Number of contact us requests via .
Number of contact us requests via from Named Accounts or Enterprises with 5,000+ employees, in America, EMEA, Australia, or Japan.
Members of the open source community with contributions to Mattermost GitHub repositories.
This does not include internal Mattermost staff
Definitions:
Customer Success Account Health Score represents the overall health of a Mattermost SFDC Account.
Account Has Open Opportunity w/ Renewal Risk Status = 'At Risk'
Account Health Score = 20%
Account Has Open Opportunity w/ Renewal Risk Status = 'Early Warning'
Number of successfully completed unique TE (Team Edition) + EE (Enterprise Edition) downloads by unique client IP address per month, from mattermost.com. Includes web browser and wget/curl downloads.
Note: Excludes GitLab Omnibus downloads, Docker (Dockerhub) downloads, and Bitnami or other cloud image downloads, as we don’t currently have a good way of measuring these downloads.
Monthly Enterprise Account Server Downloads
Number of Monthly Server Downloads among companies and organizations with over 5,000 employees.
New Monthly Server Downloads from Named Enterprise Accounts
The first contact or lead from a Named Enterprise Account who is attached to an Account either manually by an AE or by Marketo, and who provides a business email on mattermost.com/download after downloading the Mattermost server binary.
Excludes Salesforce account types equal to Customer or Partner.
Number of successfully completed desktop application downloads by unique client IP address per month, from mattermost.com. Includes web browser and wget / curl downloads.
Note: Excludes GitLab Omnibus downloads, Docker (Dockerhub) downloads, and Bitnami or other cloud image downloads, as we don’t currently have a good way of measuring these downloads.
Financial numbers cater to a wide range of teams. Below you will find information on ACV, TCV, and ARR. ACV, TCV, Bookings are relevant to Sales/CS and ARR relevant to Finance/CS.
What the customer bought
TCV measures revenue from across the entire contract a customer signs.
Recognized only in the Closed Won Month.
TCV = Total Contract Value
What the customer bought annualized for the first year of their contract
Recognized only in the Closed Won Month.
ACV = (Total Contract Value / (End Date - Start Date)) * 365
Standard financial term used to define how much sales credit is recognized for a deal. This is also the basis for all commission plans starting in FY21.
If term length >= 1 year, Bookings = ACV
If term length < 1 year, Bookings = TCV
Net New and Expansion only
Why would we have deals < 1 year?
What the customer bought normalized for one year length and reoccurs for length of contract.
ARR is the value of the contracted recurring revenue of your term subscriptions normalized to a one-year period.
Recognized from start to end of contract.
ARR = (Total Contract Value / (End Date - Start Date)) * 365
Example:
Opportunity:
Close Date: 2019-05-07
Amount = $250
Start Date: 2019-06-15
All Google Analytics data in Snowflake is at a daily level. See .
Organic Web Traffic is the daily unique visitors to *.mattermost.org, *.mattermost.com who originate from non-paid sources.
Organic Search Traffic is the daily unique visitors to *.mattermost.org, *.mattermost.com who originate from an organic Google search.
A Unique Pageview (UPV) aggregates pageviews that are generated by the same user during the same . The metric combines the pageviews that are from the same person (a user in Google Analytics), on the same page, in the same , and counts them as one.
Unique Page Views (UPV) are calculated as the daily count of distinct pageviews per user per session to view a Mattermost web property webpage ().
A session is a group of user interactions within a website that take place within a given time frame. A single session can contain multiple page views, events, social interactions, and ecommerce transactions. A single user can open multiple sessions. Those sessions can occur on the same day, or over several days, weeks, or months.
As soon as one session ends, there is then an opportunity to start a new session. There are two methods by which a session ends:
Time-based expiration:
After 30 minutes of inactivity.
At midnight.
Campaign change:
mattermost.com
https://mattermost.com/ (old mattermost.com)
mattermost.org
developers.mattermost.com
is used to pull Google Analytics data into Snowflake.
Stitch is only able to pull data at a daily level.
Aggregating up daily level to a monthly level causes double counting of users that may have visited the site more than one time in a given month.
