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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

DATA CONVERGENCE IN RETAIL

INTRODUCTIONBig-data driven changes are sweeping through the retail industry, rapidly evolving the ways in which we buy and sell. These changes are epitomized by Amazon.com’s vision of delivering products it believes consumers want but haven’t yet ordered based upon demographics, inferred preferences, and past buying behavior. While Amazon’s vision has yet to emerge from limited testing, the promise of these kinds of predictive analytic driven services is tantalizing.

The reality of big data in the $25 trillion global retail industry is more prosaic, but no less important. Big data promises payoffs at nearly every stage in the retail process, ranging from supply chain optimization to inventory management to workforce scheduling to customer personalization. A recent survey by McKinsey shows Artificial Intelligence (AI) as the next wave of digitization1. The impact of using all data with advanced analytics like machine learning algorithms to forecast anticipated product sales is already demonstrating direct impact on retailers’ business metrics such as improving earnings before interest and taxes (EBIT) by 1 to 2 percent. And for online retailers, combining real-time personalization with dynamic pricing can lead to online sales growth by 30%— that’s transformative.

The potential exists to completely transform the retail experience with the use of big data and real-time technology. Retail buying patterns are changing, and customer loyalty is becoming more elusive. Customers today are armed with better information than ever with which to make comparative buying decisions, but so are retailers.

By harnessing the tools of big data and real-time analytics, retailers can deliver customized experiences that anticipate and reward customer behavior in a way that creates not only long-term bonds but powerful word-of-mouth advocacy. Loyalty-leading brands2 like Amazon, Netflix, Dunkin’ Donuts, Samsung, Starbucks, and Zappos are setting a new baseline for customer loyalty built around unique experiences. Any retailer can now do the same with analytics.

This guide examines the trends shaping the retail industry, the big data uses having the greatest short-term impact, and the emerging use cases reshaping customer experiences and the competitive landscape.

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1 How artificial intelligence can deliver real value to companies, Mckinsey Global Institute, June 2017 2 Brand Keys Customer Loyalty Leaders List 2016

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

INDUSTRY TRENDSThe retail market in mature economies is being redefined by changing customer behaviors underwritten by knowledge and choice. Other factors impacting the retail market include the opposing forces of consolidation and specialization as well as overall declines in brand loyalty.

Customers in ControlThe internet has triggered a wholesale shift in the dynamics of the retailer-customer relationship. Consider the automotive business. At the turn of the millennium, the car buying process was heavily skewed in favor of the dealer. Most buyers knew little about what they were purchasing or how much markup the dealer made. The dealer essentially had all the information, which put the buyer in a terrible negotiating position. High-pressure sales tactics, complex financing agreements, and costly service didn’t help matters any. It’s no wonder that only one in five Americans considers car dealers to be trustworthy.3

Today, more than 90% of U.S. car buyers research their purchases online before entering a showroom.4 Buyers can learn not only exactly what the dealer paid for the vehicle but also what other buyers have paid in recent weeks. They can even play dealers against each other using online price comparisons.

This change in the balance of power didn’t destroy auto dealerships, but it did force substantive changes. Dealers overhauled their showrooms to be comfortable and inviting. Salespeople were trained to be consultative rather than aggressive. Automakers moved away from a la carte accessory pricing toward higher-margin bundled packages. Dealers overhauled their service departments to grow the repair and maintenance portion of their business. Post-sale marketing campaigns were restructured to encourage repeat buys.

The same phenomenon is occurring to greater or lesser degrees in nearly every corner of the retail market. Buyers can compare prices using their smartphones while standing in the aisle. In many markets, recommendation engines have become the most important success factor. Customers expect attentive service and quickly take their business elsewhere if dissatisfied.

Retailers are fighting back with personalized recommendations, custom offers, and even individualized pricing. Loyalty programs are ubiquitous and becoming more generous. All of this is enabled by sophisticated applications of big data.

E-Commerce PressuresDuring the first decade of the e-commerce industry, buyer shopping patterns changed relatively little. E-commerce represented less than 3% of total retail sales in 2006, but that number has grown to more than 8.5% in the fourth quarter of 2016.5 While still a relatively small share, e-commerce continues to grow at a faster rate than the overall retail market and is expected approach 15% of total retail sales by 2020.6

3 New Research: 1 in 6 Car Buyers Skips Test-Drive; Nearly Half Visit Just One (Or No) Dealership Prior to Purchase, DMEautomotove, April 15, 20144 Automotive Dealership Perception—A Lot has Changed with Lots, FARM, May 19th, 20155 Quarterly Retail E-Commerce Sales Third Quarter 2016, U.S. Census Bureau, February 17, 2017 6 Worldwide Retail Ecommerce Sales Will Reach $1.915 Trillion This Year, Retail & Ecommerce, August 22, 2016

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

Widespread adoption of consumer broadband services, combined with improved e-commerce engines, free shipping, and bottomless inventory in online markets presents both an opportunity and challenge to brick-and-mortar retailers. On the one hand, the convenience, price competitiveness, and selection of online stores poses a problem for traditional retailers who must absorb the high fixed costs of facilities, labor, and in-stock inventory. On the other, many brick-and-mortar retailers have successfully diversified into e-commerce and reaped the benefits of broader reach and geographic expansion. Some have even turned the phenomenon of “showrooming”—customers using the physical store to research products they then buy online—to their advantage by offering price matches that rescue the sale on the spot.

