Data Science: how to use it to make your business grow

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.

What is, in fact, Data Science?

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.

In practice, how does the Data Science process work?

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

1. Mapping questions

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.

1. What will my turnover be in the next months?
2. What would happen if I changed the design of the product page completely?
3. What is the right moment to approach a customer and offer my product?
4. What characteristics should a successful Affiliate have to sell my product?
5. How much do I need to invest in traffic to obtain the result X?
6. What is the ideal price for my product?
7. When do I offer a new product to my customer?
8. How many emails do I need to send to the lead before offering my product?
9. What is the best format to promote my content: posts or video?
10. What is the best month for my launch? And what is the best time to start the sales?

2. Collecting Data

Then, it is necessary to collect data that may help us answer the questions. This data may come from different sources, such as:

• Systems and Applications;
• Internet search;
• Data from organizations and companies;
• Surveys.

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:

• To predict your turnover for the next months, it may be interesting to analyze your turnover up to this moment. To do so, you need your sales history. On the other hand, if you understand that your sales results is directly connected to the traffic volume of your website, it is possible that you will find the necessary data to answer this questions with an analytics tool, such as Hotmart Analytics.
• To find out the correct time to approach the customer and offer your product, may be analysing every action taken with former customers. This may be done with both customers who purchased your product and those who didn’t, trying to understand the reasons why the customers bought the product and why some gave up.
• If your question is about the format of content that achieves the best results, posts or videos, you will need to cross-reference the data from the engagement of leads that read your posts, which can be obtained from Google Analytics, for example, and the data from the engagement of leads who watched your videos, from YouTube Analytics.

3. Processing and Organizing Data

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.

4. Data 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.

5. Development of Models and Algorithms

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:

• Create many different pages;
• Divide users into groups that would access each of these pages; and
• Find a way to ensure that the user in a certain group would have access to only one page until I finish analyzing the results of all of them.

This may look very complex, but can be done in a simple way with some applications.

6. Visualizing Data

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.

7. Decision making

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.

But how can I apply Data Science to make my business grow?

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:

• Analysis of conversions in sales funnels;
• Data analysis of behavior from visitors on your pages;
• Pricing products;
• Detecting unusual behavior and fraud;
• Analysis of user response on social media;
• Systems to recommend products to customers;
• Predicting when a customer will stop paying for a subscription (churn forecast);
• Enriching and classifying leads with the objective of prioritizing;
• Turnover forecast;
• Classifying customers according to purchase behavior;
• Optimizing sales basket through combination of products.

And finally, here are 2 practical tips for you to start using Data Science in your business today:

Tip 1 – How to use Web Analytics to detect patterns in visitor behavior on your pages and sell more

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:

• Source of visit: where your visitors came from, or which link they clicked on to get to your page;
• Average time spent on your page;
• Pages and products viewed;
• Abandonment rate: the percentage of visitors who abandoned your page without clicking on any other link;
• Parameters of links the visitor clicked on: UTMs, SRC, SCK;
• Number of visitors that took a determined action on your website (for example, the ones who clicked on the checkout link or logged in. For these cases, you will have to setup these actions on the analytics tool).

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.

Tip 2 – How to prioritize leads based on data enrichment and lead classification

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:

• Cart abandonment;
• Order cancelled;
• Chargeback;
• Order approved;
• Order claimed;
• Order refunded;
• Order expired;
• Order complete;
• Product downloaded;
• Hotmart Club registration;
• Product reviewed.

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.

For example:

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.

Analyze your data

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!