WIP
Mattermost NPS data is collected using an in-product survey on servers where the is enabled. Users answer the question "How likely are you to recommend Mattermost?" by selecting a 0-10 score and can provide additional written feedback about their experience. Selecting a score and providing feedback are optional.
Net Promoter Score is computed as (% Promoters - % Detractors) and ranges from -100 (every user is a Detractor) to +100 (every user is a Promoter).
Promoters (score 9-10): Loyal enthusiasts who will keep buying and refer others.
Passives (score 7-8): Satisfied but unenthusiastic users who are vulnerable.
Detractors (score 0-6): Unhappy users who can damage your brand.
If users edit their response to the survey on any particular server version, only the latest rating on each server version is used for computing NPS. Test server data is also removed via the excludable servers list and by trimming any responses submitted on a server version that has not been publically available for 21 days (time delay before an NPS survey is triggered after a server upgrade).
Based on the above calculation method, we can apply a time window (Quarterly Trailing NPS) and a version filter (NPS by Server Version) to represent and track the state of Mattermost NPS.
represents the NPS score computed based on survey submissions received in the last 90 days. We target a 90 day window of NPS responses because it:
Encompasses responses across all server versions that are currently in use by customers (with surveys enabled), meaning it is representative of the state of user experience that customers are facing today.
Ensures we have a representing the .
Helps us capture week-to-week variations in NPS while being less volatile than NPS by server version given the larger sample size.
represents the NPS score computed based on survey submissions on a specific server version. While Quarterly Trailing NPS provides a representation of the state of user experience that our customers are facing, NPS by Server Version provides a representation of the user experience offered by the product in particular releases. As such, it's used heavily by the PM team to track the success of particular product team initiatives as they ship.
NPS by Server Version is a lagging metric since we need to collect a statistically significant sample size before reporting NPS for a server version. Based on historical data, ~2000 unique user responses (~1.5-2 months post-ship) are required for the NPS of a particular server version to stabilize.
NPS by Server Version can be volatile since it can be affected by the upgrade cadence of heavy usage servers. We typically see a surge in responses when surveys are triggered (21 days after server upgrade) for servers with large DAU, which can impact the NPS trend for that server version. Quarterly trailing NPS is not as affected by this since the sample size is larger and responses are submitted on various server versions.
Users can optionally submit written responses to the NPS survey. These responses are reviewed and categorized by the PM team to allow us to stack rank certain initiatives for roadmap planning based on their impact to NPS, reach and effort to design, implement and test.
To determine impact to NPS, we can:
Segment the reponses based on written feedback from users who are detractors or passives (See the NPS Feedback section of the ).
Segment the responses based on the feedback category to analyze what requested product enhancements equate to the lowest NPS scores from users.
View overall number of responses by feedback category.
Renewal Metrics in simple terms:
Available Renewals (X): Total dollar amount of licenses ending is in the Renewal Qtr
Renewal Rate (Y%): Total Won Amount ÷ Available Renewals where:
Won Opportunity License start date falls in the Renewal Qtr
Calculation: Renewal Actual ÷ Renewal Target
Renewal Target
Target set in a given period (Mo, Qtr, FY) by Finance
Calculation: ∑ Gross Renewals ÷ ∑ Available Renewals
Available Renewals
Amount up for renewal at Account level by Qtr
Calculation: (∑ Gross Renewals + ∑ Forecasted Renewals) ÷ ∑ Available Renewals
Available Renewals (See Above)
Gross Renewals (See Above)
Open Renewal Amount
New: Ticket created that has not been assigned a support agent.
Waiting on customer: The support agent asks the customer a question and is waiting for their response.
Waiting on customer - Do not Close: Checkbox on the ticket. This is used for tickets that are expected to be open for a long period of time. This stops the clock.
Level 1: Urgent Business Impact: Urgent issue on production system preventing business operations. A large number of users are prevented from working, and no procedural workaround is available.
Level 2: High Business Impact: Major issue on the production system severely impacting business operations.
