Tips for Analyzing and Interpreting Digital Data

It is no secret that data-oriented search and digital marketing is on demand and many companies pay big bucks for employees, contractors or consultants with good data analysis skills. But everyone talks about data. There is an epidemic of data overload in many companies. Data collection and storage cost money and resources and interpreting that data into actionable insights can even be more complicated hence many business owners, business leaders and marketers tend to neglect their data by under analyzing it or overlook it all together. However, if data collected and analyzed properly, it can get a competative advantage to maketers and business leaders.  Below are some general tips that can help your organization with your digital marketing data analysis:

Value of Digital Marketing Data Analysis

Value of data comes when we analyze and interpret digital data to see what it could mean and what actionable insights you can take to improve your business based on that data. Analyzing data could be to study the current trends in order to predict immediate and distant future trends, discover a new product or revenue opportunities, or optimize your advertising campaigns being catered to consumers.

Carly Fiorina, Former CEO of Hewlett-Packard said, “The goal (of data analysis) is to turn data into information and information into insight(s)”.

  • Goals & Objectives: The first step of analyzing any data is to research goals and objectives. In other words, you need to have a question to answer. Once you know that question, then you want to analyze and interpret that data within a context that question in order to answer that question.  For example, one can ask, how many unique visitors visited our website last December and how many of them purchased our product?  This question will direct the data analyst to solely focus on the number of online visitors and sales from those visitors during that period of time.
  • Quantitative vs. Qualitative Analysis: Analyzing data can be quantitive or qualitative, based on the question asked.
    • Quantitive research is all about the numbers. Quantitive data analysis is usually answered closed-questions providing limited choices and responses. A good example of digital marketing is “how many unique users and sales each digital marketing channel accounted for the last December?” So analyzing that data from your Google Analytics or web analytics account, you can find out what channel brought the most traffic and sales and one action item would be to focus more on that channel and possibly boost your marketing dollars and efforts on.
    • Qualitative data analysis, however, is not about the numbers but rather understanding why. For example, why organic is doing so much better than other channels? Does that have to do with general user behavior? Or is somehow your offline marketing efforts having a postive impact on your organic or is it seasonality? These are the whys that are often answered with a qualitative analysis approach.
  • Presentation & Reporting: Answering on what is the data telling you, what is your aha moment and what these data mean, putting them into an easy to understand and actionable context is what a good report does. Reporting doesn’t mean more data, but it should be more clear and actionable insights that is presented in an easy to follow manner. A good report will also consider the following elements:
    • Speaks to the audience – This is to understand who is going to be reading the report. What were their questions and assumptions before reading this report? Does the report clearly answer their questions?  The answer to these questions will frame the report.
    • Report Completion – Ensure that report is a complete representation of project or question being asked. It provides enough data and insights about the project or questions being asked. Keep in mind that some of the readers may not know these. You want to briefly explain the methodology the research include the platforms and techniques you used to gather and analyze data and why you came up with certain conclusions and recommendations. Clearly, state the objectives and key questions research sought to address.
    • Be Clear and Concise –  Don’t leave room for alternate views or interpretation.  Also, the report shouldn’t also be a data dump of everything you learned. Address the question with enough relevant and in a logical and organized manner and provide brief but sufficient proof so there will be no room for doubt or alternate interpretation.
    • Tell A Story – Dan Heath, an American author, speaker and college professor at Duke University says “Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful”. So tell a few of those relevant stories to help make the data more meaningful. Focus and build your report around the story to keep the audience engaged on the results.
    • Explain the Results – Last step to tell what results mean to business. Conclusion and recommendation that must be connected back to the objectives and all these conclusions and recommendations should be data-oriented and no assumptions.

In conclusion, there is a saying that says people rather live with a problem they cannot solve than living with a solution they can not understand. The job of a good data analysis to make things clearer and not more confusing. The job of a data scientist is to first understand the objective or question being asked, understand what platform to use to collect the data, segment the data into quantity and qualitative, then turn that data into information and actionable insights using an easy to follow and objective report.

If you need help with your digital marketing efforts, contact us or our strategic data partners over at Analytics Clarity.