The Plan: Stage #1 of the Data Analysis Process

Welcome to the first part of our four-part series on mastering the Data Analytics Process. This article focuses on Stage 1: The Plan - the foundation of our 12-Step Data Analytics Roadmap.

By the end, you'll understand why setting clear business goals is crucial, how to ask the right questions, and which metrics truly matter for your business.

Whether you're just getting started or looking to refine your data process, this is exactly where the magic begins. Let’s dive in.

💡 Starting from scratch? Watch #1: The Analytics Plan – Crafting a Winning Strategy to learn how to create a robust analytics plan that drives results. Build a solid foundation for smarter decision-making and business growth!

The Data Analytics Roadmap is built on solid strategies and proven tactics used by thousands of our customers. It consists of four major stages:

• Plan: Set clear goals for your business.
• Collect: Gather complete, trusted data that fits perfectly into your analytics puzzle.
• Prepare: Transform and merge the data to make it actionable.
• Deliver: Present the data insights in an accessible, actionable way to drive business growth.

Stage 1, The Plan, is all about laying the groundwork for your data analytics journey. In this stage, you define the direction of your business by setting clear goals, asking critical questions, and choosing the right metrics.

Establishing a solid plan ensures that every subsequent action aligns with your core objectives and leads to meaningful insights.

Before starting any data project, it's essential to understand your business goals clearly. Think of it like setting off on a road trip without a destination- you wouldn't know where to go. Your business goals are the North Star, ensuring you're always moving in the right direction.

Whether you aim to increase sales, attract more customers, boost user engagement, or optimize marketing campaigns, having these goals upfront provides focus and purpose.

Broad goals, such as "increasing sales" or "getting more customers," can be overwhelming without concrete steps. It’s like saying, "I want to travel somewhere and have fun." Goals need to be specific and measurable to be actionable.

Have you noticed how these goals are precise and measurable? This way, at the end of your set period, you can easily assess whether you've achieved them.

Once you know your business goals, it’s time to ask the right questions. The quality of your data analytics depends on the quality of your questions. Think of them as a bridge between your goals and the detailed metrics you will monitor.

Questions can be categorized into two types:

• Strategic Questions: These focus on the big picture, such as "How can we increase user engagement on our website?"
• Operational Questions: These are more specific, like "Which webpage has the highest bounce rate?" or "What's the average time spent on our product page?"

They guide each of the following steps, from which metrics you need to measure and which data you need to collect to measure those data to how to aggregate them, group, filter, and which chart type to use to pinpoint the specifics. But asking the right questions is just half the battle won.

Not all metrics are created equal. It’s important to focus on metrics that truly impact your business goals, rather than vanity metrics that may look good but offer little actionable insight.

Vanity metrics, such as a spike in website visits, can be misleading if they don't translate into meaningful outcomes like sales or sign-ups. It’s crucial to avoid putting too much weight on metrics that don’t drive real value.

If your goal is to increase sales, focus on metrics like conversion rate, cart abandonment rate, and average order value - these are the heartbeat of your goals.

Metrics like cost per acquisition and lifetime value (actually being two main metrics you’d use when working with marketing data) become less valuable and less important to answer that specific question about increasing sales. They give you real, tangible insight into what's working, what's not, and where the business can do better.

Now that we have discussed the importance of asking the right questions, we will dive into the important metrics you need to focus on.

Understanding the difference between metrics and dimensions is crucial for data analysis. Metrics are the quantitative measures that track performance, while dimensions provide context to those metrics. Both are needed to tell a complete story, allowing you to see not just the numbers but also the factors that influence those numbers.

Metrics quantify your data, while dimensions give it context. Before diving deeper into data collection, it’s essential to understand both types:

• Metrics: These are numeric and answer questions like "How much revenue did we make?"
• Dimensions: These are qualitative and answer questions like "When? Where? Who?"

For example, in a pivot table showing store revenue by date, revenue is the metric, while the date is the dimension. Dimensions provide context that helps tell the whole story.

The key takeaways from Stage 1 are to always start with clear, measurable goals, ask the right questions, and focus on metrics that drive real business value. By doing so, you set a strong foundation for effective data analysis, ensuring that your efforts translate into actionable insights that support your overall objectives.

Data analytics is not just about numbers and charts, it tells a story - a story about your business, your customers, your products, and their journey. The first stage of this process is all about understanding the plot: setting specific goals, asking the right questions, and diving into the details with the right metrics.

In our next article, we will dive into Stage 2: Collect. This stage will focus on gathering high-quality data that will help answer the key questions you have identified. Stay tuned as we explore how to effectively collect the data you need for successful analytics.