What is Big Data and how has it transformed the sector? – WAU

Many companies don’t know what retail marketing is or how it can help them understand and improve their numbers. Others also do not know what the definition of Big Data is and how this strategy helps in the present and future understanding of the market. To help you, in today’s post we will answer the following questions: what […]

Many companies don’t know what retail marketing is or how it can help them understand and improve their numbers.

Others also do not know what the definition of Big Data is and how this strategy helps in the present and future understanding of the market.

To help you, in today’s post we will answer the following questions:

  • what is retail marketing?
  • how does it work?
  • What are your goals?
  • Big Data: how can it help you?
  • what are the types of data analysis?
  • how to leverage sales with Big Data?

Come on?

What is retail marketing?

Simply put, we can say that it is a range of activities that are done by retailers to:

  • sell more;
  • promote your store’s products;
  • understand customers;
  • and make decisions before competitors.

How it works?

Retail marketing uses the common principles of the so-called Marketing Mix, such as product, price, location and promotion.


This variable encompasses the entire product mix that a store can offer and makes that store have its own personality.

It is necessary to make decisions regarding the breadth (number of product lines) and the depth (number of items per line).

Stores that are known for their breadth are those in which you can find “anything”, while those that are for their depth please people for their specialized products.


We not only consider the individual price in this variable, but the policy adopted by the company.

The ways to identify a store is by the type of price. Some are known for their more “affordable” prices while others are “careiras”.


This variable is very important for the failure or success of any retail company.

Both the macro-location (city or neighborhood) and the micro-location (street, house or building) in which the store will be located must be taken into account.

This decision will define both the public (profile) that will be reached and the number of potential customers who will be able to access the store.


In addition to choosing a good point, it is necessary to present it in a manner consistent with the type of store.

What should be considered here is the decoration, the layout (how the goods will be distributed), the use of music and everything that can create a pleasant environment for the user.


After all the work, you need to make it known on the market, right? For this, communication is very welcome. Whether using advertising, contests and promotions.

It should be remembered that the goal is not simply to sell, but to be concerned with providing a pleasant user experience.

Thus, after purchase, your customer will want to return.

Content and Data Marketing

Personal attendance

Unlike other sales models, retail is typically characterized by personal sales.

Thus, the seller is always in contact with customers. So, it is important that he is attentive and kind during and after shopping.

Customers rely on some or all of these variables to compare and choose your favorite store, even though he is not fully conscious.

What are the goals of retail marketing?

The main objective of retail marketing is to improve each of these processes and understand market and consumer trends.

For all of this to be advantageous, it is necessary to be quick and anticipate the competition.

Now that we understand what retail marketing is, let’s see how Big Data has helped these strategies:

Big Data: How can I help you?

When we talk about Big Data, we are referring to the large volume of data (whether structured or not) that can impact every day business.

However, what matters most is not the quantity, but what each corporation does with the data.

Therefore, they can be analyzed to obtain ideas that assist in making important and strategic decisions for the success of the business.

What are the types of data analysis?

Predictive analytics

It is perhaps the most well-known type of data analysis in Big Data. Having identified old patterns in its database, the analysis aims to map possibilities for the future in that field.

It is known for trying to predict the future, using statistical and historical data mining techniques to learn about future trends.

From this, each decision made stops being decided by intuition and becomes more solid and secure.

Prescriptive analysis

Confused with predictive analysis, the prescriptive has different objectives, since it works with the consequences of each action that will possibly occur in the future.

The importance of this method can be seen in the health area. Health plans can, based on the identified standards, understand what the impacts will be for them and outline the best options for managing the business.

Descriptive analysis

Descriptive analysis can understand data in real time.

Data mining, in this sense, can analyze the information of someone who wants some type of credit, understanding all the risks involved in the release. Then, they demonstrate what the interest rate used should be.

Thus, the descriptive form demonstrates the present meanings without the need to predict what will happen in the future.

