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Web Analytics Action Hero: On the Road to Actionland

Chapter Description

In this chapter, you'll learn how to efficiently maneuver your way through Setupland so that you can punch your ticket and begin your valuable adventures in Actionland.

The Difference Between Reporting and Analysis

Sometimes the line between reporting and analysis tends to blur, so if you want a ticket to Actionland instead of a one-way ride to Robotville, you need to be able to distinguish between these two areas of web analytics:

  • Reporting is the process of organizing data into informational summaries in order to monitor how different areas of a business are performing.
  • Analysis is the process of exploring data and reports in order to extract meaningful, actionable insights, which can be used to better understand and improve business performance.

While both draw upon the same collected online data, reporting and analysis are very different in terms of their purpose, tasks, outputs, delivery, and value. Without a clear distinction of the differences, an organization may sell itself short in one area (typically analysis) and not achieve the full benefits of its web analytics investment. Take a look at how the two differ across the five key areas.


Reporting translates raw data into information. Analysis transforms data and information into insights. Reporting helps companies to monitor their online business and be alerted to when data falls outside of expected ranges. Good reporting should raise questions about the business from its end users. The goal of analysis is to answer questions by interpreting the data at a deeper level and providing actionable recommendations. Through the process of performing analysis you may raise additional questions, but the goal is to identify answers, or at least potential answers that can be tested. In summary, reporting shows you what is happening (numbers) and analysis focuses on explaining why it is happening and how you can act on it (words).


Sometimes what feels like analysis is really just another flavor of reporting (yes, there are more than 31 flavors). One way to distinguish whether your organization is emphasizing reporting or analysis is by identifying the primary tasks that are being performed by your analytics team. If most of the team's time is spent on such activities as building, configuring, consolidating, organizing, formatting, and summarizing, then you're reporting.

Analysis focuses on questioning, examining, interpreting, comparing, and confirming. Reporting and analysis tasks can be intertwined, but analysts should still evaluate where they are spending the majority of their time. It's not uncommon to find web analytics teams spending most of their time on reporting tasks.


On the surface, reporting and analysis deliverables may look similar with lots of charts, graphs, trend lines, tables, and stats. Look closer, and you'll see some differences. The first is the overall approach (Figure 2.4).

Figure 2.4

Figure 2.4 Reporting pushes information, and analysis pulls insights.

Reporting generally follows a push approach, where reports are passively pushed to users who are then expected to extract meaningful insights and take appropriate actions for themselves (think self-serve). The three main types of reporting are

  • Canned reports. These are the out-of-the-box and custom reports that you can access within the analytics tool or which can also be delivered on a recurring basis to a group of end users. Canned reports are fairly static with fixed metrics and dimensions. In general, some canned reports are more valuable than others, and a report's value may depend on how relevant it is to an individual's role (SEO specialist versus web producer).
  • Dashboards. These custom-made reports combine different KPIs and reports to provide a comprehensive, high-level view of business performance for specific audiences. Dashboards may include data from various data sources and are also usually fairly static.
  • Alerts. These conditional reports are triggered when data falls outside of expected ranges or some other predefined criteria are met. Once people are notified of what happened, they can take appropriate action as necessary.

In contrast, analysis follows a pull approach, where the analyst actively pulls particular data to answer specific business questions and provide recommended next steps with possible outcomes. A basic, informal analysis can occur whenever someone simply performs a mental assessment of a report and makes a decision to act or not act based on the data. In the case of analysis with actual deliverables, there are two main types:

  • Ad hoc responses. Analysts receive requests to answer a variety of business questions, which may be spurred by questions the reporting raised. Typically, these urgent requests are time sensitive and demand a quick turnaround. The analytics team may have to juggle multiple requests at the same time. As a result, the analyses cannot go as deep or wide as the analysts may like, and the deliverable is a short and concise report, which may or may not include any specific recommendations.
  • Analysis presentations. Some business questions are more complex in nature and require more time to perform a comprehensive, deep-dive analysis. These analysis projects result in a more formal deliverable, which includes two important sections: key findings and recommendations. The key findings highlight the most meaningful and actionable insights gleaned from the analyses performed. The recommendations provide guidance on what actions to take based on the analysis findings.