Level 3: Normal Business Impact: Moderate issue causing a partial or non-critical loss of functionality on the production system. A small number of users are affected.
First reply time: The duration between ticket creation and the first public agent reply on the ticket.
Next reply time: The duration between ticket's first reply time and next reply time, unless otherwise communicated.
SLAs
First Reply Time - Premium and E20 only
Premium
L1 (Urgent) - 1 hour
L2 (High) - 2 hours
L3 (Normal) - 8 hours
L4 (Low) - 24 hours
E20
L1 (Urgent) - 4 hours
L2 (High) - 8 hours
L3 (Normal) - 24 hours
L4 (Low) - next business day
Next Reply Time - Premium and E20 only
Premium
L1 (Urgent) - 2 hours
L2 (High) - 4 hours
L3 (Normal) - 24 hours
L4 (Low) - 24 hours
E20
L1 (Normal) - 4 hours
L2 (High) - 8 hours
L3 (Normal) - 24 hours
L4 (Low) - 24 hours
CSAT
CSAT stands for Customer Satisfaction Rating. 24 hours after the ticket is set to closed, the end-user receives an email asking them to rate their experience. In the email the end-user is presented with the question "How would you rate the support you received?". Below the question is the option to select "Good, I'm satisfied" or "Bad, I'm unsatisfied" with the option to leave a comment about their experience.
CSAT Formula
Good, I'm Satisfied / Good I'm Satisfied + Bad I'm Unsatisfied
TES stands for Telemetry-Enabled Servers. It is the count of unique, sending telemetry (“activity") data to Mattermost on a given date. Each component of TES can be described as follows:
Telemetry Enabled:
Servers have “Error Reporting and Diagnostics” or “Security Alert” enabled in System Console.
Response to Mattermost's call to collect telemetry data recorded on a given date.
TEDAS stands for Telemetry-Enabled Daily Active Servers. It is the count of unique sending telemetry (“activity") data to Mattermost on a given date with at least one daily active user.
A server is considered to have a daily active user, if at least one user recorded activity on the Mattermost server, such as viewed a channel or posted a message, in the last 24 hours.
TEMAS stands for Telemetry-Enabled Monthly Active Servers. It is the count of unique sending telemetry (“activity") data to Mattermost on a given date with at least one monthly active user.
A server has a monthly active user, if at least one user recorded activity on the Mattermost server in the last 30 days, such as viewed a channel or posted a message.
Note that an update to the metric is considered to only consider activity in the last 28 days to remove weekday variation. However, this change would require in-product changes and thus has not yet been prioritized.
There are several ways to view the TEDAS metric, and there are several more ways to create metrics that are derivatives of TEDAS. Below are some of the ways to view, pivot, and/or filter the TEDAS metric to gather additional insights regarding the overall health of the business:
:
Provides the count of Telemetry-Enabled Servers trended by their first telemetry active date.
Is an indicator of how many New, Telemetry-Enabled Production Servers are being stood up on any given date, week, month, year, etc. throughout the history of Mattermost.
TES only measures the count of active production servers. The Mattermost.server_daily_details table is used to calculate TES and only contains production servers. Mattermost.server_daily_details is derived from the Events.security table.
The Events.security table logs all server responses to Mattermost's call to collect telemetry data. The Server type responses include test, development, and production servers. Conditional logic is used to filter out non-production servers from the Events.security table and insert them into the Mattermost.server_daily_details table.
Logic to identify production servers within the Events.security table is as follows:
Mattermost Version matching recognized format
version LIKE '_.%._._.%._'
Non-production servers
Non-production servers are not included in TES calculations. Test and development servers are non-production servers that can be spun up for testing and various other use cases.
Server Age
The age of a server is determined by the server's first active date. This is the minimum date a response to Mattermost's call to collect telemetry data is recorded. It can be thought of as the server's first recorded telemetry-enabled date -i.e. first active date. The age of the server is calculated as a function of this date. It is the days between the server's first active date, discussed above, and the current date (or other relative date being used as a comparison to calculate server age at any point throughout its lifetime).
