Leveraging natural language processing to build better solutions
Consumers are tech savvy now. Avoid innovating the old way, by leveraging new technologies.
Countless industries have felt the Covid shakeup, from the Netflixification of film and TV to retail, insurance, banking and others. This tidal change has been largely driven by how easy it is to track, collect and analyse user data, whether it’s on phones, wearables or what consumers post online.
Thanks to the ubiquity of all three, big data and social analytics are a thriving billion-dollar space, and is wielded by anyone who’s anyone in nearly every sector.
However, food manufacturers, producers and ingredients companies have lagged behind in catching up with this trend. For decades, everything from whether to include specific ingredients to selecting the best packaging was deliberated on by massive focus groups and lengthy audience testing programmes costing millions. Despite this, according to a study conducted by Harvard University, around 95% of 30 000 new products launched every year, fail.
In a post pandemic world, the market has changed for good, nobody will dispute that. With rapidly advancing technologies, scientific discoveries of the power of food on our health, and shifting consumer behaviours, it simply cannot be business as usual. Yet food, ingredient and nutrition brands still hold the view that doing market research the traditional way will meet today’s consumer demands who are sophisticated, tech-savvy, health-conscious and incredibly vocal. The time has come to shift gears and adopt a more robust, agile, comprehensive analytics strategy to identify market-making trends and act on them quickly and efficiently.
In a digital economy, it is estimated that an individual generates 1.7MB of data every second. This equates to around 463 exabytes generated per individual each day by 2025 through social media, communications, wearables, sensors and videos. This is huge, but how can we make sense of this ever growing vault of data to generate deep insights on sentiment, consumer behaviour, choice and attitudes that can help brands innovate and formulate for health and wellbeing in the new normal?
Even if it was possible to deduce overall consumer perceptions using focus groups, there’s one crucial difference between the guinea pigs of yesteryear and the latest market research techniques that leverage technology: the former is based on opinions, the latter is informed by identifying hidden patterns and trends in data acquired from a variety of sources.
It is estimated that an individual generates 1.7MB of data every second. This equates to around 463 exabytes generated per individual each day by 2025
And that’s where the value is. Literally.
Inspired by the Portuguese and Spanish word ‘to see’, QinaVer uses a unique approach that combines quantitative and qualitative analysis, domain expertise and social listening to empower food, nutrition and ingredient players with the clear vision and deep actionable insights to connect with their consumers in the Personalised nutrition space.
By making use of a consumer preference programme based on advanced text analytics and machine learning, brands can focus on specific product ideas and iterate, develop new products based on real consumer needs, time launches and promotions, work more efficiently with influencers and ultimately improve general customer experience.
QinaVer helps brands to reduce product failure rates and unnecessarily wasting resources during the product development cycle, whilst maximising accuracy and maintaining richness and nuance of data checked by experts.
This is a first of its kind offering in personalised nutrition. A searchable media library powered by AI and machine learning.
What does it mean for your business?
Better solutions, enhanced trust
- Innovate and renovate based on deep insights that are closer to real-life and closer to your consumer
- Communicate with your consumers in the terms and language they use.
Respond to shifting consumer trends and needs fast
Avoid wasting resources on a trial-and error or traditional research approach.
Predict what consumers will want and do next