It is a given fact that big data analytics is revolutionizing the retail industry. As there are more and more data obtained from multiple platforms, managing big data is a pivotal concern for many retail industries nowadays. However, many retail businesses still prefer to remain using the traditional data management approach. It is because they do not have a clear understanding of how to manage massive amounts of data efficiently and effectively using advanced technologies.
The traditional document management approach such as physical files and folders relies mostly on manual data entry and processing to manage data. This can be time-consuming and subject to human errors. Apart from this factor, data silos can occur where a piece of information collected from a department or team cannot be accessible fully and easily by other departments or teams. This will lead to a failure of data integration and information sharing will be limited. Data security is also another issue to consider as the data is more susceptible to risks such as loss, damage, or misplacement when managing physical documents.
Therefore, we cannot neglect the importance of integrating the advancement of technology as well as the powerfulness of big data analytics into managing data. But before we attempt to incorporate such technologies into enhancing the way we work, we first have to grasp the meaning of big data as well as big data analytics.
What Is Big Data Analytics?
Big data analytics is the process of collecting, processing and analyzing large volumes, velocity, values, veracity and a variety of datasets obtained from various sources like social media and the company’s website to gain insights into decision-making. Besides this, retail analytics uses big data to analyze customers’ behaviours, preferences and trends to gain valuable insights into pricing optimization and supply chain.
Misconceptions Surrounding Big Data
The term “Big Data” is widely heard in today’s digital landscape no matter which industry. Seeing that we are constantly connected to the internet through not only our personal devices, but public equipment as well to which companies can gather immense amounts of information on our daily activities alone. However, with that being said, many retail industry leaders still have misconceptions regarding what big data actually is and how it works.
One of the misconceptions that surrounds big data is most people think that “Big Data” is only for big businesses. However, this is not true. It is because even a small industry can produce massive amounts of data, depending on the number of platforms they obtain the data from. Not only that, a large amount of data can also be obtained from a single platform. Therefore, the amount of data obtained is not affected by the size of the industry.
What is more, another misconception is big data contains a lot of useful information. This is not completely true. Useful information can be extracted from big data only when there are proper algorithms applied for data analysis. Data is considered useful when it integrates with complementary data sources and implements properly in a business case.
Another common misconception about big data that surrounds industry leaders is big data is hard to manage. Big data indeed contains a large volume, velocity, value, veracity and variety of data. However, to break through this misconception, there are actually many data management software available like Oracle Database and Microsoft Access which offer advanced functions and tools to manage the data.
How Big Data Works?
There are 2 categories of big data, which are structured data and unstructured data. Structured data is stored in a standardized format and has a well-defined structure, stored in a tabular form with rows and columns, with clearly defined data attributes. This type of data is normally stored in databases and spreadsheets. On the other hand, unstructured data is not stored in a standardized format and does not have a well-defined structure. It includes data gathered from emails, social media, video files, audio files, word documents, websites, feedback through questionnaires and many other platforms.
In order to make big data more realistic, big data analytics should be performed. In this blog article, we set out to list the contributions of big data and provide suggestions on the techniques available to enhance the big data analytics process. Let’s take a look at what big data analytics is and how it can be properly performed in the next section.
How Is Big Data Contributing To The Retail Industry?
Let us take a look at some of the advantages that big data brings to the retail industry.
1. Personalized Marketing and Advertising
One of the most significant transformations big data analytics brought to the retail industry is that it can personalize marketing and advertising efforts. Customers’ interactions can be tracked through their browsing history and social media interactions by using analytics tools like Google Analytics and Tableau. By analyzing this data, retailers can gain a deeper understanding of customers’ behaviours and preferences to create personalized marketing strategies and promotions. Customer-driven advertising can be done by sharing customized messages with targeted customers to increase customer satisfaction and loyalty. What is more, the brand’s reputation and credibility will increase as existing customers recommend the brand by word-of-mouth to others.
Amazon’s product recommendation system is one of the examples in which it utilizes AI algorithms to analyze customers’ browsing and purchase history to recommend suitable products according to their preferences. Other instances such as AI-powered chatbots which provide instant customer support and are able to answer queries in real time. Natural language processing (NLP) is commonly used to power the chatbot’s language side, while machine learning (ML) is applied to power data and algorithms. Examples of chatbots are Google Assistant, Apple’s Siri and Amazon’s Alexa.
