Omni-channel retailing strategy at the era of big data: They know what you are going to do!

Shahidul Kader, PhD Researcher, Auburn University, USA

Key words: Omni-channel, big data analytics, retailing

Introducing Big Data:

“You cannot manage what you don’t measure.”- An exclusive wise saying by W. Edwards Deming and Peter Drucker.  What we see in the present world: the explosion of data with short standing ideas and variations. The rapid growth of big data analytics has left many unprepared. The million-dollar question is how the big data look like. Big data is often described as a situation in which data sets grow to the massive size that the conventional technology can no longer manage to obtain the desired result. In this transition period, from corporate leaders to municipal planners and academicians, big data are the subject of attention to all, and to some extent, fear [1]. To broader extent, “big data describes large volumes of high velocity, complex and variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.” [2][4].

1. Source of big data:

There are several categories of big data sourcing. The biggest source of the big data is Social media e.g. Facebook, LinkedIn, Yahoo, Google+, Instagram, Flickr, Pinterest, YouTube, Twitter and specific-interest social or travel sites. The data is collected through individual’s profiles, demographic information, location based information as well as their sentimental inputs (#hashtags, emoticons, multimedia tags). Social influencing media like expert blog comments, user forums, Twitter & Facebook groups, RSS, IM, and other review-centric sites like Apple’s App Store, Amazon, ZDNet, etc. also are sources for big data [5].

1.1 Social media: new definition of social norms

The past decade has seen the rapid growth of mobile technology and social media [8]. In general, social media encompasses micro blogs, online communities, social networking sites (SNS), virtual game worlds, and virtual social worlds [8]. The recent growth of SNS has provided many opportunities to connect and share information with unlimited range that promotes the people to spend more time on social media [3]. Many researchers used social media to find its influence on consumers’ purchase behavior, consumption experiences, and social value [9][10]. In addition, researchers considered social media as the way to increase customer interaction and purchasing satisfaction [11].

1.2 Other sources of big data:

The other sources of big data are listed below:

• Multimedia Data: images, videos, audio, flash, live streams, podcasts. Moreover, some data storage like SQL, NoSQL, Hadoop, doc repository etc. are the source of big data.
• Various archives: Various scanned documents, statements, insurance forms, medical record and customers’ correspondence, paper archives and print streams [3].
• Docs: XLS, PDF, CSV, email, word, ppt, HTML, XML, JSON etc.
• Business apps: Enterprise resource planning (ERP), HR, talent management, Storage, procurement, Expense management, intranets, portals etc.
• Public web: Government, weather, traffic, compliance, census, Stock exchange, census.
• Machine log in data: Event logs, Server data, business process logs, audit logs, mobile location, Wi-fi data, RFID data etc.
• Sensor data: Computer and mobile device log files, aka “The Internet of Things.” This category includes web site tracking information, application logs, and sensor data – such as check-ins and other location tracking – among other machine-generated content.  But consider also the data generated by the processors found within vehicles, video games, cable boxes or, soon, household appliances.[6] [5]

2. Data analytics:

In the business world, big data is considered as the opportunity to grab the future with the aid of past. Nowadays, business intelligence and decision making mostly depend on big data analysis. According to IBM, we produce 2.5 quintillions (2.5 * 108) bytes of data for each day. However, the matter of fact that, most of the data are unstructured. The volume and overall size of the data set are only one portion of the linear equation.  Data mining is the relative term of data analytics that refers the dragging of the valuable visual data from huge unstructured data. Data cleansing also a challenging term that can be illustrated in a various way. The easier way to illustrate data cleansing is to grab meaningful data from the massive amount of unstructured data. Business decision of the large organizations are data driven. In addition, there are many companies took data analytics as their core organizational activity.

2.1 Scope of job in data analytics

Big data analytics is going to be a mainstream profession with increased adoption among every industry and repeated cycle with more people want access to even bigger data. However, often the requirements for big data analysis are not well understood by the developers and business owners, thus creating an undesirable product. For organizations who are not willing to waste precious time, money, and workforce over these issues, there is a need to develop expertise in data analytics, start the process of creating small-scale prototypes quickly, and test them to demonstrate its correctness, matching with business goals [8].

3. Data analytics in retailing

In comparing to other industries, retailing sector are changing more and more rapidly in terms of business strategy. A turbulent economy, new selling channels, advance digital technologies, and increasingly demanding are the driving force that makes retailers find new ways in business and remains competitive [7]. The purchasing decision journey for consumers involves multiple steps, many of which are now being captured, digitized, and transformed into metrics and data. As this data becomes an implied derivative of essential retail and consumer technologies, the focus is shifting from how to acquire the data to how to extract insights. The main theme of retail data analytics can be illustrated as the turning of data into product differentiation to get a competitive advantage for the retailers’ end, similarly to provide a better shopper experience to consumers. However, the main challenge of big data is just that—it is big. Massive amounts of structured and unstructured information are piling up in retailer and supplier data warehouses. Customer metrics derived from video and other sensors, social media, call centers, and mobile devices have the power to provide unprecedented insight into the purchase decision process.

3.1 Common Retail Use Cases for Big Data

1. Building a 360-degree view of the customer – Customer behavior and sentiment can be determined using Hadoop analytics, which can help retailers to refine how they interact with customers in the store, through direct mail, and using other marketing channels. Big data can correlate transaction data, online browsing behavior, in-store shopping trends, product preferences, and more [3].
2. Measuring brand sentiment – Brand studies using focus groups and customer polling techniques can be expensive and often are not that accurate. Using big data analytics, you can perform a customer brand sentiment analysis based on behavioral trends using sources such as Pinterest, Twitter, and Facebook, for example. The results are less biased and can be used to guide product development, advertising, and marketing programs [4].
3. Creating customized promotions – Big data analytics can be used to create custom offers based on browsing history and other data sources. These customized promotions can be used for localized marketing, pushing coupons and offers to smartphone users based on their location, or to drive e-commerce sales using real-time offers delivered via online advertising or social media. 
4. Improving store layout – Big data can be used to analyze customer traffic flow within the store. Sensor data such as RFIDs or QR codes can be used to track in-store traffic and shopping habits. There also are new technologies emerging that enable in-store mapping for applications such as instant coupons that can tell retailers a lot about store flow [4].
5. Optimizing e-commerce – Clickstream data and monitoring online behavior can help optimize e-commerce sites. Without the assistance of big data, the sheer volume of clickstream data would be difficult to analyze. Moreover, retailers can incorporate other metrics such as social media shares, purchase history, and more to improve performance for e-commerce websites [3].
6. Order management – Big data can be invaluable for inventory management and tracking. For example, big data can inventory needs in order to facilitate real-time delivery. It can even be used to automate order processing to eliminate “out-of-stock” goods.’

Conclusion:

Omnichannel is a multichannel approach of selling that seeks to provide the customers with a seamless shopping experience whether a customer is shopping online from a desktop or mobile device, by telephone or in a bricks and mortar store. Retailing industry is one of the important places for big data analytics [5]. Consumers purchase intention, brand loyalty, and the impulsive buying decision is several factors for the retailers. Therefore, retailer seeks the answer through the big data. By pulling together data streams from sales, operations, inventory, revenue, and other sources, big data analytics are helping retailers fine-tune their operations to reduce costs, increase customer satisfaction, and generate more profits.

References:

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5. TechAmerica Foundation’s Federal Big Data Commission, 2012
6. http://www.zdnet.com/article/top-10-categories-for-big-data-sources-and-mining-technologies/
7. https://datafloq.com/read/understanding-sources-big-data-infographic/338
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