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Customer Analytics of an E commerce store using Power BI.
Today's question: So, when do you need a Power BI analyst for customer analytics?
Answer: When you have data, but you're not sure what it's telling you.
For instance:
- What percentage of your customers are returning customers?
- How many new customers are you attracting versus retaining?
- Which customers are most likely to come back?
- Which customer segments generate the most revenue?
- Are you losing customers over time?
- What drives repeat purchases?
But beyond the numbers, what should you do with these insights?
Should you focus on customer retention, improving the experience for first-time buyers, or investing more in your most valuable customer segments?
That's where data analysis comes in: turning raw data into actionable insights that help you make smarter business decisions.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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What are your thoughts on the ongoing Power BI conversation?
In the meantime, lets have a look at this Marketing Analytics Dashboard.
Episode 5: Revenue up 40%, profit down. Why? 📉
More sales don’t always mean more money in your pocket. In this episode I break down a business that grew revenue 40% while profit fell 30%, using a simple example.
What’s inside:
→ Why discounts quietly shrink your margin
→ How costs can grow faster than sales
→ The danger of spending ahead of growth
→ Small leaks (fees, refunds, unpaid invoices) that add up
→ 3 fixes: track margin, test price before discounting, know your profit per sale
Growth is not profit. Know your numbers.
Follow Me: for more money lessons. Episode 6 is coming soon!
#CountingCrux #Accounting #SmallBusiness #ProfitMargin #BusinessFinance #Bookkeeping #FinanceTips
Great reminder! 👏 Revenue means little if profit margins aren’t healthy. Tracking the numbers and making smart pricing decisions is key to sustainable growth. 💯
A machine learning project focused on analyzing global GDP data and predicting economic output using historical data.
The project uses a world GDP dataset, data processing, and a trained machine learning model to explore economic trends and generate GDP predictions. It includes a Python application, helper functions, and a saved model for making predictions.
Built to demonstrate practical skills in machine learning, data analysis, Python development, and predictive modeling.
