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 real needs and pain points of your target audience, it is almost impossible to develop a successful product that will resonate in the market. Seeking customer feedback is not only a common practice; it is a crucial step that can make the difference between success and failure in the highly competitive world of product development.
There is an old saying: ‘test before you invest’. Whether you’re a SaaS founder or you make 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 is actually deeply ingrained in human behavior to seek feedback.
People thrive on feedback; we create ideas, we test ideas and we improve ideas. In fact, we have built large industries, such as market research, designed to obtain feedback. We have become obsessed with data as evidence to help us make decisions. In fact, we’ve adapted our data collection to become faster and more economical.
None of this is revelatory, but there is a purpose in bringing it up. In about two decades, market research has evolved from telephone and written surveys that took weeks to complete, to digital data acquisition with live face and eye tracking that took just hours. These developments have made us very efficient at collecting data, but incredibly inefficient at learning from it.
For the first time in almost a decade, the way we learn from feedback is changing. Artificial intelligence has arrived on the scene to change how we view data and how we can use it to drive innovation. It doesn’t just process information; it understands and collects and translates our diverse feedback into meaningful consumer understanding.
If we can get our data right, there is endless potential to create new ideas, optimize them, and localize them to the right audiences.
Co-founder and CEO of Zappi.
Access 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 are starting 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’ve 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 the generative AI of your choice, whether it’s OpenAI, Microsoft Azure, Bard, or one of the other tools quickly coming to market. While companies are notoriously skeptical of sharing data with AI platforms, they are becoming increasingly secure as new platforms emerge for businesses.
On top of this generative AI should be data specifically designed to train your AI and give it the data-rich context to deepen its understanding of the customers it is being created for. But it requires you to think about data in a more connected way. We can’t just send individual project data to AI and hope it fills in the gaps. This is how you get hallucinations and, well, bad ideas. Instead, we need to leverage everything we know through a cohesive, connected data asset. There is a framework for how to do this, but if you really want to leverage AI, an organized, single source of truth with data is non-negotiable.
Once you build a foundation for AI, informed by a well-connected data asset, you can use applications that make innovation faster and better, so you can democratize access to a super-trained AI that can create, optimize and localize ideas.
Data-driven and enhanced creativity
Shifting our collective understanding of how we use, connect and mobilize data will be the next evolution in the way we think about ideas. We are moving from a human-centered process of creating ideas, running workshops, testing and improving them. Our new process will be data-driven and creativity will be enhanced to look like this;
To create:
By leveraging AI’s data processing and pattern recognition capabilities, companies can channel everything they’ve learned to create an initial product concept from scratch.
Instead of sitting in a brainstorming meeting and bouncing disparate ideas off the wall until something sticks, look at a cohesive data set about your audience and what they connect to, why, what they think about their category, and any unmet needs and wants the AI starts to create. This speeds up the process and you immediately develop minimum viable ideas based on consumer insights that lay the foundation for our human intuition to refine and align our business strategy and objectives.
Optimize:
After testing these ideas and getting rich feedback from the core audience, we can add this feedback to our system, creating a learning loop powered by AI. In the optimization phase, AI immediately mobilizes this feedback to make our product reflect the desired changes.
This is a key moment when people can intervene. Experts can add their insights and knowledge on what works for the business, what guardrails are in place for the category, and ensure we avoid past mistakes.
In initial beta testing, we’ve seen that AI product optimization can significantly improve performance by up to 20% on key metrics, with human intervention.
Localize:
Based on the wealth of data collected through agile testing processes, AI can identify 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 allows companies to make informed adjustments based on real consumer understanding. This not only increases the relevance of the offer, but also significantly increases the chances of market success.
By combining the relative strengths of AI and humans, we can usher in a new era in the way we connect and mobilize feedback. With the right partnership between data, AI and people in the loop, you can become more agile and drive meaningful innovation and market success.
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