2. Optimizing Inventory and Supply Chain Operations
To optimize inventory and supply chain operations, big data analytics also plays a role. By analyzing data using predictive analytics, sales forecasts can be generated more accurately. This allows retailers to optimize inventory levels, reduce stock-outs, and avoid overstocking while minimizing inventory costs. A good example would be Walmart employing AI to optimize their inventory levels by examining previous sales data and weather patterns to forecast which products will be in high demand in that period. An automated replenishment system can also utilize data to order products when stock levels fall below a certain threshold. This technology contributes to reducing waste and improving sustainability by identifying perishable products nearing their expiration date. Retailers will be notified once the items are expired so they can take proper action to dispose of them. One of the examples of automated stock replenishment systems is GMDH Streamline. It features order planning, inventory optimization, accurate demand forecast, stockout or overstock alerts and projected inventory levels.
Additionally, big data analytics helps logistics providers determine the most efficient delivery routes to minimize fuel consumption, reduce transportation costs and optimize supply chain operations. By implementing predictive maintenance, retailers can forecast equipment failures and schedule maintenance, reducing downtime and minimizing operational disruption.
The predictive maintenance process works based on 3 stages. First, the sensors are installed to gather data on the equipment’s performance and conditions in real time. Data mining is the second stage in which the Internet of Things (IoT) is applied to allow sensors to transfer all the information to a centralized system to analyze what is happening on the data. The following stage is to perform calculations and machine learning algorithms to forecast the data’s conditions and detect anomalies if there are any.
3. Sales Forecasting
Another major contribution of big data analytics is that it can utilize predictive analytics to forecast sales. An example is Microsoft Power BI. It can visualize data by generating interactive and informative visual reports and dashboards. The visualized data can then be used to gain better insights into making strategic decisions such as promotions and pricing strategies. Retailers can then promote their products based on demographic, psychographic, behavioural and geographic segmentation. The feedback regarding the product can also be obtained through sentiment analysis. It is the process of identifying and categorizing customers’ opinions and preferences from texts by implementing natural language processing and machine learning algorithms. A particular message is broken down into several parts and each part is assigned with a sentiment score. Follow by this, the bot will sum up the scores or use each score to analyze components of the statement to determine whether it is a positive, negative or neutral sentiment. This in turn can let the retailers recommend products based on past customers’ preferences or similar products from the same categories.
4. Pricing and Retail Workflow Optimization
Not forgetting, retailers can also benefit from pricing optimization by utilizing big data analytics. A dynamic pricing strategy can be implemented to automatically adjust prices in real time based on demand, competition, inventory levels and seasonality. Algorithms like the Bayesian model, reinforcement learning model and decision tree model can be applied. The algorithms analyze data based on previous sales, product prices, production costs, current market demand, and customers’ purchase behaviours to predict how demand affects the product’s price. As a result, appropriate actions will be taken immediately to customize the products to meet fluctuating demands while staying competitive and maximizing profitability. An example is Kroger, an American retail company that uses a dynamic pricing system.
In addition, the in-store workflow can be optimized with big data analytics. Employee scheduling and workforce management can be enhanced by analyzing historical sales data and employees’ performances. Optimal schedules can be created to ensure adequate staffing during peak hours and days while minimizing labour costs.
5. Fraud Detection
Last but not least, big data analytics also come into play when detecting fraud. Data is analyzed from daily transactions and operations including purchasing, projecting sales, moving warehouses, keeping track of employee shift records and more. Furthermore, anomalies in data can be revealed using algorithms like cluster analysis and peer group analysis, which are a type of exploratory data analysis. These algorithms divide observations into various groups that share similar characteristics. To discover more about the observations, those groupings are contrasted and compared with other groups. Anomalies will be detected if there are differences or outstanding characteristics among the groups. This prompts the security officers to take immediate action if there is irregularity. Consequently, fraud prevention can be achieved as strict security measures are employed.
In summary, the retail industry has benefited in terms of efficiency in operations and sales thanks to the powerfulness of big data analytics. By harnessing the power of data analytics, retailers can gain valuable insights into customers’ preferences to make more informed decisions. Products’ pricing can be optimized with personalized marketing strategies to retain existing customers while gaining a competitive advantage. Operations can also be streamlined to align with evolving market requirements to drive revenue and profits. Most importantly, the data obtained can be utilized fully with the advancement of technology as well as enhancement of security to ensure smooth and successful retail business.
BPI Technologies, a subsidiary of the InfoConnect Group, specializes in data management and business intelligence solutions for reporting and analytics. We also offer solutions for advanced analytics to help our clients monetize the data harvested. Interested to find out more? Click here.