In our last blog, What is Big Data and How Fast is it Growing?, we discussed the basics of Big Data-
What is Big Data?
How much data is “Big Data” and how fast is it growing?
What are the characteristics of Big Data?
Examples of Big Data
So, what is the obsession about “Big Data” and “Big Data Analytics”?
“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Senior Vice President, Gartner Research.
Big Data Analytics is taking big data, creating predictive model to obtain actionable insights by building data products to communicate relevant and meaningful insights that drives value. – Carlos Somahano, Data Science London. In simpler terms, make sense of the data and draw meaningful insights.
IDC report on the study of the digital universe showed that from 2005 to 2020, data will grow by a factor of 300, from 130 exabytes to 40,000 exabytes, or 40 trillion gigabytes. In other words, the digital universe will about double every two years.
In our previous blog, we outlined the different types of data – structure, unstructured and semi-structured. It is estimated majority of data generated will be unstructured data and the real value is derived from analyzing unstructured data.
Source: Data Science Central
Application of Big Data Analytics in E-Commerce
Big Data is being used across sectors. We will take a look at how Big Data Analytics is changing the E-Commerce landscape.
In E-Commerce, most of their data would fall under two categories – structured and unstructured. Their structured data is their regular data they have been able to capture – name, address, and preferences, sex and age (in some). The more valuable piece of data which is not captured but extremely important is the unstructured data – likes, tweets, clicks, videos etc. The challenge is how to take the unstructured data and make it meaningful insights to increase conversions. According to an article from Harvard Business Review, “it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text. An exabyte is 1,000 times that amount, or one billion gigabytes.”
Big Data and e-Commerce Application – E-Commerce companies have become one of the fastest adopters of Big Data and it’s analytics importance. The dynamics of the industry is changing constantly and most companies are operating in razor thin margins.
So, how are companies using Big Data Analytics to stay on top of their game?
How can companies enhance and personalize each customer’s experience at scale? Personalization can help companies increase conversion significantly. Gilt Groupe, one of the leading e-commerce companies in “flash sales” has used it very effectively. According to Zachray Tumin, Harvard University, “The company sends 3,000 highly targeted emails each day to its 3.5 million members. These emails are based on what Gilt Groupe’s members have shopped for and liked, matched up against luxury fashion and home products that Gilt has thoroughly profiled by who likes them.” Research shows that personalization can deliver five to eight times the ROI on marketing spend and lift sales 10% or more – Harvard Business Review.
Improving Customer Experience
One of the biggest expense for e-commerce companies is user acquisition. Once the user lands in the site, it is extremely critical for them to make a conversion. The competition is tough and most consumers have wide range of choices for the same product. E-Commerce are using analytics to enhance customer experience. Companies are closely analyzing the buying path for each customer and improving customer experience making it a seamless process. Companies are analyzing data from customer service call and improving processes. bloomreach, a startup is aiming to use Big Data to enhance the ecommerce customer experience.
E-commerce companies need to have the ability constantly change pricing on millions of SKUs on a everyday basis based on competition, demand for products etc.
How can E-commerce companies predict consumer behavior? Amazon’s third party marketplace is an example of how it is less to do with individuals retailer’s marketing ability and more of Amazon’s ability to use analytics to predict what the buyer is likely to purchase. Another example is Target’s ability to predict customer pregnancy from shopping behavior. E-Commerce predictive analytics using big data can also allows companies to forecast external events and it’s ability to adapt to it.
Managing Supply Chain using Data
E-Commerce companies are dealing with lot of moving parts – vendors, logistics, warehousing, delivery, returns etc. E-Commerce companies are building efficient systems using analytics to manage the process. Companies are using Internet of things, to collect and communicate data on a wide range of conditions and redefining supply chain intelligence.
Here’s Venturesity’s Hangout with Ravi Padaki, former Yahoo product manager and Big Data expert on Personalization at Scale: