Predicting Customer Satisfaction Scores: A Practical AI Experiment

As a tech manager trying to keep up with the whirlwind that is AI, I've had a bit of an "aha!" moment. You can read all the articles and white papers you want, but to really get your head around this technology that's reshaping, well, everything, you've got to get your hands dirty.

That's what pushed me to roll up my sleeves and get some practical experience: Taking a pre-trained transformer model and teaching it to classify customer reviews.

And just to be clear, this wasn't about creating some groundbreaking, world-beating AI solution. There are plenty of those already! This was about my own learning journey, getting to grips with the fundamentals, all within the confines of my own computer setup. By tackling each step, from prepping the data to getting the model up and running, I've gained a level of understanding that you just can't get from passively reading about it.

Some Key Terms Explained (Without the Jargon!)

Before we jump into what I actually did, let's break down a few key concepts – in plain English, I promise!

AI Use Cases: Beyond the Chatbots

We all know about the fancy GenAI stuff powered by those Large Language Models (LLMs) – you know, the ones from OpenAI (GPT-4), Google (Gemini), and Anthropic (Claude) that can write poems and summarize reports. But there's a whole world of other AI applications out there that are useful for businesses.

Classification is a big one. Think of it as teaching AI to be a super-organized librarian, sorting text or data into different categories. This is how businesses figure out if customers are happy or fuming (sentiment analysis) and what they actually want (intent recognition).

Transformer Models and BERT: The Sentence Whisperers

Transformer models are a game-changer in AI. They're basically really good at understanding and working with sequences of things, like the words in a sentence. We can think of them as super-powered readers who can analyze tons of text to really grasp the context and meaning. BERT (which stands for Bidirectional Encoder Representations from Transformers – try saying that five times fast!) is an older, but still very capable, transformer model that's particularly good at understanding the context of words. That's why it's so useful for those classification tasks we just talked about, like figuring out the tone of a customer review or why someone is calling customer support.

Pretraining and Finetuning: From Generalist to Specialist

Right out of the box, BERT is already pretrained on a massive, general dataset. It's ike giving BERT a broad education in language or data patterns. But to make it really shine on specific tasks, you need to finetune it. This involves training it on a smaller, more focused dataset that's labeled with the categories you care about.

Now, the newer LLMs are like the overachievers of the AI world. They're so advanced that they often perform well on various tasks without needing this specific finetuning. Instead, we use something called "prompt engineering" to unlock their potential.

Though, even with all these fancy new LLMs, getting hands-on with BERT is still incredibly valuable for understanding the core principles of machine learning. It can help demystify recent advancements, such as the cost-effective creation of models like DeepSeek's R1. And anyone who's spent hours, fueled by coffee, watching a training process slowly grind to completion can likely attest to the often painful slowness and considerable computational cost involved in model training! 😅

What I Did

For this project, I took a pre-trained model and fine-tuned it to do one specific thing: classify customer reviews. The approach was pretty straightforward, really:

1. Grab the Data: I downloaded a dataset from Hugging Face – basically, a big collection of Yelp reviews, each labeled with a rating from 1 to 5 stars.
2. Choose the Tool: I picked BERT as my base model – like choosing the right wrench for the job. Then, I set up the training and evaluation parameters (think of it as configuring the settings).
3. Train the Model: This is where the magic (and the waiting) happens. I trained the model to spot patterns in the review text that matched up with specific ratings.
4. Build a Little App: Finally, I created a simple application where you can type in a review and get a predicted rating back. Nothing fancy, but it works!

Here on Hugging Face you can see the final implementaion.

For all the nitty-gritty technical details, check out my GitHub repo!

What I learned

Data: Garbage in - garbage out

And just as one might spend hours fueled by coffee watching a model train, a perhaps even greater amount of time (and caffeine) is often dedicated to the less glamorous, yet utterly essential, task of data preparation. While my Hugging Face dataset was a delightful anomaly, requiring minimal cleanup, I've encountered datasets before riddled with missing values, inconsistencies, and outright errors – a far cry from this well-behaved example. Real-world data often resembles an unruly garden desperately in need of weeding. This underscores the ever-present reality of "garbage in, garbage out" – a principle that keeps data scientists employed and coffee machines humming.

Computational Constraints and the Art of the "Good Enough"

Training even a relatively small model like BERT is no walk in the park, computationally speaking. My laptop, after chugging along for a solid half-hour on just a portion of the labeled data, made it clear that this wasn't a task for the faint of heart (or the underpowered of processor). While I could have squeezed out a bit more performance with even more training time (and possibly a new laptop), this brought to light a crucial trade-off: the eternal tug-of-war between model accuracy and computational reality.

This perfectly mirrors the challenges faced in real-world AI deployments. The "ideal" solution often gets sidelined by the "practical" one – the solution that delivers sufficient value without breaking the bank (or the server farm). In a production setting, this would be the point where we'd start justifying those shiny new GPUs or a move to the cloud.

It's all about finding that sweet spot: a model that performs well enough without demanding unrealistic computational resources.

Some Business Applications

So we have got our sentiment analysis, great! But how can this be useful in a business context? What's the takeaway for managers across the board – whether you're leading product development, marketing, or customer operations?

This AI-powered score prediction could be a practical tool for making smarter, data-driven decisions. Here are a couple of ideas:

• Product & Market Strategy - Competitive, Customer-Driven Innovation: By analyzing customer predicted scores next to competitor reviews on comparable products, you get a 360-degree view. Pinpoint the features and improvements that will have the greatest impact, identify unmet customer needs, spot opportunities to differentiate, and gauge where you stand in the market. It's like having a detailed battle plan, fueled by real customer sentiment, a clear roadmap for product evolution.

• Customer Operations - Smart Triage: This is about catching problems before they turn into full-blown infernos. AI-powered classification can enable businesses to automatically sort through customer feedback – reviews, emails, chats, you name it – and route it to the right team, based on what's being said and how the customer is feeling. Negative reviews highlighting specific issues? Straight to the relevant department for quick action and to prevent things from spiraling out of control. It's about empowering customer operations to be proactive, not just reactive.

The End... or just the Beginning?

So, there you have it – my journey into the world of AI, fueled by caffeine, curiosity, and a surprisingly well-behaved dataset. While this was just a small step, it's reinforced the power of hands-on learning. You can read all the theory you want, but nothing beats getting your hands dirty and building something yourself. And who knows, maybe this little customer review classifier is just the beginning... time to find another AI rabbit hole to explore!