Leading global tech company reduces noise to unlock faster, more focused customer care

Challenge

A leading technology company with large‑scale global customer engagement faced a problem common to high‑volume environments: too much noise.

Across social and owned channels — including X, Facebook, Instagram, Threads, TikTok, and LinkedIn — their customer service teams were managing a constant stream of inbound content. But not all of it required action.

A significant portion of incoming messages consisted of non‑actionable or “non‑productive” content, including general community engagement opportunities, off‑topic conversations or posts outside the scope of technical support. This created unnecessary complexity for agents, who were forced to sift through high volumes of irrelevant content to identify the issues that truly required attention.

The result was slower response times, reduced agent productivity and difficulty focusing on the interactions that mattered most.

Solution

To address this challenge, the company partnered with Sprinklr to refine its use of Intuition AI.

The focus was on improving signal quality. They wanted to ensure that only relevant, actionable content reached customer care teams. Using historical agent data, the team retrained the Intuition model to better distinguish between:

• Content requiring a brand response (true support needs)
• Content that did not require action

A key element of this approach involved segmenting training data based on whether a message historically received a brand response. This allowed the model to more accurately reflect real‑world decision‑making and classify incoming content accordingly.

As a result, non‑actionable content was filtered out earlier in the workflow, reducing the burden on agents and enabling more precise routing of customer care cases.

Outcome

Within the first month of implementing the updated model, the company achieved a one‑third reduction in non‑productive contacts.

This had an immediate and measurable impact on performance:

• Agents spent less time sorting through irrelevant content
• Productivity improved, as teams could focus directly on actionable cases
• Response workflows became faster and more efficient

By reducing noise and improving signal quality, the organization created a more streamlined, scalable approach to customer care — ensuring that teams focused where it mattered most.