(In)validating a potential revenue model via a concierge style MVP

(In)validating a potential revenue model via a concierge style MVP

Our Digital Innovators work in various industries and organisations. During one of our innovation projects, we completed a validation project for the proposition Friend’, a corporate startup within Nationale-Nederlanden.

Want to know more? Contact Jasper Brand via jasper.​brand@​thetalentinstitute.​nl or +31610960766

The challenge

Friend is an online platform where men and women over 40, who occasionally feel lonely, are matched to like-minded people. Friend’s mission is to battle the increasing loneliness rate by assisting in finding and building meaningful friendships. Our goal was to (in)validate a potential revenue stream: offering activities to their users via partnerships.

Our approach

The potential revenue stream consists of two sides: the user who is willing to book activities and the potential partner who offers the activities on the platform. We ran a concierge-style MVP over the course of three sprints where we proved the user demand for booking activities in three different cities. 

We started with customer discovery to find out preferences regarding activities of the platform current users. Preferences such as intentions for booking activities, expectations and current behaviour. These insights were used to generate and build a platform with a wide range of activities. Once we generated sufficient activities for all cities, we invited a pilot group of users

from each city to participate in our experiment. From the user interface, the activities functionality seemed to be working automatically. But it being a concierge MVP, all the mechanics in the back-end were done manually by our Digital Innovator. This included sending out booking confirmation emails, activity reminders and activity reviews. Additionally, when activities were booked, reservations were made and tickets were bought also manually. This enabled us to test the functionality with minimum involvement of development capacity.

Once the experiment ended, we ran an in-depth analysis of all quantitative and qualitative data and concluded that there was high interest among Friend users to book activities in order to meet new people. With these insights, we organized a brainstorm session and worked out the requirements for building this functionality on the platform. The data generated with this experiment supports the team in their next step to validate the supply side of this revenue model: partnering with activity provides. 

The results

  • Validation of the Friend user demand for activities.
  • Additional insights into the preferred type of activity, day and time of the activity.
  • Actionable next steps to build this feature and continue testing further.

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