Server Age = Current Date - Server First Active Date
There are additional data quality issues within the Events.security table that need to be addressed before inserting the verified production servers and calculating TES, TEDAS or TEMAS. The Events.security table is supposed to contain 1 record per server per day. This is not always the case: ~2% of server IDs have multiple rows per day. To select a single record per production server we must:
Identify the row per production server containing the maximum number of active users on the given date.
If the maximum number of active users = 0 then we select the most recently logged row based on the "hour" field value.
TEDAU stands for Telemetry-Enabled Daily Active Users. It is a metric that takes the rolling 7-day average of the sum of all "Active Users" logged by on a given date. The TEDAU calculation sums the active_user_count column in the Mattermost.server_daily_details table, and then averages that value over the last 7 days.
Currently only a subset of possible events, dubbed "whitelist" events, count towards TEDAU. The reason only a subset of events are captured is a result of exceeding Segment's (third-party event logging service) row limit. In the future, event logging will transition to Rudder (another third-party event logging service), and the list of events that count towards TEDAU will become more expansive.
Deactivating a user in Mattermost will result in TEDAU decreasing, as deactivated users are filtered out of the statistic's query. However, reactivating a user in Mattermost will increase the TEDAU statistic.
TEDAU is the rolling 7-day average sum of active users for only verified production servers that are telemetry-enabled (described in ). All other servers, and their active user counts, are ignored as they represent testing, dev, and one-off user case environments that skew the results.
Telemetry-Enabled Active Users vs. TEDAU Metric
The distinction between an individual server's Telemetry-Enabled Active Users and the TEDAU metric is important to note. Telemetry-Enabled Active Users are the collection of users hosted by a telemetry-enabled production server, that have visited the Mattermost site in the last 24 hours. The TEDAU metric is the rolling 7-day average sum of these users across all servers.
A Server Activation is defined as the first date a production server sends telemetry data to Mattermost. In order to send telemetry data to Mattermost, a production server must be set up and activated by the end user. When a production server is first activated its telemetry feature is automatically enabled, which sends security diagnostics information to Mattermost via Segment (soon transitioning to Rudder). The server will continue to send this information on a daily basis until this telemetry feature is disabled by a Mattermost System Admin. A server activation is only captured and counted on the first telemetry-enabled date associated with a specific server.
Monthly Active Users (MAU) are users that have performed an event on or within 30 days of a given date. User events are triggered when a user interacts with the Mattermost platform from their desktop or mobile device. After a user performs an event, that user will remain in MAU for 30 days. After that time the user will fall out of MAU and be classified as "Disengaged".
Monthly active users are categorized into Engagement Lifecycle Segments. There are five engagement lifecycle segments: "Persisted", "First-Time Active", "Reengaged", "Newly Disengaged", and "Disengaged". Each segment is derived from the timeline from when a user performs an event. Each Engagement Lifecycle Segment is defined as follows:
First-Time Active: The first time a user performs an event on the Mattermost platform and is classified as a MAU.
Reengaged: A user performs an event for the first time after >= 31 days of inactivity on the Mattermost platform.
Persisted: A user that performed an event in the last 30 days that does not fall into "First-Time Active" or "Reengaged" MAU Engagement Lifecyle Segments.
Server retention is calculated as Day N retention, which shows the percent of users who return to the app on a specified day after their first launch.
We do not use rolling retention, which shows the percentage of users who return to the app on a specified day or any day after that. The reason is that rolling retention continues to increase as days go by (someone who comes back to the app on day 31 will count towards day 1, day 2, up to day 31 retention).
For concrete examples, a server is included in:
day 1 retention, if there is activity 24-48 hours after server creation
day 7 retention, if there is activity 168-192 hours after server creation
day 14 retention, if there is activity 336-360 hours after server creation
day 28 retention, if there is activity 672-696 hours after server creation
All Trial Requests
Number of trial license requests via .
Enterprise Trial Requests
Number of trial license requests via from Named Accounts or Enterprises with 5,000+ employees, in America, EMEA, Australia, or Japan.
Old Contributors: Anyone that is not a First Time Contributor.