Nevertheless, e-commerce is still more of a problem than an opportunity to brick-and-mortar stores, taking a 25% bite out of their bottom lines, according to one study.7 The industry hasn’t yet reached the point at which e-commerce revenue offsets traditional revenue enough that retailers can shutter stores and go entirely virtual. If and until that happens, e-commerce poses a growing threat.

Consolidation and FragmentationCompetition in big box retailing is down to four competitors: Walmart, Costco, Amazon, and Kroger. They collectively represent more than $825 billion in revenues, and they are using their size to squeeze profits out of their supply chains. This has significant downstream impact on consumer packaged goods companies, which must play by the rules of these giant distributors or risk calamity. Many big retailers are also introducing their own store brands, whose prices and supply chains they can control more directly. This further threatens their suppliers as well as smaller competitors that can’t afford to sell their own branded products.

While consolidation continues among the giant chains, the retail market is also paradoxically becoming more diverse. New brands are emerging with completely different value propositions such as organic content, ecological friendliness, and millennial appeal. “Fragmentation...gives smaller retailers the upper hand in the market as they focus on niche products and experiences compared to the big retailers who cast wider nets,” wrote Deloitte in its 2016 Retail Volatility Index.8 The consulting firm said volatility in the retail industry has increased 250% since 2010. E-commerce has lowered barriers to entry, enabling niche retailers like Warby Parker and Zappos to build large businesses on the backs of devoted customers and social media marketing. This creates new avenues of customer choice, but also squeezes competitors in the middle.

Declining Brand LoyaltyConsidering the factors outlined above, it’s not surprising that consumer brand loyalty is at an all-time low.9 Nielsen research found that 78% of U.S. consumers say they aren’t loyal to a particular brand.10 The negligible cost of switching e-commerce providers, enabled by brokerage services such as Google Shopping, has made price comparisons a breeze. In commodity markets in which all competitors are considered adequate, customers choose on the basis of factors such as cost, convenience, and selection. While many brands still enjoy great customer affinity, retailers must cope with the fact that buyers are less inclined to automatically opt for a high-margin branded product if “good enough” competitors are available.

7 Study: E-commerce having negative impact on retailers’ operating earnings, CSA, May 3, 20168 Beyond retail trends and conventional wisdom, Deloitte, 9 Brand Loyalty Among Consumers Declining, Survey Finds, ABC News Radio, May 12, 201410 Connecting through the Clutter: Stay Ahead of Consumers to Win in Today’s Fragmented Markets, Nielsen, February 14, 2014

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

A dramatic example of this phenomenon is the retail banking industry. Banks used to be institutions in their communities, and the retail branch was a social center. People knew their bankers by name, and their loyalty was as much to those bankers as to the institution.

Automatic teller machines, online banking, and mobile apps have changed all that. Many people now seldom visit a branch. All online banking services look pretty much the same. The inconvenience of switching banks can still keep customers in the fold, but the lack of personal interaction severely limits banks’ upsell and cross-sell possibilities.

Some banks are responding by transforming their branches into miniature coffeehouses. Customers are invited to drop by to take advantage of the free coffee and Wi-Fi with the hope that they’ll also be open to discussing college planning or a new car loan. The jury is still out on how effective this strategy is, but it is an example of how technology-driven disintermediation can drive transformative change.

Brand loyalty programs, such as frequent-buyer rewards and rebates, used to be the domain only of large retailers, but today they’re accessible to any seller via a smartphone app. This raises the competitive stakes and forces retailers to differentiate their affinity programs in new ways. These programs continue to be extraordinarily powerful in driving repeat business, but increasingly they must be fine-tuned to the needs of customer segments or even individuals. This trend has raised the stakes on loyalty programs.

KEY INDUSTRY STAKEHOLDERSThe four principal stakeholders in the retail industry—customers, retailers, suppliers, and payment brokers—are united by an interest in getting the customer the right product at the right price at the right time. Big data plays a critical role for all stakeholders. Beyond that, their objectives differ and are sometimes at odds.

