Use of Python for Data Science in Digital Marketing and Analytics

Have you ever purchased items online?

Did you find yourself flooded with advertisements for similar products every time you browse the web?

Did you ever thought:

How did it happen?

Why it's happening?

And, it keeps repeating.

Indeed, it is an application of Python for data science in Digital Marketing and Analytics.

Do you know there are zettabytes of data (big data) generated in the past two years? Indeed, because of 谷歌 's cookies policy, marketers can store and use this data for meaningful business insights.?

As a user interacts with an organization's website, social media page, or POS system, it generates data captured to analyze the user behavior further and send them curated advertisements, personalized product recommendations, and special promotions.?

It's one of the most effective uses of data science in the digital marketing and digital analytics domain that has given measurable outcomes.?

Let's dive deeper and understand the top five used cases where data science and digital marketing work together, and Python for data science comes in very handy.?

1. Recommendation Systems

You must have noticed movie recommendations popping out on your favorite streaming platform. It's the recommendation system algorithm, a machine learning model built based on the user's browsing pattern input data.

Topping the list of online streaming companies operating world-class recommendation systems are Netflix , 亚马逊 , and Spotify .?

Netflix has a powerful recommendation engine: if you watch a movie on Netflix and provide it a positive rating, the next time you open the app, you'll be recommended movies of the same genre, content, and cast members.

Another powerful example is Spotify, where the recommendation algorithm learns more about your likes and dislikes and your music suggestions as you spend more time on the platform and suggest the music of your exact taste and preferences.?

Types of Recommendation Systems

• Content-Based Recommendation Systems
• Collaborative-Filtering Based Recommendation Systems

2. Sentiment Analysis

I love this machine learning technique as it's super-powerful. Please check this post that I have written on Sentiment Analysis with Python.

It's the process of determining the underlying emotion behind a piece of text and is another popular application of data science in digital marketing.?

Imagine how an eCommerce store like Amazon learns about its buyer's feedback for various products by reading individual reviews and extracting the underlying emotions from the text.?

Any small, medium or large multinational can leverage the power of Sentiment Analysis and maintain a robust social media reputation, understand what their customer wants and reduce churn.?

3. Customer Churn Prediction

Customer churn is one of the most common problems that many industries face. It mostly happens when customers find similar offerings between two products and have less brand loyalty. Thus, customers have a high chance of shifting from one product to another.

One of the most classic examples is the telecom sector, where you'll often find reports where the telecom giants report a reduction in subscriber base.

However, with the application of data science in business, you can develop a machine learning model that will help to predict the customers likely to churn.

And you can design customized offers or strategies for those customers and stop them from churning.?

4. Customer Segmentation

Identifying the correct customer group is essential to designing, and roll-out targetted promotions.

It is where customer segmentation plays a crucial role. Indeed, with the help of data science business applications, creating a buyer persona is no more a challenge.

Popular platforms like Facebook, LinkedIn, and others use machine learning algorithms for segmenting and targetting the right audience while you create any paid advertisement.

That's how powerful is data science application in digital marketing analytics.?

Facebook collects its user's demographic and behavioral data and allows companies to run advertisements that focus on custom audience groups based on this information.

Users are often segmented based on specific traits such as their location, age, gender, brands, and everyday interests and likes.?

Customer segmentation is usually achieved by building unsupervised machine learning models in Python, such as K-Means clustering.?

5. Market Basket Analysis

If you're an enthusiastic shopper, you must have noticed something interesting. There's a buying pattern with what we buy. For example, if you buy bread, you'll most likely purchase butter or Milk.?

Data scientists developed a robust algorithm that works on association mining and developed a technique called the Market Basket Analysis. It is based on the Apriori Algorithm that follows the association rule.?

Market basket analysis, also known as association mining, may be used to analyze frequently bought items.?

It is often done by processing historical purchase data to identify product combinations often seen together in transactions.

The findings from this analysis are often used by retailers to improve store design and encourage customers to purchase more items in a single transaction.?

For example, an individual who buys baby formula is also likely to purchase diapers, so stores generally place these things near each other to make them easily accessible to users.?

E-commerce platforms display highly correlated products on identical pages, ensuring they're in the users' line of sight.?

For example, if you shop online for black jeans, you'll see matching shoes at the bottom of the page, encouraging you to shop for a bundle of products instead of a single item.?

Again, this immediately increases the company's sales since customers buy more products than they came for.

Final Thoughts

Python for Data Science and its great libraries is a life savior for solving many business challenges with data-driven analytical insights. Small business owners and SMEs can quickly adopt a technology disruption and start practicing data-driven business decisions. Indeed, most of these can be low-cost and offered as a data-in-a-box solution by the SARATHI.ai - Growth accelerator for MSME team. Contact Now!