First Contribution: The first contribution ever made by someone. Any following contributions will not be considered a First Contribution.
Examples:
Person A
2019-01-02 Contribution 1
2019-01-04 Contribution 2
2019-01-05 Contribution 3
Would count as
1 First Time Contributor + 1 First Time contribution + 2 Non First Contributions.
Account Health Score = 50%
**Note: If account has open opportunity w/renewal risk status of At Risk or Early Warning, it will override all other Healthscore metrics.
Account has a completed Customer Reference
Customer Reference Score = 10 additional bonus points
Account Has No Open Opportunity w/ Renewal Risk Status = 'At Risk or 'Early Warning'
Account Health Score = Tenure Score + License End Score + Ticket Score + Task Score
Tenure Score = 25 * Tenure Health %
Tenure Health %:
Tenure <= 0.5 Years: 10%
Tenure Between 0.5-1 Years: 50%
Tenure Between 1-2 Years: 75%
License End Score = 25 * License End Health %
License End Health %:
License End <= 15 Days From Now: 10%
Ticket Score = 25 * Ticket Health %
Ticket Health %:
Count of Tickets Past 90 Days >= 5: 25%
Task Score = 25 * Task Health %
Task Health %:
Days Since Most Recent Task >= 90: 25%
True-ups
Customer owes MM 30K for Q1FY21
Booking would be 30K on the first day of Q2 (May 1)
Co-Term Renewals
Ramped Deals
Customer signs a 3 year deal on Feb 28, 2020. Year 1 = 50K, Year 2 = 100K, Year 3 = 150K
Bookings
2/28/20: 50K
New: Increase in ARR from $0 to > $0 & New Logo never seen before
Caused by a brand new and never before seen Account signing a contract
Expansion: Increase in ARR by an Account
Caused by seat increase, price increase, or product upgrade
Resurrection: Increase in ARR from $0 to > $0 & by a known Account
Caused by an Account churning and returning to Mattermost
Contraction: Decrease in ARR by an Account
Caused by seat decrease, price decrease, or product downgrade
Churn: Decrease in ARR from > $0 to $0 by an Account
Caused by an Account moving completely off of Mattermost
End Date: 2022-06-14
Metrics Breakdown (see graph below):
ACV one-time $83 in May 2019
TCV one-time $250 in May 2019
ARR $83 from June 2019 until June 2022
If a user arrives via one campaign, leaves, and then comes back via a different campaign.
integrations.mattermost.com
docs.mattermost.com
handbook.mattermost.com
licensing.mattermost.com
mattermost.gitbook.io/jira-plugin
pre-release.mattermost.com (which is old community.mattermost.com)
mattermost.zendesk.com
Won Amount <= Available Renewal Amount
Forecasted Renewal Rate (Z%): Won Amount + (Total Open Amount * Probability) ÷ Available Renewals where:
Won & Open Opportunity License Start Date falls in the Renewal Qtr
Won & Open Opportunity Product Line Type = 'Ren'
Won & Open Amount <= Available Renewal Amount
Target (A): Target Total Bookings set for the Renewal Qtr by Finance
Actual (B): Total Bookings Won in the Renewal Qtr where Product Line Type = 'Ren'
Target vs Actual aka TvA (C%): Actual ÷ Target
Total Bookings Won in a given period (Mo, Qtr, FY) where Product Line Type = 'Ren'
Renewal amount booked (up to Available Renewal Amount) where License Start is in a given Qtr
Calculation: MIN(Available Renewals,Renewal Bookings)
Example 1:
Account: Account 1
Available Renewals Q4: $100k
Renewal Bookings Q4: $130k
Gross Renewals Q4: $100k
Example 2:
Account: Account 2
Available Renewals Q4: $100k
Renewal Bookings Q4: $70k
Renewal amount for open Opportunities where License Start is in a given Qtr
Forecasted Renewals
Open Renewal Amount * Probability + Gross Renewal Amount (up to Available Renewal Amount)
Calculation: MIN(Available Renewals,(Open Renewal Amount * Probability + Renewal Bookings)
Examples include: The customer requests that the ticket remain open after a solution is provided, the customer is on vacation, or the customer is out ill.
