This is a case study on two-sided marketplace company that matches freelance labor with local demand, allowing consumers to find immediate help with everyday tasks, including cleaning, moving, delivery and handyman work.

I decided to take a closer look at performance in two of its largest categories in the company - House Cleaning and Local Moving. I will then provide analysis on what types of pros customers are interested in. I will also provide my recommendations for how the company can improve and grow their marketplace.

For the privacy of the data, I will only make the dataset visible in this Jupyter Notebook without posting the actual data on github.

• Optimize listing ranking algorithm and UX/UI to provide better searching experience for visitors, and ultimately increase the click through rate.
• Initiate marketing campaigns on each category according to the “high demand” season
• Improve UX/UI design to encourage visitors contact more pros before they make decision since this will eventually help increase the company revenue
• Hit the visitors with retargeting marketing campaign (push notifications, emails) to remind them login the app during the high conversion windows
• Send visitors call-to-action message after they perform about 9 clicks or 2 contacts - the sweet spots for conversion.
• Improve the ranking algorithm to prioritize the best matched result positions according to average rating, number of reviews, estimated cost, pro’s last-online time, and badges or awarded labels on app
• Initiate training/educating program for pros and drive their incentive to achieve badges and awards on the app to yield higher hiring rate
• Give personalized advice to pros on strategic pricing strategies
• Send reminders or in-app messages to visitors for writing reviews about pros after each service performed to improve pros number of reviews