Struggling to Catch Early Signals

For a leading F&B enterprise, monitoring public conversations was a constant challenge. Thousands of posts and comments were generated daily, yet most of the brand’s monitoring efforts captured only a fraction of them. Untagged mentions, misspellings, comment section conversations and slang-heavy complaints were often missed, leaving the company exposed to reputational risks whenever negative mentions surfaced.

Adopting dima and Exposing a Competitor’s Smear Campaign

With dima, the enterprise completely transformed its monitoring process. Coverage expanded dramatically, tracking tagged, untagged, and misspelled mentions across all platforms. More importantly, dima went beyond volume to deliver depth. By analyzing sentiment and emotion in Arabic, including slang, dialects, and Franco-Arabic, with 97% accuracy, it revealed not just what customers were saying but how they felt. On one critical day, dima detected a sudden surge in disgust-related emotion and flagged 14 alarming posts showing alleged product contamination. Instant alerts allowed the team to investigate quickly, uncovering that the posts were part of a competitor-led smear campaign and took immediate action.

From Reactive to Proactive Brand Protection

dima’s impact was measurable. Harmful content was reported and removed before it went viral, saving the brand from millions in potential losses and preserving its reputation. With 80% improvement in early negative posts detection, a 55% faster crisis response time, and a 70% increase in customer trust, the enterprise shifted from reacting after crises to proactively managing its brand image. What had once been a fragmented and reactive process was replaced with streamlined, Arabic-first intelligence that empowered decision-makers with the clarity and speed to act in no time