You may run across other hybrid outputs such as annotated dashboards (analysis sprinkles on a reporting donut), which appear to span the two areas. Remember the push-pull and numbers-words rules, and you'll be able to see the deliverable's true colors.

Another key difference between reporting and analysis is context. Reporting provides no or limited context about what's happening in the data. In some cases, the end users already possess the necessary context and background knowledge to understand and interpret the data correctly, but not always. Context is critical to good analysis. In order to tell a meaningful story with the data to drive specific actions, context is an essential component of the storyline. For example, without context a steep year-over-year drop in site visits may cause alarms to go off. When context is added (it's a weekend rather than a weekday), the data point is better understood and a fire drill is avoided.

Although they both leverage various forms of data visualization in their deliverables, analysis is different from reporting because it emphasizes data points that are significant, unique, or special, and it explains why they are important to the business. Reporting may sometimes automatically highlight key changes in the data, but it's not going explain why these changes are (or aren't) important. Reporting isn't going to answer the "so what?" question on its own.

The recommendations component is an important differentiator between analysis and reporting as it provides specific guidance on what actions to take based on the key insights found in the data. Even analysis outputs such as ad hoc responses may not drive action if they fail to include recommendations. Once a recommendation has been made, follow-up is another potent outcome of analysis because recommendations demand decisions to be made (go/no go/explore further). Decisions precede action. Action precedes value.


Through the push model of reporting, recipients can access reports through an analytics tool, intranet site, Microsoft Excel® spreadsheet, or mobile app. They can also have them scheduled for delivery into their mailbox, mobile device (SMS), or FTP site. Because of the demands of having to provide data to multiple individuals and groups at regular intervals, the building, refreshing, and delivering of reports is often automated. It's a job for robots or computers, not human beings.

On the other hand, analysis is all about human beings using their superior reasoning and analytical skills to extract key insights from the data and form actionable recommendations for their organizations. Although analysis can be "submitted" to decision makers, it is more effectively presented person-to-person. In their book Competing on Analytics (Harvard Business School Press, 2007), Thomas Davenport and Jeanne Harris emphasize the importance of trust and credibility between the analyst and decision maker. Decision makers typically don't have the time or ability to perform analyses themselves. With a "close, trusting relationship" in place, the executives will frame their needs correctly, the analysts will ask the right questions, and the executives will be more likely to take action on analysis they trust.


Finally, you need to keep in mind the relationship between reporting and analysis in driving value. Think of the data-driven decision-making stages (data > reporting > analysis > decision > action > value) as a series of dominoes. If you remove a domino, it can be more difficult or impossible to achieve the desired value.

As you can see in Figure 2.5, the path starts with having the right data that is complete and accurate. It doesn't matter how advanced your reporting or analysis is if you don't have good, reliable data. While most companies have an abundance of reports, the quality of those reports can still be an issue. Effective reporting gives a broad audience of business users an important lens into the performance of the online business. Reporting will rarely initiate action on its own as analysis is required to help bridge the gap between data and action. With decision acting as the gatekeeper to action, you usually need analysis to knock it over.

Figure 2.5

Figure 2.5 If you remove one of these dominoes, you won't be able to achieve the desired value.

Having analysis doesn't guarantee that good decisions will be made, that people will actually act on the recommendations, that the business will take the right actions, or that teams will be able to execute effectively on those right actions. It is, however, a necessary step closer to action and the potential value that can be realized through successful web analytics. When I hear a client is struggling to find value from its web analytics investment, the cause is usually a path-to-value domino is missing or misaligned. Most often it is the analysis domino. Table 2.1 highlights the subtle but important differences between the reporting and analysis dominoes.

Table 2.1. Reporting and Analysis Comparison







Monitor and alert


Canned reports

Accessed via tool

Distills data into



Scheduled for delivery

information for further analysis



Alerts company to exceptions in data





Interpret and recommend actions


Ad hoc responses

prepared and shared by analyst

provides deeper insights into business




presentations (findings + recommendations)

Offers recommendations to drive action



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