Week 3 Discussion Post Topic 2 Response:
Instructions:
Respond to the post below with any inputs or suggestions.
· All posts (both initial and responses) must be substantial (several paragraphs each) and each of your initial posts must be supported by 3 peer reviewed or authoritative sources, not including the textbook, cited properly in APA format. Responses should have proper support with at least 1 different source as applicable.
With the increasing amount of data generated by the retail industry, Big Data analytics has become a fundamental tool in the fight to reach a competitive level within it. Big data is changing all retail business processes, and the benefits of its use can explain itself.
A number of industries — including health care, the public sector, retail, and manufacturing — can obviously benefit from analyzing their rapidly growing mounds of data. Collecting and analyzing transactional data gives organizations more insight into their customers’ preferences, so the data can then be used as a basis for the creation of products and services. This allows the organizations to remedy emerging problems in a timely and more competitive manner. (Ohlhorst, 2012).
Retail industry can benefit directly from data and analytics that yield superior insights into the behaviors of their customers. The foundation of customer analytics is identifying, quantifying, and predicting the individual customer’s behavioral characteristics (propensities, tendencies, patterns, trends, interests, passions, associations, and affiliations) to identify opportunities to engage the customer to influence his or her behaviors. Some call this “catching the customer in the act.” The timelier the identification of these customer interactions, the better the chances of uncovering new revenue or monetization opportunities. (Schmarzo, 2015).
Retailers can predict future demand using various datasets, such as web browsing patterns, industry advertising buying information, enterprise data, social media sentiment, and news and event information, to predict the next hot items. Using such data as customer transactions, demographics, shopping patterns, research, and local buzz, the demand in local areas and different channels can be predicted. When combined, this information will enable retailers to stock and deliver the right products in the right amounts to the right channels and regions. In addition, retailers can improve shipments by evaluating top-selling products, making markdown decisions based on seasonal sell-through, stopping shipments for bottom selling products, and communicating more effectively with their supply-chain partners to optimize inventory. Such accurate demand forecasting will help retailers optimize inventory, improve just-in-time delivery, and reduce related costs. (Van, 2014)
The Big Data Goals are Time, Money, and Value. The foundation of customer analytics is identifying, quantifying, and predicting the individual customer’s behavioral characteristics (propensities, tendencies, patterns, trends, interests, passions, associations, and affiliations) to identify opportunities to engage the customer to influence his or her behaviors. Some call this “catching the customer in the act.” The timelier the identification of these customer interactions, the better the chances of uncovering new revenue or monetization opportunities. Understanding in detail the propensities, tendencies, patterns, interests, passions, affiliations, and associations of each of your individual customers is key to increasing revenue, reducing costs, mitigating risks, and improving margins and profits. (Schmarzo, 2015).
The best example of how to take advantage of the use of Big Data, optimizing the business process, is that of Amazon, an online retail company.
The core competency of Amazon comes from data analytics. Amazon’s ability to perform the role beyond a mere retail platform came from its aggregation and analysis of customer activity data. Amazon is utilizing the information obtained from analysis, such as customer purchase pattern analysis and association analysis, in a variety of ways including personalized recommendations, search algorithms, and grouping based on customer types. To increase traffic, it has also stepped into new areas such as fresh food and fashion based on analysis of services sensitive to customer inflow and corporate strategizing using data analytics. This can be considered a prime example of complementary role of platform expansion and data analytics. (Park, 2016).
Walmart is another big user of Big Data processing. Walmart use data to understand what’s trending in social media, as well as buying patterns among similar types of customers and what competitors are charging in real-time. For example, they learned via social media that ‘cake pops’ were popular with consumers and the company was able to respond quickly and get them into stores. They also changed their online shopping policy based on Big Data analytics, increasing the minimum online order from $45 to $50, while expanding the range, optimizing the business process, and improving the online shopping experience. (Marr, 2015)
Retailers that implement a Big Data strategy can achieve a 60 percent increase in their margins, as well as boost employee productivity by one percent, meaning there is their every reason to move forward. The retail industry collects vast amounts of data because any product purchased in a retail store or online generates data that can be analyzed for additional insights. The volume of that data will grow exponentially in the coming years, due in part to emerging new data sources such as RFID tags. Whether the purpose is to provide a smarter shopping experience that influences the purchasing decisions of customers to drive additional revenue or to deliver tailor-made relevant real-time offers to customers, Big Data offers opportunities for retailers to stay ahead of their competition. (Van, 2014)
References:
Marr, B. (2015). Big data: Using smart big data, analytics and metrics to make better decisions and improve performance. ProQuest Ebook Central https://ebookcentral.proquest.com
Ohlhorst, F. J. (2012). Big data analytics: Turning big data into big money. ProQuest Ebook Central https://ebookcentral.proquest.com
Park, P. H. (2016). Big data war: How to survive global big data competition. ProQuest Ebook Central https://ebookcentral.proquest.com
Schmarzo, B. (2015). Big data MBA: Driving business strategies with data science. ProQuest Ebook Central https://ebookcentral.proquest.com
Van, R. M. (2014). Think bigger: Developing a successful big data strategy for your business. ProQuest Ebook Central https://ebookcentral.proquest.com
