Data Science Use Cases in Retail and E-Commerce and Implications on Data Privacy
Updated: Jul 25, 2022
In this article, we highlight some key data science use cases of how both online retailers and brick and mortar stores of any size can integrate machine learning technology to stay ahead of their competitors by increasing sales and reducing costs. From shoes to groceries to cosmetics, the possibilities in the retail space are full of promise. The applications and use cases I will describe here are a fraction of the feasible machine learning projects and serve as examples of what can be done today in the retail space. Artificial intelligence (AI) and machine learning (ML) are among the top technology trends in the retail world. They are having a great impact on the industry, in particular in e-commerce companies that rely on online sales, where the use of some kind of Artificial Intelligence technology has become commonplace. However, you do not have to be a big company or sell exclusively online to take advantage of the tremendous power of machine learning. Worldwide, the retail grocery sector has become a free-for-all battleground, with e-commerce platforms like Amazon, Alibaba, Jumia, Konga, etc. entering the fray, retailers need every available weapon to counter the onslaught, and that includes predictive analytics.
Retail stores know a lot more about us than we think. With every transaction that occurs in-store, valuable data about the customer is collected and utilized by retail to draw these customers back in. In the Fast-Moving Consumer Goods (FMCG) sector, data is especially important in retaining customers and getting new business due to the low-profit margins. In order to do so, these businesses need to understand and target their audience appropriately. Considering the amount of data available today it is essential not just to freeze it but to use it for the benefit of the company. The transformation of data into meaningful insights is crucial for decision-making. The goal of machine learning is to build systems capable of finding patterns in data, learning from it without human intervention, and explicit reprogramming. What is machine learning, and why should retailers adopt it? Machine learning gives a system the ability to learn automatically and improve its recommendations using data alone, with no additional programming needed. Because retailers generate enormous amounts of data, machine learning technology quickly proves its value. When a machine learning system is fed data—the more, the better—it searches for patterns. Going forward, it can use the patterns it identifies within the data to make better decisions.
Machine learning makes it possible to incorporate the wide range of factors and relationships that impact demand on a daily basis into your retail forecasts. This is very valuable, as just weather data alone can consist of hundreds of different factors that can potentially impact demand. Machine learning algorithms automatically generate continuously improving models using only the data you provide them, whether from your business or from external data streams. The primary benefit is that such a system can process retail-scale data sets from a variety of sources, all without human labor. Machine learning is an extremely powerful tool in the data-rich retail environment. It should be leveraged in any context where data can be used to anticipate or explain changes in demand. In some instances, it can even fill in the gaps where the data is lacking. To solve the price prediction problem, data scientists first must understand what data to use to train machine learning models, and that’s exactly why descriptive analytics is needed. Experts say applying predictive analytics to pricing can show results in about six months. This leads to about a 5% increase in revenue margins. A grocer can make huge revenue gains this way because he now has the means to “price it just right”.
The old adage is common but true: “Retail is detail at a large scale.” To ensure smooth operations and high margins, large retailers must stay on top of tens of millions of goods flows every day. At the center of this storm of planning, activity stands the demand forecast. A highly accurate demand forecast is the only way retailers can predict which goods are needed for each store location and channel on any given day. This in turn is the only way to ensure high availability for customers while maintaining minimal stock risk. A reliable forecast leveraged across retail operations can also support capacity management, ensure the right number of staff in stores and distribution centers, or help buyers manage the complexities of long lead-time purchasing. Machine learning techniques allow for predicting the quantity of products to be purchased over a given future period. In this case, an algorithm can learn from data for improved analysis. Compared to traditional demand forecasting methods, a machine learning approach allows businesses to accelerate data processing speed, provide a more accurate forecast, automate forecast updates based on the recent data, create a robust system, and increase adaptability to changes.
Store owners often find themselves plagued by questions of inventory; What should be stored, and what should be thrown away? When should the inventory be stored? and when to discard it? Nonetheless, most retailers have a plan for this, based on metrics like footfalls, sales, and revenue, to name. It’s a no-brainer to replenish the stock of a product when it starts to run low. But that simply isn’t enough anymore. Predictive analytics now helps grocers remove the uncertainty factor from inventory management. Most retailers know how painful it is to have products that few customers buy. Predictive analytics accurately predicts demand and suggests better replenishment strategies. It does not end there. By deploying predictive analytics, retailers can identify where offering up a new product might increase revenue. Inventory imbalances are removed because of predictive analytics. The overall result – is a decrease in inventory costs and an increase in sales. The use cases mentioned above show that the application of data science brings numerous benefits to marketing campaigns of various brands. Considering the amount of data available today, it is essential not just to freeze it but to use it for the benefit of the company. The transformation of data into meaningful insights is crucial for decision-making.
Several repeated and complex transactions take place every day in the e-commerce industry; many of which require access to client data. Many clients have expressed legitimate concerns about disclosing personal data because of data abuse. On many occasions, highly sensitive data is unlawfully used for advertising purposes or even sold to other third parties. Running an e-commerce business requires dealing with personal data, Nigeria has a few sectoral laws on data protection, such as; CBN’s Consumer Protection Framework 2016, Consumer Code of Practice 2007 (Nigerian Communications Commission), National Health Act, and the Patients Bill of Rights 2018. The Nigerian Data Protection Regulation (NDPR) covers all transactions intended for the processing of personal data, to the actual processing of personal data and it is applicable to all “natural persons” resident in Nigeria or Nigerian citizens resident abroad. The NDPR, issued by the National Information Technology Development Agency (NITDA), is Nigeria’s primary regulation on data protection. Because individual rights are protected by the NDPR, by the provision of Article 2.13.1 an e-commerce company can receive a request from a data subject to provide all the information the company has that pertains to the data subject, the data subject is empowered by the NDPR to receive the information in a machine-readable format and in such form that the data subject can read and understand. If the e-commerce company is unable to take action on the request of the data subject, Article 2.13.2 of the NDPR mandates the company to inform the data subject without hesitating and latest within one month of receipt of the request of the reasons for not taking action and on the possibility of lodging a complaint with a supervisory authority.