What Is Macy's Omnichannel Strategy

Macy’s is an upscale department store founded in New York back in 1858. Since then, multiple stores reside in eight hundred forty locations within forty-five US states. Recently Macy’s purchased Bloomingdale’s, another upscale department store; both companies operate under the same ownership while using two different brands. The current article indicates that Macy’s goal “is to offer more localized, personalized and smarter retail customer experience across all channels.” (Rijmenam, 2012) To help with this goal, Macy’s developed an Omnichannel strategy, created data-driven websites and put to use big data. The Omnichannel approach helps in providing more shopping & sales data through the use of the desktop, mobile devices, by phone or from

a stationary store. The newer websites will help in detailing the online customers for better retention and the use of big data is used to analyze large amounts of different data that is acquired from all these different channels. This data consists of out of stock rates, prices and promotional products to the location & time of where certain product are to whom ordered or bought it. With all this set in place Macy’s has increased sales by 10% over past years, thanks to big data. (Rijmenam, 2012) By 2015 Macy’s has also become one of twenty most interesting highly recognized ecommerce and retailers using big data.
Another upscale department store is Neiman Marcus to which they would love to be noticed as a premier luxury retailer.

They use positive reinforcement informing the world that they are the best. For example, finest merchandise, most beautiful stores, best trained and high standards. Neiman Marcus owns forty-two stores, two Bergdorf Goodman stores, operates thirty-one Last Call clearance centers and their online websites. Much like Macy’s Neiman Marcus uses the Omnichannel personalization provided by RichRelevance to obtain consistency “across all channels, brands and devices”. (Podeszwa, 2015) Neiman Marcus is also using big data and Analytics through the use of Cloudera’s modern platform for data management, built on the Apache Hadoop open source tools for building big data applications. This platform is used to connect the multiple channels to collect massive amounts of data. This data is used to setup a more personalized experience for the consumer while shopping. For example the consumer can research and compare products with any device at any time; recommendations can be provided to the consumers while helping online sales, reviews can be shared on social media which are streamed in real time providing an insight to consumer’s behavior & preferences with the assistance of Hadoop. Neiman Marcus can also analyze clickstream to gain insights from ads, searches, emails and even web logs to engage the consumer. All this data is generated about individuals allowing the focus

to be put on serving unique needs for the luxury market. Neiman Marcus is also on the top twenty most interesting recognized ecommerce and retailers using big data.

For my third example, Wal-Mart was chosen because again they are part of the top twenty recognized ecommerce and retailers of big data.

In 2014, in revenue terms, Wal-Mart was considered to be the world’s largest retailer acquiring millions from 4300 retail stores in the US. How did they get so big? First Wal-Mart started out by being one of the first to use big data before the industry became known. Their idea from the start was to gather the data that they desired from consolidating ten different websites into one while storing all the data into the Hadoop cluster. Wal-Mart alone will process terabytes of new data as well as petabytes of historical data on a daily

basis.
The way Wal-Mart started gathering data, they saw an increase from 10% to 15% from online sales; all this by using data mining to which they discovered patterns within the POS data, tracking every customer individually on what they buy, where they live and their product likes and dislikes. Wal-Mart took it a few steps further by analyzing every clickable action possible from their online website, trends from Twitter to local events and analyzing how the local weather affected buying patterns.
Even though there were tools and platforms out at this time, Wal-Mart developed their own tools to help determine what data they wanted, how to obtain the data and its presentation. Amongst the tools are Social Genome, Shopycat, Muppet and Get on the Shelf. Social Genome gathered billions of data from blog postings, from tweets and YouTube videos as well as Facebook messages. Shopycat is a gift finder service that runs the Facebook information through the Social Genome technology then matches the keywords providing gift suggestions. Muppet helps in processing real-time data over all Hadoop clusters while at the same time, performing several analyses. Now Get on the Shelf was a contest used by anyone who wanted a chance for Wal-Mart to sale their product. Large online audiences would watch, vote and share these products leaving Wal-Mart with winners. These winners had their merchandise sold on Walmart.com as well as being introduced to the in stores merchandising team for shelf considerations. Wal-Mart did not stop here; they also developed a couple of big data mobile applications. Their survey indicates that mobile phone customers spend about 77% more in-stores.
Each one of these companies has a story and each has done a wonderful job at their time lines that indicate growth and what transpired this growth. These tie into our paper because each business has a roadmap that shows how they used business intelligence ideas and platforms with a description of how they increased their sales, gathered information, promotions and their expansion into other technologies.