Now, think about how far AI has come and your business today. How are you currently engaging with your customers? Product differentiation and pricing strategies only take businesses so far in the 2020s; more and more, companies need to rely on the customer experience. Could the transformative power of AI and NLP be the key to elevating your customer service to new heights? As we delve into the world of NLP, consider its potential to revolutionize your customer interactions.
Natural Language Processing, or NLP, might sound like a complex term reserved for tech experts, but its essence is something we all interact with daily. NLP is a blend of computer science, artificial intelligence, and linguistics, designed to bridge the gap between human language and computer understanding. It’s the technology behind the virtual assistants in our phones, the chatbots on various websites, and even the email filters that sort our inbox.
Imagine NLP as a skilled translator. It adeptly converts the intricacies of human conversation into a language that machines can comprehend and respond to. This translation involves several processes, such as understanding the context of a sentence, recognizing the sentiment behind a text, or identifying key information like names and dates. But this translation goes way beyond your ninth-grade Spanish class, when Ms. Hernandez asked you to translate “What is this?” and you slowly converted word by word. (“Que es eso?” by the way.)
No, a better analogy is that computers think in ancient Egyptian hieroglyphics and we’ve recently invented our digital Rosetta stone. Where we have words, computers have tokens. Just think of a token as a digital equivalent to an Egyptian glyph, which we’ll just call a picture to keep things simple. Sometimes a single picture can substitute for multiple words. On the other hand, sometimes it takes a few pictures to express what we say in a single word. (If you’ve ever played Charades or Pictionary, you probably get it!)
Regardless of whether you use the Egyptian analogy, a comparison to communicating with aliens, or some weird example with ESP, the big idea is that the last few years have seen a tremendous leap in our ability to talk like an Egyptian.
But NLP isn’t just about understanding language; it’s about deriving actionable insights from it. Take Uber’s COTA (Customer Obsession Ticket Assistant) as an example. This NLP-driven tool analyzes customer support tickets, identifying the nature of the issue and suggesting the most effective responses to the support agent.
Uber reports the COTA can reduce ticket resolution time by 10% while maintaining, or increasing, customer satisfaction (as measured via survey). It’s a demonstration of how NLP can turn a simple customer interaction into a data-driven opportunity to enhance service quality. Moreover, COTA is an example of empowering, not replacing, the human being handling the customer service issue.
NLP is more than just a technological advancement; it’s a tool that can transform vast amounts of unstructured language data into strategic business insights.
The implementation of Natural Language Processing (NLP) in business goes beyond technological novelty; it brings tangible advantages that directly impact the bottom line. By harnessing NLP, companies can achieve significant cost reductions, enhance customer satisfaction, and drive revenue growth, all backed by real-world data and metrics.
Consider American Express, a long-time leader in customer service quality. Until fairly recently, AmEx measured customer service interactions the old-fashioned way, with a standard follow-up survey that assessed the overall customer experience. In a bold move, AmEx recently implemented a natural language processing system to instead review the full experience. Their Voice of the Customer scorecard is now based on a metric derived from a machine learning algorithm. And while about 70% of interactions are scored essentially the same either way, the other 30% can now be parsed in more detail by the algorithm to learn and improve.
The Society for Human Resources management recently found that 40% of U.S. businesses are already incorporating AI into their talent acquisition programs. Natural language programming can “read” resumes and make recommendations. Often this is used to screen out candidates who are missing certain skills or experience required to interview for a role, but NLP can also look for resumes that applied for job A, but might be a good fit for job B or job C or… and so on.
And with our earlier example, Hilton sought improvement in customer service efficiency and guest satisfaction with their NLP-powered virtual assistant. Connie’s ability to handle routine inquiries was designed to reduce the workload on customer service staff and also provide guests with instant, accurate responses, enhancing their overall experience. An improvement in service quality drives increased guest loyalty and repeat business, driving revenue growth.
These examples demonstrate the return on investment of NLP implementations. By automating routine tasks, providing deeper customer insights, and enabling more personalized interactions, NLP can transform the way businesses interact with their customers. As we delve into practical applications in the next section, it becomes clear that NLP is not just a tool for efficiency but a strategic asset for customer-centric business growth.
