Learn How Data Science is Increasing Productivity in Retail Stores

Danilo Galindo
Mar 24, 2021

Contents Outline

Learn How Data Science is Increasing Productivity in Retail Stores

Mar 24, 2021 6 minutes read

The advancement and growth of new technologies and algorithms in the data science sector has given rise to many applications and use cases for all kinds of industry. Thus, making the most of the resources and processes that are indispensable to optimize and have desired results. This use case will be focused on the commercial sector specifically sales, where our main variables will be price and promotions. These are some of the variables that are commonly handled in all types of businesses whether they are small, medium or large. Regardless of the size, everyone has the opportunity to take advantage of the data. Thus, data can be collected, analyzed and presented to make important decisions, whether it is to set the right price, make an offer, offer certain products and above all, to know the right time to implement a certain strategy. All this can be managed through techniques, algorithms and models to predict and make management decisions that take the company to the next level.

image credit: Mike Petrucci

While it is true that technology has advanced to the point that most people have a mobile device to access the Internet. Thus, over time we have noticed a growth of companies offering their products online. In this way, the public can see offers and buy products in different parts of the world. This is a great advantage for customers, but also for the business itself. Currently, there are algorithms that take into account customer preference to offer products and even compare prices between the same market niche. This is easy to observe when looking for travel tickets or simply looking for a household item.

Companies that have a presence on the Internet and are associated with the sales market mostly handle e-commerce as online sales points. However, not everyone uses data science to give their business an advantage. On the other hand, companies use predictive models to adjust prices and promotions. So companies like Darwing Pricing LLC provides its customers a novel service of pricing and promotions that are automatically adjusted according to the location. For this, they use neural network intelligence algorithms to give us a real-time model of pricing and promotions. In this way, companies can produce more effective promotional campaigns and increase their productivity by more than 50%. Additionally, companies using these tools will have more time to focus on customer service.

Data science, specifically areas such as machine learning and AI (artificial intelligence) have contributed greatly to commercial business. Let's look at some statistics, results and studies of some companies that have applied machine learning or AI between 2017 and 2020.

By 2017 McKinsey told us that commercial companies in the USA saved more than 19% using data analysis. Likewise, Target company saved between 15-30% of their profits using machine learning models.

Another strong and recognized company is Amazon, which uses machine learning in many of its products. Thus 55% of their sales are handled by machine learning model recommendations. Also, Netflix is saving around 1 billion per year by using these models alone. All this is possible because many processes are optimized so that sales, pricing and promotions are totally effective.

Forbesprovides us with very useful information about how companies are revolutionized by the use of AI (artificial intelligence) and machine learning. For example, the fact that many companies save between 40% and 60% of their costs. In addition, they predict that by the current year 2020 B2B companies will invest 30% of their resources in AI. In this way, companies will be more focused on their productivity and effectiveness.

As technology advances, companies will have more opportunities to use machine learning or IA models in their processes. This is evidenced by the large investment that the retail sector has made and will make in the coming years. According to studies by Global Market Insight, this sector will invest 8 billion by 2024. This gives us a very clear picture of the importance and projection that companies have towards data management.   

On the other hand, we could ask ourselves, if all this money is directed to prices and promotions. As we can imagine, no, but these variables occupy an important percentage in the investment of companies. Let's see a little bit how companies use AI (artificial intelligence) in their different areas according to a study created by IBM.

  • 85% in supply chain planning
  • 85% in demand forecasting
  • 79% Customer intelligence
  • 75% Marketing, advertising and campaign management
  • 73% Pricing and promotion
  • 73% Store operations
What is the benefit of this retail sector investing in the previously mentioned areas? It is estimated that by 2022 this sector will save approximately 340 billion each year. This, according to the survey made by Capgemini.

Additionally, let's not forget that many machine learning and AI applications for sales companies are focused on security and customer service as we will see later on.

What if our business is not on the Internet? How can we take advantage of the science of data for our business? Well, there are image recognition and facial registration applications to know if a product was picked up or not. Additionally, you could know if a customer has more preference for certain products than others and even know the satisfaction of the service. Forbes presents a clear example of this, where it shows us how Wallmart implemented an image recognition model to determine the satisfaction of its customers.         

On the other hand, image recognition and certain learning models provide us with the security service for our products. This, especially in physical places where there is a large volume of people. In this way we can avoid any loss in business.

As we all know the prices and offers depend on many factors. Such as processes, inventory, market among many others. For each of them, there are tools in data science to manage and take advantage of each of these variables. This way we can directly impact on prices in real time. A very common example is to apply machine learning or artificial intelligence algorithms to avoid waste in manufacturing processes and be able to reduce sales prices. In addition, we could analyze the time and place where people buy. Determining also the frequency of purchase to be able to launch timely strategies.

Finally, we can conclude that any company of different size and business sector, can make use of data science tools to make effective decisions and impact on processes, products and strategies to achieve desired results. Thus, companies will not launch campaigns with their eyes closed but based on data analysis.

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