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How Data Analytics is changing the future of Retail Industry – Part 1

Blog | December 2, 2022 | By Mohit Gupta

Introduction

The world is inundated with data, and this is increasing exponentially day by day. Computer systems store vast amounts of data. Recently estimated that approximately 1 Exabyte (1 million terabytes) of data is generated annually worldwide, 99.97% of which is available only in digital form. The marketing industry is teeming with data captured by companies and the rise of social media, multimedia and the Internet will add exponential growth in near future.

The retail industry is one of the largest sectors in the world. This industry is expected to grow as the middle classes are increasing substantially in size and in buying power. Retail purchases via ecommerce and m-commerce are growing at a high rate due to the advent of high-speed internet connections, advancements in Smartphone technology and online-related technology, improvements in the product lines of e-commerce firms, a selection of delivery options and better payment options. It is estimated that consumers and large organizations generate 2.5 billion GB of data yearly and this is increasing at the rate of 40% year on year.

This growth in data is possible with the advent of high-speed Internet access and the availability of new data types for data analysis. The introduction of these data types has become possible with the introduction of Smartphones, tablet computers and other electronic devices. The data are collected because retail companies – including those engaged in some kinds of e-commerce – view them as a source of potentially valuable information, which, as a strategic asset, could provide competitive advantage. These retail data from Big Data are a powerful means of creating a way forward for marketers to accomplish their objectives in an effective manner.

Business intelligence and analytics (BI&A) and the related field of big data analytics have become increasingly important in both the academic and business communities over the past two decades. In the rising tide of retail business transaction data, these tools help distinguish what are strategic assets and what are not worth collecting in the first place. The analysis of these new data types can make the decision-making process more effective in marketing. Until recent times, appropriate software tools and algorithms were scarce in marketing research. However, with the advancement in technology, software tools and algorithms coupled with velocity of Big Data are now available to analyze content uploaded at different locations in the form of images/snaps, music, or video. For innovation, growth and to excel in competition, analysis of these large data sets (also known as big data) plays an important role. Big data not only have an impact on data-oriented managers, data analysts, etc., but also on the entire retail sector which will increasingly have to cope with the Volume, Variety and Velocity (3V’s) of big data.

Harnessing Data: Variety, Volume and Velocity

Data is being generated at a rate never before seen in history. Retailers are working to collect, organize, and leverage that data appropriately to gain insights that can drive actions and decisions leading to greater customer satisfaction and loyalty, and ultimately impacting bottom-line profits. Business Analytics makes that data capture, translation, and next best action possible.

The current digitization of virtually everything creates new types of large and real-time data across a broad range of industries. 90% of the data in the world today has been created in the last two years — and there are no signs that data creation is going to slow. In fact, the volume and speed at which data is being created will bring new challenges to retailers in the years ahead. Making matters more difficult, much of the data is in non-traditional and unstructured forms. Organizations struggle to establish the accuracy of much of the data, so they need to combine and measure data across multiple sources to create a more accurate and useful data point. The answer for many retailers is to tap into the advantages of advanced analytics technologies and strategies to extract insights that help retain existing customers and attract new ones. Business Analytics capabilities create new opportunities for retailers to meet the needs of their consumers and create a competitive edge in the marketplace.

Analytics Through the Ages:

Business Focus Investment Wave Wave 1: Visibility and ControlWave 2: Infrastructure and OperationsWave 3: Execution and ExcellenceWave 4: Customer Engagement
Analytics AgeStone AgeBronze AgeIron AgeModern Age
Focused onImproving visibility into key aspects of stock and salesIntegrating data across business functionsBuilding an enterprise data repositoryGleaning customer insights from enterprise and external data
BuzzwordSpreadsheetDecision Support System (DSS)OptimizationBig Data
Defining moment Kroger decides to participate in the experimental UPC program (1970)Wal-Mart’s database in 1999 is said to be 200TB, one of the largest in the world.Amazon’s proclamation that it is a technology company first (2011). Target predicts a teen is pregnant, before her father knows. (2013)
New RetailerTarget/Dayton Hudson (1902)Wal-Mart (1962)Amazon (1994)Apple Store (2001)

Understanding the Business Analytics Pillars

Business Analytics is a broad set of technologies and practices used to understand and enhance business performance. In this section, you take a look at the pillars of Business Analytics.

