Big Data Use-Cases – Retail, Manufacturing and Auto Industry

We are part of Big Data ecosystem wherein we enable and trigger events that produce data, capture it, analyze it and then we consume it. Our world. is now more interconnected and intelligent than ever in history. In year 2000, we had approx 800,000 petabytes of data in this world. We expect this number to reach 35 zetabytes by 2020 or sooner. Imagine the wealth of knowledge that is trapped in this data and how it touches our lives one way or other every moment.

As the amount of data available to companies is on the rise, the processing and analytical capability is on the decline. It’s very key for businesses to know which to cherry pick (“use case”) before embarking on Big data implementation. In this part of Big data use-case series, I will focus Retail, Manufacturing and Automotive industry.

Use-cases in Retail and Consumer Packaged Goods Industry:

a. Customer Sentiment Analysis

Start of this year, Coco-Cola had the most facebook fans – 35M+. The point is fans are generating major brand awareness for companies by talking to their friends and families and indirectly acting as extended sales team for your organization. It’s very crucial for Retail and CPG industry to know the market pulse and market dynamics that will allow retailers to plan for inventory and stocking before products hit the shelves.

b. Customer Care Call Centers

Without any delay, using Call center record data, retailers can now monitor customer feedback immediately at point of instance. Also based on voice/speech analysis combined with social media and competition data, retailers can retain customers providing Next Best Offer outbidding competition.

c. Campaign management and customer loyalty programs

Billions of dollars are spent by companies on ads and campaigns. It has been hard to measure the success of campaigns in real meaningful metrics. With social media data and direct customer touch-points from survey, call centers, blogs, product review boards, it has become possible to capture key metrics for campaign management. This will allow to channelize the advertising dollars in optimal medium giving highest ROI.

d. Supply Chain and Logistics

Managing and optimizing supply chain distribution and logistics is key to success for every retain and CPG company on this planet. Every sensors and RFID data can now be tracked and assessed to know exact location of product that will allow planning for optimum usage of warehouse space, distribution and delivery methods.

e. Window Shoppers

Big Data technologies such as Hadoop and in-memory computing are ideally suited to collecting and analyzing unstructured data types like the web logs that show the movements of every customer though an internet storefront. Web traffic data can then be combined with existing business intelligence applications and sales data to provide new insights.

f. Predicting Customer Purchases

The holy grail of retail has been to anticipate what consumers need even before they realize they need it. There’s no better way to beat the competition than to make an attractive offer and get a customer’s business before they even realize they need your product, or consider evaluating alternatives. Today, retailers of office supplies are able to track purchases of customers’ in-store credit cards and rewards cards and, based on purchase history, anticipate when a consumer might need to reorder a product.

Other areas where Retail industry can leverage Big data

  • Location based marketing – using smartphones, tablets and geo-location data, retailers can target their customer based on locations
  • Merchandizing – Retailers and CPG companies can optimize planning and merchandizing design by capturing and analyzing RFID, sensor data from warehouses to shelves.

Use-cases in Manufacturing Industry:

a. Supply Chain and Logistics

Managing and optimizing supply chain and logistics is key to success for every manufacturing company on this planet. There is tremendous amount of information that is generated from the Planning and Raw Material procurement to Distribution/Warehousing stage of the process. This information is very valuable to organization to simulate for potential breakdowns and delays in the process. Also every RFID data can now be tracked and assessed to know exact location of product that will allow planning for optimum usage of warehouse space, distribution and delivery methods.

b. Customer Care Call Centers

Most of the manufacturing companies have been collecting call center data records (CDR) for warranty and customer complaints. With Big data tools and technology, companies can use the Call Records to know immediately customer discontent using voice/speech analysis or text analytics. In addition, this information can be used to correlate with social media and internal reports from quality and customer/product surveys and competition analysis.

c. Preventive Maintenance and Repairs

With advancement in technology, every engineering device is now embedded with sensors and RFID that can actively transmit vital information about machines – machine variables (temperature, oil level, humidity…), production rate, waste metrics, life expectancy and breakdown information. Any production downtime is a potential huge loss of revenue to companies due to loss of production output, cost of repairs and waste generated in the process. All the machine logs can now be used by companies to proactively plan for downtime using real-time information preventing waste, preventing major repairs and minimal downtime.

d. Customer Sentiment

Customer sentiment analysis has crossed boundaries using feeds from Tweeter, Facebook, Google, Blogs, Reviews…. Eminem is officially the first person ever to get over 60 million Facebook fans. Start of this year, Coco-Cola had the most facebook fans – 35M+. The point is fans are generating major brand awareness for companies by talking to their friends and families and indirectly acting as extended sales team for your organization. Customer Sentiment analysis provides potential to reduce the loyalty decay rate, increase sales by providing vital consumer feedback on products including packaging and distribution.

