Case Study

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

While a top automotive company had a large staff monitoring social media messages, they recognized a need to optimize the process in order to more efficiently identify potential customers and respond to them. To do this, the automaker utilized the Interactions Digital Roots social media engagement platform to classify social media posts, label engagement opportunities, and offer auto-suggestions based on customer intent. This led to twice the number of customers engaged per hour for agents using the auto-suggest feature and 35% faster responses.

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The Problem

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.

The Solution

The Interactions Digital Roots 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.

The Results

While the Interactions Digital Roots 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 Digital Roots 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
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  • Reduced response composition: Average manual time writing responses decreased from 216 minutes to 143 minutes per day
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  • Faster response time: 35% faster in responding to customers across the channel

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