1. Business Intelligence

Business Intelligence (BI) includes the concepts, methods, and technologies that gather and analyze data to drive better decision making. BI has been around since 1958, when IBM researcher Hans Peter Luhn first used the term. Decades later, technologies are available that have made BI a mainstream business function. BI uses querying, reporting, analysis, scorecards, and dashboards to make it easier for business users across the organization to find, analyze, and share the information they need to improve decision making. Visualization, ad-hoc functionality, mobility, drill-down, and drill-through are just some of the capabilities that allow users to understand the current state of business and why they’re experiencing a certain level of performance. BI has come a long way, and capabilities continue to evolve, offering deeper insights that can be applied across the organization by any user. Without this evolution, organizations wouldn’t be able to cope in this world of Big Data. With modern day BI, business users across marketing, merchandising, finance, supply chain, and so on can easily access and consume data relevant to their roles.

2. Advanced Analytics

Advanced Analytics leverages historical, current, and future possibilities to help retailers see potential results if certain decisions are made and executed. Advanced Analytics includes data mining, predictive modeling, what-if simulation, statistics, and text analytics to identify meaningful patterns and correlations in data sets to predict future events and assess the attractiveness of various courses of action. Algorithms automatically find significant patterns, and models “learn” from past data and update predictions for current or new business questions.

With Advanced Analytics, retailers move from foundational reporting to differentiating and breakaway capabilities, leading to a significant competitive advantage. Advanced Analytics allows the retailer to predict a consumer’s response to an offer and use that result to draw conclusions about financial performance, supply chain requirements, and operational needs. Advanced Analytics is game-changing for retail organizations as they generate greater results and efficiencies with better information and insights into predictions about future outcomes. It allows them to become prescriptive through targeted offers and other incentives to encourage certain consumer behavior and interaction.

3. Performance Management

Business Analytics is rounded out by capabilities in Performance Management, which allows for simplified, structured, automated, and dynamic modeling to understand the implications of various scenarios. Business Intelligence and Advanced Analytics drive the input to what-if planning in Performance Management. Performance Management gives the business the flexibility to build best-case and worst-case scenarios; for example, modeling outcomes of changes in demand, then pushing those scenarios through to merchandising and operations to get a full picture of the impact, and finally driving those decisions through to finance to understand the implications to the bottom line for the organization.

Performance Management creates the opportunity to link financial and operational plans through driver-based modeling and rolling forecasts and gives users visibility and access to the right information at the right time to build confidence in the data and results. This modeling environment also ensures consistency between corporate strategy and field execution, creates a culture of continuous planning, and keeps the entire organization aligned around the strategic objectives of the organization.

4. The Analytical Decision Management Advantage

When it comes to retail, decision making is hardly automatic for most organizations. Many managers spend hours collecting data, crunching numbers, and doing collective gut checks in high-level boardroom meetings. Unfortunately, those old-fashioned methods don’t always produce new market success. The competitive advantage in today’s global marketplace is the ability to manage, optimize, and automate decisions through Analytical Decision Management (ADM), a solution that allows you to prioritize and execute business strategies faster and more effectively than the competition via informed choices.

Core Business Intelligence solutions Emerging Business Analytics solutions Optimization solutions
Core analytics tools and technologies that are foundational in nature. These tools and technologies have a high degree of adoption across retailers. Emerging analytics tools and technologies that sit on top of the foundational layer and provide advanced analytics capabilities. The adoption level of these technologies is low. Niche vertical specific solutions that utilize advanced algorithms and analytics to solve vertical specific business challenges.
  • Enterprise DW
  • OLAP and Basic Reporting and Querying
  • Enterprise BI Analytics Tools
  • Planning and Forecasting
  • Web and Social Media Analytics
  • Data Visualization
  • Digital Dashboards
  • Big Data Analytics
  • Predictive Analytics
  • Mobile Business Intelligence (BI)
  • Inventory
  • Replenishment
  • Assortment
  • Pricing
  • Distribution
  • Labor Scheduling
  • Space Planning
  • Marketing Spend

Retailers will increase technology spending on Business Intelligence and analytics from 17.7% of IT budget in 2016 to 25.7% of IT budget in 2020, representing a 9% compounded annual growth rate.

Retailers’ technology investment plans over the next 2 years line up nicely in an attempt to address these very challenges.

  • Upgrading analytics maturity: Predictive analytics tool (58% of retailers) and Big Data (47%) emerge as key areas of investment for retailers.
  • Improving delivery of insights: Retailers will invest in digital dashboard solutions (58%) and in improving data visualization (46%). Most importantly, 70% of retailers indicate plans to implement a mobile business intelligence solution.

In the next part we will understand

  • How Will Analytics Help Retailers?
  • The Retail Analytics Framework
  • Challenges in Retail Analytics
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About the Author
Dedicated analytics professional with over 8+ years of experience in Data Visualization, Data Warehouse, and Data Analysis. Passionate about transforming data into meaningful decision points for a business.
Mohit Gupta | Technical Lead - Visual Analytics | USEReady
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