10 Reasons Why You, The Search Marketer, Can Call Yourself A Data Scientist

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A standard definition for the job described as “data scientist” hasn’t yet been agreed upon, but the common denominator among the various interpretations out there identifies three key skill sets:

  • An understanding of data (what it is, how it’s collected and its meaning).
  • The ability to manipulate it to derive insights.
  • Connecting data insights to real-world value and communicating it clearly to non-data people.

Is that not what you do every day?

An Understanding Of The Data

1. Working With Metrics

Search engine marketing, whether organic optimization or paid placements, has a defined set of metrics that need to be understood at a language level of native proficiency.

If you don’t understand what those metrics are and why they are important, then you just can’t be a search marketer.

And it’s more than just knowing what they are. Anyone can learn what a bounce rate or click-through rate is, but what makes SEMers data scientists is they understand how these metrics were captured, the math behind them and what their value is to marketers.

Professional search marketers can easily take a quick glance at a table of these metrics and understand if they’re good, bad or ugly.

Data is a key language of your world. You are a data scientist.

2. Mastering Tools

Whether they’re using Excel, an SEM-focused platform or any other data tool, search engine marketers have become masters at bringing in data sets and prepping that data so it’s in a good state to analyze. Our proficiency in these tools is similar to that of a data scientist who uses SQL or MicroStrategy or other analytics platforms.

Search pros also have an expert knowledge of the value of their various tools and which one to pull out from their toolbox depending on the analysis required at that moment. I would imagine the average search marketer works with at least five data tools. Of course, these tools may have multiple purposes, but there will always be a data component of the platform.

Data Tools Of Paid Search Marketers: Native engine platforms, bid managers, website analytics, Microsoft Excel, reporting or optimization systems and more.

Data Tools Of Organic Search Marketers (SEOs): Website analytics, webmaster tools, keyword research platforms, ranking trackers, link miners and more.

Do you have tools to pull and view data? Of course you do. You’re a data scientist.

3. Inventing New Ways To Look At The Data

As this marketing discipline has matured, so has the the data science behind it. Great SEMers obsess about metrics and how to find new ways to slice and dice the data.

Every new curveball in our industry requires new thinking about how we can manipulate the data in different ways to see new sides of the problem and potential solutions.

This is an important consideration for any scientist. Data science is an evolving art, and it requires smart professionals to think outside the box to move that particular discipline forward. This is how breakthroughs happen in the various fields of science, as well as in search engine marketing.

The very best search marketers in your company or agency are always looking to build their own custom metrics. One of the baseline requirements is for search tools to allow marketers to take any two metrics and build custom calculated fields, such as “cost per” whatever. The off-the-rack metrics work in most situations, but not for every situation.

Data scientists like you are always thinking outside the box to bring new thinking to how the data can be sliced and diced.

Ability To Analyze & Derive Valuable Insights

4. Analyzing Data

This is at the heart of data science and the real value of what this role brings to the table. Search marketers are on a constant search for little golden nuggets of truth that help paint the picture around what happened and how to either continue that success or change the negative trend.

In seems like all great SEM stories begin with, “I was looking through the data and saw…”

Even if you don’t consciously realize it, while going through your search data, you swiftly switch from one type of analysis to another and another as you dive for insights.

The six recognized archetypical analysis types are below. Think about how you use most of these — if not all of them — every day.

  • Descriptive. The discipline of quantitatively describing the main features of a collection of data.
  • Exploratory. An approach to analyzing data sets to find previously unknown relationships.
  • Inferential. Use a relatively small sample of data to say something about a bigger population.
  • Predictive. Use the data on some objects to predict values for another object.
  • Causal. Find out what happens to one variable when you change another.
  • Mechanistic. Understand the exact changes in variables that lead to changes in other variables for individual objects.

Do you see your tasks in that list? Of course you do, you’re a data scientist.

5. Running Experiments

What is more scientific than running experiments every day? In fact, next time someone asks you what you do, tell them you run experiments for a living.

It wouldn’t be untrue, would it?

When you think about it, every decision you make to improve search results is an experiment. That is the right attitude for the professional search engine marketer. Nothing is assumed. Every opinion is simply an assumption (a hypothesis) that will be tested and then analyzed. Rinse and repeat.

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Question: Have you ever made a change in your search strategy, gone back later to see if it worked, and then made a new decision (whether to keep things the same or change) based on those results?

That’s the scientific method, Professor, which means you are a data scientist.

6. Utilizing Other Datasets To Build Advanced Insights

Data scientists view raw data as ingredients that will be used to build a new understanding about the goals at hand. Because of this, in the same way a chef will go to his or her spice rack to add a new flavor to a dish, the data scientist will look for different datasets to help “flavor” their own.

