Skip to main content

Frequently Asked Questions: Hook's Engagement Level

Most commonly asked questions about Engagement Levels in Hook.

Updated over 3 months ago

Hook's Model

Can more features be added to Hook’s model? Can features be removed?

Each time we build a model for a customer, we use a feature correlation process so that only the most important features that contribute to renewal likelihood are included in the model.

It's possible for more features to be added or existing features to be removed from Hook's model. This can happen during implementation or for scheduled model rebuilds.

Contact the Hook administrator for your organisation to give feedback on any features you'd want included in a future remodel.

How does new data from each customer impact the model?

Machine learning models improve their performance when they are built using a large training set of data. Therefore we expect new data on renewals and churns to improve the accuracy of Hook’s model.

We update models for our customers each quarter. This ensures that Hook’s churn predictions remain at a best in-class level of accuracy.

How do I know Hook’s model is more accurate than my organisation's previous health score, or my gut feeling about an account?

Hook's engagement models typically achieve 75% or above accuracy in predicting customer churn*, which is above best-in-class for the CS industry.

The other 25% will be from external factors that data can't predict such as company strategy changes. This information is discovered during conversations with customers and therefore not included in the data we use to build Hook's model. Risks can be used to flag positive or negative notes on accounts that cannot be seen using data alone and can be configured to override Hook's Engagement Level.

Our data science team often performs analysis to compare Hook's accuracy to your organisation's previous health score when presenting new models to customers.

Why are subscription metrics (e.g. subscription value, time as a customer) contributing to the Engagement Level? CSMs do not have control over this.

Subscription information is included in the model because it’s necessary for model performance. This is not always key information for a CSM but is important for building Hook's model. An example feature would be subscription ARR.

There are two other buckets of data we use for Hook's model: insightful data and actionable data. Insights are helpful for CSMs to understand a customer's health. For example, number of support tickets. Actionable data is information a CSM can take action on to improve the Engagement Level of an account. For example, increasing the number of active users.

We would not showcase suggested actions for a customer for subscription information, since this is not realistic for a CSM to change.

How are edge cases handled by the model? (e.g. CSMs being aware of upcoming M&A events for customers, core champion soon to leave the business, customer has emerging budget constraints etc)

If these edge cases are captured in company data, we can include them in Hook’s model. Otherwise, risks can be used to flag edge cases for each customer. In risk settings, these can be configured to override Hook’s engagement level.

Understanding Engagement Levels

Related Article: Hook's Engagement Level

How regularly does the Engagement Level update?

Engagement Levels for customers update weekly. This can be seen in the Engagement timeline on the Overview page

A customer has a High Engagement Level but some of their key metrics haven’t changed in several weeks. Why do they have High Engagement in Hook?

Each key metric has a different level of importance in contributing to renewal likelihood. If a metric that is not an important indication of renewal likelihood hasn't changed for a customer, this will not significantly change the engagement score.

A customer has high product usage but their Engagement Level is Low. Why is that?

Hook's model will be unique to your organisation and makes renewal predictions using various different data sources. Product usage data informs the engagement score, but so does meetings, support and subscription data. One of the most important features of the model could be data within one of these other categories.

I’ve had a conversation with my customer saying that they’re unlikely to renew. Why doesn’t this line up to the High Engagement Level for this customer in Hook?

We only include data in Hook’s model from your company’s data. Unless this information is available in your company’s database and has been provided for implementation, our model will not include it for making churn predictions.

We encourage CSMs to add Risks for customers in these cases. Find out more about risks here.

What happens if I disagree with the Engagement Level for a customer?

Risks in Hook enable CSMs to indicate positive or negative flags for an account that cannot be seen using data alone. For example, a customer sharing in confidence that they're considering another software provider. Risks can be configured in risk settings to override Hook's Engagement Level. This will be reflected in the Overall level field, and the original level in the Engagement field in the Customers table.

Why does a customer have an Inactive Engagement Level?

A customer may have an Inactive Engagement Level if they have are either zero users or no users with activity. Scoring takes place weekly, so any updates to user activity will be reflected in the next engagement score for an account.

Why does a customer have a No Data Engagement Level?

A customer will have a No Data Engagement Level if they did not receive a score in the most recent scoring. This could be for a couple of reasons:

  • The account is new or has recently renewed, so wasn't included in Engagement Level scoring that week.

The current subscription was created in your CRM after the account renewed, so we have not included the account in this scoring.

*Disclaimer:

The reported accuracy of approximately 75% reflects the model's performance during internal testing and may vary depending on specific use cases or data inputs. While we strive to improve model quality continuously, predictions generated by this system are not guaranteed. Users are encouraged to review outputs and apply human judgment where appropriate.

Did this answer your question?