What Retail Can Gain from Big Data

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23/09/19 Technology Reference this

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Data Science Technology Review

What Retail Can Gain from Big Data

Contents

Abstract

I. Introduction

I. AN INSIGHT OF BIG DATA

II. BIG DATA SOURCES IN RETAIL INDUSTRY

III. Big Data Applications in RETAIL

IV. Challenges

V. Technology Players and Examples

VI. Conclusion and Future work

References

List of Figures

Figure 1: 3 Amazon retail Ecommerce sales …………………………………………..6

Figure 2: Amazon: 10-year view……………………………………………………

Figure 3: DIKW model ……………………………………………………….. 8

Figure 4: Data sources in retail………………………………………………….. 10

Abstract

Data has become a critical part of our daily lives.  In this era of the digital world, data are generated from various sources, and the fast transition from digital technologies has led to the growth of big data.  The term Big Data is a popular buzzword in today’s market.  Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques.  While “Big Data” really means many things to many people, it is being used by almost every industry including retail.  The objective of this paper is to discuss the insight of big data and how retailers around the world are leveraging it to transform their processes and their organizations.  This paper also describes the challenges and realities retail industry face by using big data technologies. Finally, the paper includes some examples of successful retailers who are taking advantage of the power of big data in their daily process. 

KeywordsBig Data, retail, loss prevention, prediction

I.   Introduction

Information technology is evolving rapidly, and now we are living in the artificial intelligence (AI) age which is considered a smart society where Internet of Things (IoTs) connecting to intelligent devices.  Thanks to the advances in technology, retail is among the industries that are most affected by digital transformation.  With this transformation, the consumer is changing their shopping experience and behavior from seeking products or shops, comparing, reading reviews to in-store, online and finally writing reviews, contacting customer service.  The growing demands of modern consumers for excellent shopping possibilities gives the modern retailer room to innovate and understand shopper’s behavior better in response to the need of modern consumers.  Retailers are dealing with a quickly changing retail landscape and newer competitive threats due to the new and improved changes in technology.  To address these changes, retailers are now using big data solutions to collect massive amounts of data to gather more in-depth insights into their business to recognize customer expectations.

Today’s advanced analytics and machine learning give the retailer the ability to predict the next customer action, provide outstanding customer service, understand consumer demand, predict priceless market trends, make smarter pricing decisions and create valuable cross-channel shopping experiences. To uncover the hidden patterns, trends, and relationships, big data analytics involves examining large amounts of data to gain more insights to make proper business decisions retail industry. 

I.   AN INSIGHT OF BIG DATA

According to EMarketer Retail 2018, Amazon will drive 80% of E-commerce growth, and e-commerce sales hit $258.22 billion in 2018, up 29% over 2017 (EMarketer Retail, 2018). 

Figure 1: 4 Amazon Retail Ecommerce Sales

By the end of 2018, Subway closed 500 stores. Rite Aid closed 600 stores, Toys R Us closed 700 stores, and Teavana closed 379 stores.  These are the brick-and-mortar retail stores had the most closings in 2018 (Stebbins & Sauter, 2018).  Brick-and-mortar retailers across the United States have been forced to shut down to maintain profitability in recent years since the era of digital transformation. 

We live in a world where consumers have opportunities to research and select the best brands they are buying with good quality and reasonable price.  With the sources from social media preferences, browsing behaviors, devices preferences, and geographical demographics make it easier for Analytics to help retailers to understand consumer’s need on a deeper level.  More brick and mortar stores close lately and competitive e-commerce environment is taking place, personalizing the customer experience is the number one priority to send the right message to the right person at the right time. How do retailers accomplish that?  Big data can be a solution.  Gradually, AI is growing as the best way to offer a truly one-to-one experience to customers.  According to Garner “By 2020, customers will manage 85% of their relationship with the enterprise without interacting with a human.” And by 2022, more than half of customers will select services based on AI instead of traditional brand (Accenture).

Figure 2: 4 A 10-year view on the future of AI and how it could impact the consumer experience and organization of the future (Accenture, 2018)

What Exactly Is Big Data?

