Streamlining data analysis with AI

1. Streamlining data analysis with AI

So far, we've understood how AI can streamline research, ideation, and creation. Now, we'll learn how to AI can be used to measure, optimize, and communicate about our marketing initiatives.

2. Why marketers need strong analytical skills

Marketing is one of the most data-driven fields out there. Social media teams need to analyze post metrics to understand which content drives the most engagement. Paid advertising teams constantly optimize for metrics like return on ad spend and click-through rates. Content marketers measure how their articles perform in organic search. And demand-generation teams track how campaigns impact revenue and demo requests. All of these activities require marketers to have strong analytical skills.

3. The AI augmented analytics workflow

AI can help accelerate these existing skills by helping you analyze datasets faster, build reports and visualizations more efficiently, and tell data stories that drive impact.

4. What's behind the drop?

Let's illustrate this with a practical example. Imagine you're a content marketer. You are accountable for the traffic to your company's blog. You know from your company dashboard that traffic has decreased, but you don't know why. Let's uncover the answer with AI.

5. The dataset

First, we export a dataset of all of the blogposts on your site, and their month over month traffic. We're showing a few rows here for simplicity's sake.

6. Step by step analysis with AI

We then upload our dataset to Copilot or any other AI tool of choice. Our approach will be iterative. We break down our analysis into small steps, each time providing clear goals, context, and limitations. In this first step, we provide Copilot our goal, and context over the dataset. We specifically ask Copilot to visualize total traffic month over month in a line chart.

7. Traffic decline visualized

Just like most AI tools, Copilot will use Python to visualize total traffic. We can confirm that total traffic has gone down. But is it all the articles or just a handful? Let's break it down even further.

8. Uncovering the declining articles

In the same thread, we ask it to visualize a multi-line chart, where each article is a line in the chart. To ensure our multi-line chart isn't cluttered, we ask it to only visualize articles with average traffic above 5,000 a month.

9. Exploratory data analysis with AI

By looking at the created visualization

10. Exploratory data analysis with AI

We can see that only 4 articles are responsible for the decline.

11. Isolate the declining articles

Let's isolate these articles. We ask Copilot, to return the articles that have decreased, ranking them from most decreasing to least decreasing.

12. The culprits

We confirm what we saw in the visualization. The decline is driven by four articles only.

13. Next steps

Time to create a report. We ask Copilot to summarize our findings into a short report. We ask it to cover the following: Describe total traffic decline, assess which articles can be attributed to the decline, how we plan on addressing the decline by refreshing these articles, and ensuring that the report is a simple 1 pager.

14. From data to report

In just a few prompts, we've gone from data to report with actionable insights.

15. Bad prompts vs good prompts

Before we practice our skills, let's take a step back and understand best practices and risks when working with AI for data analysis. First, clear communication is essential. As always, provide your AI system with clear goals, contexts, and limitations about your data, and what you hope to achieve with it.

16. Context management

Second, context management is key. As you saw, we've been iterating within the same chat window, and maintaining that context is essential for successful collaboration. If you feel like your AI assistant is veering too much off track, don't hesitate to restart in a new chat.

17. Data privacy and PII

Third, be mindful of uploading personally identifiable information in AI systems. Always check company privacy policy, and AI usage policy.

18. Quality and iteration

Fourth, double-check the quality of the results. AI systems can make mistakes, and this is where data and programming literacy are still essential for verifying your AI output. Check out DataCamp data analysis courses to sharpen your skills there.

19. Let's practice!

Now it's your turn to put these concepts into practice! Let's analyze some marketing data with AI.

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