goals.
Optimize:
After testing these ideas and getting the rich feedback from core audiences, we can add this feedback into our system, creating a learning loop powered by AI. At the optimize stage, AI immediately mobilizes this feedback to have our product reflect the desired changes.
This is a key moment when humans can intervene. Experts can add their insights and knowledge about what works for the company, what guardrails exist for the category and ensure we avoid mistakes that have been made before.
In initial beta testing, we’ve has seen that AI product optimization can substantially improve performance by up to 20% across key metrics with human intervention.
Localize:
Drawing from the wealth of data collected through agile testing processes, AI can pinpoint the preferences and trends unique to different consumer segments.
Whether the goal is to tailor a product for men, adapt a service for younger women, or customize a campaign for residents of West Texas or Colombia, AI enables businesses to make informed adjustments based on real consumer understanding. This not only enhances the relevance of the offerings but also significantly increases the chances of market success.
Combining the relative strengths of AI and humans, we can usher in a new era of how we connect and mobilize feedback. With the right partnership across data, AI and humans in the loop, you can become more agile and drive meaningful innovation and market success.
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