Data Science: how to use it to make your business grow
Learn more about this field of studies that helps you make more assertive decisions.
Learn more about this field of studies that helps you make more assertive decisions.
If you keep up with the latest news of the digital world, you have probably heard of Data Science, Machine Learning, Artificial Intelligence, Big Data, and other related terms. These keywords are increasingly present in the news, every time a tech company launches a new product.
Artificial intelligence is present in many of these new things, such as self-driving cars, or even in those robot vacuums that clean houses with any human help.
What not many people know is that the same principles in technology that help us develop self-driving, or autonomous cars, can also be used to optimize business ventures, especially digital ones.
Another important point that may go unnoticed is that the use of these technologies is actually simpler than it looks. But before we move on to practical examples, let’s take a deeper dive and better understand what Data Science is.
Data Science is a field of studies that uses the scientific methodology to extract knowledge from data and to support decision making.
In a broader sense, companies use Data Science techniques to analyze data and make decisions to make their business grow.
Making the best decision is not always easy, and that’s why Data Science is a multidisciplinary field, encompassing knowledge of Math, Statistics, Computer Science, and Business.
The good news is that technological advances have led to a greater democratization of Data Science processes. Today, many tools can help ordinary people to use Data Science in business, even without knowledge of statistics and math.
In this post, we will give you some practical examples of how people are using these techniques to make better decisions, increase sales, and make their businesses grow.
Putting theory aside, the first step to start using Data Science in any kind of business is to understand, in a practical way, how the process works and what the necessary stages to make the best decisions are.
There is no consensus on the the most appropriate way to work with Data Science, but usually the process is divided into 7 stages:
1. Mapping questions > 2. Collecting Data > 3. Processing and Organizing Data > 4. Data analysis > 5. Development of Models and Algorithms > 6. Visualizing Data > 7. Decision Making
Usually, the Data Science process begins with a question that needs to be answered. Below, we have listed 10 common questions for people who work with digital products.
Then, it is necessary to collect data that may help us answer the questions. This data may come from different sources, such as:
The truth is, there is a myriad of sources of data, and it’s extremely important to find a source that presents information in a trustworthy, structured way. Let’s take a look at some examples:
After the data has been collected, it’s important to clean, standardize, process, and organize the information. This happens because, frequently, the data generated comes with inconsistencies that may hinder the analysis and lead to the wrong decision being made.
When the data has been organized and processed, you will be able to start the analysis.
There are many different kinds of analysis, from simple ones to extremely complex ones. But it’s important to remember that, most of the time, a basic analysis may reflect in a valuable result for the business.
The reason why this occurs is a very simple one: as many people and companies still don’t have the habit of looking at numbers, the ones who start analyzing data (even a simple analysis) are usually one step ahead of the competition.
When data analysis becomes very complex, or ends up generating more questions that answers to the initial question, it may be a sign that you need to develop statistical models and algorithms to find the solution that will bring more value to the business.
These models and algorithms are usually necessary when the “human mind” can no longer find the best patterns to solve the problem, or when finding a solution to the problem can take too much time.
An algorithm may be used to find patterns that escape human perception, or even to analyze millions of scenarios in a few minutes, leading to a more assertive decision in a short period of time.
In the example below, we can see how this works.
If I have a base of 5,000 leads, and sent 7 email messages per month to them in the last 4 years, there are more than 1.6 million events that I would need to analyze to find a behavioral pattern for my leads.
Even if I concentrated all my efforts in this analysis, it would probably take too long to find patterns that an algorithm would identify in seconds.
If I want to understand the best way to promote products on Facebook, I could analyze over 50 different indicators for every ad I have ever run.
But how would I find out which indicators are really relevant to my audience?
If I want to test which details in my page’s design increase the chances that the visitor will buy my product, I would have to:
This may look very complex, but can be done in a simple way with some applications.
After the use of models and algorithms, you will need to visually analyze the results to make sure the conclusions are aligned with the object of the study.
This visual analysis is done through graphics, that make it easier to detect patterns and make decisions.
When the data is ready to be analyzed, we have reached the most important moment: making strategic decisions for your business.
When you verify the patterns, you will be able to notice what works and what needs to be improved. This will enable you to implement new actions and carry out tests to boost your results.
These decisions, of course, depend on the kind of business you have and which aspect you need to optimize.
The important thing is that, after deciding on the action that will be taken, you analyze the data you already have, and choose the most assertive decision.
As it is a multidisciplinary field, Data Science may be applied in practically every challenge faced by people who run a digital business.
Some common example of the use of Data Science to achieve results in digital businesses are:
And finally, here are 2 practical tips for you to start using Data Science in your business today:
One of the main advantages of a 100% digital business, when compared to a brick and mortar one, is the amount of information you can get your hands on online.
When you install an analytics tool on your website, you immediately start collecting information that may be analyzed to generate better results to your business.
Some examples of relevant information obtained from these tools are:
Hotmart Analytics, for example, comes with a series of resources for people who work selling digital products on the internet.
After having installed the tool, the metrics start being collected and you have the data you need to make the best decisions.
One of the best ways to sell more on the internet is to segment the way communication with each customer is done.
Offering a product at the right time, when the lead is likely to make the purchase is one of the best ways of getting results without being seen as a spammer who only sends emails messages selling products, all the time.
To carry out this kind of analysis and understand the best time to promote your product, you will basically need two tools.
The first one is an email marketing tool.
Then, you will need to use ListBoss, a Hotmart tool that enables you to integrate Hotmart and the email marketing service you have chosen.
With the integration, you will able to configure it so that your email marketing service receives events whenever your lead takes one of these actions:
This is the first point to decide the right time to send an email to recover sales.
A customer who visited your checkout and abandoned the shopping cart has a greater chance of buying your product than a customer who doesn’t yet know you have a product to sell.
So, this will be a crucial piece of information for you to send a new email to this customer.
With all this information being registered in your email marketing service, you can start attributing actions to each case.
You can send a welcome email message when your customer downloads a product, or a thank-you message when a customer reviews the product.
These actions lead you to build a stronger relationship with your customers, and to earn the trust of your buyers.
The tips we have provided above deal with some of the many possibilities of using numbers to make your business grow.
The tendency is that the complexity of analysis increases as the business becomes more mature, and consequently, presents a greater volume of information.
But the most important thing is to start collecting, organizing, and analyzing data from you business. This is the real key to success!
And if you want further information on metrics, read our complete post about it.
See you soon!