What is data science? How do you use data analysis to expand your business?


If you follow the digital world, you may have heard of the term Data Science as well as the terms Machine Learning or Artificial Intelligence, Big Data or any other similar term.

These keywords have gained a lot of sparkle in our daily news, every day technology companies release new developments in the market.

Artificial intelligence is present in many of these developments, such as autonomous cars, which can take trips without a driver inside. Another example: cleaning robots that sweep dust and clean homes without any human assistance.

What few people know is that the key technical principles that help develop these autonomous cars can also be used to improve businesses, especially digital businesses.

Another point that few people notice is that using these techniques is much easier than it seems, but before we get to practical examples it is important to understand more deeply what (data science) means.

What does the concept Data Science mean?

It is an English term that means data science. It is a field of study that uses scientific methods to obtain knowledge through data, to provide support for decision-making.

Companies generally use data science techniques to analyze data and make decisions that help expand business.

Making the most hit decisions is not always an easy task, which is why data science is a diverse and multidisciplinary field of education, incorporating knowledge in mathematics, statistics, computers and business.

data science fields
Diverse fields of data science

A good point is that technical advances have democratized data science. Today, there are many tools that can help ordinary people use Data Science in business, even without mastering the techniques of statistics and mathematics.

In this post we will provide some practical examples that explain how people use these techniques to make better decisions, increase sales and expand business.

But, how does Data Science work in practice?
Away from theory, the first step to start using data science in any business is to practically understand how Data Science works, and identify the necessary stages that help make the best decisions.

In the picture, we find boxes for the stages of decision-making
Summary of the stages that help in making decisions

There is no consensus that there is a specific and convenient way to work with data science, but the process is generally divided into 7 stages:

Phases of data science application
The stages on which the application of data science is based

  1. Ask questions
    The Data Science process usually begins with a question that must be answered. And then we organize a list of 10 most common questions that are directed to those who deal with digital products:

1- How much will the sales figures be in the coming months?

2- What will happen if I completely change the design of the product page?
3- What are the qualities that determine the success of the affiliate marketer in selling the product?

4- What is the appropriate time to go to the customer and present the product?

5- What is the amount of money that should be invested in the field of network traffic to get the specific results?

6- What is the ideal price for the product?

7- When you can introduce a new product to the customer?

8- How many emails must be given to the lead before the product is presented to him?

9- What is the great form for content marketing? Articles in a blog or through videos?

10- What is the best month to do launch events? What is the ideal time to start selling?

  1. Data collection
    Then it is necessary to search for data that can help answer these questions. YOu can collect This data from c different sources, example:

Systems and applications.
Internet searches.
Data of organizations and companies.
Research.
The truth is that there are countless sources of data, and it is very important to find a source that presents the information in a reliable and structured manner. Here are some examples:

To forecast sales figures for the coming months, it can be useful to analyze sources that verify sales figures to date. For this, you will need to extract your sales history. On the other hand, if you notice that your sales result is directly related to your traffic volume, you can find the necessary data to answer this question by using an analytics tool like Hotmart Analytics!!
One way to discover the ideal moment at which to approach a customer and present your product is to analyze all the actions you have taken with your old customers. This can be done either with those who bought your product or with those who did not do so, trying to understand the reasons that led customers to buy, or made them back away from it.
If the question is which content format works best – blogs or videos – you will need to compare the interaction data of leads who read blog articles with the interaction data of leads who watched videos. This data is brought to you by the Youtube Analytics tool.

  1. Data processing and organization
    With the collected data, it is very important to clean, coordinate, process and organize the information. This happens because the resulting data often suffers from inconsistencies that may negatively affect analysis and wrong decision-making.

When the data is processed and organized, the analysis process can begin.

  1. Data analysis
    There are several types of analytics. It varies from simple to very complex operations. But it is important to remember that in most cases the analysis of basic data leads to results

Very valuable for your business.

The reason is very simple: With so many people and companies not in the habit of looking at numbers, people who start out with data analysis (even if it’s just a little bit) usually get ahead of their competitors.

  1. Develop models and algorithms
    In cases where data analysis becomes complex, or raises more new questions than it provides answers to the main question, it may be time to create statistical and algorithmic models to find the solution that will bring the most value to your business.

These models and algorithms are necessary as the “human brain” cannot find the best models to solve a problem or when it finds a solution to a problem it may take a lot of time…

An algorithm can be used to find patterns that humans are unaware of, or even to analyze millions of scenarios in just a few minutes, leading to a more on-target decision in a short period of time.

In the example the following, we can get understand how this is working.

Let’s say I have a base of 5,000 leads, I send out 7 emails per month for the last 4 years, and there are over 1.6 million events that need to be analyzed in order to try to find a standard model that explains the behavior of leads.

Even if I focus all my attention on this analysis, it will probably take me a lot of time to find standard patterns that a single algorithm can detect in a matter of seconds.

If I wanted to understand the best way to advertise on Facebook, I could analyze 50 different indicators for each of the ads I set up.

