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DATA & ANALYTICS BLOG

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8 Applications for Data Analytics in Retail

Posted by LPA Software Solutions on Aug 4, 2020 3:17:32 PM

 

The global market size for the retail industry will reach a staggering $29.763 trillion in 2023. Year after year, the retail industry has been on an upward growth trend as sales continue to skyrocket. 

Of course, the impressive growth of the retail sector does not make it less competitive, especially for small retailers. Businesses that want to stay ahead must find ways to optimize service business processes to fully satisfy the expectations of their customers. One way to do so is by taking advantage of big data analytics and big data science.

But what exactly is data analytics? How does it differ from data science? More importantly, how can you apply data analytics in retail?

These are some of the issues we address in this comprehensive guide. Keep reading to learn more. 

What Is Data Analytics?

Data analytics refers to the analysis of raw data with the aim of making conclusions about the information. Most of the processes and techniques of data analytics can be automated into algorithms and mechanical processes that analyze raw data for human consumption. 

With data analytics, it’s possible to learn of metrics and trends that could otherwise be lost in all that mass of information. You can then use this information to optimize business processes and boost your business’ overall efficiency.  

Data Science Vs. Data Analytics 

You’ve probably heard the words data science and data analytics used interchangeably. The truth, however, is that the two are unique fields that have different scopes. 

The term data science refers to a group of fields used to mine huge datasets. On the other hand, data analytics is a focused version of data science that can rightfully be considered part of a larger process. Data analytics focuses on getting actionable insights that may be promptly applied based on specific queries. 

Another major difference between data science and data analytics has to do with exploration. Data science does not concern itself with specific queries. Instead, it deals with large datasets, many times using unstructured techniques to expose useful insights.

Data analysis, as we hinted earlier, works best when focused. In other words, it uses existing questions that require answers that are based on already existing data.  The main focus of data science is asking questions, while data analytics seeks answers. 

 The Process of Data Analysis

There are four steps involved in data analysis. Below, we look at each of these steps more closely.

Determining Data Requirements or Data Groupings 

There are many ways to sort data. These include age, gender, income, demographics, and so on. Data values can either be numerical or divided into different categories. 

Collecting Data

Step two in data analytics involves collecting the data itself. There are many ways to collect data, including personnel, computers, cameras, online sources, and environmental sources.

Organizing Data

After you’ve collected data, you need to organize it so you can then analyze it. This organization can take place on spreadsheets or other software for statistical data.

Cleaning Data

This step involves scrubbing and checking data, so there are no errors or duplication of content. The process is also meant to ensure that the data is complete. Any errors in the data are also fixed.

Types of Data Analytics

Generally, there are four main types of data analytics:

These four types of data analytics collectively play different essential roles in a retail business. 

Data Analytics in the Retail Industry

Data analytics is applied at various stages of the retail process in both big and small retail businesses globally. In this section, we take a look at some of the top data analytics applications. Read on to find out.

Price Optimization

Algorithms in data analytics do various essential functions that help with price optimization. These include tracking the demand for products and services, checking inventory levels, monitoring competitors' activities, and so on. Since all this information is provided in real-time, you can take the appropriate actions regarding prices.

Before data analytics, retailers used to bring down prices when demand for a product diminished or the buying season was over. Analytics gives you the insight needed to enable gradual price reductions when demand shows signs of sagging. The reverse is also true. 

Future Performance Prediction

Each customer interaction you have has a huge impact on existing and potential relationships. One wrong decision could lead to huge losses in the future. 

Data analytics helps provide insights into how a certain product or service will be received by the target audience. This is done by comparing the performance of a test group to that of a well-matched control group.

Demand Prediction

Top retailers go out of their way to learn customers’ buying trends. This way, they can target areas that have high demand. Information about customers’ buying trends can only be gotten by collecting demographic, seasonal, and occasions led data to create clear patterns of buying behavior across the target market.

To Select the Highest ROI Opportunities

The most successful retailers make use of data-driven intelligence to understand their existing and potential customer base. Often, the idea is to model expected reactions to marketing campaigns. With this information, it’s easy to predict which marketing approaches will generate the highest return on investment (ROI).

To Forecast Trends

Modern retailers have a variety of cutting-edge tools to gain a clear understanding of current trends. There are sophisticated algorithms that can accurately forecast trends using buying data. This way, it’s easy for retailers to understand which products need to be promoted by the marketing departments.

To Identify Customers

Data analytics can also help you identify which customers are most likely to desire a certain product or service. This data is especially useful for companies that ship products to customers only after they order. With information regarding individual geographic locations that depend on demographics, these businesses can fulfill orders more efficiently.

Data Analytics Can Boost Your Retail Business

Data analytics delivers huge returns in the retail industry. It’s the reason top retailers are increasingly relying on it to boost customer loyalty, improve brand visibility, and boost promoter scores.

Are you interested in ways to boost your retail business by leveraging big data? Please contact us today.

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