Ways to use your product analytics metrics

Summary

Explains and gives examples of how Accoil Analytics can be used to monitor Account and User engagement.

How this helps

Understanding and optimizing Account and User engagement, focusing on new user activation, account retention, and reducing churn to boost customer numbers and revenue.

Understanding and optimizing account and user engagement in your product is a key driver of SaaS growth. Things like new user activation, account retention, and churn reduction are ways to increase your customer count and revenue.

With Accoil Analytics, you can monitor user activity and identify important moments in your users' journey. This lets you make data-driven decisions to optimize your customers' experience and increase product usage.

By leveraging these insights, you can drive product adoption, increase user engagement, and minimize churn.

Consider the following examples as thought starters.

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1. Product Engagement

Metric	Examples to spark ideas	How to do this in Accoil Analytics *`note add links` for relevant feature documentation*
Identify my Most and Least Engaged Users (I can monitor metrics showing which users have the highest and lowest engagement rates)	Segment Engagement: Identify frequent feature users versus those who rarely log in. Highlight Features: Email highly engaged users about new, unexplored features. Re-engage Inactive Users: Offer special discounts and recommendations to least engaged users. Dedicated Support: Provide priority email support or a dedicated account manager for highly engaged users. Assist Less Engaged Users: Use surveys, live chat, or a detailed FAQ to understand and support less engaged users.	Use Engagement Profile: Use customized profiles and segments. “Suggested” segments like 🌶️ Red Hot and 🥶 ice cold are pre-built and ready to use. Learn more on specific segments details Read the Use Case
Monitor the Engagement Trends (I can see changes in user engagement over time)	User Engagement: Tracking a user whose engagement score has increased by 20% over the past month might highlight a positive trend, whereas a user whose engagement score has dropped significantly needs attention. Focus development on features with the highest user engagement for better prioritization. Spot early engagement declines to proactively prevent churn and retain customers.	Use Engagement Score: Create your Profiles and filter your Engagement score for changes over time. Other metrics to watch (calculated by Accoil) Filter by Frequency Filter by Adoption Rate Read the Use Case
Activity Analysis (I can view specific actions or lack thereof by users)	User activity patterns: Users who consistently use core features but ignore new ones might need targeted education about the new functionalities. For example, some users use the comment feature a lot but don’t use the file upload feature. Early task completion: Users who complete their first task within 48 hours show higher retention. Integrations drives activity: Users who connect integrations within the first week are more active.	Use Engagement Score: Create a new Profile to include the features you’d like to track. You can also update an existing Profile to include these features. Read the Use Case
Account Activation and Stalling (I can see the status of account activation processes)	Activation patterns: Discover accounts that quickly complete all key steps of the onboarding process versus those that stall after the initial setup phase. Activation time frame: Learn if accounts completing onboarding within a week show higher potential compared with accounts stalling at profile setup and who might need additional support.	Configure Activation: Configure activation metrics at account- and user-level. Use: Average activation rate Activation rate Read the Use Case
Key Activation Points (Identifying when crucial engagement steps happen)	Recognizing high value feature activation: Noting that users who utilize a particular feature are more likely to stay engaged can help focus efforts on promoting such feature to others.	Configure Activation: Configure activation metrics at account- and user-level. Use: Average activation rate Activation rate Read the Use Case
Feature and Event Impact (Which features or events drive user engagement)	Impact patterns: Analyzing if a newly released feature has increased overall platform activity or if certain events lead to spikes in usage. For example, a new messaging feature increased daily active users (DAU) by 15	Focus on Events: Average adoption rate (by selected time frames) Average adoption change Average engagement score by event Individual account or user event usage (filter by adoption over time, % change, average score, event count etc) Read the Use Case

