Platform Principles: Unit 4 - Reporting

Analytics Academy

Platform Principles: Reporting Overview

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Introduction

Once your Google Analytics data has been collected and processed, you’ll use the Google Analytics reporting interface or the Google Analytics reporting APIs to retrieve your data for reporting and analysis.

Reporting overview

All Google Analytics reports are based on different combinations of dimensions and metrics. When viewing your data in a standard Google Analytics report, the first column you see in the table contains the values for a dimension, and the rest of the columns display the corresponding metrics.

Most often, you’ll use the Google Analytics reporting interface to access your data, since it’s easy to use for a majority of reporting and analysis needs. You can think of this interface as a layer on top of your data that allows you to organize, segment and filter your data with a set of analysis tools.

In addition to the reporting interface, you can use an API, or an Application Programming Interface, to extract your data directly from Google Analytics. Using the APIs you can programmatically add analytics data to your custom applications, like an internal dashboard. This can help you automate and customize the reporting process for your business.

Most of the time, when you request data from the reporting interface or the APIs you’ll receive your data almost immediately. But in some cases, where you request complex data, Google Analytics uses a process called sampling. Sampling helps Google Analytics retrieve your data faster so there’s not a long delay between when you request the data and when you receive it.

Conclusion

It’s important to understand the building blocks of the Google Analytics reporting system. When you know how Google Analytics generates reports in the UI and how you can build your own reports using the APIs, you can simplify the reporting process and better integrate analytics into your organization.

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Platform Principles: Building Reports with Dimensions & Metrics

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Introduction

The building blocks of every report in Google Analytics are dimensions and metrics. By combining different dimensions and metrics Google Analytics can generate almost any report you need to measure your marketing activities and user behavior.

Dimensions in Google Analytics

A dimension describes characteristics of your data. For example, a dimension of a session is the traffic source that brought the user to your site. And a dimension of an interaction a user takes on your site could be the name of the page they viewed.

Metrics in Google Analytics

Metrics are the quantitative measurements of your data. They count how often things happen, like the total number of users on a website or app. Metrics can also be averages, like the average number of pages users see during a session on your website.

Combining dimensions and metrics in reports

Dimensions and metrics are used in combination with one another. The values of dimensions and metrics and the relationships between those values is what creates meaning in your reports.

Most commonly, you’ll see dimensions and metrics reported in a table, with the first column containing the values for one particular dimension, and the rest of the columns displaying the corresponding metrics.

However, not every metric can be combined with every dimension. Each dimension and metric has a scope that aligns with a level of the analytics data hierarchy -- user-, session-, or hit-level. In most cases, it only makes sense to combine dimensions and metrics in your reports that belong to the same scope.

For example, the count of Visits is a session-based metric so it can only be used with session-level dimensions like trafficSource or geographic location. It would not be logical to combine the count of visits metric with a hit-level dimension likePage Title.

Here’s another example: the metric Time on Page is a hit-level metric. It measures how long users spend on a page of your site. It is not possible to use this metric with a session-level dimension like traffic Source or geographic location. In this case you would need to use a session-based time metric, like Average Visit Duration.

Conclusion

Understanding what dimensions and metrics are and how they can be combined in your reports will help you get the meaningful data you need to analyze your business and improve your performance.

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Platform Principles: The Reporting APIs

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Introduction

In addition to the online reporting interface, Google Analytics also gives you simple and powerful reporting APIs. These help you save time by automating complex reporting tasks.

Using the Google Analytics reporting APIs

For example, you can use the APIs to integrate your own business data with Google Analytics and build custom dashboards.

To use the reporting APIs, you have to build your own application. This application needs to be able to write and send a query to the reporting API. The API uses the query to retrieve data from the aggregate tables, and then sends a response back to your application containing the data that was requested.

Each query sent to the API must contain specific information, including the ID of the view that you would like to retrieve data from, the start and end dates for the report, and the dimensions and metrics you want. Within the query you can also specify how to filter, segment and order the data just like you can with tools in the online reporting interface.

You can think of the data that gets returned from the API as a table with a header and a list of rows. The header describes the name and data type of each column -- these are either the dimension or metric names.

Conclusion

Writing an application that can access Google Analytics data can be a complex process and requires the help of an experienced developer. But with a little effort, the reporting APIs give you the power to automate and streamline complex reporting tasks for your business.

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Platform Principles: Report Sampling

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Introduction

Report sampling is an analytics practice that generates reports based on a small, random subset of your data instead of using all of your  available data. Sampling lets programs, including Google Analytics, calculate the data for your reports faster than if every single piece of data is included during the generation process.

When does sampling happen?

During processing, Google Analytics prepares the data for your standard reports by precalculating it and then storing it in aggregate tables. This lets Google Analytics quickly retrieve the data you request without sampling.

However, there might be times when you want to modify one of the standard reports in Google Analytics by adding a segment, secondary dimension, or another customization. Or, you might want to create a custom report with a completely new combination of dimensions and metrics.

When you make any of these kinds of custom requests, either through the reporting interface or the reporting APIs, Google Analytics inspects the set of aggregate tables to see if the request can be met using data that’s already processed and is in the tables. If it can’t, Google Analytics goes back to the raw session data to process your request on-the-fly. When this happens, Google Analytics checks to see how many sessions should be included in your request. If the number of sessions is small enough, Google Analytics can calculate the data for your request using all of the sessions. If the number of sessions is too large, Google Analytics uses a sample to fulfill the request.

For example, let’s say you create a Custom Report with the dimensions City and Campaign and the metrics Visits andConversion Rate. This combination of metrics and dimensions is not already pre-calculated in any of the aggregate tables. So, if you choose a date range for the report that includes a very large number of sessions, your report will be calculated from a sampled set of data.

Adjusting the sample size

The number of sessions used to calculate the report is called the “sample size.” You can adjust the sample size using a control in the reporting interface or by specifying the size when you query the API. If you increase the sample size, you’ll include more sessions in your calculation, but it’ll take longer to generate your report. If you decrease the the sample size, you’ll include fewer sessions in your calculation, but your report will be generated faster.

The sampling limit

Google Analytics sets a maximum number of sessions that can be used to calculate your reports. If you go over that limit, your data gets sampled.

One way to stay below the limit is to shorten the date range in your report, which reduces the number of sessions Google Analytics needs to calculate your request. Google Analytics Premium also offers an Unsampled Reporting feature that will pull unsampled data for custom requests, even for large reports that exceed the sampling limit.

Conclusion

Session sampling is an effective way to reduce latency while maintaining a high level of accuracy for your reports. It helps Google Analytics process your custom data requests efficiently, so you get timely answers to your business questions.

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