A #FRAMEWORK FOR #DIGITALANALYTICS PROCESS _ INTERNATIONAL INSTITUTE OF #DIGITALMARKETING ™ – FESCH.TV

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A #FRAMEWORK FOR #DIGITALANALYTICS PROCESS | INTERNATIONAL INSTITUTE OF #DIGITALMARKETING ™

Digital Analytics processes can be used to accomplish many things. Yet, in its most valuable form, the process should be viewed as a means to familiarize business users with data that is potentially available to them and to create efficiency around how that data is collected, analyzed, and provided back to the business.
Most organizations have organic processes that grew out of necessity, but in my experience few have developed formal processes for taking in analytics requests, for data quality management, or for new tagging requests.
Digital Analytics New Request Tagging & QA Process
This process is designed using a Scrum methodology, which can easily fit within most companies development cycles. At the conceptual level, the Analytics Tagging & QA Process provides a method for business users to communicate their data needs, which are then used to: 1) Define requirements, 2) Create a Solution Design, 3) Develop Analytics Code, 4) Conduct QA, and 5) Launch new tracking functionality
Tagging & QA Process — Starting Point:
The tagging and QA Process is one that is typically used by organizations multiple times throughout website redesigns, feature/function improvements, and general updates.
The starting point for this process includes a “Digital Analytics Brief” that will be used to identify goals, measurement objectives, and specific elements that need to be tracked with analytics.

Step 1: Defining Business Requirements
With the Digital Analytics Brief in hand, the business analyst should have pertinent information necessary to begin the process of defining business requirements. Depending on the scope of the project, this part of the process should take between one and five hours to complete with the Digital Analyst leading the effort and stakeholders collaborating with details.

Step 2: Creating A Solution Design
Often one of the most important yet overlooked aspects of digital analytics is documentation. Documentation provides an organization the ability to clearly define and record key components of its digital analytics implementation.
Companies can rely on internal resources to generate documentation, or if using an agency or consulting partner, ask them to provide documentation that should serve as the foundation for your analytics implementation.

Step 3: Developing Code
Unlike traditional development, digital analytics (especially Adobe Analytics) requires its own specific code base that includes events, eVars, and sProps to work properly.

Step 4: Quality Assurance
As with all development projects, digital analytics requires QA testing to ensure that tags are implemented correctly and that data appears within the interface as expected. At Analytics Demystified, we have developed our own processes for administering QA on digital analytics tags.

Step 5: User Acceptance & Launch
In closing, there are many different processes that can (and should) be applied to digital analytics. By building process around mission critical tasks, businesses can create efficiency in the way they work and bring new levels of standards and accountability to staff. By creating a process for new analytics requests, we’ve witnessed that organizations become more skilled at deploying tagging and reports in a timely manner with fewer defects.

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