DATA SCIENCE

Why Every Marketing Department Should Implement Data Science Techniques

In this blog, I walk you through my transition from Marketing to Data Science and why you should consider doing the same

One of the questions that I get the most as a data scientist with a background in marketing and business intelligence is why I switched careers. That is interesting because I have never decided to move away from marketing and start a move to another field. It was an evolution of my job that brought me to data science. In fact, I have a Bachelor of Science in marketing, which is a type of marketing focused on data analysis and business intelligence. Thus, this transition was going to happen at some moment in my career.

Since my day one working in marketing departments (8 years ago), I have been using data to help me in my decision process in every project I have worked on. Every sentence I wrote for a marketing campaign and every picture I chose with specific colors for an ad had some data analysis behind it. In marketing, it’s common for teams to use data to explain decisions that were made before any investigation. I always used data to make decisions, never the opposite.

As learning data analytics was becoming more accessible with tons of online courses coming up every day, adding tools such as Python, Power BI, Tableau, and Pandas was inevitable to keep me at the top of my game. There was a point where Excel could not do what needed to be done, so I had to move to more powerful tools. Data is growing, and its analysis is essential for every business. Data mining, data manipulation, analysis, and create predictions became crucial to my career. There was no turning point, and I had to move forward. My first step was to start learning new tools, such as Python, and refine my statistics. As I was learning more and looking for more resources, I began to hear about data science, and when I explored the field, there was a Eureka moment. That’s what I wanted to do, and that’s what I have been doing in the past three years. Now, that’s enough about me. We are here for how you can use data science in marketing departments to take businesses — and your career — to the next level.

Creating reports

The way marketers show how successful a campaign is and identifying areas for improvements is through reporting. However, they can take a lot of our precious time. There are tools such as PowerBI and Tableau that can create beautiful visualizations. However, even these powerful tools can have their limitations. That’s when Python, Pandas, Matplotlib, Plotly, and Seaborn shine. You can do data cleaning, exploiration, and create visualizations to help you with your decisions.

Find the right audience

Multiple companies such as Facebook and Google promise to drive the best users to websites through ads. Although these companies are more intelligent than ever, they have their limitations. We need to limit users to keywords that you think would work to attract them to your business, but the truth is that most of the time we are just guessing. However, you can use your data to help companies such as Facebook using lookalikes to find the best users for you. It’s more complex and accurate than any keywords.

Targeting your audience correctly

This a big one. Some companies believe that users’ preferences can be defined with one keyword of a box that they checked two years ago. Users are people and people’s preferences cannot be defined based on three or four options. There are dozens of factors that can influence people to click on an ad or buy a product. It’s nearly impossible to analyze all that without data. Thus, there are no excuses for you not to put your data to work. You can use machine learning to find the best target in e-mail marketing campaigns, for example.

Find the right channel

Many marketers only look for the number of page views of a specific page or the number of clicks on a particular ad to analyze if a campaign was success or not. They ignore numbers such as where the user is coming from, how much time they are spending on a website, how many pages were viewed, what actions were taken, etc. Data science can help you analyze all the data you collect from users and help you take insights.

Predictions

You can use data science to create informed predictions. It is very important for any company, but it should be a must for marketing. Instead of guessing, time series models, for example, can make informed predictions of page views, sales, revenue, profit, losses, and much more.

Content Strategy

You might think that content strategy should be the job of SEO professionals, but anyone can use data to understand which keywords or type of articles bring the most users to your website. There are sophisticated ways to analyze and classify articles using Natural Language Processing, for example. The good news is that there are multiple platforms where you can find the data you need to understand what your reader likes to see, such as Google Search Console and Google Trends. Then, you can use multiple tools, such as Tableau to make analysis.

Improve Customer Experience

Once you understand your customer, what they like to read, and what actions they take on a website or app, it’s time to improve your website’s experience. The truth is that you can have the most beautiful website, best app, or most incredible product, but if a user doesn’t have a good experience on it, they won’t be your client for much longer. Customer experience can go from design to support, and data can tell you what actions should be taken.

Find new groups of users with clustering models

This one is often ignored by companies because they tend to think they know their consumers and what they want. Unless the company is Amazon, they probably do not. Clustering models with unsupervised learning can solve this issue. The cluster groups of data together based on their similarities. So on top of clustering users in groups that you already know, it’s also capable of finding new groups, allowing you to expand your products.

Develop a recommendation system

Recommendation systems are powerful tools and work from recommending a movie on Netflix or showing the products to users on Amazon. Recommendation systems are a fantastic way to show content that users are actually interested in. Although its algorithm is complex, its implementation is not as difficult as it sounds. I have worked on many recommendation systems, and the results that I have seen are fantastic.

Final thoughts

As a final note, let’s clarify that both marketers are just as crucial as data scientists and vice-versa. It’s ok if marketers are not interested in moving towards data science. It’s a preference that I respect and understand. However, bringing the opportunities mentioned in this article can be a game-changer for companies and should be considered. Marketers don’t need to become super technical to implement these solutions, but they should be aware that these options exist and try to bring them to their department. Marketers and data scientists should work together to develop the best solutions for the company and the clients. I just can’t wait for the products that are yet to come from these two great fields.

Data Scientist | Machine Learning Engineer | Based in NYC | Writer | http://bit.ly/linkedin-ismael

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