The Data Analytics Mistake You’re Making

You have a lot of data, but are you using it effectively? Are you making the most of it to achieve your goals? Or are you making a common mistake that is costing you time, money, and opportunities?

Find out what it is and how to avoid it in this edition of #MarkyticsChronicles.

Data is the new oil, they say. It fuels your business, drives your decisions, and powers your growth.

But data alone is not enough. You need to analyze it, interpret it, and act on it.

That's where data analytics comes in.

Data analytics can help you answer questions like:

• Who are your most valuable customers?
• What are their needs, preferences, and behaviors?
• How can you attract, retain, and delight them?
• What are the best channels, strategies, and tactics to reach them?
• How can you measure and improve your return on investment (ROI)?
• How can you identify and solve problems, risks, and opportunities?

Sounds great, right? But there's a catch. Data analytics is not easy. It requires skills, tools, and methods that are not always available or accessible to everyone.

And even if you have them, you can still make a big mistake that can ruin your results and waste your resources. What is it?

The data analytics mistake you're making is: not having a clear goal.

Yes, you read that right. The most common and costly mistake in data analytics is not knowing what you want to achieve with your data.

Without a clear goal, you are like a ship without a compass, sailing in the dark. You don't know where you're going, why you're going there, or how to get there.

You end up collecting, analyzing, and reporting data that is irrelevant, inaccurate, or incomplete. You miss the big picture, the key insights, and the actionable recommendations. You lose focus, direction, and value.

How can you avoid this mistake? By following these four steps:

1. Define your goal

The first step in data analytics is to define your goal. What is the purpose of your data analysis? What is the question you want to answer, the problem you want to solve, or the opportunity you want to seize? What is the desired outcome or impact of your data analysis?

Be specific, measurable, achievable, relevant, and time-bound. For example:

• Increase sales by 10% in the next quarter
• Reduce customer churn by 5% in the next month
• Improve customer satisfaction by 20% in the next year

2. Identify your data sources

The second step in data analytics is to identify your data sources. Where will you get the data you need to achieve your goal? What are the types, formats, and qualities of your data? How will you access, collect, and store your data? Be careful, reliable, and ethical.

For example:

• Customer relationship management (CRM) system
• Website analytics tool
• Customer feedback survey

3. Analyze your data

The third step in data analytics is to analyze your data. How will you process, clean, and transform your data into insights? What are the methods, tools, and techniques you will use to explore, visualize, and model your data?

How will you validate, test, and refine your data analysis? Be rigorous, creative, and objective.

For example:

• Descriptive statistics
• Data visualization
• Regression analysis

4. Communicate your results

The fourth and final step in data analytics is to communicate your results. How will you present, share, and explain your data analysis to your audience? What are the key findings, conclusions, and recommendations of your data analysis? How will you support, illustrate, and persuade with your data? Be clear, concise, and compelling.

For example:

• Executive summary
• Dashboard
• Presentation

To illustrate how these steps work in practice, let's look at an example from the retail industry.

Example: How a retail store increased sales with data analytics

A retail store wanted to increase its sales by understanding its customers better. It followed these steps:

- Goal: Increase sales by 10% in the next quarter

- Data sources: CRM system, website analytics tool, loyalty program data

- Data analysis: The store analyzed the data to segment its customers into four groups based on their purchase frequency, recency, and value. It then calculated the lifetime value, retention rate, and profitability of each segment. It also identified the characteristics, preferences, and behaviors of each segment. It found that:

• ?Segment A: High-value, loyal, and frequent customers. They accounted for 20% of the customers, 50% of the sales, and 60% of the profit. They preferred quality, variety, and service. They were mostly female, aged 25-45, and lived in urban areas.
• Segment B: Low-value, loyal, and frequent customers. They accounted for 30% of the customers, 20% of the sales, and 10% of the profit. They preferred price, convenience, and speed. They were mostly male, aged 18-35, and lived in suburban areas.
• Segment C: High-value, occasional, and recent customers. They accounted for 10% of the customers, 15% of the sales, and 15% of the profit. They preferred quality, variety, and service. They were mostly female, aged 35-55, and lived in rural areas.
• Segment D: Low-value, occasional, and lapsed customers. They accounted for 40% of the customers, 15% of the sales, and 15% of the profit. They preferred price, convenience, and speed. They were mostly male, aged 45-65, and lived in rural areas.

Results: The store communicated the results of its data analysis to its management and staff. It recommended the following actions to increase sales:

• Segment A: Retain and reward these customers with personalized offers, loyalty programs, and premium services. Cross-sell and upsell them with complementary and higher-value products. Encourage them to refer and review the store online and offline.
• Segment B: Increase the value and profitability of these customers by offering them discounts, bundles, and incentives to buy more and higher-value products. Improve their experience and satisfaction with faster delivery, easier returns, and better support.
• Segment C: Reactivate and retain these customers by sending them targeted emails, coupons, and reminders to visit the store again. Offer them free shipping, free trials, and free samples to entice them to buy more and more often. Provide them with relevant and helpful content and advice to build trust and loyalty.
• Segment D: Acquire and convert these customers by creating awareness and interest with online and offline advertising, social media, and events. Offer them attractive deals, freebies, and guarantees to overcome their objections and doubts. Follow up and follow through with them to ensure their satisfaction and retention.

By following these actions, the store was able to increase its sales by 12% in the next quarter, exceeding its goal.

That's it. That's how you can avoid the data analytics mistake you're making and achieve your goals with data.

I hope you found this article helpful and informative. If you have any questions, comments, or feedback, please let me know. I'd love to hear from you.

And now, I have a question for you:

What is your data analytics goal and how are you going to achieve it?

Don't Forget to checkout my Data Analytics Portfolio here: https://www.behance.net/ishtiaqmarwat