2. Product Adoption

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
Adoption Rate Over Time (I can track adoption rates over different periods.)	Dropping recent adoption: An account with high all-time adoption but low recent adoption might need re-engagement strategies or feature updates. Impact of feature adoption: Weekly adoption rate increased by 10% after feature update. Flattening long term trend: Yearly adoption rate remained flat despite new features.	Use Engagement Profile: Use customized profiles and segment Filter by Adoption all time vs recent adoption. Add time dimension to see longer term trend. Read the Use Case
Deep Feature Engagement (I can monitior the depth of feature usage by accounts.)	Feature adoption: Accounts using a wide range of features regularly indicate high adoption and potential for case studies or upsell opportunities. eg: Accounts using 8+ features monthly are highly engaged. User adoption: Accounts with 80% of users accessing all features regularly.	Use Engagement Profile: Use customized profiles and segment Filter by Adoption - recent adoption and activation rate > 70% Read the Use Case
Specific Problem Solving (I can identify if users are leveraging the platform for specific needs. )	Low all-time and recent adoption might indicate a user solving a particular problem, suggesting the need for better product marketing to highlight other features.eg: Users only using the invoicing feature might need education on reporting.	Use Engagement Profile: Use customized profiles and segment Filter by Adoption all time vs recent adoption. - filter for < x score Read the Use Case
High Engagement Accounts Accounts with consistently high and increasing engagement.	High usage: Accounts showing a trend of high usage over both recent and all-time periods are prime candidates for upsell opportunities and customer success stories. Frequent log ins: Accounts with consistent weekly logins are good upsell targets. Rising usage: Accounts increasing feature usage monthly are ideal for case studies.	Use Engagement Score: Create your Profiles and filter your Engagement score for change over time. Other metrics to watch (calculated by Accoil) Filter by Tenure Filter by Adoption Rate Filter by Average active users Read the Use Case

3. New User Product Activation

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
Account Activation Rate (Percentage of new accounts reaching activation).	Monitoring the percentage of new users who complete the onboarding process and engage with core features within their first month. Reach target within time frame: eg accounts reaching activation within a week have higher retention. 70% of new accounts activate within the first 10 days.	Configure Activation: Configure activation metrics at account and user level. Use: average activation rate activation rate. Read the Use Case
*Time-to-Activation* (Average time taken for accounts to become active. )	Change to time to activation: ⬇️ If the average time to activation is decreasing, it indicates that the onboarding process is becoming more effective ⬆️ If it is increasing, look for friction points to resolve. Speed matters: Faster activation correlates with higher user satisfaction.	Configure Activation: Configure activation metrics at account and user level. Use: frequency based filter average activation rate activation rate. Read the Use Case
*Activation by Account Type* (Activation rates segmented by account types. )	Onboarding strategy variation: Using customized profiles, compare activation rates of free trial users versus paid users to understand the effectiveness of different onboarding strategies. 85% of paid accounts activate within a week. Free trial accounts show 60% activation within 10 days.	Consider multiple profiles for each account type Configure Activation: Configure activation metrics at account and user level. Use: average activation rate activation rate. Read the Use Case

4. Identifying Account Opportunities and Risks

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
*Accounts Prime for Expansion* (High engagement accounts with increasing activity).	Upsell targets: Analyzing accounts with upward engagement trends to identify those ready for upsell opportunities. eg: Accounts with a 20% increase in feature usage monthly.	Use Engagement Profile: Use customized profiles and segment. “Suggested” segments are pre-built or built your own defined filter. Review shortlisted upward engaged accounts for % change Activation % Active users % Tenure Adoption Frequency ratio Read the Use Case
*At-Risk Accounts* (Low or falling engagement accounts).	Churn risk: Regularly reviewing accounts with declining activity to implement re-engagement strategies. eg: Accounts with a 30% drop in logins over three months. Accounts showing reduced interaction with core features.	Same as above but in reverse - looking for low or falling engagement Read the Use Case
Feature Adoption at Account Level (Comprehensive use of features by accounts).	High feature usage: Ensuring that most features are used by at least some users in each account to maximize product value. High account user usage: Accounts where 90% of users utilize an important feature. eg: messaging.	Use Engagement Profile: Use customized profiles and segment. Filter by: Average score Average % of active users Average no of active users Average activation Read the Use Case
*Segment-Specific Engagement* (Engagement based on account segments like tenure or engagement).	Prioritization focus: Prioritizing customer success efforts for high-value accounts with declining engagement. Change in long tenure accounts scores: Long-term accounts showing engagement drops require tailored support.	Use Engagement Profile: Use customized profiles and segment. Filter by: Average score Average % of active users Average no of active users Average 30 adoption Average all time adoption Read the Use Case

