Consumer-facing businesses are sitting on troves of data, but it's often in silos. Think of all the focus groups and surveys that were conducted in the past, which are now gathering metaphorical dust at the bottom of the team's shared drive. Or think of the hundreds, if not thousands, of online comments, product reviews, and in-store surveys that are inundating businesses on a monthly basis. Teams rely on institutional knowledge to build upon previous conclusions, which introduces human bias and other risks.
As consumer-facing brands are overwhelmed by large quantities of open-ended text data from customers, this presents a significant opportunity to leverage natural language processing AI. With more data, it becomes easier to recognize patterns, and thus easier for the algorithm to predict future results. That is what Native AI is doing with its Digital Twins solution. Digital Twins, built dynamically using online and offline sources such as social media discussion, e-commerce reviews, and survey responses, can emulate real humans with shocking precision. The crux of Native AI's technology is its ability to train new large language models (LLMs) rapidly, so each set of Digital Twins becomes unique to each brand.
Digital Twins
The cool thing about Digital Twins is that they can be hyper-specific or more comprehensive. For example, a Digital Twin could range from representing a very small demographic or purchase channel to the brand's (or their competitors') entire customer base. Imagine chatting with your competitor's customers about what would make them switch to your brand. Or asking an audience that gave negative feedback about what specific improvements they'd like to see. Or comparing your holiday shoppers' interests to your everyday customers' interests. With thousands of ways to slice and dice your customer segments, the possibilities are almost endless.
This technology is poised to completely reimagine how consumer brands and market research firms operate in the modern data-rich, time-constrained world. Product Managers use Digital Twins to identify whitespace opportunities for new products. CPG Sales professionals use Digital Twins to create data-backed pitches to acquire distribution partnerships. Marketing folks use Digital Twins to test positioning and explore the differences between various audiences in order to drive hyper-personalization and brand loyalty. Over the next few years, we will see brands and market researchers start to gain significant competitive advantages when they have an AI assistant.
Leveling the Playing Field
Native AI's co-founder Frank Pica is aware that, in the past, access to new technology has not been equitable. The pandemic further accelerated the shift to e-commerce, and with it came a new wave of smaller direct-to-consumer brands. Frank believes that enterprise-grade AI should not be restricted to businesses that can afford in-house Data Scientists. Therefore, Native AI's platform is built on the principle that it is easy for anyone to use. While many other solutions require heavy lifting, Native AI has created out-of-the-box dashboards and step-by-step walkthroughs to help businesses get started quickly and easily.
What's Next for Native AI
In order to remain the industry leader in generative AI for market research and consumer insights, Native AI has an ambitious roadmap. Next, Native AI will turn its sights to more advanced trend lifecycle tracking and harness the power of social listening to predict future consumer behaviors and motivations at scale.
To learn more about Native AI and its mission to make data-driven decision-making accessible, visit gonative.ai.
* This is a contributed article and this content does not necessarily represent the views of techtimes.com
