Reporting/Metrics Dashboard

This page would contain all the metrics dashboards which we will be using post launch and for internal reporting purposes.

Overall metrics:

Adoption of the tool

This tracker gives the summary of all important L0 metrics on total users/signups, DAU's, revenue etc on all key dimensions like daily, weekly, monthly, client-wise etc

# number of clients # number of signups: total users # number of daily active users (at least some update) # % of daily active users (of total signed up users) # time to first activity (avg time taken from sign-up to first activity) # revenue # revenue/user (per-client) # number of referrals # number of referrals/user # time to refer

Churn

# avg time since last visit # activity of last visit (attributing drop to a certain feature?) # % of users who haven't been on the platform for more than 1 week

Link to the tracker

Deep dive metrics:

This tracker give a full 360 view of what's happening on the tool. All the above mentioned metrics on segment/cohort level as well additional inputs to understand and suggest improvements to the OKR process for a client and to improve the tool adoption.

Dimensions for all our data: Daily, weekly, monthly, quarterly, by client name, by client type (engagement versus not engagement), by teams are a must-have for all the below metrics

Engagement

We would see our customer journey funnel to gauge engagement: Total users from sign ups -> any OKR's assigned/created -> any OKR's accepted/Rejected -> OKR's with status/progress changed -> OKR's with any check-in -> OKR completed/archived.

Another very important thing is the average time taken between each of these stages.

# % of daily active users (of total signed up users) A daily active user is a user who logs in and does some activity (reading a notification could also be an activity along with any other change made to the system. This would be characterised as per the length of the session. Any session with < 1 min length would be filtered and removed for daily active users. # no. of times a user checks-in on the tool in a day/week/month Again the same definition of sessions >1 minute would be considered. # comparison of user-activity across various user segments : senior level, mid level and root level etc. Have to check feasibility of getting this.

Efficiency of OKR process (what's working and what's not working?)

# % of independent objectives/ unlinked objectives (not linked to company goals) # avg no. of objectives getting accepted per user / % of objectives accepted # avg time taken from objective creation to acceptance # avg no. of objectives completed( > 0.9 progress) or # % completions on OKR's company as well as total (% completions are measured with the base as accepted objectives) # % of objectives being carry forwarded to next quarter

Analytics Layer in tool and recommender system (out of scope for MVP)

Analytics layer for each client and recommender to cover the below focus areas: # What % of tech/Product objectives are tied to company objectives? # What is the overall % completions for all OKR’s in the company? # Automated RCA questions on objectives with lower completions% # Recommendations on how to improve the OKR process

Health Metrics

# Customer Feedback (on a scale of 5) or NPS # Number of customer complaints/ queries # Most-used and least-used features # Feature ranking with highest no. of complaints # Number of bugs reported # Distribution of users with login time # avg time spent per user in a day