External data helps to provide more context to the source dataset. For example, very early on in paid search, marketers starting using organic tools, as well as website analytics, to build a stronger understanding of the value of their strategies.

I remember the first time I saw bounce rate in a paid search report, maybe around 2004. It was an “aha” moment for me. Of course, I would want to understand the behavior of paid visitors once they made it to the site! We were tracking conversion events within the paid search tools, but it had never occurred to me to understand if they bounced out without going beyond the landing page.

Over the years, I worked with clients and analyzed second-page bounce rate and third-page bounce rate to build an even deeper understanding of the paid traffic.

Attribution tools have also now become commonplace with search marketers. For years, we only looked at our own conversion rates for optimization. However, Search — as much or more than any other channel — is absolutely impacted by the other channel activity.

Consumers don’t just wildly come up with the idea out of the blue to search for long-tail keywords such as product SKUs without being driven to that action by other marketing and research touch points. It’s very important to understand the influences on search marketing, as well as how your search initiatives impact other channels.

Looking at your dataset and realizing there’s more information you need is how a data scientist operates. You already think like that because you are a data scientist.

Connecting Analysis To The Real World And Clearly Communicating Value

7. Knowing What Needs To Happen To Improve Performance

The final link in the chain for a data scientist — after he or she has pulled the data, manipulated it and analyzed it — is to take action. After all, what value do data have on a page? None. It’s what you do with data that makes them powerful.

This is undeniable proof that search marketers are data scientists. You use data to enact change and elicit positive results on behalf of your company or clients. If you can’t do this, then you can’t be a search marketer. This is at the core of what data science is all about; studying the data and applying the insights of your analysis to make things better.

Whether you are validating a previous strategy, finding proof to take a different direction, or even verifying that more evidence is needed to make a decision, data are at the heart of this discipline.

This is what you do every day because — you guessed it — you are a data scientist.

8. Using Data Visualizations To Tell The Story

Sometimes a picture is worth a thousand words, and the data scientist knows how to bring data to life using the proper visualizations.

Try putting a data table in front of a group of peers. Each of them will come to different conclusions as their eyes wander through the columns and rows. However, if you use the right combination of charts and graphs, the group will be able to easily see what you need them to conclude.

The right bar graph will highlight an outlier in the data — such as a day that had a big traffic spike. A good pie chart will clearly demonstrate how one tactic is eating up the budget. Line charts show how performance over time has slowly been growing. These are all insights that aren’t clear in spreadsheet form but pop out in visualizations.

Get to know all of the different charts and why you would use one over the other. It will take your search marketing to the next level.

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Data scientists understand how different visualizations affect viewers’ perceptions and use them to their advantage. Use them properly, and you will be able to tell the data story in a third of the time with three times the impact.

You eat pie charts for breakfast and bar graphs for lunch because you are a data scientist.

9. Communicating To Others

Reporting what we’ve discovered so the client or your peers can understand what you’ve found is a crucial skill for search marketers.

After all, even if you’ve discovered the best approach to solving a problem, if you can’t clearly articulate the rationale behind your conclusions, in a sense, your conclusions aren’t valuable.

The core reason data science is the sexiest job of the 21st century is that those who do it aren’t like the classic quants of old who talk like computers and aren’t able to relate to anyone in the office.

The data scientist is not someone who sits on the other side of the building and is never involved with key decision making. They are the folks executives enjoy having in the boardroom who can contribute to the conversation and bring data to the table.

I learned early on in my career that almost anyone can learn to analyze data. But the ability to apply it in real-world situations and explain it to others in a way that will make sense for them is what distinguishes a data scientist from an analyst.

But you are good at that, right? You can sit with your boss or your client and walk them through the tables and charts so they can conclude for themselves why you’re recommending a certain course of action.

But you already knew that, right? Because you are a data scientist.

Summary

There are different definitions of data scientists. Some are very strict and require a working knowledge of various type of coding and database management. However, if you look at most of the definitions, they all converge on the three points we’ve discussed here today:

  • An understanding of data, what it is, how it’s collected and its meaning.
  • The ability to manipulate it to derive insights.
  • Connecting data insights to real-world value and communicating it clearly to non-data people.

My final piece of evidence is that you have already realized that the title promised 10 reasons, and I’ve only covered nine.

10. Recognizing Discrepancies

This is a crucial skill for data scientists and search marketers, because sometimes the data aren’t right. They’ve either been corrupted by a hiccup in the system or accidentally partially deleted as the spreadsheet changed hands so many times.

You have to have a critical eye to notice these things, or you’ll spend your entire afternoon spinning your wheels on data that will never reveal proper insights because they’re not correct.

This is something only a human can do — computers can only work with the data fed to them. Unless they have instructions on how to verify the validity of the dataset, they’ll just keep crunching the bad information.

So go get yourself a lab coat. Because you are… well, you know what you are.

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