I often get questions about tools for digital marketing. Great topic for a blog post I thought, but when I started to write it became so massive that I realised I need to make a blog series out of this topic. In this post, I’ll walk you through the tools I use for data processing and data analysis.
Finding tools for digital marketing
With a background as a tech journalist, I have this weird interest in new technology. Whenever I find something new I can try out, I get excited. I love spending time on ProductHunt to see what new products that might help me at work. Maybe not the most normal thing to do on a Sunday night.
I’m always on a hunt for two types of tools: 1. technology that can simplify or even automate part of my current job or 2. technology that can give me possibilities that I don’t have today.
1. technology that can simplify or even automate part of my current job. or 2. technology that can give me possibilities that I don’t have today.
However, I started like most people: writing reports, making presentations and sometimes using spreadsheets to do calculations. I guess most of my time is still spent putting thoughts on paper, and my every day “martech stack” is still pretty basic.
Everyone can learn data analysis
Since I’m going to talk about tools for data, I want to say that my background is not in statistics or engineering. I do have some university credits in basic statistics, and I once knew how to perform a significance test in SPSS. However, I’ve learned most of my marketing data skills by doing. I started testing with small side projects, spending time with both MOOCs and tutorials online has been a pretty good way for me to learn.
But I would also say that part of why I learned it all was that no one else around me knew how to do data analysis, so if I didn’t try to figure it out on my own, I wouldn’t have any quantitative insight at all. Not using the data I had access to in some way felt more stupid than to try to do some data analysis on my own.
My tools for marketing analytics and data analysis
I regularly walk clients over slides with data analysis nowadays. So I do everything from the first export to the visualisation on my own. I think I have three types of reoccurring projects where I need my data skill set:
- Auditing – Looking at historical data, in a delimited and pre-defined context, to find how something performed
- Monitoring and Measuring – Visualising data in real-time, creating opportunities for better decision-making
- Research – Looking at trends and decoding information in an unknown context
The tools I use are either for processing, analysis, or visualising and presenting data. However, it wasn’t long ago I didn’t know how to split up a CSV-file without help from Google; Hence, you shouldn’t be intimidated or feel like it’s something you cannot do yourself.
Tools for Data Processing
The free cloud-based tool Google Sheets is a great way to work with certain types of data, especially medium-sized data structured in rows and columns. My first real relationship with spreadsheets started here, and its interface made me feel safe(r than other spreadsheet tools). I also realised I could find the answers to most of my Google Spreadsheet related questions online.
The first time I used Google’s spreadsheets was for personal budgets and other types of simple calculations. However, I like tracking stuff, so most of my courses at Uni had a spreadsheet with all the tasks and deadlines and suggested readings, and I kept track of my progress using colours and conditional formatting. I used it more to create well-structured files than to process data or calculate anything. I later started to do “Content Calendars” for clients in Google Spreadsheets, since it was easy to get an overview.
My introduction to using Google Sheets
My first real use of Google Sheets for work was to create monthly performance reports. I exported standard data files from Facebook, Twitter, Pinterest and the other sources I needed. In my Spreadsheet I had one “master sheet” calculating all my KPIs, referring to data from sheets named per data source. When I wanted to update my KPIs, I just overwrote the data in my import sheets, and all calculations in my master sheet refreshed automatically – as long as all the columns in my export were in the same place, but usually, they were.
I later started to write Google Script to make more automatic data imports, not having to download and upload files. Using scripts to get access to data from an API makes it possible to have automatically updated data in your sheets. This method makes it possible to use Google Sheets as a simple dashboard solution if you need to. I also use tools like Supermetrics to automatically import data from different APIs through a Google Sheets plugin (more on that further down).
While more advanced users might disagree, I would say that Google Sheets and Excel are very similar. You can do most things on a fundamental level in both tools. Some things are more comfortable to do in one than the other. I guess this is why I continue to use them both.
I tried to stay as far away as possible from Excel for very long. It felt like Excel was something only dull people needed, and my biggest fear in life was to seem boring. So, I stayed far away. For a very long time, I opened my .xlsx files in Google Sheets.
When is Excel better than Google Sheets?
The more I work with data, the more time I spend cleaning and prepping data files for data analysis. Additionally, my data files got heavier (and still do). So, the first feature I needed in Excel for was to use it without an internet connection. I know you can work with Google Sheets in offline mode too, but it doesn’t feel as safe.
