For many businesses, the contact center continues to be an unoptimized source of revenue, with significant potential for improving lead conversion and reducing cancellations. Until now, optimizing sales in the contact center has been challenging because of limited insight into the factors that drive sales conversions and retention. Current evaluation methods, which often rely on small samples and use only sales rates to assess agent performance, fail to capture the nuances of agent behavior. Also, inefficient processes for managing outbound leads can result in missed customer connections and reduced sales performance overall.
Now, however, a generative AI (gen AI) speech analytics engine can help businesses pinpoint buying triggers, track how customers purchase across digital and in-person channels, and identify the most successful agent tactics for preventing cancellations. To fully realize these benefits, businesses need to invest strategically in sales use cases and pair them with the right business expertise to configure AI solutions to their specific needs.
Here, we continue our exploration of early applications of gen AI in the contact center, building on our article, “Gen AI in customer care: Early successes and challenges,” and our recent blog post on quality assurance, “AI mastery in customer care: Raising the bar for quality assurance.” This blog post explores the potential for gen AI to increase sales conversion and reduce cancelations and offers guidance on getting started.
Increasing conversions to optimize sales
To maximize sales opportunities, contact center sales leaders can start by developing a comprehensive understanding of what actually drives sales success, including the specific tactics that resonate with customers and the capabilities required for effective sales conversion. Once these drivers are defined, the next crucial step is to assess how well sales agents are supporting and executing these drivers.
A successful sale depends on various parameters, including customer needs, product offerings, pricing, and agent skills. However, the performance of customer service sales agents varies considerably, and current manual evaluation methods for assessing their effectiveness typically cover less than 5 percent of total call volume, giving few clues into how agents are really performing. Sales rates provide a basic measure of performance, but they don’t reveal the underlying tactics agents use or how frequently they leverage key steps that lead to sales conversions. This lack of insight makes it difficult to understand which specific behaviors contribute to success and which do not.
Agents can leverage a mix of primary skills, such as comprehending customer issues and identifying needs, and secondary skills, such as objection handling and empathy. In a four-week pilot analysis conducted on a group of agents at a mobility company, gen AI was able to identify instances of agents demonstrating all of the predefined skills during calls. Data analysis on agents’ skills showed that primary skills like identifying customer needs—for example, urgency, flexibility, and trust—were associated with effective conversion, but secondary skills did not significantly affect the outcome. By comparing agents with top converters, gen-AI-powered analysis was able to identify specific coaching opportunities.
Reducing cancellations to optimize revenue
To optimize revenue in the contact center, reducing order cancellations is as critical as increasing sales conversions. Cancellations occur for a variety of reasons, among them pricing issues, product dissatisfaction, and problems with delivery timing. Understanding why customers cancel their orders and employing effective saving strategies can significantly improve overall sales performance. If a customer is concerned about pricing, for example, the most effective way to prevent cancellation might be to offer a discount or flexible payment option.
In the mobility company’s pilot program, a separate analysis highlighted that top-performing agents who consistently applied certain effective sales techniques achieved significantly lower cancellation rates than their counterparts. Supervisors employed this insight to tailor their coaching approach more effectively, resulting in more successful interactions and higher conversion and retention rates.
The mobility company’s gen AI pilot is already finding improvement opportunities across sales and order cancellation. Its gen-AI-enabled voice analytics program is providing detailed insights into agents’ sales tactics, save tactics, skill adherence, and need identification. The company expects that use of this technology will deliver a 5 to 10 percent boost in conversion rates, a 10 to 20 percent decrease in canceled orders, and a 10 percent enhancement of customer experience ratings.
Overcoming hurdles to get the best of AI
While the potential impact on revenue is significant, gen AI needs to be designed and deployed with care to address the inherent risks and challenges associated with this still-new technology. For example, gen AI can produce inconsistent results when tasked with multiple logically related tasks in a single prompt, such as simultaneously seeking to understand a customer’s reason for canceling an order while also determining the outcome of a call. Generating accurate and logical outputs will require precise, prompt engineering—and careful iteration thereof.
Similarly, gen AI models need well-defined parameters so that results will be useful. Consider an analysis of sales drivers across 1,000 customer transcripts; the gen AI output might yield 1,000 unique results, which is overwhelming and impractical. In contrast, gen AI using a model with a predefined list of drivers can produce clearer, more actionable insights.
Bridging the gap between technical and business expertise
A further challenge is the common misconception that deploying gen AI will automatically lead to impactful insights and results. Many organizations believe they can simply feed data into AI and immediately extract valuable outcomes, but the reality is more complex. Success with gen AI deployment in the contact center requires a structured development process and ongoing collaboration between technology and business teams. Without this alignment and human validation, organizations risk producing inaccurate outputs or impractical insights that cannot be easily implemented.
A successful gen AI implementation, therefore, requires individuals who can bridge the gap between business objectives and technical execution, ensuring that AI tools are properly tuned to meet the specific needs of the business. And while gen AI is powerful, it is important to understand that it is not a magic solution.
In essence, success lies in guiding the AI with independent knowledge and integrating it into a broader, well-structured strategy informed by deep business insights. The real power of AI emerges when it’s used as part of a well-informed approach, combining both the AI’s capabilities and an expert understanding of the business context.
Gen AI holds enormous potential to revolutionize sales and retention within the contact center, but the technology needs to be thoughtfully deployed. Companies that get the best returns from their AI investments are likely to be those that invest in the right expertise, adopt a well-planned approach, and are willing to iterate on their strategies.
The authors wish to thank Kartik Rawal and Nitish Gupta for their contributions to this blog post.