Big data is a big buzzword when it comes to modern business organization. Unlike database structured data, big data is not about the size of the data but about the value within the data in large datasets that analytics can discover patterns and trends in human behavior.  Big Data will grow from 4.4 zettabytes to 44 zettabytes (or 44 trillion gigabytes) by 2020 (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).  Major advances in hardware and software and nowadays makes it easy for a data scientist to bring data from different sources, study it, and make the decision. To understand how raw data turns into information, let us take a look at the Data-Information-Knowledge-Wisdom (DIKW) model (Rao, 2018) as follows.

  • Data: this raw and unorganized data and is not useful much since it is just a collection of facts, signals, or symbols.
  • Information: data at this point is useful since it is arranged and consistent
  • Knowledge: data is valuable at this form since it is a collection of information with its associated context
  • Wisdom: is the ability to select the best way to reach the desired outcome based on knowledge

Figure 3-4: 3 DIKW model (Rao, 2018)

II.   BIG DATA SOURCES IN RETAIL INDUSTRY

  1. Large-scale enterprise systems: The integration of different applications, protocols and formats are considered Enterprise systems to allow companies to integrate business processes like sales, deliveries, and accounts receivable together. According to (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016), enterprise systems like Enterprise resource planning (ERP), supply chain management (SCM),  and customer relationship management (CRM) systems have been deploying data for two decades now (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).
  2. Online social graphs: With the combination of the major social networks such as Facebook, Twitter, Weibo, and WeChat, we have close to two billion people interacting with friends, accessing media, and socially networking.  By doing so, they are leaving a digital trail that can be tracked, graphed, and analyzed (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).
  3. Mobile devices: This is another source of big data as every action was taken by a user can be tracked and potentially geotagged (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).
  4. Internet-of-things: The emerging sensor enabled Ecosystem to connect objects with each other and with humans. This is the foundation of tomorrow’s smarter physical ecosystems, using sensors to connect physical objects (homes, automobiles, even garbage bins, and street lights) while generating big data in the process (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).
  5. Open data/public data: Data about topics including weather, traffic, maps, environment, and housing are increasingly becoming available (Baesens, Bapna, Marsden, Vanthienen, & Zhao, 2016).
  6. Capturing data from multiple sources: In the retail industry, the majority of data are coming from Retail transactions. Purchase history and transaction details can tell customer’s behavior, geographic information, gender information, time of purchase, and length of time shopping can be recorded throughout these shopping actions. This information are available since they will be cross-checked with other data sources to procedure more complete customer profiles (Microsoft, 2016)



Figure 4-4: Data Sources in
Retail (Microsoft, 2016)

III.   Big Data Applications in RETAIL

  1. Use of Big Data Analytics to Solve Advertisers Problem and Offer Marketing Insights

Big data analytics can help change all business operations such as the ability to match customer expectation, changing the company’s product line and helping with the marketing. By observing the online activity, monitoring the point of sale transactions, and ensuring dynamic changes in customer trends, the company can gain insights on customer behavior by collecting and analyzing the customer’s data.  (Kopanakis, 2018). 

  1. Big Data Analytics for Risk Management: Big data analytics has contributed greatly to the development of risk management solutions by helping the company to foresee a potential risk and justifying it before it occurs. To achieve this, organizations collect the internal data first to gain clear insights that will benefit them. A proper big data analytics system helps ensure that areas of weaknesses or potential risks are identified (Kopanakis, 2018).
  2. Recommendation Engines.  Based on a customer’s purchase history, big data can help analytics to predict what customer likely to purchase next.   By training machine learning models on historical data, the retailer can generate accurate recommendations before the customer leaves the Web page (Woodie, 2016).
  3. Market Basket Analysis and Path to Purchase.  Market basket analysis is a standard technique used by merchandisers to figure out which groups, or baskets, or products customers are more likely to purchase together. analyzing and understanding how a customer came to make a purchase or the path to purchase is an excellent way for retailers to understand customer buying patterns.  The social media source such as the behavior of consumers who love to talk about what they just bought also helps because it generates much more data to work with on path-to-purchase to give customers suggested grouped items to consider (Woodie, 2016).
  4.  Loss Prevention.  Retail fraud is a major headache for retailers in recent years.  There are many types of retail frauds such as employee fraud, refund fraud, discount abuse.  Big data analytics can help retailers fight fraud in many ways. For example, they can use predictive capabilities to create a baseline sales forecast at the SKU level. If a product turns noticeably outside of that range, it could indicate some fishy business. For other types of fraud, the power of big data technology can create more transparency into internal activities to find the patterns. (Woodie, 2016).
  5.  Demand.  By understanding data-based insights on customer habits, retailers can guess which of their products and services are most in demand and which ones they should potentially stop offering. Not only can these insights serve to save money and where to place investment, but it will also help brands to give the consumer exactly what they want (Lebied, 2018)
  6.  Prediction: Trend forecasting algorithms in big data can help brands make key market predictions and forecast consumer trends. Retailers monitor the demand of consumers in real-time and can develop products that will provide them with the best return on investment (Lebied, 2018).
  7.  Pricing:  By gaining access to insights on real-time customer transactions, retailers can use big data to better understanding which prices yield the best results on particular products. Big data technology can also be utilized for ‘markdown optimization’ – an understanding of when prices on specific items should be dropped. (Lebied, 2018).
  8.  Visualization and display: Dashboards can reveal basic sales data in new ways, automatically calculating the factors that correlate most closely with the actions of significant segments of the retailer’s audience. For example, a visual display of data could show sales of outdoor clothing to primary market segments, cross-reference that with the time and date of purchases and the weather, and statistically determine the portion that is most heavily
  9.    Easier and more secure online payments.  Big data integrates all different payment functions into one centralized platform to reduce fraud risks in real time.  Every time a purchase is made, big data can detect payment money laundering transactions that appear as legitimate payments and notify customers and credit card companies before allowing a purchase to go through (Bohrer, 2018).

