Zulily is an online retailer with a mission of bringing its over four and a half million active customers (primarily moms) fresh and interesting products every day. Zulily have combined the strengths of both Apache Hadoop® and cloud computing to deliver a highly scalable unified data platform for structured and unstructured data. This allows Zulily to scale both storage and analytics on demand, and accelerate decision making processes.
The Power of Empirical Data for Retailers
Connected Data Platforms from Hortonworks dramatically reduce the cost of capturing, ingesting, storing and analyzing data. When integrated with existing systems and operations, retailers can analyze enough data to make statistically confident observations on empirical retail data, rather than rolling the dice with customer panels, in-store surveys or focus groups to guess what drives sales.
Build a 360° View of the Customer
Retailers interact with customers across multiple channels, yet customer interaction and purchase data is often isolated in data siloes. Few retailers can accurately correlate eventual customer purchases with marketing campaigns and online browsing behavior.
Connected Data Platforms gives retailers a single view of customer behavior. It lets them store data longer and identify phases of the customer lifecycle. Better customer analytics increase sales, reduce inventory expenses and retain the best customers.
Analyze Brand Sentiment
Enterprises lack a reliable way to track their brand health. It is difficult to analyze how advertising, competitor moves, product launches or news stories affect the brand. Internal brand studies can be slow, expensive and flawed.
Connected Data Platforms enables quick, unbiased snapshots of brand opinions expressed in social media. Retailers can analyze sentiment from Twitter, Facebook, LinkedIn or industry-specific social media streams. With better understanding of customer perceptions, they can align their communications, products and promotions with those perceptions.
Localize and Personalize Promotion
Retailers that can geo-locate their mobile subscribers can deliver localized and personalized promotions. This requires connections with both historical and real-time streaming data.
Apache Hadoop® and Apache NiFi bring the data together to inexpensively localize and personalize promotions delivered to mobile devices. Retailers can develop mobile apps to notify customers about local events and sales that align with their preferences and geographic location (even down to a particular section in a specific store).
In time for the 2013 holiday shopping season, Macy’s launched a test in two flagship stores with Apple’s iBeacons technology. This article describes how, “down the road, Macy’s might also ping shoppers on a department-by-department basis, possibly telling them about sneaker sales when they’re in the shoe section, or even recommending nearby products.”
Online shoppers leave billions of clickstream data trails. Clickstream data can tell retailers the web pages customers visit and what they buy (or what they don’t buy) on their site. But at scale, the huge volume of unstructured weblogs is difficult to ingest, store, refine and analyze for insight. Storing web log data in relational databases is too expensive.
Apache Hadoop can store all web logs, for years, at a low cost. Web retailers use information in that data to understand user paths, do basket analysis, run A/B tests and prioritize site updates. This improves online conversions and increases revenue.
Optimize Store Layouts
In-store layout and product placement affect sales. Retailers often hire extraneous staff to make up for a sub-optimal layout (e.g. “Are you finding what you need?”). Brick-and-mortar stores lack “pre-cash register” data about what in-store shoppers do before they buy. In-store sensors, RFID tags & QR codes can fill that data gap, but they generate a lot of data.
Apache Hadoop can store that huge volume of unstructured sensor and location data. Once analyzed, the resulting intelligence allows retailers to reduce costs and simultaneously improve customer in-store satisfaction. This improves same-store sales and customer loyalty.
Big Data Analytics for Retail with Apache™ Hadoop®
A Hortonworks and Microsoft White Paper The Big Data Opportunity for Retail Big data analytics unlock big opportunities for retailers—actionable intelligence that boosts productivity and margins. A growing array of retail channels and increasingly connected consumers have made a wide variety of new data types and sources available to today’s retailer. Structured transactional and operational…
How Machine Learning Keeps Retailers Ahead of Trends
This blog is jointly submitted by Alexander Gray, Ph.D., is chief technology officer, Skytree, a Hortonworks Technology Partner, and Eric Thorsen, general manager, consumer products and retail, Hortonworks. As consumers increasingly reveal their shopping habits online, retailers can access social media, purchase history, consumer demand and market trends to better understand their customers, maximize spending…
Modern retailers collect data from a multitude of consumer engagement channels, including point of sale systems, the web, mobile applications, social media, and more. They hope to use this data to derive greater customer insights, promote increased brand engagement and loyalty, optimize pricing and promotions, streamline the supply chain, and enhance their business models. Data…
Retail Data Analytics – A Hot Topic at NRF Big Show 2017!
The NRF Big Show is here and it’s no surprise that retail data analytics are a hot topic. It’s an exciting time for retailers as we continue to discover the power of data to improve our ability to personalize the customer experience, drive brand loyalty and increase sales. Two key trends are emerging - retailers…
Provenance, Lineage & Chain of Custody The models of Provenance, Lineage and Chain of Custody are used in fine art to determine when a piece was created, the sequence of locations where it was held, how it was touched along the way, and who has owned it since creation, all with the purpose of authenticating the piece.…
Want Fries With That? Build A Digital Recommendation Engine That Generates Revenue
In the US fast food industry, this is a common question when you order a burger. ‘You want fries with that?’ It’s in the American psyche at this point, and has become common parlance. I was recently heard this exchange: ‘Hey, can I get a copy of your targeted promos report?’ ‘Sure! You want…
Future of Retail: 5 Quick Insights From Top European Retailers
Last week I had a unique opportunity to present to a group of C-level retail industry leaders. Here are five stories I heard that you might find interesting. These are leaders in Merchandising, Marketing, Infrastructure and IT in top European companies. The common link was dinner and retail. I spoke briefly about my experience in retail and adoption of…
Data Wrangling and Visualization on a Future-Proof platform
It sounds like the Wild West, and when it comes to data, sometimes it looks like it too. The concept of “wrangling” brings to mind a lone cowboy on horseback, rounding up a herd of cattle. Back in the day, a really good wrangler could take charge of livestock, guiding them in the right direction…
Streaming analytics to create an accurate single buyer identity in real-time The 4th and final demo of the Data Hacks & Demos session, at Hadoop Summit San Jose, was done by Simon Ball and it showcased how Apache NiFi moved parallel streams of streaming data into Spark and then more analysis could be done by…
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