On Hold: The support agent reaches out to an internal team and is waiting to hear back. Internal teams include Product, Development, or Customer Success. Any ticket that is tied to a Jira ticket is placed On Hold.
Solved: Support agent provides a solution to the customer. A ticket remains in a Solved status for 48 hours before it is set to Closed. While the ticket is in a Solved status any response from the customer will reopen the ticket.
Closed: If no response is recorded after 48 hours, a ticket in a Solved status will automatically be set to Closed. Any response to the ticket after that period of time will open a new ticket.
Level 4: Low Business Impact: Minor issue on non-production system or question, comment, feature request, documentation issue or other non-impacting issues.
Servers:
Servers host Mattermost instances for teams and organizations.
Each team/organization can have one-to-many servers installed to host their instance.
Small/Medium teams typically leverage a single server.
Large teams can leverage Enterprise Edition features to create server clusters that allow them to scale their instance.
The count of Telemetry-Enabled Servers that are >= 7 days old since their first telemetry active date w/ >= 1 active user logged on the server.
The rate (percentage) at which Telemetry-Enabled Servers leave the platform (churn) or disable telemetry within a given number of days since the server's first telemetry active date.
Typically (as of 4/15/20), 75-80% of new, Telemetry-Enabled Servers churn within the first 7 days of their first telemetry active date.
Other version formats are not valid because they indicate a test, development, or custom built instance.
No "Dev Builds" and "Ran Tests" recorded
dev_build = 0
ran_tests = 0
Registered Users >= Active Users
user_count >= active_user_count
Data anomalies occur causing servers to show active users > provisioned users - these servers must be excluded.
Disengaged: A user, that was previously in MAU, that has not performed an event > 31 days of their last event. A "Newly Disengaged" user becomes "Disengaged" on their 32nd+ day of inactivity.
You can't impact what you don't measure. So at Mattermost we have defined standard operating metrics that we monitor to run our business. This section is dedicated to this effort.
Our main company measures are defined and tracked by way of our Standard Operating Metrics (SOM)
SOM: Metrics we believe give the best overall understanding of how the company is performing across departments.
Metrics LifeCycle
Choose them
Define them
Document them
Automate where possible
Reports and Dashboards
Monthly Going Well, Not Going Well & Next Actions (GNN) Review by MLT and MLX.
Tenure > 2 Years: 100%
License End Between 31-60 Days From Now: 75%
License End Between 61-90 Days From Now: 90%
License End > 90 Days From Now: 100%
Count of Tickets Past 90 Days Between 3-4: 75%
Count of Tickets Past 90 Days Between 1-2: 100%
Days Since Most Recent Task Between 30-60: 75%
Days Since Most Recent Task <= 30: 100%
2/28/21: 100K - 50K = 50K
2/28/22: 150K - 100K = 50K
Gross Renewals Q4: $70k
We use Focalboard to track our company-wide goals.
This page summarizes our processes and instructions.
Go to Goal - By the 2nd Tuesday of the month, in the left tab, click on Goals by DRI. In the board, scroll to the goals under your initials.
Share Forecast - Open each goal and update the "forecast" field with your forecast for the end of the period. E.g. For "Q1 Forecast", put in your forecast for Q1 ending number.
Set "Status - Status is a summary of how you feel progress is going.`
GREEN - If you believe you'll hit your target by the end of the period, set status to Green.
YELLOW/RED If you feel your target is off-track, but there's still a chance to hit your target, set status to Yellow
Share "Going well" and "Not going well" - Add a comment to share more context about your goal. Include at least the following:
Going Well: Share wins, successes, and good news in general. Share "Thank yous" to people who have helped, and @mention them if you feel appropriate.
Not going well: Share the key issues you're facing. If the status for the goal is not Green
Update the "Last Forecasted" - Update the field "Last Forecasted" with the date of your forecast so MLX knows it's been completed.