 

 

IV.   Challenges

The main challenges associated with the above-mentioned applications are people, supplier management, and IT integration.

  1. Big Data project.  What are the most critical questions the company needs to answer? What data sources should they analyze? Are these already available? Is the data clean and reliable? (Brooke, 2018)
  2. Ensuring that the data collected is accurate and identifiable.  In a retail business, there are multiple factors that data could go wrong.  Accurate data is essential to have the right prediction.  For example, a customer may use various payment methods for a single transaction.  The way data is qualified in a big data scenario is extremely important and an important hurdle, since the customer is generally unpredictable who cannot be pigeonholed into fixed segments of behavior (Kumar, 2018).
  3.   Ensuring data security and compliance.  With data breaches (the recent problems with Equifax, for example) being widespread, the aspect of data security is a hurdle that one must pass for a successful big data implementation (Kumar, 2018).
  4.  Ensuring timely adoption of implemented technology.  In the retail business, there are many ways to capture data.  For example, if data is gathering from customers viewing products while their views captured by eye-scanners or an entry into the inventory software for every new warehouse delivery, then there is a device that either needs to be run by a human or it needs some routine maintenance (Kumar, 2018).
  5. Customer Concerns over Privacy.  Lastly, customer concerns over privacy may delay the retailer from implementing big data applications because it can prevent retailers from using customer data for specific operational decisions.

(Aktas & Meng, 2017).

