Skills Web Analysts and Mobile App Analysts Must Have


Chapter 20

User

The Four Tasks

As a web analyst and/or a mobile app analyst, your role requires you to perform four major tasks.

  • Perform trend and data reporting.
  • Analyze online marketing acquisition strategies and explore new opportunities and/or new strategies.
  • Understand on-site (and/or on-app) visitor behavior and experiences.
  • Stay connected with the trends and the details.

The Three Phases

In order to accomplish the four major tasks, the web analysts and/or mobile app analysts will actually have to go through three phases.

  • Data Collection – Collect and store the raw data which is required for building reports.
  • Data Reporting – Process the raw data and have the data presented as analytics reports. The reports can be in table format or graphical format or both.
  • Data Analysis – Go through the data reports hoping you will be able to spot data spikes and/or insights for improving your business.

The three phases are traditional phases in analytics (for websites' and mobile apps' data). Let's examine each phase.

Phase 1: Data Collection

Your website's data can be captured through web server log files and/or web analytics tools.

Web Server Log Files

Web server log analytics captures and stores raw data in log files.

Before using log file data as your analytics data, your first set as a web analyst is to ensure your website is correctly configured to capture and store log files.

Web Analytics tools

Most websites use at least one or sometimes multiple web analytics tools.

To enable a web analytics tool to capture your website's basic data, the first step is to implement the required JavaScript based tracking codes onto all pages of your website.

When additional user behavior data is required, you will have to implement some advanced/customized tracking setup.

Mobile App Analytics tools

Most mobile app analytics tools provide SDKs (i.e. SDK for iOS app & SDK for Android app). To enable the mobile app analytics to collect data from your apps, your first step is to install the SDKs onto the mobile apps.

Phase 2: Data Reporting

Once the data is collected, the next phase is to extract the data for the end users. Raw data collected in phase #1 should be converted into reports that are for two major purposes:

  • Regular Data Reports
  • Ad Hoc Data Reports

Regular Data Reports

These reports need to be received on a regular basis which may be once per day, per week, or per month. These reports are categorized into different levels depending on who the receivers are. An executive (e.g. company CEO) will need high-level reports showing key revenue figures for each major division of the company. Operational managers will need mid-level data reports which allow them to track “potential issues” of the products that individual teams are responsible for.

Ad Hoc Data Reports

These reports won't be processed regularly with any fixed intervals. Normally, ad hoc reports are required for review purposes for any once-off online campaigns. Ad hoc reports are also required when deep diving into data to figure out issues. Issues may be certain KPI numbers have decreased over the past two weeks, and operational managers will need to go through ad hoc data reports in details to figure out the reasons behind the decrease.

Web & Mobile App Analytics tools

Typical web analytics tools and/or mobile app analytics tools provide many basic data reports in user-friendly graphical user interfaces (GUI), and usually the reports can be downloaded as spreadsheets. The pre-built reports allows you to quickly see high-level data trends.

Whether you can perform advanced segmentation and/or compile custom reports, it depends on the specific web / mobile app analytics tool you use. Performing advanced segmentation and/or compiling custom reports allow you to go one (or several) levels deeper into figuring out some real business issues.

Web Server Log Files

With web server log analytics, log files usually are very large files that can be very difficult to process. A mid-size website's daily log file can easily exceed 25 gigabytes in size. You may have to use a third-party log analyzing tool to compile the data into reports.

You must be very familiar with the principles of how web server log files capture data and what data is available. Web server log files give you the ability to “record” all the files that were loaded by the user when they accessed your websites, and you can easily see which “components” of your websites aren't responding to user requests.

Transaction Data and/or Customer Data

Your business intelligence (BI) team may already have captured transaction data and/or customer data and have them stored in a data warehouse software (or multiple databases) e.g. Cognos, Microsoft Reporting Services, etc. At this stage, the transaction data and/or customer data should already been processed into human readable reports.

The next immediate step is to connect the transaction data to your web analytics data and mobile app analytics data. Only after this data connection is established, then your reports will show close to full pictures of your users from how they interact with your website (or mobile apps) to what they have transacted (i.e. purchased).

To build the connection, you may have to use your SQL query writing ability to extract the data directly from the databases (or data warehouse).

Analytics for Advertising Campaign Tracking

If you use one or some of the third-party platforms (e.g. Kenshoo, Marin Software, etc) to track advertising campaigns' performance, then you will have to extract the data and connect it to your web / mobile app analytics data and transaction data.

Phase #3: Data Analysis

Excel & Visualization Reporting Tools

For data analysis, tools such as Excel, and some third-party Visualization reporting tools (e.g. Tableau) are essential.

As an analyst for websites' and mobile apps' data, spending 80% of your time solely on Excel as a tool may be normal. Being able to use the two Excel feature Pivot tables and Vlookup will be important.

R & Python

Tools with steeper learning curves include R, Matlab, and even Python. You may need one or some of them to help you build data models when sophisticated analysis is required.


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Gordon Choi's Analytics Book has been available since August 2016.







Content on Gordon Choi's Analytics Book is licensed under the CC Attribution-Noncommercial 4.0 International license.

Gordon Choi's Analytics Book

Gordon Choi's Other Books:
The China Mobile SEO Book
Mobile Website Book