Occam's Razor

AUGUST 22, 2011

Key To Your Digital Success: Web Analytics Measurement Model. " Measuring Incrementality: Controlled Experiments to the Rescue! Barriers To An Effective Web Measurement Strategy [+ Solutions!]. Measuring Online Engagement: What Role Does Web Analytics Play? "Engagement" How Do I Measure Success?

KPI 124

KPI Metrics Measurement ROI 124

Occam's Razor

JULY 24, 2018

Even simple questions like “ How effective is our analytics strategy? Soon, your digital analytics strategic framework that you hoped would provide a true north to the analytics strategy question looks like this …. To name a few: Digital Marketing & Measurement Model | Analytics Ecosystem | Web Analytics 2.0.

Analytics 93

Analytics Metrics Strategy Measurement 93

The Unofficial Google Data Science Blog

JULY 22, 2020

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

Experimentation 95

Experimentation Statistics Testing Strategy 95

Alation

SEPTEMBER 28, 2022

DataOps strategies share these common elements: Collaboration among data professionals and business stakeholders. In DataOps, data analytics performance is primarily measured through insightful analytics, and accurate data, in robust frameworks. Easy-to-experiment data development environment. Value working analytics. Embrace change.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. One way that we can get around this is to use the proportion of tags that fall into a given class as a measure of our degree of confidence in that class association. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Corinium

APRIL 25, 2019

Most of my days focus on understanding what’s happening in the market, defining overall product strategy and direction, and translating into execution across the various teams. For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. For example, we might want to stop the process if we measure harmful effects early. What if their uptake rate is not uniform?

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

JULY 31, 2016

The analysis can be straightforward, especially when it's safe to assume that individual observations of an outcome measure are independent. The outcome measure we care about is an average of the students' test scores, and so the unit of observation is a student. To figure this out, let's consider an appropriate experimental design.

Experimentation 42

Experimentation Testing Measurement Metrics 42

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

FEBRUARY 8, 2021

From observing behavior closely, and from my own experimentation and failure, I've noticed consistent patterns in what great employees do and great bosses do. Explain strategy. And, critically, why that strategy. They find the external author of the statistical algorithm I want them to use, and ask them for guidance.

Strategy 132

Strategy Statistics Cost-Benefit Marketing 132

Occam's Razor

JANUARY 9, 2012

A huge part of the last few years for me have been about bringing more data, better strategies, more powerful tools, ever more impactful keynotes to people around the world. Most companies are astonishingly blasé about data and possibilities of measurement. " Sad, unimaginative measurements of their sad, unimaginative campaigns.

Marketing 140

Marketing B2B Measurement Risk 140

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Part of it fueled by some Consultants. I suppose the rational is: self preservation before all else. It has simply not had a break to catch a breath and mature.

Analytics 118

Analytics Consulting Metrics Experimentation 118

O'Reilly on Data

DECEMBER 9, 2019

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics.

Statistics 27

Statistics Uncertainty Risk Marketing 27

Cloudera

NOVEMBER 25, 2020

And soon also sensor measures, and possibly video or audio data with the increased use of device technology and telemedicine in medical care. How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data?

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Domino Data Lab

AUGUST 26, 2019

To support the iterative and experimental nature of industry work, Domino reached out to Addison-Wesley Professional (AWP) for appropriate permissions to excerpt the “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Beyond this, we also get a measure of the variability.

Testing 79

Testing Modeling Machine Learning Metrics 79

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61