5. Tailoring Strategy by Opportunity Size

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
Deal Size Metrics	Customize engagement: Adjusting the timing and intensity of engagement efforts based on the potential value of the account.	Configure activation and set your own engagement strategies with your team.
Small Opportunities: 80%-100% Activated (I can focus on accounts that are near full activation.	Watch & wait: Waiting for smaller leads to reach high activation levels before engaging, ensuring they are close to conversion to maximize efficiency. eg: Send promotional offers to accounts nearing activation.	Configure Activation: Configure activation metrics at account and user level. Use: average activation rate activation rate. Filter by score or by score trend Read the Use Case
Mid-Size Opportunities: 60%-80% Activated (I can engage earlier when activation is moderate).	Set engagement outreach rule: For mid-sized accounts, initiate contact when they are 60%-80% activated, especially if the account involves multiple users or decision-makers. eg: Contact mid-sized accounts at 70% activation to offer support. Mid-size accounts at 65% activation receive targeted webinars.	Configure Activation: Configure activation metrics at account and user level. Use: average activation rate activation rate. Read the Use Case
Large Opportunities: 25%-50% Activated (I can intervene at earlier stages of activation).	Decide upfront on outreach scope: For large accounts, start engagement efforts when they are only 25%-50% activated. However, care must be taken to avoid premature sales efforts that could overwhelm the user. Begin personalized outreach for large accounts at 30% activation. Large accounts at 40% activation receive dedicated account managers.	Configure Activation: Configure activation metrics at account and user level. Use: average activation rate activation rate. Read the Use Case

6. Engagement Scoring Metrics

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
New Users (Users who signed up within the score period).	Define monitoring practice: Tracking the number of new sign-ups. New users' feature usage in the first 30 days is critical eg:in the past month and monitoring their initial engagement levels. Monitor engagement levels of new users within the first week.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Most Engaged (Scores above 75.)	Showcase opportunities: Users who interact with multiple features daily and provide high-value feedback, showing a strong affinity for the platform. eg: Users with scores above 75 are prime candidates for case studies. Highly engaged users can be advocates for new features.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Slightly Engaged (Scores between 50 and 74).	Modify outreach tactics: Users who log in regularly but may only use a limited number of features, indicating room for growth in engagement. Slightly engaged users should receive targeted feature tips. Regular users not exploring new features need more education.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Passive (Scores between 25 and 49).	Trial & test: Users who log in occasionally and have minimal interaction with the platform, requiring re-engagement efforts (encourage experimentation). Passive users get automated re-engagement emails. Send personalized messages to re-engage passive users.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Least Engaged (Scores below 25).	Outreach focus: Users who have minimal to no activity, indicating a high risk of churn and needing immediate attention. Immediate support outreach for least engaged users. Least engaged users receive special incentives to boost activity.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Recently Inactive Users/Accounts (Last active between 30 to 60 days ago).	To salvage or not: Users who haven't engaged with the platform for a month but might still be salvaged with targeted re-engagement strategies. Trial Recently inactive users get targeted re-engagement campaigns. Personalized follow-ups for users inactive for over 30 days.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Red Hot (Trend increased by 10 or more since the last scoring period).	Change to engagement: Users whose engagement has significantly increased, indicating a growing interest and potential for upsell. eg: Users who recently started using advanced features extensively.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Ice Cold (Trend decreased by 10 or more since the last scoring period).	Disengagement change: Users whose engagement has dropped sharply, signaling potential dissatisfaction or shifting priorities. eg: Users who stopped logging in after a consistent daily activity. Users who unsubscribed from notifications and haven't logged in recently.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Mature Users/Accounts (Signed up more than 180 days ago).	Look for stability of usage: Long-term users who provide consistent feedback and have a stable usage pattern, forming the core user base. eg: Users who participate in beta testing and provide feedback regularly. Accounts with detailed usage histories and consistent feature use.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Active Today (Last active today).	Current status: Users who logged in and interacted with the platform today, showing ongoing engagement. eg: Users who have logged in and completed a task today. Users who have interacted with customer support today.	Pre-configured segment available after building your Engagement Profile Read the Use Case
Activated Users (Activation rate = 100).	Pinnacle of success: Users who have completed all onboarding steps and actively use the platform's core features, representing successful activation. eg: Users who completed all profile setup tasks and started using core features. Accounts where all users have engaged with the primary toolset.	Pre-configured segment available after building your Engagement Profile Read the Use Case

