Thumbtack helps customers search for the right local professionals to get projects done. Our search product collects project details from customers and matches them against preferences from professionals. Afterwards, our ranking algorithm displays the professionals most likely to result in a job well done. We tackle the search ranking problem by scoring professionals that match the customer’s requirements and then sorting them by score. Earlier this year, we changed our search ranking algorithm from a heuristic scoring system to a machine learning (ML) based scoring system. This change was very challenging but impactful. In this blog post, we’ll discuss why we wanted to transition our search ranking algorithm to use machine learning,
At Thumbtack, millions of customers each year, across the entire nation, seek the help of hundreds of thousands of service providers (“pros”) to get jobs done. Analysts and economists at Thumbtack can use the marketplace activity data generated by these interactions to help drive efficiency in the marketplace and to understand where we can do better to help serve our customers and pros.
In this post, we showcase an example of a project, completed by our analytics team, where we visualized how Americans are moving across the nation. Specifically, we visualized a sample of approximately 200,000 long distance move (50+ miles) job requests our customers made through Thumbtack over the past few years.