Digital Marketing And Data Science: What’s The Relation?

By Author: nagaraju
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What is Digital Marketing?
Digital Marketing is a process where we use online platforms like the search engines, emails, YouTube / other video platforms, apps, websites to reach our prospective customers who would be willing to buy our products or services. And that’s the reason why any business exists – To sell there products and services and make profits.

Data Science: Basic Overview
Let’s look at some popular data science models that surround us each day.

Amazon’s Recommended For You
Data Science : Recommended for you by Amazon
Data Science Example: Recommended Products
You must have seen “ Recommended For You” section on your favorite shopping site like Amazon. And there has been a number of instances when I have bought products from that section. In a nutshell, it’s a Data Science model that suggests products to you based on your purchase history, browsing history, cart, profile parameters among others. It’s a win-win situation since you get what you want and the e-commerce sites are able to increase their sales and profits.

Google’s RankBrain
Or ...
... take the case of data science-driven Machine learning model of Google called as Rankbrain. It’s based on reinforcement algorithm; not only it provides great accurate organic search results but learns to improve on them by user’s feedbacks.

What do I mean by that?

Data Science: Machine Learning RankBrain Concept
Coffee example to understand Rankbrain
Imagine searching for ” How to make coffee” and coming across these two posts. Imagine clicking on the first post and coming across a sub-standard content and leaving the webpage in a few seconds. And then you click on the second result [ 9 Rules for how .. ] and came across a great article. Reading word by word, you end up spending a few minutes in reading the complete post.

The RankBrain is going to consider all these things. If more and more users start clicking on the second post and stay on that webpage for long, Google would give it a rank boost. This is Data Science Application at the best level

Let’s take a real-world example to understand Data Science Basics.

Hypothetical Football Match Example
Imagine a Football Stadium where a match is held each day. Some 30,000 people come each day to enjoy their favorite matches.You are a Data Scientist who need to make sure that there are sufficient Food and water supplies while also ensuring that your company ( who manages all these supplies ) make some great profits?

Digital Marketing and data science: Football example
Football Field And Spectators to illustrate the example.
Here, we basically want to build a model that would allow us to make predictions about Food and Water Supplies so as to maximize profits and minimize wastes.

How would you go about this problem? There are basically 4 steps.
1. Big Data: Processing And Preparation
I always used to wonder why Big Data terms like Hadoop would just slip in while talking about data science?

It’s because you are dealing with huge data – whether it’s 20,000 people in our stadium or millions of people who use banking or a billion of people using the search engine like Google.

What do you do here? Look at the past data. We may not require all the parameters/fields in the data. So, we process the data and prepare it for Analysis.

2. Select Suitable Algorithm
The next step is to select a suitable algorithm based on our requirement. Why do you need an algorithm? To model the huge amount of data that we have so that we can visualize it better and make exact conclusions. That’s where your math, statistics, and programming skills play a role.

3. Tuning Parameters To Optimize Results
Now, we need to tune the parameters to optimize our results. Sound complicated? Yes, it is. It’s basically a process where our model should not only generate good accurate results, but it should be generalized to be applied for future predictions. It takes time and experimentation to do that.

4. Model Validation
What good is a model if it’s not giving accurate results? Imagine that you deployed your data science model – it can make food and water predictions based on weather, match type, and other parameters. Can we call it good if it deviates a lot and makes inaccurate predictions regarding supplies? No. Not at all. So, it’s vital to check the model’s accuracy and make sure it’s practical.

As you can make out, Data Science is a multi-disciplinary Field that is primarily concerned with extracting knowledge and insights from big data and using them to create automated models to solve real-world problems. Hoping that you understood data science basics, let’s clear the relation between digital marketing and data science.

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