Solving Product Innovation Challenges By Building A New Technology Stack

Solving Product Innovation Challenges By Building A New Technology Stack featured image

Table of Contents

4 mins read

Our latest blog on how AI can help you overcome the challenges in 2021 planning and demand forecasting proved to be a flashback of sorts for my co-founder Himanshu and I.

While discussing the unique challenges of the food world that changed even more drastically in 2020, we had one of our ‘back-to-the-roots’ conversation again.

It reaffirmed our belief that foresight about emerging consumer trends and preferences is going to accelerate and optimize the innovation funnel for CPG, food and beverage, and retail companies. We believe this will hold not only during pandemic times but also in the long-term.

But isn’t foresight already built into the systems based on the insights derived from traditional, tried, and tested market research, consumer surveys, and business analyst reports?

A decade-long journey in the food industry and our Eureka moment

When I worked at Givaudan, regularly discussed pain points with customers in the food and flavor industry, I realized there was a common thread to their challenges, irrespective of their product portfolio and geographies.

These challenges can be grouped into three areas:

  1. Smoke before fire: Brands desire early discovery of an emerging consumer trend.
  2. Staying ahead of the curve: Brands want to be agile in their response to rapidly evolving consumer preferences and emerging trends.
  3. Innovation funnel success ratio: Brands need to ensure a greater probability of success for new product concepts and launches.

When I began my quest for solutions to these challenges, I heard a lot about the emergence of data in volume, at velocity, and in variety around food, from unconventional sources.

I was getting closer to my Eureka moment!

Me and Himanshu Upreti in 2018 coding in between the prep for Demo Day

The Abundance of Big Data and the Need for a Technology Stack

All of us in the food industry had started realizing that the Internet not only has an abundance of data but also continues to make more data available in real-time.

We were staring at this unstructured and raw but highly contextual data, which consumers were making available, as they went about:

  • searching food, flavour , recipes, ingredients on Google, Bing, Naver, Baidu, and other search engines
  • sharing reviews, posts, their feelings and experiences), likes and dislikes, videos and photographs across social media platforms
  • ordering food from restaurant aggregators and food delivery platforms
  • shopping food, beverages, and grocery products from e-commerce websites

This trail of data, unlike traditional market research and customer survey data, is devoid of bias, both in-built biases from researchers and response bias from consumers.

On the other hand, it was neither humanly possible to find meaning from this data in its raw and unstructured form nor derive context and insights using legacy analytics tools and systems.

To overcome this situation, food and beverage companies needed a technology stack that could capture, process, filter, derive, and predict based on historical and real-time data from search, social, retail product, and a variety of other sources.

But no tech stack at that time could offer such capabilities.

A new tech stack: Artificial Intelligence to assist Human Intelligence

The Eureka Moment arrived when Himanshu and I found synergies in our vision for technology solutions for the food industry.

Himanshu’s deep expertise in Big Data Analytics and AI gave us the confidence to work on Natural Language Process and Machine Learning technology as a way of overcoming the industry’s three challenges.

Fast-forward to June 2018 – we successfully launched Foresight Engine:

  • It captures big data from the web, across search, social media platforms, retail product data, restaurant data, and food recipes.
  • It is built on a Natural Language Processing stack, ensuring that brands are not merely listening but deriving context and insights from this data.
  • It is language-agnostic which means it can understand context beyond English-language data. It reads and understands images too.

Our Foresight Engine Solution

Ai Palette’s Foresight Engine — Identify F&B trends in real-time

Foresight Engine was built with a vision to enable brands in the food industry to:

  • Take the guesswork out of predicting emerging consumer trends and their future trajectory
  • Be agile in their response to rapidly evolving consumer needs and preferences
  • Increase ROI by offering an Innovation Funnel pivoted for success

Our team at AI Palette work tirelessly to walk this talk. We partner with key stakeholders in the food and beverage industry to bring this vision to market in a way that constantly addresses their needs for a more agile product innovation process.