Customers are motivated by the buying experience, but experience means different things to different people. To some customers, it’s low price or environment, while others value customer service, selection, or personalization. Big data presents retailers the opportunity to understand how different customer segments define customer experience so they can deliver against those expectations. For example, an electronics retailer may outfit a lower income neighborhood store with bargain merchandise while decking out its locations in prosperous suburbs with the latest pricey new gadgets.

Retailers care most about sales, margins, and growth, though not necessarily in that order. There are many ways to apply big data to these areas, ranging from inventory, supply chain, and workforce optimization to targeted marketing to fraud detection.

Suppliers want to increase sales and margins on the products they sell through retail channels. They can use big data to better understand customer preferences and to tune offers appropriately. They can also use their own big data insights to better guide retailers toward mutually beneficial promotions and in-store displays. Finally, they share an interest in supply chain optimization with their channel partners and can use big data to better predict demand and adjust supplies accordingly.

Financial institutions are interested in putting a dent in the $16 billion annual credit card fraud problem, of which retail businesses are a prime channel. Improved fraud detection using big data and streaming analytics shows great promise.

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

KEY DATA, DATA SOURCES, AND INDUSTRY STANDARDSData Source Description Relevant to*SALES DATA The most valuable source of data retailers have and the one they can

control most easily. By correlating sales data with such factors as the individual buyer, date/time, demographic statistics, items in the shopping basket, discounts or coupons applied, and external factors such as weather and time of year, retailers can learn much about customer behavior that can help them to optimize inventory, target promotions, and achieve the highest margins.

As the Internet of Things takes hold, retailers will also be able to integrate data from in-store cameras, sensors, coupon dispensers, and point-of-sale terminals to better understand traffic patterns and improve displays and promotions. Sensors can also monitor inventory to warn retailers when stocks are dwindling and reduce the risk of outages.

R,S,F

SYNDICATED DATA Nielsen, Information Resources Inc., and SPINS are examples of syndicated services that aggregate data from many retailers and sell summary information or specialized cuts to their customers for use in marketing and supply chain optimization. Trade associations such as the World Alliance for Retail Excellence & Standards and the National Retail Federation also publish data and reports for their members.

R,S

INFORMATION BOUTIQUES

Dunnhumby, D&B Hoovers, and Zeta Global are three examples of the many specialized consulting and market intelligence firms that provide specialized retail data and analytics.

R,S

PUBLIC DATA SOURCES The U.S. government publishes an extensive library of public domain research, such as the Monthly Retail Trade and Food Services report from the U.S. Census Bureau and the Retail Sales Workers report from the Bureau of Labor Statistics. Many state and local agencies also publish their own versions of retail data or demographic statistics, which can be useful in customer profiling. Special purpose information such as weather data, traffic statistics, state and national holiday, and real-estate transactions may also be useful in certain applications. GPS data can be used for segmenting customers according to geography.

R,S

CUSTOMER TRAFFIC Analyzing traffic patterns in stores or on websites can yield valuable insight about how customers shop, what catches their attention, and what they miss entirely. Brick-and-mortar retailers can use webcams, sensors, and RFID labels to correlate sales to shelf or aisle placement. Website analytics such as visitor paths, referring sites, cart abandonment, and visual hotspots can help retailers improve website navigation to guide customers successfully to a sale.

R

MAINSTREAM AND SOCIAL MEDIA

News outlets and consumer publications continually monitor trends and can tip off retailers to a new style or craze. Social media can do the same and also has benefits as a source of feedback on product and customer experiences. Most of this data is in unstructured format, making it ideal for big data tools like a new converged data platform and NoSQL databases.

R,S

RETAILER DATA Walmart’s Retail Link is a prominent example of a data service provided by a retailer for use by its supply chain. Several other large retailers also furnish sales data either on a subscription or need-to-know basis.

S

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

CUSTOMER SATISFACTION DATA

Whether obtained directly through surveys or inferred through analysis of social media and ratings data, this information is useful in designing customer experience, making decisions about which products to carry, and assessing the quality of the customer experience.

R

SUPPLIER DATA Retailers can use information from their supply chain to plan inventory and promotions, reduce shortfalls and overstocks, and prepare for new products.

R

*Principal Beneficiaries. R=Retailer, S=Supplier, F=Financial Service

USE CASESPotential applications of big data span nearly every area of the retail environment, from operations to marketing to supply chain management. The following use cases address some of the areas with the greatest impact.

Upselling/Cross-Selling Retailers know that the best way to increase sales-per-customer is to reach buyers when they’re in the store. The second best way is to reach out with customized offers that entice customers back into the store for reasons that are compelling and personal.

Big data can help in both respects. By better understanding shopper behaviors, retailers can optimize promotions and shelf placement to encourage add-on sales. They can dispense coupons to individual shoppers upon entry or checkout that encourage purchases of known preferred products. Promotions can even be sent to shoppers’ smartphones in real time.

