To better understand how our users are working with our product, we are aggressively trying to contact them by phone to get interviews. At the same time, we are also attempting to build in data analysis into our product offering. These data analysis stem from a set of metrics that we have designed. This set of metrics is designed to extract the value that we have created rather than the value we have extracted from our users. This will give us a better grasp of how our product is doing in generating value for our users.
Inspiration: Andrew Chen Benefit-Driven Metrics: Measure the live you save, not the life preservers you sell.
Idea: Value creation generates revenue and traffic. Therefore, do no focus inwards on self-interested gain, focus on value creating for customers.
User benefits metrics:
- Want people to view their event. [measure # of click through per event from home page]
- Want people to recommend it to their friends [measure # of source from friend’s newsfeed to other means to enter our site] NB. Against all other ways to enter the site, eg. from direct link we place on BBS/we give to friends
- Want to know if people will RSVP/like their organized event through our site [#actions towards like or join/total number of people who visit the site]
a) Want to know if their friends have signed up. [track the evolution of people signing up over the duration which the event is on the page]
b) Want an events management page [track # times a user goes into his ‘profile’ every time he enters the site]
c) Want to find an event that he can go to [% of times he entered the site and actually signing up to any event]
d) Finds it a good site to search for events [# of times per day a unique user revisits the site]
e) Site is attractive enough to keep user interested [average length of time a user spends on the site]
- Google map feature vs poster wall feature [# of click through to events from map/# click through to events from poster wall]
- Other features which might be interesting later on eg calendars.
- Flow of user [what is the fist click and last click of each user before he leaves the site and length of stay in the site]
- Individual page views [# of views per page/total # of views]
- Efficiency of user acquisition/ Virality coefficient [# of source from friend’s newsfeed to # of source from our provided site link]
- Unique visitors per day