Zehra Cataltepe is the CEO of TAZI.AI an adaptive, explainable Machine Learning platform. She has more than 100 papers and patents on ML.

AI is transforming customer management in financial services, retail and SaaS, among many other verticals. Highly changing environments and limited budgets increase the interest in this technology and its benefits. Not using AI or generative AI is becoming a non-viable option for many, considering the evolving competition, customer demands and costs.

In this article, I outline strategies for personalizing customer support using AI, GenAI and analytics, accompanied by specific use cases.

Customer Management For Demand And Retention

In every industry, keeping customers happy and loyal is crucial. Both AI and GenAI can help by determining what each customer likes, allowing you to offer personalized services. This approach can help improve customer satisfaction and keep them coming back, which is important in a competitive market. With AI and GenAI, companies can quickly spot and meet customer needs, helping to ensure every experience is smooth and rewarding.

Let’s see which business problems can be aided by AI, GenAI or analytics and how.

AI For Customer Complaints Classification

AI helps categorize customer complaints by severity, from non-issues to serious concerns. First, it requires plenty of examples (complaint texts and their classifications) to learn from. The AI, like a neural network, trains on these examples to classify new complaints accurately. If it doesn’t perform well, you might need to adjust the process or get more data.

GenAI For Customer Complaints Classification

GenAI with zero- or few-shot learning needs fewer examples for customer complaints classification. You set up the model with basic instructions expressed in a prompt. If you want to utilize few-shot learning, you also provide a handful of examples. Then, when classifying new complaints, the model uses these guidelines to make accurate classifications.

Especially if you use few-shot learning by means of selecting appropriate past examples for each complaint, the GenAI solution adapts over time, learning from each new case. This approach is great for quickly adapting to new types of complaints without needing a lot of data upfront.

GenAI And AI For Trending Topics And Sentiment

Using GenAI, you can understand the mood (sentiment) and main topics in customer messages. The new topics may emerge in time. By analyzing the sentiment and topic messages over time, you can spot trends in what customers are talking about or how they feel. The trending topics information can help with call center automation.

Analyzing both the trending topics and sentiments alongside product and customer data can enhance the effectiveness of AI models, aiding product teams in refining their strategies. Additionally, AI can swiftly identify shifts in trends or sentiments, offering insights into potential issues or opportunities.

GenAI For Reducing Response Time

Retrieval augmented generation (RAG) can help enhance customer support operations for companies with complex products, especially in scenarios where there’s high turnover among call center staff.

RAG can help with knowledge retention, training, onboarding and transfer among the staff as well as consistency in responses. For complex products, customer queries can be intricate and require detailed technical knowledge. RAG can quickly pull specific information related to the product’s features, troubleshooting steps or usage guidelines, assisting support staff in delivering comprehensive and precise answers.

As RAG systems can be updated with new information and data, the customer support provided via these systems can continually improve.

Dynamic Data

In a rapidly changing environment, the ability to adapt to new data is crucial for both AI and GenAI models. Change can happen due to market shifts, competition, regulatory changes, product changes and improvements and evolving customer needs.

Continual learning AI systems can learn both from domain expert feedback as well as newly created data. GenAI systems can also get feedback from domain experts in terms of updated prompts or updated examples chosen for few-shot learning.

Trustworthy AI And GenAI

Building trustworthy AI and GenAI systems is essential, especially for applications that affect people’s lives. The European Union has set standards to ensure AI’s safety, fairness and transparency, emphasizing seven criteria: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, societal and environmental well-being and accountability.

In the implementation of these criteria, involving human domain experts in the loop for the design and monitoring of the systems, using only the necessary data, avoiding PII or remote LLMs unless there are strict guarantees and involving legal and IT teams at every project for security and compliance are necessary steps.

In this article, I have outlined how you can utilize AI, GenAI and analytics for improving customer satisfaction. The use cases I have outlined are some initial examples. There are plenty more decisions you make during any customer’s journey, many of which can utilize AI, GenAI or analytics to increase the efficiency and satisfaction of your teams as well as increase your customer satisfaction.

Zehra Cataltepe

Forbes Councils Member

Forbes Technology Council

Also Published on Forbes: https://www.forbes.com/sites/forbestechcouncil/2024/03/05/customer-management-with-ai-and-genai-in-a-highly-changing-environment/