Marketing & Consumer Insights

Social media trend detection

In a market where consumer behaviors change faster than decision cycles, a customer needed to anticipate trends instead of experiencing them. We built the platform that turns social media noise into actionable signals.

Problem

The client's marketing team made decisions based on quarterly market research and insights. Consumer trends, on the other hand, were evolving in real time on social networks, new consumer segments, breaking habits, changing perceptions. By the time information was sent back through traditional channels, the competition had already moved. Without a tool capable of collecting, analyzing and scoring these signals on a large scale, the customer was structurally behind the curve in its market.

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Solution

What we built

We deployed a team of 2 Data Scientists and 1 Data Engineer to design a complete platform for detecting trends in real time.

Step 1 — Data lake and collection pipelines. Implementation of an automated collection infrastructure capable of ingesting a very large volume of data from social networks continuously: texts, images, metadata, interactions.

Step 2 — Text and image analysis. Development of NLP and computer vision algorithms to understand the content of publications: themes addressed, products mentioned, associated visuals and context of use.

Step 3 — Sentiment analysis and scoring. Each detected signal is scored based on its velocity, volume, and associated feeling. The system distinguishes a passing noise from a real emerging trend.

Step 4 — Visualization application. Development of an interface in ReactJS and VueJS that allows the marketing team to follow trends in real time, filter by segment and trigger actions directly from the platform.

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