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Cohort-Based Forecasting: A Technical Deep Dive

Updated: Oct 18

Cohort Based Forecasting

At TensorOps, we specialize in implementing AI solutions that drive business growth. One powerful application of AI in the business realm is cohort-based forecasting. In this blog post, we'll dive into the technical aspects of cohort-based forecasting, explore how it differs from traditional time series models, and provide code snippets to help you implement it. If you're interested in user acquisition using Machine Learning please check out our blog post in which I reviewed a few ML and Deep Learning techniques for user acquisition and retention.


Introduction to Cohort Analysis

What is a Cohort?

In data analysis, a cohort is a group of users who share a common characteristic within a defined time frame. This characteristic could be:

  • The date they signed up for your service

  • Their geographical location

  • Their initial purchase behavior

By grouping users into cohorts, we can analyze behaviors and patterns that are not apparent in aggregate data.


Why Use Cohort-Based Forecasting?

Cohort-based forecasting is a method of predicting future trends by grouping individuals or entities (such as customers or users) into cohorts based on shared characteristics, such as the time they started using a product or service. By analyzing how these cohorts behave over time, businesses can make more accurate predictions about future performance, retention, or growth. This approach is particularly useful in identifying patterns and trends that may not be visible through traditional forecasting techniques.

Unlike Traditional forecasting methods often rely on aggregated time series data, which can mask underlying patterns and behaviors of different user segments. Cohort-based forecasting breaks down this data into more granular groups, allowing for:

  • Improved Accuracy: By modeling each cohort individually, we capture specific behaviors that enhance overall forecast precision.

  • Better Insights: Understand how different user segments contribute to key metrics over time.

  • Scenario Planning: Simulate "what-if" scenarios for specific cohorts to inform strategic decisions.


Cohort vs. Time Series Forecasting

Traditional Time Series Models

Time series models like ARIMA or Prophet focus on patterns over time in aggregated data. They are excellent for capturing trends, seasonality, and overall patterns but may miss nuances in user behavior.


Cohort-Based Models

Cohort-based models transform time series data into a tabular format with both registration dates and event dates (e.g., purchase dates). This structure allows us to apply regression models and capture non-linear relationships and interactions within the data.


Implementing Cohort-Based Forecasting

Let's walk through the implementation of cohort-based forecasting using Python. We'll use synthetic data for illustration.


Data Preparation

First, we need to create a dataset that includes:

  • Registration Date: When the user joined.

  • Event Date: When the user performed the action we're forecasting (e.g., made a purchase).

  • Age: The time difference between the event date and registration date.

Exploratory Data Analysis

Visualizing the number of events over time helps us understand the decay pattern of user engagement.

Observation: Events typically peak shortly after registration and decay over time, often following a power-law distribution.


Feature Engineering

To capture the non-linear relationship between user age and event probability, we can use transformations like logarithms or B-splines.

Modeling with Generalized Linear Models (GLM)

We can use a GLM with a Poisson distribution to model the count data.

Forecasting Future Events

With the model trained, we can forecast future events for each cohort.


Advantages of Cohort-Based Forecasting

Granular Control

By modeling cohorts separately, we can adjust for:

  • Marketing Campaigns: Measure the impact of specific campaigns on user behavior.

  • Product Changes: Analyze how updates affect different user segments.

  • Seasonality and Trends: Apply seasonal adjustments at the cohort level.


Improved Accuracy

Regression models can capture complex relationships and interactions that time series models might miss, leading to more accurate forecasts.


Scenario Analysis

Easily simulate different scenarios by adjusting cohort sizes or behavior patterns to predict future outcomes.


Challenges and Best Practices

Data Volume

Cohort-based models can generate large datasets, especially with long user histories. Ensure efficient data handling and storage.

Feature Engineering

Non-linear relationships require careful feature engineering. Consider using:

  • Log Transformations

  • B-Splines

  • Interaction Terms


Model Selection

Choose models based on your needs:

  • GLMs: Good for interpretability and handling specific distributions.

  • Gradient Boosted Machines (GBMs): Handle non-linearities automatically but may struggle with trend extrapolation.


Validation

Use rolling walk-forward validation to mimic real-world forecasting and avoid look-ahead bias.


Conclusion

Cohort-based forecasting provides a robust framework for predicting key metrics by capturing the unique behaviors of different user groups. By leveraging this method, businesses can gain deeper insights and make more informed decisions.

At TensorOps, we're committed to helping organizations harness the power of AI and machine learning. If you're interested in implementing cohort-based forecasting or other AI applications, contact us to learn how we can assist you.



 

About TensorOps

TensorOps is a leading AI consulting firm specializing in the implementation of advanced AI applications. Our team of experts helps businesses unlock the full potential of their data through cutting-edge machine learning solutions.

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