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Schaman + ChatGPT: a new paradigm in Customer Service See more

Learning from Data and Patterns. When analytics is your ally to improve CX one step further

Could you imagine that every interaction that arrives at your Contact Center could help you improve your service? AI, data analytics, and pattern analysis come to the rescue to help you achieve it!

AI: The never-ending learner 

AI is currently at the forefront of technology trends, primarily referring to Machine Learning (ML). ML involves computational models that learn autonomously. But, how exactly does a machine learning model operate? 

To simplify, ML models (such as neural networks) can be seen as black boxes in the initial stages, where we focus on how to utilize them rather than their internal mechanics. Once an ML model is built, the pivotal step is its training. Many emphasize that the quality of training your AI model receives can be even more crucial than the model's inherent quality. 

Effective ML model training requires a collection of previous interaction data to provide background information. This is where each previous interaction contributes to enhancing your model.  

Once trained, the model can work its magic, solving problems and providing the right answers to customers. Moreover, ML models continuously learn from each interaction, whether right or wrong, using feedback to improve accuracy with each interaction.

Data and Metrics: Unleashing the power of analytics

Data surrounds us, and its processing and quantification have been a fundamental human need, as Galileo once asserted, "The universe is written in the language of mathematics."

But, how can data and analytics help us improve our CX? 

First of all, let us distinguish structured and unstructured data. Structured data is measurable, while unstructured data is not. For instance, NPS results fall under structured data, being easily quantifiable, while a user's text comment is an example of unstructured data, composed of words, meanings, and nuances that are challenging to measure directly. 

For this article's purposes, we'll focus on structured data as it's more straightforward and objectively usable. The key lies in collecting data and processing and analyzing it to extract value for your CX. A prime example is the NPS, where just obtaining a score from users can offer insights into your Contact Center's performance and provide more detailed analyses about agent performance, areas of improvement, or the convenience of specific solutions from the customer's perspective.

Clustering: Unveiling surprising insights through data combination

Clustering involves analyzing data groups that share common characteristics. For instance, if we had thousands of living beings' names, we could cluster them into categories like animals or plants based on their similarities. Clustering necessitates a more detailed analysis, often assisted by AI, to identify characteristics that frequently occur together. This knowledge can significantly enhance your processes. Some examples of the potential of clustering for the CX world will be provided in the next section.

Schaman: Leading Data analytics to allow you going a step further

Schaman's core mission is to continuously enhance CX and prevent recurring root causes. Schaman leverages AI to comprehend customer intent, identify issues or requirements, and offer optimal solutions. The Schaman team excels in AI training and deployment, a critical factor in achieving successful results with ML, ensuring the ML engine is optimized and responses are highly accurate. 

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Image: AI-powered chatbot giving the best reply and resolution to the customer

Additionally, Schaman offers its Analytics Module, enabling real-time analysis of your Contact Center's performance to identify areas for improvement. For example, if solvency rates for a specific root cause are low (or similarly, re-appearance rates are high for a given root cause), it's time to enhance the resolution process for that issue, rather than focusing on already successful processes.  

Image: Example of Solvency Rate VS. Volume graph, for each root cause

Schaman also features an AI-based module for massive incidence characterization, helping detect potential service outages and hidden issues that are difficult to spot without this type of analysis. 

Furthermore, Schaman aids in delving deeper by analyzing correlated parameters through characterization, allowing you to uncover hidden issues that might be challenging to detect otherwise. For example, if you notice that a specific issue occurs more frequently at the end of the month, or in a certain software version, it may be time to reevaluate processes or update equipment to rectify the problem. Another example: a clothing shop detecting that a particular t-shirt size change is frequent, and most times for a bigger size, could indicate a sizing issue for this particular product, or the need for clearer size information to guide customers.

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Image: example of data clustering, unveiling insights from data combination

In summary, data analysis empowers enterprises to understand their Contact Centers better and build superior user experiences through AI, analytics, and advanced data processing. At Schaman, we specialize in adapting AI models to enhance your Contact Center. Our powerful analytics module aids in pinpointing areas for improvement, while AI-driven data processing identifies potential massive incidents. Pattern analytics and clustering enable you to delve deeper and uncover hidden problems, taking your CX enhancement to the next level.



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