Data Science and Big Data. What about marketing?
Posted: Sun Dec 22, 2024 8:49 am
When we talk about digital marketing , we see a world of possibilities. But based on its real meaning, we can say that it is basically a business strategy that consists of a set of tactics practiced in different types of channels in the online environment , used to reach your target audience and expand your business .
Digital marketing also has many connections with current buzzwords , such as: data science , big data , machine learning , artificial intelligence and many others that are directly related to technology. But after all, when we put it clearly and objectively, what do these terms really mean for marketing? What is the real impact on my business? In what scenarios will they be useful and applicable?
We will start a series here to finally demystify these terms, to bring clarity to you, marketing professional, manager and even business owner who needs to understand what the benefits of marketing combined with technologies are and what they really represent. Follow the thread!
Data science and Big data, how to use them?
To have data science , we first need to have data! If we think of analytical marketing as if it were building a house, data would be our foundation. And a foundation has materials from different sources , right? Cement, mortar, wires, beams… and so on.
Here, our initial structure will consist of data from various sources, which will make up our code number of philippines big data, which is nothing more than the analysis and interpretation of a large volume of data . And yes, this data already exists if you already have an online environment, even if it is not yet mined or if your website does not have proper tagging in analytics. So, it's time to organize it!
Some examples of useful data sources in digital marketing are:
Social media and email marketing (engagement).
In practice: Are people who like my page also subscribing to my newsletter? Are people who are impacted by my actions converting to purchases?
Number of visits and clicks on my website (marketing funnel).
In practice : Is the journey I designed for my client working as planned? Does the content I think adds value actually add value?
Post-purchase engagement (customer relationship).
In practice: The best after-sales service is the one that generates a new purchase. But can I clearly identify my customer's interests in repurchasing and suggest new products that will contribute to a new conversion?
In other words, these various sources provide us with invaluable information to generate a database. However, there is no point in having so many digital traces if we don't know what we can do with them. So let's get started!
Benefits of Data Science for Digital Marketing
Considering this data generated by digital marketing and its applicability in business (no complicated theories here), data science consists of analyzing a large amount of information, originating from different platforms – the famous big data, already mentioned above – in order to transform these discoveries into valuable knowledge for your business ( insights , another buzzword that we will address later).
But what kind of insights can I obtain using data science methods ?
By using statistical models to describe the behavior of a group and not just a single individual, you will be able to:
Predict customer behavior;
In practice: If I know my customer is searching for sweatpants, I can assume they want a matching top and then make a cross-sell, or I can also add them to an interest list for comfy clothes to generate even more interest in other products.
We call this a contextual offer, understand more in this post here !
Establish success metrics;
In practice: Let's assume that my target audience is people interested in comfy sets , on my website I have a large number of hits, but my conversion rate into purchases is very low.
When I evaluate the path my customer takes through the data trail they leave, I can find out the real reason they are not making a purchase, understand which stage of the funnel they are not progressing through and thus develop specific intelligent metrics to validate my consumer's journey.
Improve and customize marketing strategies for my target audience;
In practice: Still thinking about the example mentioned above, could the low conversion rate on my website be due to the fact that my strategy is not attracting an audience interested in comfy clothes ? Could it be that my visitors are interested in pajamas, for example? In other words, data analysis is essential to help us understand whether the audience attracted is, in fact, the same one that was intended for my persona.
Ultimately, there are many practices available when it comes to understanding data, but the main objective of combining digital marketing, big data and data science is to “understand the customer’s pain”. Using technology to your advantage to solve gaps in the best way possible, that is, having a Data-driven strategy , which is, literally, being guided by your data!
To help you even more, we have developed an ebook that explores Artificial Intelligence applied to marketing , so that you can understand the types of AI analysis and its benefits. Download it now from the link below, it's free!
Digital marketing also has many connections with current buzzwords , such as: data science , big data , machine learning , artificial intelligence and many others that are directly related to technology. But after all, when we put it clearly and objectively, what do these terms really mean for marketing? What is the real impact on my business? In what scenarios will they be useful and applicable?
We will start a series here to finally demystify these terms, to bring clarity to you, marketing professional, manager and even business owner who needs to understand what the benefits of marketing combined with technologies are and what they really represent. Follow the thread!
Data science and Big data, how to use them?
To have data science , we first need to have data! If we think of analytical marketing as if it were building a house, data would be our foundation. And a foundation has materials from different sources , right? Cement, mortar, wires, beams… and so on.
Here, our initial structure will consist of data from various sources, which will make up our code number of philippines big data, which is nothing more than the analysis and interpretation of a large volume of data . And yes, this data already exists if you already have an online environment, even if it is not yet mined or if your website does not have proper tagging in analytics. So, it's time to organize it!
Some examples of useful data sources in digital marketing are:
Social media and email marketing (engagement).
In practice: Are people who like my page also subscribing to my newsletter? Are people who are impacted by my actions converting to purchases?
Number of visits and clicks on my website (marketing funnel).
In practice : Is the journey I designed for my client working as planned? Does the content I think adds value actually add value?
Post-purchase engagement (customer relationship).
In practice: The best after-sales service is the one that generates a new purchase. But can I clearly identify my customer's interests in repurchasing and suggest new products that will contribute to a new conversion?
In other words, these various sources provide us with invaluable information to generate a database. However, there is no point in having so many digital traces if we don't know what we can do with them. So let's get started!
Benefits of Data Science for Digital Marketing
Considering this data generated by digital marketing and its applicability in business (no complicated theories here), data science consists of analyzing a large amount of information, originating from different platforms – the famous big data, already mentioned above – in order to transform these discoveries into valuable knowledge for your business ( insights , another buzzword that we will address later).
But what kind of insights can I obtain using data science methods ?
By using statistical models to describe the behavior of a group and not just a single individual, you will be able to:
Predict customer behavior;
In practice: If I know my customer is searching for sweatpants, I can assume they want a matching top and then make a cross-sell, or I can also add them to an interest list for comfy clothes to generate even more interest in other products.
We call this a contextual offer, understand more in this post here !
Establish success metrics;
In practice: Let's assume that my target audience is people interested in comfy sets , on my website I have a large number of hits, but my conversion rate into purchases is very low.
When I evaluate the path my customer takes through the data trail they leave, I can find out the real reason they are not making a purchase, understand which stage of the funnel they are not progressing through and thus develop specific intelligent metrics to validate my consumer's journey.
Improve and customize marketing strategies for my target audience;
In practice: Still thinking about the example mentioned above, could the low conversion rate on my website be due to the fact that my strategy is not attracting an audience interested in comfy clothes ? Could it be that my visitors are interested in pajamas, for example? In other words, data analysis is essential to help us understand whether the audience attracted is, in fact, the same one that was intended for my persona.
Ultimately, there are many practices available when it comes to understanding data, but the main objective of combining digital marketing, big data and data science is to “understand the customer’s pain”. Using technology to your advantage to solve gaps in the best way possible, that is, having a Data-driven strategy , which is, literally, being guided by your data!
To help you even more, we have developed an ebook that explores Artificial Intelligence applied to marketing , so that you can understand the types of AI analysis and its benefits. Download it now from the link below, it's free!