Consumers today are becoming more discerning about which brands they trust and support.
71% of surveyed consumers believe that trusting the brands they purchase from or use is now more crucial than it was in the past. Gen Z appears to prioritize this trust even more. The report also reveals that consumers are interested in continuous interactions with brands beyond just buying a product or service.2023 Edelman Trust Barometer Special Report
These ongoing engagements help them establish trust and answer important questions, such as:
- Is this brand competent? Is it performing to my expectations?
- Is this brand ethical? Is it attempting to do good in the world?
- Is this brand relevant? Does it fit my lifestyle and identity?
As consumers become increasingly discerning in their brand interactions, companies are seeking new approaches for more transparent and personalized engagement. In this endeavor, the role of artificial intelligence (AI) is becoming indispensable. AI makes it easier for companies to provide customers with the real-time personalization they desire. AI is adept at recognizing critical permutations and nuances in each conversation, offering valuable information and human-like interactions to individual consumers.
Lessons Learned From Implementing AI in the Customer Experience
We’ve had the privilege of experiencing all aspects of how artificial intelligence and the customer experience are evolving together, sometimes producing stellar outcomes — and sometimes not. Here are the most important things we have learned about how AI technology is progressing to deliver real-time personalization:
1. Content Matters, But Context Matters More
The right context will consistently deliver better content. To achieve this, AI must rely on excellent emotional mining or sentiment analysis tools to comprehend the context of the conversation.
How can sentiment analysis be used to improve the customer experience, exactly? With the aid of tools like sentiment analysis, AI systems can detect emotions, sarcasm, and incorrect word usage. These tools enable companies to promptly identify individual customer sentiments and respond accordingly.
Natural language processing (NLP) enhances the customer experience (CX) by allowing your AI solutions to understand intent and context. Rather than a robotic response to a customer inquiry, your AI solution can deliver personalized responses based on individual conversational patterns and preferences that emulate human interactions.
2. Serve AI the Right Data
For machine learning (ML) to achieve the personalization your customers are hoping for and relate to your target audience, it needs the right data.
When it comes to collecting data, collect often, but focus on gathering only the data you want to measure. It takes a lot of data to produce intuitive and meaningful results, and depending on the industry, it can also take a long time. Rather than gathering every single bit of data available, track data at regular intervals to compare factors and trends longitudinally.
Compare and experiment with different machine learning models to fully understand their strengths and weaknesses. Keeping a pulse on different models can help you choose the best machine-learning framework and scale your personalization efforts more quickly.
3. AI Hallucinations Are Real — Lean on HITL
When training and utilizing AI, it’s essential to acknowledge that AI models can sometimes generate incorrect or inaccurate results, most commonly known as hallucinations. Researchers from Mass General Brigham conducted a study on the accuracy of ChatGPT in clinical decision-making and found that it had an accuracy rate of only 72%.
Needless to say, nobody wants their AI model to go haywire while it’s engaging with a customer. To counter the issue of hallucinations, many businesses rely on the human-in-the-loop (HITL) strategy during the training stage.
During the early stages of AI development, it is important to have human oversight to help reduce the number of mistakes and add creativity and newness when needed. These experts often include data scientists, data vendors, ML engineers, data analysts, and domain experts.
As AI models continuously receive human feedback with HITL, they can improve at faster rates and become far superior to models left to train on their own.
4. Start Small and Grow Outward
Nurturing an AI model to work in an intuitive, customer-friendly manner, where it provides value and conversions, can seem like a daunting task. We’re not going to lie; it’s not a simple task, and it can’t be done overnight. It requires dedication, patience, troubleshooting, and the knowledge of experts. The best way to ensure a successful outcome is to start small.
Begin your journey with a domain area you’re comfortable with. Spend some time observing, learning, and testing before you bring your AI solutions to scale. Work with your legal team to flesh out potential pitfalls and outline the arena your company is comfortable testing in. As you discover which models work well, what your customers are responding positively toward, and how much oversight you need, you can confidently step into other areas.
AI in Customer Service: The Future Looks Bright
Your customers want more connection, and AI has the ability to provide that personal touch at scale. You can start your customer experience AI journey today: