Marketing Data Science: Introduction to Data Blending

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Marketing Data Science: Introduction to Data Blending

One of the truths we don’t talk about nearly enough in marketing data science is the need to blend data. There are very few data sources that are a single source of truth by themselves, that need no augmentation or supplementation to provide maximum value. Even our most favorite, most used tools like Google Analytics, Hubspot,, etc. lack data that would make them truly comprehensive.

Why is Data Blending Necessary?

Why? In many ways, the evolution of these tools means they should be growing more focused, not less – and adding more data that isn’t broadly applicable. is the wrong path to go. As a result, point applications like SEO tools or nurturing tools crop up to fill those gaps – and that’s okay. Just as the major tools do, these new spot applications become experts in their little niches as well, allowing them to iterate and improve quickly.

Except… they have their own data. Which then means we have to learn how to blend data. This isn’t anything new – if you’ve ever used any of the lookup functions in Excel like VLOOKUP or XLOOKUP, you’ve done data blending. Applications like Alteryx and IBM Watson Studio Data Refinery have existed for years that streamline data blending for larger enterprises. New applications on the market like Tableau Data Prep allow smaller companies to access more of these capabilities. Open-source data languages like R and Python make data blending free and relatively straightforward to anyone with the skill to write in them.

But regardless of the tool, data blending is an essential skill for anyone working with marketing analytics data.

What Is Data Blending?

First, let’s be clear: data blending is a stupid name. When we think of a blender, we think of a device where we put a bunch of ingredients in and get a mashed, mixed version of them out. Put a bunch of fruit in, get a smoothie. Put a bunch of kale in, get a bowl of sadness and regret.

Data blending is nothing of the sort. You can’t just put a bunch of data in a machine and have a data smoothie come out. Well, you can, but you really shouldn’t, since it has absolutely no utility that way. But we’re stuck with the term for now.

So what IS data blending?

Data blending is the bonding of two or more data sources together, often to create analysis or insights you can’t obtain by looking at one data source, or looking at data sources separately.

The key ingredients to data blending are rectangular data and keys. Let’s examine what these mean.

Rectangular data means data that is in some kind of table format, like a spreadsheet or a delimited text file.

Rectangular data

Rectangular data has specific requirements- it’s data that has distinct columns and rows, with rows and columns being of equal length. For example, you can’t have one column with 72 rows and another column with 15. In data science, you’ll often hear these tables of data referred to as data frames.

Keys are values that different tables have in common.

Table keys

For example, suppose you have an export from Google Analytics and an export from your social media monitoring software. Both tables have lots of data in them, but they have a column in common – the date. In this case, the table key would be the date.

Or, you have data from your marketing automation system and your CRM, and both have the customer’s email address in common. In that case, the key would be email.

Keys are essential for data blending because they’re what allow you to glue together two or more data sets. As long as the keys are identical in terms of format, you use them to merge data together.

Data Blending: SEO Example

Let’s look at a practical example of data blending. Here’s the data I get from Google Search Console about my website:

Search console data

Alone, there’s a good amount of information here, information I could use to make decisions. We see each page on my website, the query that Search Console says that page is being found for, the number of clicks that page and query combination have received, and a few other metrics.

Could I build SEO strategy just from this list? Could I make decisions just from this table of data? Yes, of course. I could look at this and determine what I’m getting the most clicks for, or look to see how often I show up for terms that have a lot of impressions, but not a lot of clicks.

But something’s missing. Search Console shows data that is specific to our own websites. I can’t see how many people search for something like ethics in marketing, just the number of times my site has shown up in search results (that’s what impressions means).

Does that data exist? Yes! It exists in the SEO tool of your choice.


If I export all the queries Google Search Console says I show up for and run that through the SEO tool of my choice, I can now see how much any given term is searched for overall, whether or not my site shows up for it.

If we think about this in terms of addressable markets, then what comes out of Search Console is my obtainable market – the searches that my site could plausibly obtain. What comes out of my SEO tool is my addressable market – the maximum amount of overall searches for a given term.

Which means, if I could blend this data together, I could see what percentage of my addressable market I am currently obtaining.

Look carefully at both tables. Do you see the key? It’s the query – both tables have query in common. With that, we can join them together.

Joined table

I’ve now got my data, joined by the query key, all in one place, all in one table, which means I can start doing math on it. Let’s do that computation – my site’s impressions for a given term divided by the total number of searches for that term, to see how much of the market I’m addressing.

Addressable market

For the most part, I’m showing up for less than 10% of most of the terms. That means in terms of upside, I’ve got a lot of potential still. I haven’t tapped out these specific terms. If I was capturing something like 80-90% of the volume? I’d be hitting diminishing returns on my optimization efforts for those terms and topics.

What decisions could I make with this data? If I brought all the data into a common, shared set of measures – like a 0 to 100 score for each column, where 100 would be high opportunity and 0 would be low opportunity, I could build a prioritization score:

Priority scoring

What does this merged score tell us? These are the best balance of opportunities – search volume, likelihood that someone will click on my stuff specifically, appearances in search, keyword difficulty, etc. By merging everything together and reducing it to a single score, I can now use that score to make decisions. The pages with the highest score need love. They need to be optimized and tuned, linked to, shared, the works. Anything older than a year probably needs to be freshened up and augmented.

We couldn’t get to this level of insight without data blending. By merging two datasets together, we gained greater view of our search opportunities and built a straightforward rubric to make decisions.

Bear in mind, this still isn’t the whole picture; there are even more things we could do with additional data blending, like bringing in competitive data, but this is sufficient to illustrate the point.

How To Get Started With Data Blending

Start simple. Learn how to use functions like VLOOKUP and XLOOKUP in tools like Excel and Google Sheets. That’s the easiest place to begin; from there, you’ll explore more advanced tools until you reach the point where you start writing code for truly customized data blending. As your skills and knowledge grow, you’ll be able to take on bigger and bigger datasets until you’re blending incredibly complex amounts of data and generating insights no one else can.

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