V.   Technology Players and Examples

  1. Wal-Mart.  The world’s biggest retailer with over 20,000 stores in 28 countries, is in the process of building the world’ biggest private cloud, to process 2.5 petabytes of data every hour (Chutke, 2017).
  2. Amazon’s purchase recommendation engine.  The e-commerce Amazon has used an algorithm based on a user’s purchase history, the items they have in their cart already, items they have rated or liked in the past, and what other customers have viewed or purchased recently. It has been reported that over 35% of all Amazon sales are generated by the recommendation engine – a testament to the importance of product recommendations (Greene, 2018).
  3. Kohl’s.  Kohl’s is a brand with big data plans. The company has invested over $2 billion in technology and big data initiatives. Product recommendations aside, the brand is on a mission to use big data firstly for the benefit of its customers, as well as to make the stores more profitable.  Kohl’s also uses its big data to create tailored marketing campaigns, which have been produced with customer data in mind. The brand now plans for data science to assist merchandising allocation, including external data like macro-economic conditions and social data, which will determine which products are stocked. This will ensure that products fly off the shelves faster (Greene, 2018).
  4. Starbucks.  Starbucks has the uncanny ability to open a number of branches on the same block and enjoy a healthy level of profit from each. By using big data analytics to its advantage, Starbucks can predict the growth potential of each new store by looking at metrics such as location, traffic, area demographics, and customer behavior.  With reported revenue of $22.39 billion last year alone, it’s fair to say that Starbucks is a real retail winner (Lebied, 2018)
  5. IKEA.  IKEA uses big data to enhance customer experience and boot sales.  Customers could scan through the catalog with their mobile devices to highlight products they were interested in, and from this, the brand offered personalized digital content and reviews to inform their purchase.  The company also use image-recognition technology, with which customers can scan catalog items and virtually place them in their own homes to see what they would look like.  They can then select the colors and sizes that work best in the space, without having actually to go to store and purchase the product. This allowed the catalog readers to make informed purchases, resulting in higher customer satisfaction and fewer returned items (Greene, 2018).
  6. Costco.  Costco tracks what you buy and when. A California-based fruit packing business warned Costco about the potential of listeria contamination in its stone fruits. Instead of sending out a blanket warning to all who shopped at Costco in recent weeks, the company was able to notify the specific customers that bought those particular fruits, first informing them by phone, followed by a letter (Lebied, 2018)
  7. PepsiCo.  The company uses warehouse inventory and POS inventory to forecast production and shipment needs to ensure they have the right product and right volumes at the right time (Greene, 2018).  
  8. Macy’s: The Traditional Department Store is Ahead of Its Time.  Macy’s uses big data to offer a smarter customer experience. The brand analyzes multiple data points, such as stock (Greene, 2018)
  9. Nordstrom: fusing the online and offline shopping experience.  Nordstrom’s marketing team tracks Pinterest pins to identify which products are trending and then employs this data to promote the right products in its physical stores.  Over 30% of Nordstrom’s budget is spent on technology, having established the ‘Nordstrom Innovation Lab’ based in Seattle for product development and testing. On top of this, Nordstrom hosts interactive touchscreens in changing rooms to allow customers to order products and view stock online (Greene, 2018)

VI.   Conclusion and Future work

Today, the world is business driven, and retail industries are also stepping up to increase their business values and provide good quality care to its customers. Technology has made the world a small place and big data analysis has made it even easier for the retail industry across the globe to face the challenges and find a solution for it. Despite the promise of big data analysis in the retail sector, but it has its own challenges and shortcomings that need to be taken care of.

In the near future, with big data and Internet of Things (IoT), stores will be enabled with sensors that detect a nearby shopper with the app on their phone or tablet.  Also, it is expected that Big data would help retailer zoom drones through the skies to deliver consumers packages that they haven’t even ordered yet (Chutke, 2017).

References

  • Accenture. (2018). Virtual Assistant Overview.
  • Aktas, E., & Meng, Y. (2017). Sector, An Exploration of Big Data Practices in Retail.
  • Baesens, B., Bapna, R., Marsden, J., Vanthienen, J., & Zhao, L. (2016). TRANSFORMATIONAL ISSUES OF BIG DATA AND ANALYTICS IN NETWORKED BUSINESS. Mis Quaterly.
  • Bohrer, C. (2018). 5 Ways Big Data and Analytics Will Impact E-Commerce in 2018. Retrieved from https://dataconomy.com/2018/02/5-ways-big-data-analytics-will-impact-e-commerce-2018/
  • Brooke, S. (2018). 5 Amazing Improvement Big Data Can Bring to Retail.
  • Chutke, V. (2017). Big Data Analytics In Retail.
  • EMarketer Retail. (2018). Retrieved from https://retail.emarketer.com/article/amazon-will-drive-80-of-ecommerce-growth-this-year/5b3a75dbebd4000b24140976
  • Greene, V. (2018). Top 7 examples of big data retail personalization.
  • Kopanakis, J. (2018). 5 Real-World Examples of How Brands are Using Big Data Analytics.
  • Kumar, A. (2018). Six Challenges of Big Data Analytics for Retail. Retrieved from https://datafloq.com/read/6-challenges-big-data-analytics-for-retail/4900
  • Lebied, M. (2018, April 5). The Impact of Big Data on The Retail Sector: Examples And Use-Cases. Business Intelligence,.
  • Microsoft. (2016). What brick-and-mortar retail can gain from big data. Gartner.
  • Rao, V. R. (2018). From data to knowledge.
  • Stebbins, S., & Sauter, M. (2018). Subway, Rite Aid, Toys R Us, Teavana: Retailers closing the most stores in 2018, so far. 24/7 Wall Street.
  • Woodie, A. (2016). 9 Ways Retailers Are Using Big Data and Hadoop. Datanami.

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