How Industry-Specific Data for Businesses Can Unlock the True Power of Generative AI
At the forefront of AI’s rapid evolution, conversation analytics is becoming an increasingly vital tool, evolving the way businesses understand, engage with, and ultimately sell to their customers. As the demand for both bespoke and industry-specific solutions grows, the importance of augmenting these tools with vertical data has become increasingly evident. Now, more than ever, companies can and must make use of their unique and proprietary data to cater to the individual needs of their customers.
Having access to a large catalog of customer conversations within a particular industry unlocks the ability to identify important, emerging trends that drive mission-critical insights for businesses of all sizes within that industry, an advantage that companies are eager to embrace. Fortunately, with the evolution of Generative AI (GAI), new tools are available to extract meaningful insights from valuable data processed from hundreds of millions of conversations between businesses and their customers.
Before AI, this simply wasn’t possible, especially at scale.
The Impact of Generative AI
Since the launch of ChatGPT in November 2022, companies worldwide have been racing to integrate Large Language Models (LLMs) and other emerging AI technologies into their products and workflows, to better understand and cater to customer needs. As the early adopters have come to terms with the capabilities of these cutting-edge tools, one technology – GAI – has proven to be indispensable in the realm of conversation analytics, where it has emerged as a powerful catalyst for unlocking nuanced comprehension across various industries.
ML-Powered Efficiency: Aira, VMware, and Broadcom’s 5G Network Management
Even on its own, Generative AI offers a wealth of conversational intelligence to businesses of all shapes and sizes.
As an example, consider a sentiment analysis tool that can be used to determine the overall sentiment from a transcript of a phone conversation between a business and a customer. By analyzing the content of the conversation, a Generative AI tool, such as a Large Language Model (LLM), can classify the customer interaction as positive, negative, or neutral; data the business can then review and act upon.
Though these data points are indeed valuable, they lack specificity and likely miss critical details unique to individual industries. This limitation is akin to diagnosing a patient with only a cursory examination; the symptoms may be treated, but the root cause is not addressed.
However, when businesses are equipped with Generative AI trained on high-quality, industry-specific data, they can dive deep beyond surface-level sentiments into an ocean of mission-critical insights, previously unreachable. In the home services vertical, for example, these tools can identify pain points in home repair experiences down to the product level, ranging from customer dissatisfaction due to unsatisfactory service, to defective or broken parts, and more.
Such discoveries enable a targeted and effective approach to addressing the individual challenges a business is facing.
Customization is Key
Proprietary data that is rich with industry-specific details empowers companies to tailor their AI models with precision, ensuring that recommendations are both accurate and relevant to the context of a given industry. This level of customization is particularly crucial when the stakes are high, and a one-size-fits-all approach falls short.
For instance, success in the automotive industry generally requires an understanding of rapidly evolving technologies, market trends, relevant inventory, and consumer preferences that are distinct from other domains. Custom AI models, built to recognize unique names and entities (such as vehicle makes and models), or trained to identify the presence of an opportunity for a location’s specialty service (oil change, new tire), can have a profound impact not just on a company’s bottom line, but on customer experience and reputation management, as well.
Sentiment analysis tools, while effective at surfacing generalized patterns, may struggle to consistently infer why specific issues, such as customer frustration and dissatisfaction, persist in customer interactions or may struggle to identify how an issue relates to a broader brand or industry trend.
GAI can look at the nuance and context of each conversation, identifying not only the small percentage of customers whose frustration and dissatisfaction might lead to negative online reviews but also why these customers are dissatisfied with their service.
Conclusion
In this new era of increasingly democratized AI, the true worth of conversation analytics for businesses lies less in the sheer volume of data they can easily process, and more in the richness and applicability of the signals produced. Buried in the thousands of customer interactions SMBs and enterprise companies participate in daily is not just a vast quantity of hidden insights, but the story of their customers, and their business, waiting to be read and understood.
Re-occurring pain points, emerging product trends, vocally frustrated callers – all stories that would take humans ages to identify on their own. With the proper tools and the right set of data, businesses can use verticalized GAI to unravel these disparate customer narratives into an actionable strategy that can be put into practice.
Comments are closed.