7. Churn Indicators

Metric	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
Low Activation (Accounts with minimal initial interaction or completion of onboarding processes).	New user with incomplete profile: If a new user signs up but doesn't complete the tutorial or set up their profile within the first week, they might not understand the platform's value, risking early cancellation.	Configure Activation: Configure activation metrics at account and user level. Use: frequency based filter average activation rate activation rate. Read the Use Case
Low Engagement (Users or accounts with consistently minimal activity over a set period).	Infrequent log in: An account that logs in infrequently and interacts with only a few features shows a low engagement level, which could indicate a higher risk of churn.	Configure Activation: Configure activation metrics at account and user level. Use: frequency based filter average activation rate activation rate. Read the Use Case
Drop in Engagement (A significant decline in user activity over time).	Sudden drop in activity: If a previously active user suddenly reduces their activity by 50% over the past month, it might suggest dissatisfaction or a shift in priorities.	Use Engagement Profile: Use customized profiles and segment. Filter at Account level by: Average score Average % of active users Average no of active users Average 30 adoption Read the Use Case
Inactivity (Extended periods without any user interaction).	No signs of life. Users who haven't logged in or engaged with the platform for over 30 days are at a high risk of churn.	Use Engagement Profile: Use customized profiles and segment. Filter at Account level by: Average 30 adoption Last active Frequency (last 30 days) Read the Use Case
Farewell Events (Actions that suggest a user is preparing to leave the platform).	Watch for exit signals: If a user exports their data or removes integrations, these are strong indicators that they might be planning to leave.	Use Engagement Profile: Set profile so that “key exit events” are set at 10 and the rest <5 to trigger. (Useful where a matrix of “exit” features are in play. It also provides a visible focus as a specific segment. Can also use view at an individual event level and filter by: top accounts by event top accounts by score Read the Use Case

8. Common Use Cases

Use Case	Examples to spark ideas	How to do this in Accoil Analytics *note add links for relevant feature documentation*
Determine Which Accounts Are Prime for Expansion (I can analyze engagement scores reveals opportunities for expansion).	Several mid-sized accounts are in the "Most Engaged" category (scores above 75) with a "Red Hot" trend (engagement increased by 10 or more). These accounts represent great opportunities for expansion. Initiating contact with these accounts at 60%-80% activation can drive further engagement and potential upsells.	Use Engagement Profile: Set your profile and segment with pre-built suggested segment 🌶️ red hot Use of filter type by activation rate is between 60%-70% Pre-configured segment available after building your Engagement Profile Read the Use Case
Identify At-Risk Accounts (I can review engagement scores to identify at-risk accounts).	Several "Recently Inactive" accounts (last active 30-60 days ago) with "Ice Cold" trends (engagement decreased by 10 or more) are at risk and need immediate attention. Targeted re-engagement efforts should focus on understanding their needs and addressing any issues to prevent churn.	Use Engagement Profile: Set your profile and segment with pre-built suggested segment “recently inactive” Use of filter type by trend less 10 vs last x days (nominate time frame) Read the Use Case
Improve Feature Adoption at the Account Level (I can examine feature usage to enhance adoption).	A "Mature Account" (signed up more than 180 days ago) with a "Passive" engagement score (25-49) is only using a limited set of features. Tailoring training sessions to highlight underutilized features can help increase overall engagement and product value for this account.	Segment with pre-built suggested segment “mature account” Use of filter type by score is between 25-49 Read the Use Case
Ensure Important Account Segments Receive the Right Attention I can segment accounts by tenure and MRR to prioritize efforts).	High-value, long-term accounts ("Mature Users/Accounts" with high MRR) have varying levels of engagement. Prioritizing customer success efforts for accounts with "Passive" or "Slightly Engaged" scores (25-74) ensures these key segments receive the necessary support to boost their engagement and satisfaction.	Segment with pre-built suggested segment “slightly engaged” View your important segment. Read the Use Case

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