RedUNREACHABLE If you feel it's impossible to reach your goal, set your status to UNREACHABLE. Your MLT member will work with you to bring this to the monthly review meeting to discuss how we can adapt as a company.
DATA NEEDED If you don't feel you're able to provide a forecast, or are unsure about anything in the process, set status to DATA NEEDED.
Next actions: Share key actions to be taken in the following month, with owners and checkins. Be specific.
The document linked below has been created in hopes of creating a more self-serve Analytics infrastructure for product, product managers, and other employees to perform their own analysis. It provides up-to-date metric definitions, as well as Looker links to pre-filtered jumping off points for modeling the data. This documentation was presented in series of Self-Serve Analytics workshops that were hosted in early June FY22. A recording from the workshop has also been provided below for additional context.
This section outlines metric definitions maintained by finance for reporting to investors.
These are metrics for MLT discussions
Any metrics shared by a department with the MLT will be asked to work with business operations to define the metric to be listed on this page under standardized naming and MLT definition checklist
Definitions center around ARR, GMA Magic Number and NPS
MLT definitions checklist
Qualifiers precede metrics names
i.e. use ""Gross Margin Adjusted Magic Number" instead of "Magic Number, Gross Margin Adjusted" to avoid ambiguity when label names are truncated
Metric names should only have one possible interpretation
50% complete
New ARR: ARR from new logos signed, with license start dates in the respective period.
Expansion ARR: ARR from existing customers with a cross-sell/upsell deal, with license start dates in the respective period (e.g. increased licensed seat count, upgrading from E10 to E20).
Contraction ARR: Reduction in ARR from existing customers, but where they are still paying Mattermost based on date of license change.
50% complete
Monthly Server Downloads: Number of successfully completed unique TE + EE downloads by unique client IP address per month, from mattermost.com. Includes web browser and wget / curl downloads.
Note: Excludes GitLab Omnibus downloads, Docker (Dockerhub) downloads, and Bitnami or other cloud image downloads, as we don’t currently have a good way of measuring these downloads.
Monthly Enterprise Account Server Downloads: Number of Monthly Server Downloads among companies and organizations with over 5,000 employees.
10% complete
Total Active Users: The total number of user accounts created on a single Mattermost server. Excludes deactivated accounts, deleted accounts and bot accounts. This is also the “Total Active Users” measure shown in System Console > Site Statistics.
Registered Authorized Users: Same as Total Active Users.
Total Registered Users: The total number of user accounts created on a single Mattermost server, including deactivated and deleted accounts.
1% complete
GMA Magic Number: Gross Margin Adjusted Magic Number (1%): Net New ARR in a period multiplied by Gross Margin in the period, divided by total Sales & Marketing expense in prior period.
NGMA Magic Number: Non-Gross Margin Adjusted Magic Number (1%)
Gross Margin: Net sales revenue minus cost of goods sold
50% complete
Organic Traffic:
Organic Web Traffic: Monthly unique visitors to *.mattermost.org, *.mattermost.com who originate from non-paid sources.
Organic Search Traffic: Monthly unique visitors to *.mattermost.org, *.mattermost.com who originate from an organic Google search.
1% complete
Enterprise Trial Requests: Number of trial license requests via from Named Accounts or Enterprises with 5,000+ employees, in America, EMEA, Australia or Japan.
Enterprise Contact Us Requests: Number of contact us requests via from Named Accounts or Enterprises with 5,000+ employees, in America, EMEA, Australia or Japan.
MQL: Marketing Qualified Lead:
10% complete
90s Hiring vs Goal (P0, P1, P2): Difference between hiring plan and hires broken out by priority level (P0, P1, P2), function and time.
P0 - Roles which hiring managers have flagged as high need.
P1 (1%) - Roles which hiring managers have flagged as medium need.
50% complete
Product NPS: The product net promoter score (Product NPS) measures user satisfaction of the product, calculated based on single question “How likely are you to recommend Mattermost?”. The score is based on a -100 to 100 scale, with the _**_gathered through in-product survey.
End User Product NPS: The Product NPS calculated among end users only (ie. not among Team or System Admins).