Diagnostic analysis

As the name can show, this type seeks to understand the causes (who, when, how, why and where) of a given event.

A marketing team can predict, with this means, all the reach and impact that will happen after the realization.

The data alone does not mean anything. The analyst can and should use them to help the company achieve its goals.

How to leverage sales with Big Data?

Using the immensity of available data to seek to optimize the results is a great challenge. In this sense, we have chosen some points where using Big Data can help your company. Look:

Analysis of consumer behavior during purchase

The internet is being very useful for training information, which makes Big Data move.

A study by Forbes Insight and Turn showed that 65% of companies used the web to collect data from their customers.

Thus, this type of media has become the greatest form of data storage, which was previously in the hands of call centers.

In 2012, the Massachusetts Institute of Technology was able to calculate how many people were in a large American shopping center at any given time on Black Friday.

All of this from the crossing of geolocation data available on mobile devices.

With this, it is possible to understand consumer behavior.

marketing research

Most efficient segmentation on the market

Nobody doubts that the principle of retail is selling. And in times of crisis, all employees of a company are together to be able to attract the attention and desire of consumers.

The tactics are varied, from direct contact to large salons. However, when purchasing power is short, none is more efficient than full knowledge of the customer.

Thus, one can know what their main wants and needs are.

A good example is Netpoints, a London company that works with a loyalty program focused on retail.

From Big Data, the company can analyze and understand what are the specific offers that must be made to promote products according to the interest and consumption profile.

When it comes to the best age, the profile showed a predominance of women (64%), who normally shopped in the morning. They were able to understand that the purchase of cheese for this audience was higher.

From that, Netpoints promoted some actions to increase the frequency and average ticket of this profile.

For example, they offered tastings and courses on cheese and exercises with teachers in the parking lot of partner stores before they opened.

The strategy proved to be efficient and increased sales by 15.6%.

Demand forecasting

Companies that work with software development can collect customer usage data (if authorized) and mentions of the topic on social networks to identify demands on application failures, dissatisfaction and, especially, new features that may correspond to the needs of customers. customers.

Amazon, based on predictive analysis, identifies which products someone has purchased and calculates how long they tend to end on average. Thus, it sends new products to buyers who will not even have the trouble of asking for them.

Development of products that meet the exact needs of an audience

With the use of Big Data tools, companies can understand and develop new products.

The collection of ideas extracted by data mining generates greater knowledge about users, their preferences, their personalities and their desires.

In addition, the data can be used to understand what users think of certain brands.

From all these data, one can explore the strengths and weaknesses of products and companies to develop products that are solutions for them.

THE Nike, in partnership with an application, started to collect data on heartbeat, speed and distance covered in training.

Thus, it was possible to understand what were the main problems of their customers and indicated promotions and demonstrated how they could improve the performance in each of their races.

Competition Analysis

As we have seen, data can be used to understand the competition.

In addition to understanding strengths and weaknesses, you can:

  • analyze what is the possible future of it;
  • how to make better and more profitable decisions;
  • create specific promotions;
  • increase stocks of products that are most in demand.

Thus, your business will be in tune not only with its own causes, but analyzing the market as a whole.

The Walmart chain, for example, acquires more than 2.5 petabytes of data per hour from capturing transaction information.

From that, she can control her stock well.

Decrease client output

Customers who wish to cancel their services or return products can indicate the reasons why they want to leave the company.

From this, it is possible to identify small signs that may mean a massive demand in the future and, thus, avoid losing consumers.

THE Nissan, for example, uses Big Data to understand what are the processes that most lead people to go to dealerships looking for repairs.

After that, it acts preventively and anticipates the most frequent needs of its customers.

Retail marketing is very useful for small, medium and large companies. With these strategies – including Big Data – you can anticipate trends and get closer to your customer.

Are you interested in knowing more about Big Data? Then read “Big Data: why every marketing strategy needs this ally“.