In addition to promoting add-on sales of preferred products, retailers can also identify and address “gaps of opportunity,” which are products that customers should buy but don’t, perhaps because they’re accustomed to purchasing them elsewhere. Knowledge of personal information such as birthdays and anniversaries can be used to trigger promotions that are customized to each shopper’s individual tastes. These one-to-one interactions can also improve customer loyalty by providing a more relevant, personalized online experience.

Recommendation engines are a particularly powerful manifestation of this idea. By understanding which products are frequently purchased together, retailers can suggest up-sale opportunities based upon the buyer’s shopping cart.

Customer Segmentation Understanding the preferences and behaviors of different customer segments underlies nearly everything a retailer does to reach, cultivate, and convert prospects into sales. Everything from store location to floor plans, displays, promotions, and marketing messages should be tuned as finely as possible to the interests of different segments, and the smaller those segments are, the better. Big data enables segmentation to go beyond simple demographics to encompass psychographic factors like lifestyle preferences, attention to trends, price sensitivity, and peer group status. Retailers can now create much finer segments than were possible in the pre-big data age, and they can constantly update those definitions based upon observed behavior. They can also combine data from online and offline sources to extend segments across multiple outlets.

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

11 Big Data, Analytics and The Future of Marketing and Sales, Forbes, July 22, 2013

For example, segments can reflect activity-based data such as visitor paths on a company website along with e-commerce sales, call center activity, mobile app use, and response to various incentives. Social network profiles indicate preferences for groups, topics, and institutions. Retailers can also fold in social influence and sentiment data, online comments and reviews, and observations of customers with similar characteristics. From this, they can develop aspiration- and lifestyle-based segmentation profiles that bring those segments to life, such as “trendy thrill-seekers” and “cautious couch potatoes.”

Personalization Research has shown that shoppers value personalization, but only to a degree. Personalized mailings and offers are appreciated; being greeted by name by a stranger at the checkout counter probably isn’t. Big data gives retailers extraordinary personalization capability. For example, by analyzing customer purchases over time, a store can create a customized coupon booklet based upon individual purchase histories. Many chains already do this to some extent. New technology is now emerging that enables retailers to personalize in-store displays via screens that display content targeted at individual customers and push customized offerings to their smart phones upon entering the store. This kind of extreme personalization may not be worth the cost in every case, but costs always come down. One proven tactic that is both affordable and commonplace is to create custom emails based upon a customer’s buying history and stated preferences.

Recommendation Engines Already widely used by e-commerce sites, recommendation engines mine the history of groups of shoppers or individuals to identify products that are commonly bought together. While some pairings are obvious—a customer buying skis can be reasonably assumed to be in the market for boots and bindings as well—these engines really shine in their ability to identify correlations that aren’t evident on the surface. They’re also good at correlating purchase behavior with demographic characteristics. For example, pregnant women tend to purchase the same types of products depending upon the stage of their pregnancy. E-commerce is a natural application of recommendation engines, but recommendation engines can even be used in store, for example, by dispensing offers at the checkout counter based upon past purchases of a customer in a loyalty program.

Targeted Marketing This application of big data spans a wide variety of scenarios, ranging from customized email greetings to completely individualized content packages and offers. Website and social media marketing technology can also follow customers online and present targeted ads based upon previous buying behavior. Some email marketing services can even generate promotional campaigns in which no two emails are the same. The only limitation is the retailer’s willingness and ability to collect and apply data at the necessary level of granularity. Targeted marketing has demonstrable payback. A McKinsey analysis of more than 250 engagements estimated that companies that base their marketing and sales decisions on data improve marketing ROI by 15%-20%.11

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

Sentiment Analysis Customers have many more ways to express opinions today than they did a few years ago. Through tweets, likes, mentions, online reviews, and even web petitions, they share, recommend, and warn their social networks about experiences they have with brands and outlets. Retailers can use social listening to gather intelligence that helps them anticipate hot new trends or get early warning of developing problems. Text analytics with big data tools like a new converged data platform enable retailers to detect patterns in unstructured text like blogs and tweets and to identify fans, critics, ambassadors, and influencers. This information can be related back to individual products, stores, experiences, and personnel to identify outstanding performance or areas for improvement.

Loyalty Programs Merely having a loyalty program used to be distinctive, but today such incentives are table stakes. The challenge now is to entice customers back to the store when the competitor down the street offers a program that’s basically the same. Loyalty programs yield valuable data for personalization and targeted marketing because customers essentially trade off some anonymity for other benefits. Retailers can use this demographic information to identify information that can be extrapolated to a larger unknown audience.

Retailers can use big data to create innovative extensions to their affinity programs that go beyond discounts and giveaways. For example, customers can accrue rewards through referrals or by recommending the retailer in social media. Retailers can also partner with each other and with their suppliers to create unique offers based upon combination buys.

