May 17, 2023

Transforming Trend Discovery With AI-NLP+Advanced Analytics

Date & Time (GMT):
May 17, 2023 12:47 PM
Date & Time (EST):
May 17, 2023 12:47 PM

Everyone knows that there are early conversations in social media that are forerunners of the next Rosé trend or the move to avocado showing up as an ingredient in your hand cream.

However, using this data in a way that predicts which of these early trends will grow versus those that will fizzle away, has been elusive. Last week, in4mation insights, had the pleasure of participating in a webinar with Converseon.AI, to discuss the exciting work we are doing with them to convert the deep ocean of social media data into a platform for ingredient, flavor, color and packaging trend predictions.

In addition to the webinar’s host, @Rob Key, CEO of Converseon.AI and our partner in this work, we were lucky to have a surprise guest from Unilever, @Anthony Cece, Senior Data Science Manager, join us. He kicked off the discussion with the following thoughts:

“…as a big corporation, we are well aware of the rising trends towards micro segmentation and personalization… people now have more choices and know much more about products….before we needed to meet the average needs of the average consumer while today that has all changed and we need to innovate fast”  - Tony Cece, Unilever

Tony continued to share that while social media data is certainly extensive and “real time” – using it in the right way and extracting the information in the right way, was not an easy task. He emphasized however that if you want to be a leader rather than just jump on the bandwagon of an existing trend, boldness was key.

What is going to help companies make these bold decisions? Here is what Tony thought was particularly relevant about this work:

“Mining social media data with a level of confidence that comes from the combination of AI enabled machine learning filters and statistical rigor that allows a company to make the right decisions on what early stage trends to take advantage of so you have the opportunity to strategically position yourself in the marketplace.”

Some of the key findings that were shared by Mark Garratt, co-founder of in4mation insights and an award-winning marketing scientist, included:

Taking the research grade data that we get from Conversion.AI and then curating it to get it ready for time series analysis. We have discovered several critical steps are needed to do that – and one is around establishing the seasonality trend of an ingredient.

We have discovered how to identify what we call “ingredient/flavor trend migrations”– a critical component for anyone managing the next sensory innovation opportunity. For example, imagine you are managing innovation in the spirit category, wouldn’t you want to know what botanical trends from personal care or sparking beverages will be migrating to alcohol in the next 6 months? Or vice versa?

The concept of ingredient / flavor trend flocking or said another way - “trends of a feather flock together.” Yes, we are discovering how ingredients and flavors group together and this is leading to a whole new range of exciting insights like - to name just one example - what kind of ingredients are bound for a decline versus an upsurge.

And while we are not ones to use a lot of hyperbole, we do feel pretty good about what clients’ that attended the webinar had to say about our Signal Spotting Trend Prediction (SSTP) engine:

“Just attended this webinar and was blown away… how can we take advantage of what you covered today for where we are focused?”

“The work that was shown here was fantastic and really exciting.”

This new approach that has been part of a multi-year development journey to power a new way to build confidence around innovation bets and trend spotting. The future is here and we are unlocking the predictive power of social media data!

You can listen to the full webinar hosted by our partners Converseon.AI and please let us know how this kind of work could help up your innovation game.

This blog first appeared on Linkedin.

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