Communicating with Impact — Storytelling Training

A common representation of sales comes in the form of someone aggressively trying to sell you something you don’t need. However, this isn’t the only application of sales, and the act of selling is used in more scenarios than you may think. Money may not be changing hands, and you may not be exchanging physical goods or services, but you’re more than likely selling something to somebody every day. …

P-values and Confidence Intervals

Ro Data Sip-and-Share Q1 2019

Getting a firm understanding of key statistical concepts is critical in interpreting and sharing results from various analyses. Using Daniel Laken’s Improving your statistical inferences course, I set out to get precise definitions of two widely used but commonly misconstrued concepts: p-values and confidence intervals.

P-values

A p-value is the probability the getting the observed (or more extreme) result of a test if the null hypothesis (H0) is true. The null hypothesis for a test is that there is no difference between two groups. …

Analyzing Survey Results: Efficiently Performing Lots of Chi-Squared Tests

Surveys can provide a direct source of data into your members feelings, thoughts, and opinions. Connecting survey results to other existing data can unearth deeper insights about your member base.

One way to go about this integrated analysis is to find relationships between existing member characteristics (ex: age and gender) and the survey responses. For example, after sending out a survey, you may hope to answer some questions like:

- Do older members think frequency of contact is more important?
- Are female members more likely to recommend the product to their friends? …

Census Data for your Data Warehouse

Demographic data by ZIP code can be useful in getting a better understanding of your members and/or user base. Are your members from urban or rural areas? How much money do they likely make? How educated are they? Where do they fall as far as demographics go?

The most complete free-to-use data source we found was from the American Census Bureau. We’ve pulled, cleaned, consolidated, and reformatted their data around basic demographic information — such as age and gender — but also more detailed attributes like income, health insurance coverage, and gross rent. …

Cohort Analysis in Looker

Motivation: Create a Looker Dashboard that will allow you follow the behavior of a member over their entire life cycle, depending on which month they first joined your business/service. This type of analysis helps identify patterns of a typical member that started in a certain month, and compare how they differ to the behavior of members that started in other months.

Some tiles that we find crucial to our dashboard are:

1. To compare how many orders were placed by members across different cohorts.
2. To track the percent of members that have ordered in the last month over time as a metric for how engaged a cohort is. …

Ro-Gazer: Bulk Looker Content Management

At Ro, we have a development workflow that uses two Looker servers: staging and production. Although LookML code can be migrated and managed across multiple Looker servers, it is tough to do the same for the user defined Looks and Dashboards. The gzr utility was a step in the right direction to manage Looks and Dashboards between our two Looker servers, but after experimenting with various gzr commands we found that they were geared towards managing individual Looks or Dashboards and not tailored for bulk migration or management.

We have built Ro-Gazer as an easy-to-use script to bulk view, download, and upload Looks and Dashboards between multiple Looker servers. …