1luke2 + analytics   7

From Project To Process | Simo Ahava's blog
A typical example is in Ecommerce tracking. Ecommerce comprises such a huge number of variables that successful comparison across date ranges would require that these variables be encoded consistently across different points in time. How often does that happen? I’m willing to say rarely, and the likelihood decreases with time. However, still we see people comparing Revenue from last quarter (which included shipping and tax) with Revenue from a year ago (which did not include shipping and tax). The very fact that they have different aggregation methods means that they can’t be compared unless you know how these methods differ. Unfortunately, this information is often misplaced when teams and organizations change.
In other words, data collection is an investment. It requires labor, love, and a crystal ball. It’s one of the most difficult things to get right, and requires a lot of experience. But there’s nothing, nothing as frustrating as data that’s missing or incorrectly collected.
2. Analytics as a PROJECT is the root of all evil

Here we go: diving into the controversial end of the pool. Let me juice it up with another claim.
Ecommerce  GTM  Analytics  gameplans  Business  advice 
4 days ago by 1luke2
The ULTIMATE eCommerce Analytics Guide | Oribi Blog
Any eCommerce website owner’s main goal is to increase its sales and revenues. The most effective way to do so is choosing the right analytics tool (or tools) for your needs, learning how to make the most out of it, and taking data-driven decisions based on the detailed data provided.
Ecommerce  Analytics 
4 days ago by 1luke2
Sophie Bakalar on Twitter: "Some KPI benchmarks for consumer brands: - AOV (online): $60+ (ideally $100+) - GM%: 30-40% for bev/frozen, 40-60% for food, 50%+ for apparel, 70%+ for personal care - Retention (subscription): 1M: 80%+, 6M: 30-40% - CAC: $25-7
Some KPI benchmarks for consumer brands:
- AOV (online): $60+ (ideally $100+)
- GM%: 30-40% for bev/frozen, 40-60% for food, 50%+ for apparel, 70%+ for personal care
- Retention (subscription): 1M: 80%+, 6M: 30-40%
- CAC: $25-75
- LTV/CAC: 4x+
- Organic acq.: 25%+
ecommerce  analytics 
8 days ago by 1luke2
GTM Guide: dataLayer.push with examples - Analytics Mania
DataLayer.push is a way for you/developers/3rd-party-plugins to pass some useful data to the Data Layer. And from there you can “play with it” in Google Tag Manager.

DataLayer.push should be the only way how you should be adding data. Even though Google Tag Manager official documentation still mentions/recommends using a regular Data Layer declaration (dataLayer = [{}]), you should not fall for that.
GTM  googletagmanager  Analytics 
16 days ago by 1luke2
What Product Managers Can Learn From Digital Analytics - Blair Reeves
Nancy: One of the big things I’ve learned is that educating everyone else around me at my company was a big part of my role, and no one (including myself) really anticipated that. The biggest items I had to continually preach: Implementation is not a “set it and you’re done” exercise. Any and all change introduced to a website or app can disrupt or break tracking. It’s so much more fragile than anyone ever thinks. The fact that QA had to happen with every release – and simply to try to preserve the tracking and data collection we had in place. And, focusing on what was truly important to measure, and making sure our data collection was rock solid.
Analytics  Business  Interview 
16 days ago by 1luke2
A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments | the morning paper
A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments Dmitriev et al., KDD 2017

Pure Gold! Here we have twelve wonderful lessons in how to avoid expensive mistakes in companies that are trying their best to be data-driven. A huge thank you to the team from Microsoft for sharing their hard-won experiences with us.
abtesting  metrics  analytics 
january 2018 by 1luke2
Picking a cloud database for analytics: the SQL options | Parse.ly
This post will compare:
1 Amazon RDS (Postgres)
2 Google Cloud SQL (MySQL)
3 Amazon Redshift
4 Google BigQuery
It will also serve as a guide for choosing between these options, including the technical pro’s and con’s of each.
Mysql  BigQuery  SQL  Analytics 
december 2017 by 1luke2

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