Occam's Razor

SEPTEMBER 15, 2014

In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. Media-Mix Modeling/Experimentation. For the first couple of interactions, give her/him that data. Surely you are not surprised that digital finally beats TV.

Metrics 141

Metrics Reporting Analytics Marketing 141

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

Occam's Razor

AUGUST 28, 2017

The first eye-opening learning for me came from the Google Research team’s post on Learning from Large-Scale Interaction. The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models). Most robots are very robotic because they follow a sense-plan-act paradigm.

Deep Learning 139

Deep Learning Machine Learning Marketing Strategy 139

Occam's Razor

JULY 26, 2010

Maybe Google is really good at Volunteers and not optimal for attracting people who donate. Will take you off on a completely different line of inquiry, all from adding June 2009 to look at June 2010. So why is that your mental model? And what to do better. If you focus only on donations you'll devalue Google.

Metrics 96

Metrics Measurement Dashboards Reporting 96

Occam's Razor

JUNE 11, 2014

What if the first interaction with your brand is digital? According to eMarketer in 2010 we spent 3:11 (hrs:min) on digital content consumption and 4:24 on TV in the US. From a data perspective this is reflected in our obsession with multi-channel attribution modeling or user based visitor segmentation. See-Think-Do YouTube.

Marketing 150

Marketing Strategy Sales IT 150

Occam's Razor

MARCH 23, 2010

My problem with these mistruths and FUD is that they result in a ton of practitioners and companies making profoundly sub optimal choices, which in turn results in not just much longer slogs but also spectacular career implosions and the entire web analytics industry suffering. This is sad. Even a little frustrating. Aim for this.

Analytics 118

Analytics Consulting Metrics Experimentation 118

O'Reilly on Data

JANUARY 25, 2021

We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start).

Machine Learning 363

Machine Learning Software Testing Technology 363