Bike sales analysis

The Europe bike sales data had 18 columns but gave decision-makers little visibility into which regions sell best, how customer groups behave, or whether things like children or commute distance influence a purchase.

I cleaned and prepared the data in Excel and Power Query, answered a fixed set of business questions with pivot analysis, then built an interactive Power BI dashboard so the answers could be explored.

1. Kept an untouched copy of the raw table, then cleaned a duplicate in Power Query.
2. Removed duplicates, checked missing values and standardised inconsistent entries.
3. Analysed purchase rate by age, gender, income, marital status, commute distance and region.
4. Built a Power BI dashboard with KPI cards, charts and filters.

• More customers did not buy a bike than did, so conversion is the main opportunity.
• Middle-aged customers were the most likely to buy.
• Higher-income customers bought more often.
• Customers with shorter commutes bought more often than those travelling further.
• Married customers were slightly more likely to buy than single customers; the gender gap was small.
• Purchases varied noticeably by region.

• Focus marketing on middle-aged, higher-income customers with short commutes.
• Sell bikes on convenience, cost savings and health for short commutes.
• Invest more in strong regions and investigate why weaker ones lag.
• Offer instalments and entry-level models to reach lower-income customers.