Here in Pecan, to start your modeling journey faster and clearer, we created a set of templates for you to choose from.

Let's go over what each template means and which one you should use.

As a best practice, we recommended first writing in simple words what we are trying to achieve with our model.

For example:

After we write our use case, we can continue to choose the right template for our model.

We offer two model variants: binary and regression. The appropriate model type will be selected automatically when you choose the relevant template that aligns with your use case.

Binary models predict if an input belongs to one of two classes. For instance, they can determine if a customer will cancel their subscription (True) or not (False). They categorize inputs into positive ("1" or "True") and negative ("0" or "False") classes.

To learn more about the probability score for our binary model, click here

Regression models involve predicting a continuous value using input features. For instance, they can estimate the potential revenue a customer might generate based on various input factors. This might result in a range of values, such as $122, $80, $261, and so on.

Event based models make predictions based on user actions or occurrences, while Frequency based models prioritize timely insights by predicting outcomes at regular intervals, which is especially useful when time is crucial for predictions.

Frequency-based templates are utilized when our business use case is time-dependent, and we intend to conduct predictions at regular intervals of X time units.

Event-based templates are utilized when our business use case relies on specific user actions, and we aim to conduct predictions based on those actions.

Our Starter and Advanced templates were created for you to start modeling faster, or if you feel comfortable with SQL you can start from blank.

The starter templates are ideal for your initial journey with Pecan. Within these templates, you'll discover commonly used use cases and straightforward text-based formats for each scenario.

Once you've established a model and gained a deeper grasp of Pecan's functionality, we recommend exploring our advanced templates. Similar to the starter selection, these templates offer enhanced customization options, enabling you to align them more closely with your unique use case.

If you want to create your own model and start with an empty SQL code, you can do so by clicking "Start without a template" at the bottom.

Below, you will find Pecan's templates and their recommended use case.

Retention is the act of keeping or maintaining a behavior over time.

Lifetime value helps us understand what will be the total value of something at a certain point in time.

Conversion helps us to predict if something will change its status after a certain amount of time when the change is reflected in another table.

Predictive question:

Churn is the act of stopping or suspending a behavior over time.

Predictive question:

Keeping or maintaining a behavior once a time period comes to an end.

Predictive question:

Predicting every Month/Week/Day the likelihood a new behavior will happen.

Upselling is the practice of encouraging customers to purchase higher-priced or additional products or services beyond their initial choice. In Pecan, we can analyze the likelihood of a customer performing an upsell.

Predictive question:

Predicting the likelihood of something to be top performing after a certain amount of time.

The High Value Customers template helps create predictive models to pinpoint customers with the highest potential for exceptional performance. These predictions assist in prioritizing customer focus, highlighting those of high value.

Predictive question:

Predicting if there will be a status change for someone/something when the change is reflected in the same table.

Chargebacks occur when a customer requests a transaction to be reversed and funds are returned. Predicting chargebacks helps businesses prevent fraud and prepare for potential problems by identifying risky transactions.

Predictive question:

Regularly predict the value of something for the following X days.

Revenue forecast helps us to predict or estimate how much money a business or organization expects to earn in a specific period.

This helps businesses plan better, make smart decisions, and set goals for growth.

Predictive question:

Consistently forecasting the fluctuation of a value for the upcoming X days.

Engagement decrease refers to when the level of interest, interaction, or involvement with a product, service, or platform goes down. Predicting engagement decrease allows us to anticipate and address issues that might lead to reduced customer interest and interaction.

Predictive question: