There’s a lot of talk about Predictive Analytics and how AI can unleash a new paradigm, but most are confused about how to use it and what its impact will be. Especially marketers who are wondering - what is predictive analytics?
Predictive analytics employs algorithms and machine learning to anticipate outcomes and patterns, aiding decision-makers in risk assessment, process optimization, and fraud detection.
Marketers have been leveraging these models for campaign forecasting, trend analysis, customer segmentation, lead scoring, and churn prediction. It allows marketers to make data-driven decisions and tap into new market opportunities by enhancing their customer engagement, optimizing marketing efforts, reducing costs, and ultimately driving higher revenue.
Despite utilizing predictive analytics for a number of tasks, a notable 84% of marketing leaders find it challenging to interpret and extract actionable insights from it.
This could change with the advent of Large Language Models (LLM). With ChatGPT, there is so much more that can be done with predictive modeling - You can go beyond just predicting outcomes.
Here’s how. Read more: How Can You Extract Data Insights Using ChatGPT?
ChatGPT is not a traditional “predictive model.” It may or may not give you numerical outcomes like Tableau or RapidMiner, but it has vastly improved how we look at data.
Typically, the predictive analytic process consists of data gathering, model selection, training the model, model evaluation and then deploying it. Here, we’ll look at how ChatGPT can help simplify the process for you.
1. Model selection: Start with gathering data from various sources and cleaning it for redundancy and irrelevancy. Once your data is ready, you need to select a model that fits the nature of your dataset, the campaign, the degree of interpretability required, KPIs, and the computational resources that you have at hand.
How does ChatGPT help: Selecting the right model can be tricky based on your dataset. This could become complicated if you’re a beginner or have just started learning about predictive analytics.
Try using these ChatGPT prompts to help choose the right model:
2. Training the model: Once the model has been selected, you should add historical data. This allows the model to learn from the data, where you can adjust the parameters to optimize both learning and training.
How does ChatGPT help: Think of ChatGPT as your go-to person for quick tips and insights during the model training process. It can provide a step-by-step process, ensure data formatting, assist with data preprocessing, offer recommendations on initial values or ranges to consider, interpret training logs, and also raise red-flags for overfitting. You can even troubleshoot problems when you’re stuck.
Try some of these ChatGPT prompts:
3. Evaluating the model’s accuracy: Check for accuracy of the model on a subset of data. Various metrics, such as accuracy, precision, recall, and the F1 score, offer insights into the model's performance, with each metric providing a unique perspective based on the problem type, be it classification or regression.
By plotting predictions in graphical formats, such as line charts alongside the actual values, faults and anomalies in the model's predictions become clear.
How does ChatGPT help: When users encounter unexpected results or problems in their evaluations, ChatGPT can offer troubleshooting advice, potential solutions, and sample code snippets using popular libraries like scikit-learn or TensorFlow. It can also help refine the models based on evaluation results, providing techniques like hyperparameter tuning, additional training, or model adjustments.
Use some of these prompts:
There are various concerns around AI - bias, ethical use of data, privacy, security, user data collection, and data integrity.
And while OpenAI has announced the launch of ChatGPT Enterprise, which aims to combat data concerns, there are still risks that you should be aware of before using any AI prediction tool.
While the advantages of AI outweigh the disadvantages, it’s important to acknowledge the complexities and ethical considerations that arise - especially as we integrate AI further into our business and marketing strategies.
Have you been using AI for your predictive analytics?
Let us know your experience here.