Case Study

Automaker Engages with 2x More Customers Using Auto-Suggest AI

A leading automaker optimized social media monitoring with Interactions, which classified posts, labeled opportunities, and auto-suggested responses based on customer intent. This doubled engagements per hour and improved response times by 35%.

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About the Customer

A leading automaker partnered with Interactions to transform customer engagement using AI-powered virtual assistants. This solution doubled customer interactions, improved support availability, and enhanced satisfaction with seamless, scalable service experiences.

Industry

Automotive

Company Size

Fortune 100

Key Findings

2 x

as many customers engaged within an hour

35 %

faster response time

34 %

decrease in response composition

Challenges

With information readily available on the Internet, purchasing a vehicle has become simpler over the years. In fact, 70% of the buying process is complete before customers are even willing to engage with a live salesperson. This means customers want to be engaged the moment they’re ready to start talking—and not a second later. Consequently, this auto manufacturer needed to cut through the clutter of social messages to find the leads and capture their attention in near real-time.

Solutions

The Interactions social media engagement platform offered a two-prong solution to the automotive company’s challenge. First, the platform was built with smart moderation AI, which classifies social media posts in real-time and labels engagement opportunities based on customer intent (i.e., sales leads, assistance, etc.). By implementing this technology, the company reduced the time agents spent manually identifying which posts required responses.

Second, Interactions worked with the automotive company to enhance the platform with a new auto-suggest feature. This feature utilizes natural language processing to generate and compose real-time responses to customer posts. Additionally, auto-suggest learned from agent responses, understood brand voice, and self-adapted to improve responses over time.

Results

While the Interactions platform offered these three key results, the entire process was optimized and enhanced from start to finish compared to the previous agent-driven system. The platform started with real-time classification and engagement labeling, so customers could be identified based on intent. Additionally, the automotive company saw optimization and adaptation from the auto-suggest feature. The AI not only learned from agents to enhance its automated responses, it also understood brand voice—ensuring that customers received a cohesive, on-brand experience every time.

In social media, fast engagement is key, and the Interactions social media engagement platform ensured that this major auto manufacturer increased productivity for agents and both the quality and quantity of conversations with customers.

Increased customer engagement: On average, twice as many customers were engaged within an hour
Reduced response composition: Average manual time writing responses decreased from 216 minutes to 143 minutes per day
Faster response time: 35% faster in responding to customers across the channel