retentioneering-tools VS dbt-fal

Here are my top off-the-shelf data science models for Marketing. Would be interested which other marketing data science tools you use?

Product Recommendation on Your Website with Metarank (https://github.com/metarank/metarank)

Metarank is a tool that helps you easily build an advanced recommendation engine for your products or content on your website. To get started you only need historical performance data of your products (e.g. number of clicks) and additional metadata like product rating, genre, ingredients or price. In a YAML file, you define the features and the model parameters (e.g. number of iterations, modeling technique). The API service integrates with Apache Flink and can be easily integrated into Kubernetes clusters.

User Journey Analysis on your Website with Retentioneering (https://github.com/retentioneering/retentioneering-tools)

Retentioneering helps you to understand the user journey on your website. Retentioneering is a Python library that allows you to easily connect your Google Analytics data (in Bigquery). You define user-id, event-type and time stamp. From this data input a comprehensive graph network is created with gains and losses as you know it from a customer journey. In addition, customer segments are created that have a similar customer journey. This reduces the complexity of a purely descriptive view of the data.

Marketing Mix Modeling with Robyn (https://github.com/facebookexperimental/Robyn)

Less third-party cookie means less attribution models. The answer to this is Marketing Mix Modeling. Marketing mix models are regression models that use statistical probability to calculate the effect size of marketing channels and other independent variables. The advantage is that business context can be modeled much more realistically. For example, Google Searches for the own brand can be integrated to determine the share of the own brand strength in the revenue. Likewise, offline advertising measures can be modeled with other metrics in this context (e.g. offline advertising with GRPs). Robyn takes into account adstock effects, ROAS calculation and multicollinarity in the marketing channels. In addition, with simple functionality, budgets can be optimized using the predictions and results from marketing tests can be integrated into the model for calibration.

Happy data scientists use fal and dbt

fal (https://github.com/fal-ai/fal) helps with this! In fact we wrote a blog post about feature engineering with fal and dbt recently

We built a dbt adapter that helps you run local Python code with your dbt project with any other data warehouse. You can see it here: https://github.com/fal-ai/fal/tree/main/adapter

This new adapter helps you run your dbt Python models with isolated Python environments using our open source library: https://github.com/fal-ai/isolate

Have you considered fal? https://github.com/fal-ai/fal

Something to maybe comment on the post is that dbt is introducing Python transformations on the data warehouse offering (e.g. Snowspark) soon and that there are tools like fal that enable these Python transformations to run in a different environment which you have control over.

Fal-AI ( https://github.com/fal-ai/fal ) Fal helps to run Python scripts directly from the dbt project. For example, you can load dbt models directly into the Python context which helps to apply Data Science libraries like SKlearn and Prophet in the dbt models. This especially improves the data science capabilities within a data pipeline. What I extremely like about fal is that it extends dbt from a interesting angle.

- 🐍 fal ai: Fal helps to run Python scripts directly from the dbt project. For example you can load dbt models directly into the Python context which helps to apply Data Science libaries like SKlearn and Prophet in the dbt models.

I haven’t tried it yet but I know https://fal.ai/ helps you run python alongside dbt.

Yes I agree with you that having fivetran/airbyte and dbt covers a lot of the airflow use cases.. That being said you might still want to run some scripts after the DBT transformation is over, we ran into this exact problem and built a useful CLI tool for running python scripts alongside the dbt run.

Great write-up! For your logging integration, you might have a look at fal. There's an example of sending events to Datadog