November 1, 2023

Automation & AI's Role in Enhancing Predictive Analytics for Marketers

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?

Pecan AI survey reveals that while marketers have adopted AI predictive analytics, they struggle with its smooth execution. Could leveraging LLMs like ChatGPT and other AI tools help navigate these challenges?
Pecan AI survey reveals that while marketers have adopted AI predictive analytics, they struggle with its smooth execution. Could leveraging LLMs like ChatGPT and other AI tools help navigate these challenges? Source.

How can ChatGPT improve the process of predictive analytics?

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. 

  • Automated data pre-processing: With the right plugins and precise prompts, ChatGPT can not only read data but also clean, pre-process, and generate textual data, making it more suitable for traditional predictive modeling tasks. Furthermore, in case of missing data, ChatGPT can predict or assign missing textual data based on the context and information available in the dataset. This ensures that the data fed to predictive analytics tools is of the highest quality, leading to better and more accurate predictions.
  • Improved textual predictions: When it comes to processing textual information and natural language, ChatGPT can outperform traditional models. For predictive tasks involving texts such as sentiment analysis or text classification, ChatGPT can offer more nuanced analysis.
    You can try prompts such as “The Excel contains subject lines for various email campaigns, could you help classify the subject lines that can be marked as ‘Spam’ based on the latest trends?”
  • Model development and adaptability: Since LLMs can interact with users in real time, it is quite capable of adapting to feedback. This ability enables it to assist in predictive model development, hyperparameter tuning, and even data analysis.
  • Interpretability: ChatGPT’s inherent ability to understand and respond in natural language allows it to generate human-readable explanations for predictions made by other models. This can exponentially help with increasing model transparency and its trust factor.
    Try this prompt next time you’re not able to understand the results of the predictive model: “The document shared contains results from a predictive analytics tool. Can you help summarize the results and share the main highlights of the report?” 
  • Knowledge integration: ChatGPT is running on GPT-4 which has been trained on 1.7 trillion parameters, plus, it can now access data in real time. Given its vast knowledge base and its ability to harness external knowledge, if ChatGPT is integrated into predictive models, it can enhance the model’s contextual knowledge and accuracy.

Examples of how ChatGPT can simplify the predictive analytics process

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:

> I need to run predictive analytics on this dataset. Which model would provide the most accurate results for [forecasting goal/objective]?
> I need to perform churn predictions. Which predictive analytics model is widely recognized to deliver the highest accuracy?
> I'm working on a project with tight deadlines. I need a predictive analytics model that's quick to train but still offers decent accuracy. Do you have any suggestions?

Read more: How ChatGPT can help you choose the best forecasting model and extract insights

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:

> Can you provide a sample code-snippet for training a linear regression model using TensorFlow?
> Is there a method to visualize the training process? Could you guide me through it?
> I've come across [term] in machine learning. Can you explain what it is and how to implement it?

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:

> I've noticed some anomalies in my model's predictions. How can I fine-tune it to get better results?
> I want to see the distribution of errors in my regression model. How can I plot a residual graph?
> My model seems to be overfitting based on the evaluation results. Could you guide me on techniques to address this?

FLow diagram showing Predictive Analytics Process

Read more: How AI tools like Tableau and Qlik Sense help with data visualization

Risks and ethical issues you need to know

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. 

  • Overfitting: This undesirable behavior often happens with complex AI models where they perform exceptionally well on the training data but work poorly on new, unseen data.
  • Interpretability: Advanced AI models are often described as "black boxes" because it's difficult to understand how they arrived at a specific prediction or decision. This lack of transparency and trust is significant in critical sectors such as healthcare or finance, where it’s important to understand the reasoning behind a decision.
  • Accountability: The question of who should be held accountable for an AI model's decision is not straightforward and the answer can vary depending on the context and stakes involved. Is it the developer of the model, the user who interacts with it, or the organization that deploys it?

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.

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