Quite quickly I also learned that Excel was a bit more stable than Google Sheets when I was working with larger files. (It’s not like Excel is perfectly durable though, I’m very often looking at the spinning rainbow ball when I work with Excel). One elegant Excel feature is that you can turn off automatic calculation and make all your changes to the document before it calculates what’s in your cell. This feature might give you some extra processor power when you need it.
Today I often use more advanced functions in Excel than I did in the beginning. I could probably do the same thing in Google Sheets with a plugin, but it’s neat that they are already part of Excel. However, the type of data imports I do in Google Sheets, using Google Script is not something I do in Excel, even though it’s possible. Also, as soon as I need to share my documents with someone else, working in Google Sheets is often much more accessible.
Atom is a text editor (like TextEdit on a Mac or Notes on a PC). So why do you need a text editor to work with data you might think, it doesn’t make any sense? Well, at times when I work with data processing, I get across data stored in JSON or XML-files. When you try to read what’s in there, it looks like gibberish. A good text editor is very helpful when decoding these files.
However, any text editor won’t help you; you need a good enough one (like Atom or Sublime). A good text editor formats the text in your file to make it more readable. This process is often called “prettifying”, unfolding code that is hard to read into a structure that is friendly and easy to understand.
For example, Facebook Ads API display the targeting data per ad as a JSON-object. If you try to read it in a spreadsheet column, you will struggle. Atom formats JSON beautifully if you activate one of the “prettyfiers” that comes with the tool. You create a JSON-file, paste your FB-targeting data into it, and save it to your hard drive. On the save it will magically become formatted and (almost) readable. Also, the same is true for many other file formats that you might need to decode.
Tools for Data Analysis
I felt intimidated when I first found Tableau. Partly because I didn’t know about the tool, nor that there’s a field called Business Intelligence (with people doing data analysis for a living). Also, because it felt like I needed an exam to have the right to use it.
The primary usage for Business Intelligence software is data analysis. Naturally, this makes them pretty good at this. So I decided not to care about my lack of previous knowledge and downloaded a demo version. I quickly found myself with two problems: 1. It’s hard to get started with Tableau as a beginner, 2. Tableau is super expensive.
When should you use Tableau?
I use Tableau to extract information from my data. I look for learnings or insights that I could never see without help from software. For instance, are we spending our advertising money on the content our clients engage with or the material we think is best? How are different segments of our users behaving when they interact with our product? I ask questions to my data through Tableau. Most of the time it shows me that my first gut feeling is incorrect. At the same time, Tableau often shows me things that I had no clue about and would have never found through empirical studies.
Tableau is hard. This is because it’s different from most other software you’ve used before. It’s hard to translate prior knowledge into Tableau. Your data becomes dimensions and metrics, and in the beginning, nothing makes sense.
Teaching myself Tableau is probably the best thing I’ve done – it made me understand data on a deeper level. I’ve always known about the difference between boolean variables, strings and integers from my background in programming. However, it became much more tangible when I started to look at different data sets, trying to extract information from them.
Tableau has excellent tutorials online, so you are not alone in this process of not having a clue. There is also a great forum where you can read and post. Users always help each other out which is nice. But Tableau is not (yet) as well documented online as Google Sheets and Excel. When you have a problem, you might not find the answer in your first search. Stack Overflow is another place if you need help.
Last but not least, R is a tool that I don’t have to use that often. It is a useful backup tool for analysing data. R can solve some problems that are not possible with tools I’ve talked about earlier in this post. If you have a coding background, you will have fun learning R. If you don’t, R will seem pretty hardcore since you are interacting with the program through writing code and not through nice visual interfaces. It is similar to the terminal on your computer, in many ways.
I only use R when I have extensive data files, or if I need to calculate relationships between data points or data sets. Neither of these needs appears very often in my everyday life in marketing. However, I did a network analysis once, that wasn’t possible to do in any of the other tools. One key feature is that you can run your calculations on a server in the cloud if your computer cannot handle the size of your data sets or calculations. I’m not saying you should start doing that, but it’s good to know that it’s possible.
Overlaps between data sets are also hard for the other tools to handle, but R nicely calculates differences and draws Venn diagrams. R can do a lot of graphs and visualisation, but they are not the most visually appealing, so I don’t recommend to use it only for that.
How to learn R
If you think you’d like to learn R, multiple MOOCs can help you get started. I’d recommend taking one of them. However, if I were you, I’d start with one of the other tools; I only use a fraction of the functionalities in R. But sure, I plan on getting better at it, I just have some other things in life that I might prioritise before that…
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