360° Customer View The overarching goal of all of the cases listed above is to achieve the vaunted 360° customer view, or a complete profile that includes preferences, motivations, behaviors, and outcomes. Big data platforms are the ideal nexus point for information that may exist in silos around the organization, in public databases, and in supplier and partner customer relationship management systems.

Aggregating data from multiple sources was Lazada Group’s goal when it selected the MapR Converged Data Platform to power its e-commerce operations in Southeast Asia. Lazada, the number one online shopping and selling destination in the region, has more than 10 million customers and a catalog of nearly 16 million products. Lazada is using MapR to leverage Apache Spark and Hadoop to centralize marketing data from systems around its coverage area. This helps the company see its entire marketing landscape holistically and get immediate insights on segments, campaigns, and products in order to optimize marketing spend and provide more personalized services.

Price Optimization Big data brings science to a discipline that was heretofore mostly art. Pricing no longer needs to be a seat-of-the-pants decision. Sellers can now incorporate competitors’ prices, supply-and-demand information, and time factors into their own decisions. For example, not long ago retailers waited until late in the buying season—when demand had nearly evaporated—to discount seasonal merchandise. However, analytics showed that gradual reductions in price beginning early in the season generated more overall revenue in the long run. Ride-sharing companies charge passengers more or less depending on factors like time of day, demand, weather, and traffic congestion. Retailers can mine competitors’ websites to compare prices and instantly adjust. New dynamic pricing technology even enables sellers to change prices on tagged merchandise in the store.

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12 Overcoming 5 Major Supply Chain Challenges with Big Data Analytics, Computerworld, February 19, 201613 Don’t Play it Safe When it Comes to Supply Chain Risk Management, Accenture Global Operations Megatrends Study, 201414 37 Cart Abandonment Rate Statistics, Baymard Institute, January 9, 2017

Demand ForecastingIt’s easy to predict that demand for overcoats will surge in the fall. The trickier task is to predict when a viral video, celebrity quote, or new food craze will have customers suddenly beating down the doors. Big data can help by comparing a wide variety of factors to look for correlations that aren’t obvious on the surface. The technology can also be used to monitor unstructured data sources like news outlets and social media feeds to spot trending topics. Trends can then be tested on retailer e-commerce sites to determine their validity.

Supply Chain Optimization Every retailer’s nightmare is to run out of a popular product and have no idea when replenishment will occur. That’s closely followed by a different nightmare in which the store orders too much and has to sell off inventory at a loss just to clear its stock.

Both are examples of problems in the supply chain.

Supply chains are notoriously difficult to model. They’re subject to all kinds of disruptive forces, ranging from weather to labor strikes to misfiled forms. One recent academic research paper identified 52 different data sources that affect supply chains, ranging from GPS telematics to RFID tag information. Most of these sources are external to the organization. Big data analytics is uniquely suited to ingest and process this large volume of data in order to spot opportunities to enhance efficiency. It’s not surprising that 64% of supply chain executives consider big data analytics to be important.12

Big data is particularly appropriate to mitigating supply chain risk. Retailers can combine demand forecasting with supplier production plans to predict shortages or surpluses and adjust accordingly by establishing alternative supply sources or adjusting prices. Accenture reported that 61% of the companies it surveyed enjoyed ROI of at least 25% through risk mitigation.13

Retail Space Optimization With retail floor space costs running as high as $200 per square foot in midtown Manhattan, retailers place a premium on maximizing every inch of display space. New technology that combines visualization, shelf space optimization, and customer traffic pattern analysis can help map customer journeys through a store to identify the best placement for high-margin products. Big data can also be used to optimize shelf placement by, for example, co-locating products that are frequently bought together. Retailers can also realize bonus revenues by charging suppliers for display space in high-traffic areas.

An online corollary to retail space optimization is clickstream analysis. Online sellers can capture, analyze, and derive actionable insights from data across multiple channels including search, ads, email, and web logs. By analyzing how customers arrive at a decision, how they navigate through choices on an e-commerce site, and when they commit or click away, retailers can better design their websites for conversion and reduce the 70%-plus shopping cart abandonment rate problem.14

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Fraud Detection Retail fraud ranges from bogus returns/refunds to customer service abuse and purchases of untraceable assets such as gift cards. Retailers and financial services companies can deploy streaming analytic tools like Apache Kafka and Spark to monitor credit card transactions in real time and check against databases of stolen cards and compromised accounts. Emerging technologies like machine learning and behavioral analytics can also now be applied to detect potentially fraudulent transactions based upon behavior, such as large gift-card purchases or numerous transactions that fall just below a verification threshold. Real-time analytics now enable businesses to lock down these transactions before they are committed and prevent potentially large financial losses.

