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Luxury car company conquers the common name game in social listening

Luxury car company
Company Size
350K+ employees
Featured Product
messages processed in first three months of deployment
messages identified as spam in first three months of deployment
accuracy in identifying spam mentions in first three months of deployment


An automotive company pins decades of success to its unrivaled understanding of the luxury needs of its customers. As conversations about its brands have expanded to digital channels, the company has also expanded — listening to and engaging with customers on social and other digital channels.

But social listening has proved problematic for one of the companies’ brands. It has struggled to get good insight from online sources because the brand name is also a common noun. As a result, the company has gotten reams of irrelevant data in its listening reports — comprising as much 10% of their data set. Worse, there was no easy way to filter out the irrelevant data. Someone had to manually vet and track complex queries and regularly scrub reports in an attempt to get accurate data and real insights. It was an inefficient, labor-intensive effort. But without it, the team would consistently miss out on content that could provide valuable data and insights.

The company needed an automated solution that would enable it to identify only brand-relevant social conversations — and weed out the rest. This would provide a complete picture of the brand’s online market perception, including share of sentiment, competitive intelligence, and percentage of online conversations related to the brand.


The company implemented Sprinklr’s AI Studio to eliminate irrelevant social listening results.

The team worked with Sprinklr to create a custom AI model that predicts what is relevant to the brand. This disambiguation model now enables them to capture data about the brand — and nothing else. This included developing and validating custom AI text classification models to empower teams to filter and categorize messages.


The company now has access to relevant, actionable data that drives critical brand, business, and operational decisions. The custom-built brand disambiguation model has removed the manual work that was previously required to scrub the data.

With AI now working for them, the company has increased the number of channels it monitors with social listening to include regional channels and messaging apps. Within just three months of implementation, the company captured 30 million more earned mentions— and achieved 85% accuracy in identifying and removing irrelevant mentions.

“Gauging the impact of social media on our brand is contingent on good data, and Sprinklr has leveled-up our ability to capture good, clean data for actionable insights,” says the company’s head of global social media.