Starter Projects for Quick Wins
Integrating NLP technology can bring significant benefits. For businesses eager to embark on this journey, here are a couple of starter projects that could provide quick wins with manageable costs and resource requirements.
1. Implementing Basic Chatbots
A simple yet effective entry point into NLP is setting up a basic chatbot for customer inquiries. This can be as straightforward as a text-based system on your website or social media platforms, capable of handling FAQs and basic customer interactions. Tools like Google’s Dialogflow or Microsoft Bot Framework offer user-friendly interfaces and integration options, making this a low-cost project with immediate impact on customer engagement and support efficiency.
2. Implementing Sentiment Analysis Tools
Another accessible project is employing sentiment analysis to understand customer feedback. This is often applied to social media monitoring, but works as well for customer reviews or survey responses. There are dozens of tools available at fairly low cost; some of the better known include Idiomatic, Repustate, Lexalytics, and MonkeyLearn. Tools like IBM Watson or Google Cloud Natural Language provide APIs that can be integrated into existing data analysis workflows. The investment in these tools is relatively modest compared to the depth of understanding they provide about your customer base.
These starter projects are not only feasible in terms of cost and technical complexity but also offer immediate benefits in customer engagement and market understanding.
Challenges & Best Practices
While NLP offers exciting opportunities, approaching its implementation with a clear understanding of challenges and best practices is important. Keeping realistic expectations maximizes the chances of successful integration into your business.
- Quality Data and Iterative Refinement: A common misconception is that NLP solutions work flawlessly out of the box. In reality, they require a foundation of quality data and continuous refinement. Uber’s COTA evolved through iterative improvements; your NLP project will need ongoing adjustments and updates based on user interactions and feedback.
- Change Management and Integration: Integrating NLP into existing business processes can be challenging. It requires careful planning and change management. Start with small pilot projects to understand the impact and gradually scale up. Ensure that your team is trained and comfortable with the new tools. For instance, Hilton’s deployment of Connie involved not just technology integration but also staff training and customer education.
- Ethical Considerations, Bias Mitigation, and Data Privacy: NLP systems can inadvertently reflect or amplify biases present in their training data. Regularly auditing and updating your models is essential to prevent biased outcomes. Amazon famously developed a resume-screening system that couldn’t be used because it exhibited a consistent bias towards men.
- Collaborative Approach: A collaborative approach involving different stakeholders – from IT professionals to end-users – is essential. Regular feedback and open communication help identify potential issues early and ensure that the NLP solutions align with business objectives and user needs.
By acknowledging these challenges and adopting best practices, businesses can effectively navigate the complexities of NLP implementation.
Embracing the NLP Revolution
Natural Language Processing (NLP) stands as a transformative force in the realm of customer service. From Hilton’s early assistant, Connie, to Uber’s COTA and American Express’ customer satisfaction, NLP is not just a futuristic concept but a present-day tool driving significant business improvements.
As an executive, your role in navigating the evolving business landscape is pivotal. To harness NLP’s potential, create a dedicated internal team to identify and implement pilot projects. This team should:
- Assess Current Customer Service Processes: Identify areas where NLP can make a significant impact, such as automating routine inquiries or enhancing customer interaction analysis.
- Pilot NLP Projects: Start with manageable projects like implementing a basic chatbot or using sentiment analysis tools. These projects can offer quick wins and serve as a foundation for more complex NLP applications.
- Collaborate and Iterate: Foster a culture of collaboration and continuous improvement. Engage with your teams, gather feedback, and refine your approach based on real-world experiences and outcomes.
The NLP journey is iterative and collaborative, enhancing human interaction with efficient AI-driven tools. As Nobel laureate Dennis Gabor wrote, “The future cannot be predicted, but futures can be invented.” By integrating NLP into your customer service strategy, you’re not just adapting to change – you’re leading it.
For more columns from Michael Bagalman’s Data Science for Decision Makers series, click here.