But how can I find out which indicators are important to my audience?

If I want to test design details on my page that increase the chances of visitors buying my product, I will need:

  • Generate many different pages.
  • Separate the groups of users who will access each of these pages.
  • Finding a way to ensure that a user from a specific group accesses only one page until I finish evaluating the results of the different pages.
  • This is a bit complicated, but it can be done in a simple way through some applications.
  1. Data preview
    After using models and algorithms, it will be necessary to analyze the results visually to ensure that the analytical results are consistent with the objective of the study.

This visual analysis is done on the basis of graphs, which facilitate the process of checking the models and help in making decisions.

  1. Make decisions
    Now that this data is ready for analysis, we get to the most important moment: making strategic decisions for your business.

By checking the patterns found, you can see what works well, and discover areas that need improvement. This allows you to apply new procedures and tests to improve your results.

These decisions will of course depend on the type of business, and on the appearance that needs improvement.

It is important before deciding what action to take, that you analyze the data that you have, in order to choose the most appropriate decision for your business.

How can I apply Data Science to expand my business?
Being a multi-angle field of education, data science can be applied to practically all challenges facing digital businesses.

Here are some common examples of using data science to generate results from a digital business:

Conversion Analysis in Sales Funnel.
Analyze visitor behavior data on your pages.
Product pricing.
Investigate uncommon and fraudulent behavior.
Analyzing the feelings of followers on social networks.
Product guidance systems for customers.
Predicting when a customer will stop paying a subscription (churn rate or churn forecast).
Enrichment and classification of leads with a view to prioritizing.
Forecasting the total sales rate.
Classification of customers according to their buying behavior.
Improve the shopping cart by adding more products and forming groups of products.
Finally, here are two practical tips to get you started with Data Science in your business right now:

Tip 1: How can you use Web Analytics to investigate patterns of visitor behavior on your pages and sell more?
One of the main advantages of a 100% digital business when compared to an offline business, is the amount of information that can be obtained online.

When you install an analytics tool on your site, you immediately start gaining tons of information that can be analyzed to generate better results for your business.

Some examples of important information obtained through these tools are:

The origin of the visit: Where did the client come from? Or on which link we click to get to your page?
The average (avg) time visitors stay on your page.
Pages and products visited.
Abandonment rate: This is the percentage of visitors who leave your page without clicking any other link.
The modalities of the links the visitor clicked on are: UTMs, SRC, and SCK.
The number of visitors who performed a specific action on your site (eg clicked the link in checkout or logged in). In such cases, you will need to set up these procedures through the Analytical Tool).
The most well-known and frequently used analytics tool is Google Analytics, but there are many other complementary tools, and choosing the best tool depends on the unique characteristics of each business.

Hotmart Analytics, for example, contains a series of resources for anyone who sells digital products on the Internet.

After the tool is installed, the metrics are collected and you have the data you need to make decisions.

Tip 2 – How do you prioritize leads based on data enrichment and leads ranking?
One of the best ways to make more sales online is to classify the ways to communicate with each customer.

Presenting a product in the

The moment when the lead is ready to buy is the best way to increase the results, so that you are not considered to be a spam offering to sell products all the time.

To do this kind of analysis and understand when is the right time for your product, you’ll basically need two tools:

The first is an email marketing tool, ie: mail chimp and LeadLovers. or other tools.

Next, you should use ListBoss, a Hotmart tool that allows integration between Hotmart’s digital platform and your chosen email marketing service.

After the integration is done, you can set up your marketing tool of choice to receive events and notifications every time a lead does one of the following:
Invoice issuance.
Abandon the cart.
Cancel the purchase.
A purchase coin is subject to a chargeback.
A purchase is accepted.
A purchase subject to a complaint.
Money back purchase.
An expired purchase.
Completed purchase.
Download a product.
Register in the Hotmart Club (the membership area for hosting digital courses).
Product evaluation.
This is the first point in order to decide when it is appropriate to send an email aimed at recovering unrealized sales.

A customer who visits the Checkout page and abandons the cart has a much higher chance of buying the product from a customer who doesn’t even know you have it for sale.

So this information is critical to determine the moment you send a new email to this customer.

With all this information subject to registration via the email marketing tool, you can start setting actions for each of the mentioned cases.

Example:

You can send a welcome email as soon as the customer installs the digital product, or even thank them when they evaluate their purchase. These actions help you build a closer relationship with your customer and increase trust among buyers.

Analyze your data
The tips we provided above address some cases, but there are countless other possibilities for using these numbers and expanding your business.

The complexity in data analysis tends to arise when a business begins to mature a lot and so we have large amounts of information.

But the most important thing is that you start collecting data.. We have a lot of useful tips that help digital content producers use metrics to get the best results. Feel free to read and learn about it!

What’s your opinion ? Do you consider that you have learned what is important in the field of Data Science? You can comment about your experience or opinions in the comments space and we are waiting…

Good luck!

Leave a Comment

Your email address will not be published. Required fields are marked *