We’ve talked about it more than once.
The ability to have millions of user information data at our disposal has changed the way we understand marketing.
And, in large part because of this, what is now known as digital marketing has emerged.
Data helps us make strategic decisions based on objective criteria.
In other words: we don’t do it based on our instincts and personal experience.
And you know one of the maxims of digital marketing: measure, measure and measure.
In this way, unlike traditional marketing, we can perform our actions by being more certain if they are actually impacting the target we want.
In short, data is essential in marketing, and we want to show you some of the most useful apps they have today.
However, let’s start at the beginning because… do you really know what Data Science is and how it differs, for example, from Big Data?
We’ll reveal everything in this article.
What is Data Science
As the term suggests, Data science is the science that studies data.
But this general explanation leaves us with many doubts, what exactly does it mean?
Basically, it takes care of extracting information from large amounts of data and then interpreting and applying it, for example, in our Digital Marketing actions.
The goal of Data Science is to make decisions using a set of tools that allow knowledge to be extracted from the data.
Large data processing is not achieved only by using traditional analysis methods.
Data Science therefore involves programming, data mining, machine learning, statistical, mathematics, and data visualization skills, in addition to the business knowledge of the sector in which it is being applied.
It’s quite a world.
What is Big Data
The concept of Big Data is used to describe large volumes of data.
Big Data includes structured data, semi-structured data, and unstructured data.
We tell you what they are.
- Unstructured data:digital images, audio or video files, mobile data, sensor data, web pages, social networks, emails, blogs, etc.
- Semistructured:XML files, system log files, text files, etc.
- Structured data: transaction data, databases, etc.
This differentiates Big Data and Data Science
Big Data and Data Science have undoubtedly transformed today’s digital and technological age.
Both terms are closely related to each other.
So much so that the main difference between them is that the concept of Data Science falls within the concept of Big Data.
Data Science is conducted within the realm of Big Data to gain useful information through predictive analytics, where results are used to make smart decisions.
Come on, without Big Data there wouldn’t be the concept of Data Science.
And without Data Science, Big Data would have no value.
3 Main differences between Big Data and Data Science
- Large volumes of data (Big Data) are distinguished by 3V: variety, speed, and volume.
Data Science, for its part, provides the methods or techniques for analyzing them.
- Big Data focuses on technology (Hadoop, Java, Hive, etc.) and analytics tools and software.
Instead, Data Science focuses on decision-making strategiesand data dissemination using mathematics and statistics.
- Big Data extracts information from large volumes of data while Data Science uses machine learning algorithms and statistical methods so that computers can get as accurate predictions as possible of the data obtained.
How Data Science helps in marketing
Data is everywhere and grows incessantly.
But they don’t bring value in themselves.
It is necessary to assimilate them and extract useful information that facilitates decision-making within companies.
Specifically, in Marketing it helps to make strategic decisions.
How data is interpreted
The data is obtained through different channels:
- Mobile devices
- Social media
- Online stores
And these are just some of the fonts used.
Our tastes, routines or movements generate data of great value for companies that want to know their customers in detail.
However, the interpretation of unstructured data does not add any value to companies.
For data interpretation, Data Science includes:
- Data cleansing and restructuring
- Data analysis
- Defining the right business questions to meet the company’s goals and can be treated analytically
- Visualization of data with graphs to extract intelligence from them.
- Presentation of insights and business recommendations
- Creation of data-centric products for companies that use analytics to generate new technology solutions.
Data Science requires (in addition to analytical capacity) business knowledge and business vision to extract and transmit recommendations tailored to the needs of the company.
Data Science in Digital Marketing
In today’s digital marketing world we have large amounts of information that we can extract through numerous channels:
- Data obtained by installing applications
- Virtual stores and websites
- CRM systems
- Customer databases
- Advertising platforms
- Social media
- Analytical web traffic tools like Google Analytics
These are just some of the channels from which we can extract information for our Digital Marketing and Inbound Marketing strategies.
But data is received in large volumes and at an ever faster rate, so if it is not known to interpret it effectively and at the right time, they lose all their value for the right decision-making and only generate one thing:
With a good implementation of Data Science,you can obtain crucial information and achieve levels of marketing segmentation and user interaction that until recently we could not have.
Data Science in Digital Marketing
Data Science applications in SEO
Years ago, positioning in search engines was the equivalent of giving blind sticks.
It was, in large part, about testing and error when the algorithms responsible for positioning a website were unknown.
Today, thanks to Data Science, we are much more accurate in determining what works and what doesn’t.
In the case of SEO, Data Science helps a lot thanks to machine learning functions.
- Detects patterns. Google and other search engines use machine learning to detect published content and spam.
- Helps interpret images. the unstructured Big Data data we’ve discussed before.
Using Data Science in Ads
Data Science has made life much easier for marketers in charge of online advertising.
Above all, in Display advertising,
And today, thanks to data, we can define where we want our ads to show and who we want them to show to.
Once, in the offline world, you put your ad on a busy street in Barcelona, for example, and what you were making sure of was that a lot of people would see it.
But you couldn’t determine how many impacts on your target audience you’d make.
Or exactly what kind of audience would see it and take action later.
Thanks to Data Science, you can:
- Choose much more accurately the location where we want our Display ads to show page by page.
- Consider what type of ad we want to show based on the location where it’s shown.
For example, we may have two versions of an ad for the same product.
One more focused on a millennial young audience and another on an audience of 30-year-olds.
In ads only the copy has been adapted by attacking some pain points or others.
Thanks to the data, they will tell us which page to place an ad on or another based on the content of the page, the type of traffic you have, etc.
In other words: we will be able to optimize the results more, since we will be segmenting the advertising more effectively based on the interests of the user.
Data Science Applications in Email Marketing
Of course, one area where Data Science has been received as May water is Email Marketing.
Without the analysis and use of the data, it would be impossible for us to make the mass sending of emails that we carry out every day.
Some Data Science applications for Email Markeitng are:
- The ability to make product recommendations that are really relevant to the customer.
Using predictive analytics, custom emails are prepared for each user in the list.
In this way, each person receives offers of products that are more interesting to them, either because they have previously interacted with one of those products on the web, or because they are similar to one they have already purchased.
- Boost re-purchase. Data Science helps determine when a customer may be about to be exhausted to send a purchase reminder.
For example: imagine that you are responsible for marketing an online cosmetics company.
It’s been a month since a customer purchased one of your shampoos.
Because you know your shampoos usually last a month, that customer may be about to run out.
The data science will have already detected it, and it will generate an automatic email that will be sent to that customer to motivate them to repeat their purchase.
Already using the power of Data Science in your Digital Marketing actions?
Depending on each company, the analysis needs are different and you can find very varied uses to the data.
In any case it is essential to clearly establish the objectivesto define the data that is most interested in knowing.
The today’s digital marketing and online advertising companies require professionals from more scientific sectors and with a business and analytical profile, who also have the necessary knowledge for the application of the Data Science tools to leverage the data obtained and make effective business decisions.
If you want your marketing strategy to have the best results, it’s imperative that your digital partner masters Data Science.