Robyn VS ethical-ad-server

With all this talk about Google and other platforms deprecating 3P tracking in favor of more aggregate "tracking", my team is considering a marketing mix modeling tool. One that comes to mind is this tool - Robyn

Robyn - R Library

Furthermore, both Google and Meta have quietly conceded that a lot of the digital attribution data they generate is pretty bunk. It’s why Meta developed Robyn, which uses MMM techniques that have long been used to measure effectiveness of traditional channels: https://github.com/facebookexperimental/Robyn

Here's my first question - are there other approaches that model media spending and media exposure metrics separately? I have noticed that Robyn has included both exposure metrics and media spending and got them transformed via a non-linear model called the Michaelis-Menten function to establish the spend-exposure relationship, as you can find it here. What if we want, or is it possible to keep only media exposure metrics instead of media spending? If yes, what would be the alternative approaches apart from Logarithmic Regression, Ridge Regression, Bayesian approach, etc?

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.

Try this https://facebookexperimental.github.io/Robyn/ And for more advanced project you can try this https://siyasgupte.medium.com/causal-impact-understand-the-inner-workings-to-optimize-your-results-506ed442619

For Data Scientists super interesting but still in WiP: We are implementing a advanced Marketing Mix Model soon. It combines bascially Prophet + Ridge Regression + Nevergrad (it is already a R library from Meta: https://github.com/facebookexperimental/Robyn

Take a look at Robyn by Facebook Meta, if you haven't

MMM (R): Robyn

submitted to HN in 2022: https://news.ycombinator.com/item?id=32650878

> [...] a privacy-first ad network. We don't use any cookies, and target ads based on content. The code is all open source (https://github.com/readthedocs/ethical-ad-server), and we're slowly working to help fund open source projects. We only show developer-related ads on developer sites. No ads about a product whose site you visited last week and nothing off topic.

Please report the page views (total and unique visitors) alongside the revenue, if possible.

I'm running ethicalads on my site (vadosware.io) and it's been pretty great so far (though I suspect revenue is much lower than other alternatives) -- they also have an open source server which they run an instance of and give you access to:

https://github.com/readthedocs/ethical-ad-server

Stats and lots of information about the advertisements and click rates/etc is available.

Been pretty smooth sailing so far, was going to write about it in the future.

EthicalAds

Hi HN!

We've been working on our ad network for a couple years, but we just launched the 1.0 of our open source code, so it seems like a great time to do a Show HN! We have more info on our 1.0 post here: https://www.ethicalads.io/blog/2022/08/going-v10-the-backsto...

A few years back, we were building Read the Docs, a documentation platform for open source. We had millions of monthly visitors and the obvious way to monetize was ads. However, we cared about the privacy of our visitors and we didn't want ad companies to track our users around the web. We went to a few ad networks and asked if we could proxy the ad traffic or even just run ads without cookies. They weren't willing to do what we wanted, so we built our own ad network.

We started to build a privacy-first ad network. The code is all open source (https://github.com/readthedocs/ethical-ad-server), and we're slowly working to help fund open source projects. We only show developer-related ads on developer sites. No ads about a product whose site you visited last week and nothing off topic.

Next, we built a crawler that indexes the sites on our network to help target our ads. Using an ML classifier (built with SpaCy), it can tell if a page is about data science or about full stack development. This allows advertisers to target the niche they are focused on and ensure the ads perform well, without doing any user tracking.

We're pretty excited about the future ability of our ML to improve ad targeting without any information about the user. The coolest thing is that our business gets better as we understand the content we're serving ads on better, instead of tracking more about our users by harvesting more data about them.

We've been a true network beyond just ads on Read the Docs for a couple years and we now have ~130 publishers. We gross just under $60k per month in ad revenue of which 70% goes to publishers. Most of our publishers are small sites or open source projects so to send them ~$40k/mo feels great.

Do you have a developer site that you're looking to monetize? Or are you an advertiser trying to reach developers? Or are you just curious about privacy and advertising? Happy to answer any questions!

Ethical ad server