System Admin Product NPS: The Product NPS calculated among System Admins only.
1% complete
Customer NPS: Customer satisfaction of the product, calculated based on single question “How likely are you to recommend Mattermost to a friend or colleague?", sent two days after booking or renewal is closed. The score is based on a -100 to 100 scale.
Support Metrics (E10 and E20): Metrics calculated based on Zendesk tickets opened by E10 and E20 customers. Tickets opened by non-subscribed organizations are not counted towards these metrics.
Tickets Created: Number of net new Zendesk tickets created.
For technical analytics definitions not covered here, see the .
To be added.
DRI for this page is Jason Blais
Metrics will inevitably be shortened, pre-emptive definition avoids collision
Total ARR: Total ARR ending in a specific period.
IARR: Incremental Annual Recurring Revenue: (New ARR + Expansion ARR) - (Contraction ARR + Churn ARR)
Count of New Logos (1%): Count of new logos signed, with start dates in the respective period.
Count of Churned Logos (1%): Count of logos lost where an existing customer is no longer paying Mattermost.
New Monthly Server Downloads from Named Enterprise Accounts (1%): The first contact or lead from a Named Enterprise Account who were attached to an opportunity either manually by an AE or by Marketo, and who provides a business email on mattermost.com/download after downloading the Mattermost server binary. Excludes Salesforce account types equal to Customer or Partner.
TEDAS: Telemetry Enabled Daily Active Servers: _**_Number of unique servers that have “Error Reporting and Diagnostics” or “Security Alert” enabled in System Console, and send telemetry “activity data” (such as number of users).
DAU: Daily Active Users: The total number of users who viewed the Mattermost site in the last 24 hours. Excludes bot accounts. This is also the “Daily Active Users” measure shown in System Console > Site Statistics.
TEDAU: Telemetry Enabled Daily Active Users: The total number of users averaged over the last 7 days, who viewed the Mattermost site in the last 24 hours from servers that have “Error Reporting and Diagnostics” or “Security Alert” enabled in System Console, and send telemetry “activity data” (such as number of users). Excludes bot accounts.
MAU: Monthly Active Users: The total number of users who viewed the Mattermost site in the last 30 days. Excludes bot accounts. This is also the “Monthly Active Users” measure shown in System Console > Site Statistics.
Active User Count: A measure of the number of active users last 24 hours. Legacy measure, do not use this for analysis or decision-making.
Net Sales Revenue:
Cost of Goods Sold:
PQL: Product Qualified Lead: Installed free TE/E0 version from a Target Account interested in EE features.
Monthly Active Contributors: Number of unique contributors in a given month who have posted at least one message on the Mattermost forums OR have committed at least one line of code to any of the publicly available Mattermost repositories on GitHub, including Mattermost Staff and those participating in a paid R&D candidate audition project.
Note: Excludes translators, integration/solution creators and QA/UX contributors, as we don’t currently have a good way of measuring these contributors.
Monthly Code Contributors: _**_Number of unique contributors in a given month who have committed at least one line of code to any of the publicly available Mattermost repositories on GitHub, excluding members in the Mattermost GitHub organization.
EE Product NPS: The Product NPS calculated among all users in licensed E10 or E20 servers only.
First Response Time [Median, hours]: The median number of hours from when a ticket was opened in Zendesk to when the first response was sent to the customer.
% First Response >8 Business Hours: % of newly opened Zendesk tickets whose first response time is greater than 8 business hours as defined in https://mattermost.com/support/.
Resolution Time [Median, hours]: The median number of hours from when a ticket was opened in Zendesk to when the ticket is resolved.
% Resolution Time >7 days: % of newly opened Zendesk tickets whose first resolution time is greater than 7 days.
% Resolution Time >14 days: % of newly opened Zendesk tickets whose first response time is greater than 14 days.
Number of CSAT Responses: # of new CSAT (Customer Satisfaction) survey responses from customers whose Zendesk ticket was resolved.
Customer Satisfaction Score: % of newly submitted CSAT survey responses who responded “Yes” to the question .