The impact of data science in retail and e-commerce is constantly increasing, and the pandemic has only accelerated the digital transformation. Retailers turn to big data to implement smart automation and get better return on investment (ROI), and this direction of software development is becoming more relevant every day.
In this article, we’ll consider the role of data science in retail, challenges on the path of implementing it, and features of deploying such solutions.
Data Science in Retail in 2021: Application and Challenges
By 2021, data science solutions have been implemented in almost all industry’s processes. They are most commonly used for the following purposes:
- Customer experience and recommendations improvement,
- Data-driven pricing optimization,
- Better logistics and inventory management,
- Fraud detection, and
- Forecasting trends via social media.
Among the core challenges of applying data science in retail are:
- Issues of storing user data. You can overcome leakage threats and risks of confidentiality loss via multi-stage data protection, including biometric authentication, secure payment gateways, data storage in a trustworthy cloud, etc.
- Predictive analytics errors. Algorithms that study customers’ purchase history can make an erroneous conclusion that is offensive and unacceptable for a person. So, you should not forget about the quality of data processing when using predictive machine learning (ML) models.
- A growing number of data sources. Data science algorithms have to aggregate data from a vast number of sources. Retailers must leverage advanced relational search engines, artificial intelligence (AI), and robust cloud solutions to handle vast quantities of unstructured data.
Data Science Use Cases in Retail
Using customer data seems innovative, but retail started to do it 100 years ago. The first such cases date back to 1923, when Arthur C. Nielsen, Sr. founded a buyer behavior research firm. Today, Nielsen Corporation is a globally famous consumer data analytics company.
The breakthrough inventions of the 1980s helped broaden data science’s scope, and a brave new world began at Amazon. The company used algorithms that recommended goods based on customers’ buying habits to optimize pricing. In the 2000s, retailers realized the potential for data that remains from online shoppers and social media, and applying data science for driving sales has become ubiquitous today. For example:
- Netflix uses global data on user preferences and habits. Based on their analysis, the company makes recommendations for content and offers videos of interest to a specific person.
- Valve’s Steam, a video game distribution platform, has implemented a pricing policy based on big data analysis and ML models.
- Starbucks uses data analytics to decide where to open its next coffee shop. The company analyzes the area’s demographic parameters and traffic to determine how profitable a new shop will be.
How to Apply Data Science in Retail
Data science combines data processing, statistical analysis, data mining techniques, and AI applications. The development pipeline can be represented as:
- Research and discovery phase,
- Data mining and modeling,
- Development and testing, and
- Delivery and maintenance.
First off, we have to define the business and technical requirements, the scope of technologies needed to create our product, and the lineup. Then, we can go to data processing.
Even the advanced ML algorithms won’t work without appropriately collected and prepared data. Most often, engineers use the Cross-Industry Standard Process for Data Mining (CRISP-DM). It is divided into business understanding, data understanding and preparation, modeling, evaluation, and deployment. In most cases, engineers apply steps cyclically and, besides deployment, repeat them several times.
Next, you have to create, train, and test the ML model. Training is feeding data to apply statistical weights that allow the model to perform needed actions automatically. When the model is trained and tested, you can evaluate and deploy it.
In general, data science-driven app development consists of the usual steps (except for CRISP-DM). You need to:
- Think over the architecture of the solution,
- Design the user interface,
- Create the frontend and backend, and
- Test the app with the help of QA engineers.
When your app is thoroughly tested, you can place it in app stores. In the future, you will likely have to improve the models and regularly update the system (not always, but usually) so that it supports the latest OS versions.
Retail companies constantly test new data mining models and process everything they know about a customer. And it works: data science truly gives retailers the tools to improve the customer shopping experience, better manage risk, increase productivity, and, as a result, generate more revenue.
Therefore, to keep up with competitors, you cannot stand aside and ignore a trend that will remain for a long time. Try data science for your retail business, and you will understand how much it has to offer retailers right now.
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