Spot Data Issues From the Start With Weekly Analysis

When marketers think of weekly analysis, the focus is almost always on understanding how a campaign performed. This could be the number of leads generated, the CPA (cost per acquisition) metric, or anything that measures the different aspects of a marketing effort. While this kind of analysis is important and meaningful, it should not overshadow a different kind of analysis, one that focuses on uncovering data quality issues before you have to generate monthly, quarterly, or other regularly scheduled reports.

For example, would you want to uncover that a website functionality was broken, preventing users from triggering a KPI, before collecting an entire quarter's worth of data? 

Your answer is probably a resounding, “YES!” followed by, “But how can I do that efficiently and effectively? Well, if you keep reading, I share how to easily conduct weekly monitoring. 

1. Understand that it’s not as difficult as it may seem.

It is very easy to become intimidated by the sheer number of reports you have to analyze for a true analytics solution. Google Analytics is one example. Each main heading in the image below goes much deeper than one level and at first glance, going through each one of them can seem daunting. This is when you need to remind yourself that it’s not as difficult as it may seem. When you are first starting out, just focus on going through the reports and the speed will come naturally. Once you start knowing what to look for, you get into the groove, and you will fly through it. 

Data points in google anlaytics - Seer Blog

2. Each round of analysis is a building block.

Having done these analyses for numerous clients now, I have learned that there is a slight learning curve as you look at an organization’s data week after week. As you build upon that, it will become easier for you to spot unusual patterns and learn what’s worth reporting on. So, although you might find yourself spending more time than you anticipated in the beginning, you will continuously get faster and the payoff will be worth the upfront time investment.

3. You should make a list of drastic percentage changes.


The great thing about making a list of these numbers and associated dimensions is that after you have gone through all your reports, this list will help you uncover data points that are related. You can make this list anywhere: notepad, Microsoft Word, Google Docs, etc. As long as you know which reports those numbers are pulled from, that’s all that matters for diving deeper later. At this point, you might be wondering how many of these should you write down, because there could be hundreds of drastic changes.

Keep in mind what I said earlier; each round is a building block and after a few rounds, you will learn which ones are important.

In addition to building upon your findings each week, you can get an idea of what might be important by talking to the stakeholders that will be looking at your analysis. I remember looking at a client’s Google Analytics account and seeing huge fluctuations for email traffic every week. At first, I used to spend a significant amount of time writing down changes for email campaigns to go back and dive deeper.

However, I soon realized that such fluctuations were normal since the client sent emails periodically. Then it occurred to me that maybe I should ask the client about whether that’s something important. To my pleasant surprise, the client said that they already track email performance using another system so although they were OK with some information regarding email traffic, looking at each email campaign from Google Analytics wasn’t that valuable. I could then focus on more effective data analysis for that particular client's needs.

4. Restrain yourself from diving deeper into a report until you have looked at all reports.

I cannot stress how important it is to NOT go all 'Sherlock Holmes' in a specific report UNTIL you have thoroughly mined your reports for all percentage changes. Doing so prevents you from wasting time on a specific report because there might be something important that you could miss if you get distracted by the first drastic change you see. Skimming other reports also helps you find related data points making it easier to decide whether what you have found is interesting and important or interesting only.

For example, as a web analyst you notice 3 data points that stand out:

1. Traffic to /digital-marketing-careers jumped 67%

2. Digital Analyst appears in Top 10 Site Search Terms list

3. department_of_labor_report_on_digital_analyst_profession.pdf receives 300% more clicks

In this example, we have landing page, site search term, and file click observations. If you start investigating only one data point the second you notice it, you might end up spending a lot of time uncovering campaigns that drove the traffic, which geographical area traffic originated from, and what pages were viewed in the session. In doing all that, you might run out of time to see other changes that tell a bigger story. Think about what a recruiter in your company, for example, will be able to do with only one data point vs. seeing all three or how much more impactful your analysis would be when you share all the findings vs. just one.

5. Decide whether something is interesting and important vs. only interesting.


Now it’s time to filter out any points that are not worth digging into. The goal of a great weekly analysis is to find information that can result in actionable recommendations. This also tends to be the most fun but challenging part of the process. It’s fun because you end up getting to know the website or other digital assets really well. You find out cool facts about the website and user behavior that may be unknown to many people.

However, it’s challenging because sometimes there’s not much you can find and that can make you feel as if you aren’t doing your job. When that feeling starts to sink in, keep in mind that it’s perfectly OK to find a few actionable items and not to look for something that is not there. The opposite of a great analysis is regurgitating data. 

Your job as an analyst is to look at the data and interpret it in such a way that the reader feels enlightened to take action. Your job is NOT to copy paste data from your analytics solution into a fancy PowerPoint slide for the sake of doing so.

So, how do you determine whether something is interesting and important?

Let’s look at another example. A few weeks ago, I was looking at content grouping data for an organization and noticed that pageviews for a certain grouping quadrupled. That was interesting because I had never seen such a rise before. To find out more, I visited some of the web pages in that content grouping and discovered that selecting choices from a drop-down resulted in the page reloading. That was the important piece of my finding because now I found something that my audience could change that will positively impact data quality as well as improve user experience.

What I have discovered is this: items that are definitely worth including in the analysis end up being interesting and important because your audience can do something with that finding. Items for which the most you can say is traffic to xyz landing page increased by 76% tend to be interesting only. I’m not saying that it’s completely wrong to highlight information that is interesting only.  What I am saying is to try and find information that will help your audience take action. My yardstick for whether something is important is if my finding has a concrete action item.

6. Always get a second pair of eyes on your analysis before sharing with your stakeholders.

You may have found some really cool and important things that your stakeholders will be grateful for, but it only matters if they understand what you’re saying. That is why it is extremely important to have someone else take a look at what you have written. It’s important to know whether someone else will be able to understand it.

To summarize, while it’s important to analyze marketing efforts, it is equally important to comb through and analyze data points more frequently than once a month. So now that you know what I have learned, let me know how these guidelines work out for you.  I would love to hear other tips to make this process smoother and better! SIGN UP FOR NEWSLETTER

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