THE MAPR CONVERGED DATA PLATFORM IN RETAILBy pursuing our data-centric vision for a new generation of applications, MapR has created an applications platform that converges the management of data of any size, speed, and format. It was for this work that we were recently awarded a patent (US9,207,930). The MapR Converged Data Platform integrates Hadoop, Spark, and Apache Drill with real-time database capabilities, global event streaming, and scalable enterprise storage to power a new generation of big data applications. The MapR Platform delivers enterprise grade security, reliability, and real-time performance while dramatically lowering both hardware and operational costs of your most important applications and data.

Open Source Innovation on a Trusted PlatformThe MapR Converged Data Platform is designed to deliver utility-grade data services and commercially supported open source innovations to development teams, IT operations, business analysts, and data scientists. Open source technology provides a fantastic creative force when looking to tackle the sophisticated new challenges that big data—and especially new data—can uncover.

Without a converged data platform, critical information can get stuck in data silos and an inefficient use of hardware resources can result in a costly “cluster sprawl” of under-utilized servers and storage. With the MapR Platform, businesses can enjoy real-time insights based on secure, protected, high fidelity data.

OPEN SOURCE ENGINES AND TOOLS

ENTERPRISE-GRADE PLATFORM SERVICES

MAPR-FS

High Availability

HDFS API POSIX API HBase API JSON API Kafka API

Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace

MAPR-DB MAPR STREAMSWEB-SCALE STORAGE DATABASE EVENT STREAMING

Search and Others

Cloud and Managed Services

Custom Apps

COMMERCIAL ENGINES AND APPLICATIONS

UNIFIED MANAGEM

ENT AND MONITORING

PROC

ESSI

NGDA

TA

MAPR CONVERGED DATA PLATFORM

“MapR continues to deliver innovative enterprise solutions that just work.”Manny FuentesCTOAltitude Digital

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Seamless Integration with Existing Enterprise SystemsOne of the most profound design decisions made by MapR was to create an enterprise-grade file and storage system to house the data of the Hadoop ecosystem. The MapR File System, based on the trusted POSIX/NFS standard, makes it vastly easier to get data in and out of the MapR Platform using familiar enterprise tools. MapR also provides developer programmatic access to data with standard interfaces like SQL, HDFS, HBase, JSON, Kafka, and more.

Continuous Trusted OperationsWith our consistent focus on the integrity of data, MapR has created a hardened, clustered platform that can withstand multiple hardware failures, data center outages, and malicious attacks and intrusions from cybercriminals. Many proven methods of data protection—such as failover, redundancy, and access controls—are built into the MapR Platform.

Big Data with Enterprise StabilityGame-changing big data applications and analytics will continue to rely on open-source software. As a company founded in and contributing to the open-source world of Hadoop and Spark, MapR continues to define enterprise requirements and best practices for successfully using the latest open source innovations. We deliver monthly updates to open source software packages to ensure you have the latest innovations.

Retail Application Architecture

DATA SOURCES INGEST INSIGHTS STAKEHOLDERS

POS Transactions

Online Purchases

CRM Loyalty

Web Clickstream

Holiday Events

Weather

Email

Social Media

Streaming Data Ingest

POSIXNFSFile Ingest

Data Exploration

Dashboards

Analytics

Applications

Search

Customers

Retailers

Suppliers

Financial Institutions

UpsellingCross-selling

Ad PerformanceOptimization

Optimized Pricing

CustomerSegmentation

SentimentAnalysis

Supply ChainOptimization

FraudDetection

DemandForecasting

PersonalizedOffers

TargetedMarketing

Customer 360o Analytics

RecommendationEngine

USE CASES

OPEN SOURCE ENGINES AND TOOLS

ENTERPRISE-GRADE PLATFORM SERVICES

MAPR-FS

High Availability

HDFS API POSIX API HBase API JSON API Kafka API

Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace

MAPR-DB MAPR STREAMSWEB-SCALE STORAGE DATABASE EVENT STREAMING

Search and Others

Cloud and Managed Services

Custom Apps

COMMERCIAL ENGINES AND APPLICATIONS

UNIFIED MANAGEM

ENT AND MONITORING

PROC

ESSI

NGDA

TA

MAPR CONVERGED DATA PLATFORM

PROCESSING

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Fashion Retailer’s Data Driven Supply ChainA 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:

• Weather, world events, and logistical data is collected in real time via MapR Streams, allowing for real time analysis of potential logistical impacts and rerouting of inventory.

• Apache Spark is used for batch and streaming analytics processing, and machine learning for predicting supply chain disruptions.

• Data is stored in MapR-DB providing scalable, fast reads and writes. Apache Drill is used for interactive exploration and preprocessing of the data with a schema-free SQL query engine.

