Small retailers that think big data analysis is only important
for larger retailers should think again. The fact is using big data
analytics is key for small businesses that want to compete with the larger
companies.
And it’s even more critical to help online retailers interact
with their customers in real time, according to the article in
Practical eCommerce.
Practical eCommerce offers six ways online retailers can use big
data and big data
analytics to improve ROI.
1. Personalization. Different customers shop
with the same retailer in different ways. That means online retailers have to
process the data from these various touch points in real time so they can
provide personalized content and promotions to each customer.
“For example, do not
treat loyal customers the same as new ones,” says Gagan Mehra, author of the
article. “The experience needs to be personalized to reward loyal customers. It
should look attractive and ‘sticky’ for new customers.”
2. Dynamic Pricing. Dynamic pricing is a type
of “price discrimination that companies use to change prices on the fly based
on circumstances and estimated user demand,” according to Money Crashers. Online retailers need dynamic
pricing if their products compete on pricing with other sites.
Dynamic pricing means
taking data from a number of sources, such as competitor prices, product sales,
regional preferences, and customer actions and then figuring out the price
needed to close the sale for a particular product. Supporting this functionality
will give businesses a big competitive advantage, according to Mehra.
3. Customer Service. The success of an
e-commerce site depends in large part on superior customer service. To excel at
customer service, online retailers must use big data analytics to give customer
service representatives a 360-degree view of each shopper’s interactions with
their companies.
“For example, if a
customer has complained via the contact form on your online store and also
tweeted about it, it will be good to have this background when he calls
customer service,” Mehra says. “This will result in the customer feeling
valued, creating a quicker resolution.”
4. Manage Fraud. Online retailers can use
big data analytics to process their sales transactions against known patterns
of fraud, to detect fraud in real time – or it could be too late to catch the
criminals.
5. Supply chain visibility. Retailers can use big
data analytics to give customers information on the availability, status and
location of their orders, Mehra notes. “This will require your commerce,
warehousing, and transportation functions to communicate with each other and
with any third-party systems in your supply chain,” he says. “This
functionality is best implemented by making small changes gradually.”
6. Predictive analytics. Online retailers can use predictive analytics to predict the revenue from certain
products in the next quarter. “Knowing this, a merchant can better manage its
inventory costs and avoid key out-of-stock products,” Mehra says.
Reference:
One of the most widely used areas of big data analytics for the retail industry is in retail industry. Market basket analysis is a marketing method used by many retailers to determine the optimal locations to promote products. The term market basket analysis in the retail business refers to research that provides the retailer with information to understand the purchase behaviour of a buyer. In this method, a huge amount of data is collected on movements of clients shopping in supermarkets and retail sector, and then a special modeling technique is developed based on the idea that if one buy a certain group of items, then he/she is more likely to buy (or not to buy) another group of items. This information will enable the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept). An apocryphal early illustrative example for this was when one super market chain discovered in its analysis that customers that bought diapers often bought beer as well, have put the diapers close to beer coolers, and their sales increased dramatically. For an example watch the following video on youtube.
ReplyDeletehttp://www.youtube.com/watch?v=QYDtrFdwbC0
referance:
http://en.wikipedia.org/wiki/Market_basket
I agree with you. In the current years, more and more retailer have started to introduce big data to their business and they do get lots of benefit from them. As we know, in the supply chain of retail industries, huge datas could be collected to be analyzed. Let's see the development of big data in Walmart. When there were not so much organizations understanding the word terms Big Data in retail industry in 2012, Wal-Mart began to use Big Data and moved from an experiential 10-node Hadoop cluster to a 250-node Hadoop cluster. In addition, they tried to move all their existing data from Oracle, Netezza and Greenplum hardware to their own Wal-Mart systems. Wal-Mart wanted to put ten different websites together into one website and keep all data in the new Hadoop cluster. The reason why Wal-Mart could be first place in the retail industry and be so special among all retail organizations is that Wal-Mart always knows the importance of data and takes action before all the other competitors in the industry. With the data, Wal-Mart could make a prediction about customer buying behavior; for example, Wal-Mart invested four billion dollars to build up sales database, RetailLink, to use bar codes and EDI and other new technologies at that time to deal with data, and Wal-Mart also used the new technologies in collecting data. In 2001, Wal-Mart rejected to share the sales data with other firms to prevent the internal data from the other retail companies’ getting, and at that time, in 2004, Wal-Mart had more than 460 terbytes of data, and the internet just had half of the amount. Based on these, New York Times said that Wal-Mart is“more data about the products it sells and its shoppers’ buying habits than anyone else in the business”. Is that interesting?
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