Metrocar is a fictional ride-sharing company who is seeking to indentify user drop-offs across the stages and plans to implement dynamic pricing to boost revenue during high demand.
-7 FUNNEL STEP QUERY-
The 1st. query summarizes count of users & count of rides x each of the step funnel (the stages that the user does: Download, Sign-up, Ride Request, Ride Accept, Ride Complete, Payment & Review), x each of the 3 platforms, x 5 age_ranges & x each download_date. Its purpose is to get insights from the step funnel at a deeper level (platforms, age_ranges, download_date). I used this dataset in Tableau to make viz and being able to answer these business questions:
- What steps of the funnel should we research and improve? Are there any specific drop-off points preventing users from completing their first ride?
- Metrocar currently supports 3 different platforms: ios, android, and web. To recommend where to focus our marketing budget for the upcoming year, what insights can we make based on the platform?
- What age groups perform best at each stage of our funnel? Which age group(s) likely contain our target customers?
- What part of our funnel has the lowest conversion rate? What can we do to improve this part of the funnel?
-RIDES REQUESTED X HOUR QUERY-
This query summarizes all the rides has been requested in each hour of the day. Its purpose is to summarize x each hour how many rides has been requested, with that then being able to use it in Tableau and viz it to answer the business question:
- Surge pricing is the practice of increasing the price of goods or services when there is the greatest demand for them. If we want to adopt a price-surging strategy, what does the distribution of ride requests look like throughout the day?.