• ODBC with Drill provides support for existing BI tools.

• MapR Technologies’ enterprise capabilities provide for global data center replication.

The fashion retailer’s data driven supply chain provides the required in-season agility, leading to increases in sales and fewer markdowns.

WEATHER AND EVENT DATA

APPLICATIONLOGS

STREAM

SERVE DATADATA SOURCES COLLECT DATA STREAM PROCESSING

PROCESS

DERIVEFEATURES

MODEL

Build Model

Models

Update Model

Feature Extraction Machine Learning

BATCH PROCESSING

TOPIC

STREAM

TOPIC

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Fortune 100 Retailer’s Enterprise Data Analytics Facilities A Fortune 100 retailer’s enterprise data analytics facilities, built on top of the MapR Converged Data Platform, enables the processing of tens of petabytes of data with 24/7 availability and reliability.

Data is collected from point of sales transactions, inventory status and pricing, competitive intelligence, social media, weather, and customers (scrubbed of personal identification), then pulled together on the MapR platform, allowing for a centralized analysis of correlations and patterns that are relevant to improving business.

Big data algorithms analyze in-store and online purchases, twitter trends, local sports events, and weather buying patterns to build innovative applications that personalize customer experience while increasing the efficiency of logistics. Point of sales 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, in order to reduce overstock and stay properly stocked on the most in-demand products, helping to optimize the supply chain.

Key MapR features for this customer were:

• NFS for moving data onto the platform.

• Multi-tenancy overcoming the need for separate clusters for each team or application.

• Volumes for scaling without any data loss.

CONSUMERDATA

SOCIAL DATA

SHOPPING

DATA SOURCES

MOBILE SHOPPING APPS

RECOMMENDATIONS

DISCOUNTS

PRICINGOPTIMIZATION

INVENTORYOPTIMIZATION

NFS

MAPR-FS MAPR-DB MAPR STREAMSWEB-SCALE STORAGE DATABASE EVENT STREAMING

ETL Into Operational Reporting Formats

(e.g., Parquet)

MULTI-TENANCYJOB/DATA PLACEMENT CONTROL, VOLUMES

ACCESS CONTROLSFILE, TABLE, COLUMN, COLUMN FAMILY, DOC, SUB-DOC LEVELS

AUDITINGCOMPLIANCE, ANALYZE USER ACCESS

SNAPSHOTSTRACK DATA LINEAGE AND HISTORY

MAPR CONVERGED DATA PLATFORM

DATA ANALYTICS

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

Agile Reporting FarmIRI provides market and shopper information, predictive analytics, business intelligence, and expertise to help leading CPG, retail, and over-the-counter retail companies increase revenue, build brands, and drive growth. IRI clients include 95% of the Fortune Global 500 CPG and retail companies.

IRI wanted to provide clients with deeper reporting and analytical insights. At the same time, they wanted to cut costs by decreasing their mainframe load and build the foundation for a more cost-effective, flexible, and expandable data processing and storage environment. The mainframe data extraction process had performance issues. They wanted to achieve random extraction rates averaging 600,000 records per second, peaking to over one million records per second from a 15 TB fact table. This table feeds a large multi-TB downstream client-facing reporting farm.

The solution was to keep the fact table update and maintenance processes, which were too costly to migrate, on the mainframe. A synchronized copy of the fact table is kept on the MapR cluster. The relatively simple extraction processes, which represented the majority of the mainframe load, goes against the MapR cluster, significantly reducing the mainframe load. IRI was able to achieve between two million and three million records per second extraction rates on a 16 node cluster.

MapR was chosen to maximize file system performance, facilitate the use of a large number of smaller files, and take full advantage of its NFS capability so files could be sent via FTP from the mainframe directly to the cluster. With MapR, IRI saved over $1.5M annually on their data management and storage platform due to reduced workload on their mainframe platform.

IRI not only saved money, but the new solution processes data much faster, which in turn speeds up insights delivered to clients and provides a flexible platform that can easily scale to meet future corporate growth.

MAINFRAME

MEDIA DATA

CONSUMERDATA

DATA SOURCES

MAPR-FS MAPR-DB MAPR STREAMSWEB-SCALE STORAGE DATABASE EVENT STREAMING

ETL Into Operational Reporting Formats

(e.g., Parquet)

MULTI-TENANCYJOB/DATA PLACEMENT CONTROL, VOLUMES

ACCESS CONTROLSFILE, TABLE, COLUMN, COLUMN FAMILY, DOC, SUB-DOC LEVELS

AUDITINGCOMPLIANCE, ANALYZE USER ACCESS

SNAPSHOTSTRACK DATA LINEAGE AND HISTORY

MAPR CONVERGED DATA PLATFORM

SOCIAL DATA

CUSTOMER SEGMENTATION

TARGETING

FSP

ANALYSIS

INNOVATION

APPLICATIONS

NFS

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

Customer Engagement Platform for the Restaurant IndustryFishbowl’s restaurant marketing SaaS platform helps over 70,000 restaurant locations 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.

