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Editor's Note: The retail industry is on the verge of a major transformation through the use of advanced analytics and big data technologies like the MapR Data Platform. The 'MapR Guide to Big Data in Retail' provides a comprehensive view behind the scenes of how big data technology is impacting the retail industry. Download your complimentary copy here
With the advent of eCommerce, online shopping, and fierce competition for customer loyalty, retailers are increasingly using new sources of data and big data analytics to stay relevant or stay afloat. Analysis of information on consumer behaviors as well as data influencing consumer behaviors can answer questions about where, when, and what customers are buying, which can be used to predict trends and improve shopping experience.
Big data analytics are being used at every stage of the retail process to understand customer behavior, predict demand, and optimize pricing.
Most retail big data use cases fall into these main categories: System-Wide Cost Reduction, Data-Driven Adaptive Supply Chains, Improving Online and In-Store Customer Experience, and Real-time Analytics and Targeting. Let’s look at a few customer examples.
Fishbowl’s restaurant marketing SaaS platform helps over 70,000 restaurants leverage data from over 160 million restaurant guests to drive predictable sales growth. Due to rapid growth, Fishbowl needed to scale and also provide support for aggregating a variety of data sources to provide a comprehensive view of restaurant guests. The solution architecture is shown below:
This solution enabled Fishbowl’s interactive query speed to increase by 5-10x beyond that of competitive products, with 1/10 the spending on licenses and 1/3 the spending on storage. Fishbowl was also able to deliver the solution one quarter earlier than expected by leveraging the MapR Platform’s multi-tenancy and security infrastructure.
A Fortune 100 retailer’s Enterprise Data Analytics facilities, built on top of the MapR Data Platform, enables the processing of tens of petabytes of data with 24/7 availability and reliability.
Data is collected from point of sale transactions, inventory status and pricing, competitive intelligence, social media, weather, and customers (scrubbed of personal identification) and then pulled together on the MapR Platform, allowing for a centralized analysis of correlations and patterns that are relevant to improving business.
In-store and online purchases, Twitter trends, local sports events, and weather buying patterns are analyzed by big data algorithms to build innovative applications that personalize customer experience while increasing the efficiency of logistics. Point of sale transactions are analyzed to provide product recommendations or discounts, based on which products were bought together or before another product. Predictive analytics is used to know what products sell more on particular days in certain kinds of stores, to reduce overstock and to remain properly stocked on the most in-demand products, helping to optimize the supply chain.
Key MapR features for this customer were:
DataSong uses the MapR Data Platform to provide retailers with a marketing analytics service.
A wide variety of data (including observations of user behavior on websites via clickstream logs, advertising impression logs, direct mail logs, and transaction data) is loaded onto the MapR Platform, processed, and analyzed to provide clients with reports, showing the effectiveness and incremental impact of their marketing spend. Having more sources and a longer time span of data to analyze provides more accurate customer reports. MapR’s Platform provides the scalability and cost effectiveness needed to grow Datasong’s business.
A major fashion retailer wanted to increase in-season agility and inventory discipline in order to react to demand changes and reduce markdowns. The data-driven supply chain solution architecture is shown below:
The fashion retailer’s data-driven supply chain provides the required in-season agility, leading to increases in sales and fewer markdowns.
Because of the rapid evolution of the way we are buying and selling, the growth opportunities for retail and big data are huge. Success in retail is driven by competition: the successful retailers of the future will profit from their data-driven knowledge of their customers and the current market as well as from an ability to predict future trends, driving competitive advantage. The MapR use cases, previously discussed, show how retailers can profit from the huge amount of information in their data to optimize merchandise selections and pricing, improving their customer's experience. For more information and use cases:
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