Planned Feature: Employee Tracking + Leaderboard
Hi Climbers, we are planning a new feature around employee tracking, and before building it we want to collect your feedback in detail, because we think this could become one of the most powerful ways to help local businesses generate more reviews in a natural and consistent way. The idea is this. The business owner will have a dedicated section called Employees Links, where they can add the employees who are responsible for asking customers to leave reviews. Adding an employee will be very simple: the business just inserts a photo, a name, and an alias, which can also simply be the employee’s real name or nickname. Every time an employee is added, the platform will automatically generate a dedicated review link for that employee. It will also be possible to download the related QR code. Technically, this employee link will simply be the general review link with an extra URL parameter, something like employee=alias. In this way, we will be able to track the unique visits to each employee’s review link. So if an employee asks a customer to scan their own QR code or open their own review link, the business will be able to measure how many unique visits that employee has generated. On top of this, we want to build a leaderboard, inspired by the Skool style, where the business can see the ranking of employees based on the number of unique visits to each employee’s review link. The leaderboard will be viewable for the last 7 days, the last 30 days, or all-time. This would already create an interesting use case for businesses. For example, the company could decide that, on the last day of every month, the employee with the highest number of unique visits receives a bonus in payroll, because that employee made a stronger effort to ask customers for reviews. But we want to take this feature much further. Our idea is that if, inside the text of the review, the customer mentions the alias of the employee, then that review will be automatically assigned to that employee. For example, if the review says “Luke was very kind with us”, the review will automatically be associated with the employee whose alias is “Luke”.