• The first part of the solution workflow is to get the data on the MapR Converged Data Platform, which is easy via the Network File System (NFS) protocol.

• Apache Hive and Apache Sqoop are used for ETL and processing with the customer matching datastore in MapR-DB. ETL results are stored in columnar Parquet files for fast data queries with Apache Drill.

• Apache Drill is used for interactive exploration and preprocessing of the data with a schema-free SQL query engine.

• Tableau is used on top of Apache Drill to provide rapid BI dashboards as shown below.

CAPTURESOURCES

Point of Sales Transactions

Loyalty Info

Reservations

STORE SERVEPROCESS

NFS

Check CountYTD 156,846

GROWTHQ/Q -11.7%Y/Y -37.3%

J F M A M J J A S O N D

Check AverageYTD $54.23

GROWTHQ/Q 2.6%Y/Y 22.5%

J F M A M J J A S O N D

Net SalesYTD $8,505,778

GROWTHQ/Q -8.9%Y/Y -6.4%

J F M A M J J A S O N D

Customer CountYTD 347,938

GROWTHQ/Q -7.5%Y/Y -6.5%

J F M A M J J A S O N D

Customer Mix

UNMATCHED

HIGH

LOST

LOW

MEDIUM

$4,101,287

$ 660,197

$ 973,614

$2,770,679

0.4%

29.2%

-25.2%

-86.0%

-23.2%

17.2%

-120.8%

53.0%

Sales YTD

% Change Q/Q

% Change Y/Y

Market Effectiveness

SENT COUNT

OPEN RATE

REDEEMED COUNT

11,519,791K

0.03%

1,750

79.32%

10858.32%

83.20%

79.32%

269.31%

83.20%

YTD Q/Q Y/Y

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MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

This solution enabled Fishbowl’s interactive query speed to increase by 5-10 times that of competitive products, with one-tenth the spending on licenses and one-third the spending on storage. Fishbowl was also able to deliver the solution one quarter earlier than expected by leveraging the MapR platforms multi-tenancy and security infrastructure.

Marketing Analytics Service for RetailersNeustar uses the MapR Converged Data Platform to provide retailers with a marketing analytics service.

A wide variety of data—observations of user behavior on websites via clickstream logs, advertising impression logs, direct mail logs, and transaction data—is loaded onto the MapR Converged Data 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. The MapR Converged Data Platform provides the scalability and cost effectiveness needed to grow Neustar’s business.

CUSTOMER INTERACTION OBJECTIVE OUTCOME

ID L

EVEL

TIM

ESTA

MP

DATA

SUBSCRIPTION-CENTRIC

MAC

RO D

ATA

BEHAVIORAL

MARKETING

SERVICE

LOYALTY

TELEMATIC

PRICE/PROMOTION

COMPETITION

SEASONALITY

PURCHASE

SITE VISIT

CUSTOMER SERVICE

UPGRADE

LEAVE

DEFAULT

RESPONSE MODEL

INFERENCE

PREDICTION

TIME-TO-EVENT

POINT-IN-TIME

TIME APPROACH

MapR and the MapR logo are registered trademarks of MapR and its subsidiaries in the United States and other countries. Other marks and brands may be claimed as the property of others. The product plans, specifications, and descriptions herein are provided for information only and subject to change without notice, and are provided without warranty of any kind, express or implied. Copyright © 2017 MapR Technologies, Inc.

For more information visit mapr.com

MAPR INDUSTRY GUIDE TO BIG DATA IN RETAIL

CONCLUSIONBecause of the rapid evolution of the ways in which we are buying and selling, the growth opportunities for retail and big data are huge, but only if retailers understand their markets and customers at a fine-grained level. The successful retailers of the future will profit from their data-driven knowledge of their customers, the current market, and an ability to predict future trends, driving competitive advantage. The highlighted use cases show how retailers can profit from the huge amount of information in their data to optimize merchandise selections, improve pricing, and enhance their customer’s experience.

The retail industry will continue to be buffeted by constant change driven by demographic changes, cultural trends and competition from new sectors. The winners will be the companies that embrace data-driven decision making, move quickly to respond to changing buyer preferences and develop innovative customer engagement programs. No matter what the challenge, data will be the center of their strategies.

FURTHER INFORMATIONRetail Use Cases

Harnessing Social Media to Increase Point of Sale Purchases (with Datameer)

HelloFresh Case Study

Fishbowl Deploys MapR to Provide Customer Engagement Platform for the Restaurant Industry

Gurunavi Case Study