Use-cases in  Automotive Engineering Industry:

a. Vehicle Insurance:

If you look at any of the fancy German cars to Ford cars, you will find inbuilt tablet shaped computer screen on your dashboard showing key information about your car using “telematics”  (telecommunications and informatics). Many embedded sensors on your vehicle are constantly collecting information. This will include distance, speed, duration, geo-spatial/gps locations – city/highway/street, neighborhood, driving patterns, parking style and many others. These are key valuable information for assessing the driver risk for potential accidents. All these real-time streams and machine logs from thousands of cars and trucks on the road will constitute Big Data for insurance companies. Based on the information, Insurance companies will be able to proactively provide alerts, warnings on real-time basis and also personalized insurance pricing.

b. Personalized Travel & Shopping Guidance Systems

Your ride will be able to give you optimized travel and shopping experience by providing personalized bargain deals based on GPS locations to nearby outlets/malls/stores and individual’s recent likes on Social Media sites – Facebook, Twitter, LinkedIn, Instagram, Pinterest, Google+, … and mashed with Groupon, Bargain Deals and coupon sites.

Also using telematics and geospatial information, vehicles will deliver personalized recommendations for travel on vacation spots using individual preferences from social media sites and localized travel destinations. If individual has expressed interest in Hiking on Social media site, it will show local hiking trails; if someone liked casino or bars, it will show local casino and bars along with coupons and deals.

c. Supply Chain/Logistics

Providing real-time RFID/sensor data to Distributions/Logistics Fleet providing for aggregated real-time dashboards using streaming feeds. This will allow Distribution and Logistics companies to plan for better distribution of products,

d. Auto-Repairs

Vehicle sensors will actively transmit vehicle information to nearby authorized dealers that will be bidding for your business for repairs and maintenance. For large fleet companies, this will allow for proactively repairs and maintenance needed on the vehicles based on analytics using potential downtime, availability of parts at repair shops, probability of failure before next maintenance, loss of business … The decision to send the vehicle to repair shop will be automated and guided by systems unless human override occurs.

e. Vehicle Engineering

Based on real-time streams of data feeds from RFID and active wireless sensor networks, vehicle engineering can be improved significantly making cars safer, efficient and cost effective.

f. Vehicle Warranty

Vehicle sensors will be providing proactive information to Warranty centers about potential failure in vehicle parts and control systems. These timely alerts to owners will enable preventive maintenance and timely repairs that will reduce repair cost during warranty period

g. Customer Sentiment

This is something marketing, advertising and sales will spend their every dollar to know what customer sentiment on your product line is. In addition, it will be also key to know what consumers think about your competition. Companies can mine this information from Tweets, Facebook, …..

h. Customer Care Call Centers

Every companies has been collecting call center data including recorded voice but didn’t know how to use mine this information. With Big data tools and technology, companies can use the Call Records to know immediately customer discontent using voice/speech analysis or text analytic. In addition, this information can be used to correlate with social media and internal reports from quality and customer/product surveys.

While all these use cases just makes up the tip of an iceberg for Big Data potentials, the intention here is to give you sample use cases that will trigger your thought process that will allow you to think using big data to your organization needs for new opportunity or competitive advantage. Big Data is full of valuable, voluminous, velocity and variety of information! Though 10 years from now, most of the companies would have implemented same or similar use-cases, the leader will be one who chose to elect the right use case for Big data path. While opportunities are endless, key is to know where you begin…. Think Big but Act Small!


Big Data making the difference in Retail industry!

The retail industry is one of the largest industries globally. Measured solely by revenue numbers, the U.S. is the undisputed leader of retail. Wal-Mart is not only the largest global retailer, it is also one of the largest companies of any kind in the world. According to Fortune Magazine’s 2010 “Global 2000” list, 54 of the largest companies of any type in the world are U.S.-based companies that are solely retail companies or have significant retail operations. Of the world’s 10 largest retail companies in the world, five of them are from the States and five are from Europe. These top 10 had combined sales of $1.15 trillion in 2009, according to international consulting group, Deloitte.

It is well known that retailers who know their customers — and apply what they know about their customers’ preferences — are finding a competitive advantage in the marketplace. If you’ve done any shopping online recently – you’ve probably seen big data in action. We’ve all experienced it: You go shopping for a pair of shoes online, put them in your virtual shopping cart, but then for some reason change our mind. Afterwards, seemingly every site you visit features an ad for that very pair of shoes at that same online store. The reason? Online retailers can give you a virtual identification number and track you as you go from site to site, and purchase targeted ads for products they already know you’re strongly interested in1.

Recently in my engagement at an ad-network client, I was reviewing the information architecture and helping develop the set of business analytics to drive optimal online ad placement and targeting. Millions of records from online cookies were being sourced and captured daily by this ad-network company. This data was then processed, segmented and fed into a rules engine that used the information to target future placement of ads using demographic, ad size, location, pixels and ad placement on web page.

Typical consumers wait year-round for the best shopping deals on large items, especially during “Black Friday” and “Cyber Monday.” The reason it’s termed as “Black Friday” that many people may not realize is that this period signifies the approximate date on the calendar when many retail businesses move from operating in the red and start to actually make a profit for the entire year.  Online retailers saw record holiday sales at the end of 2012. ComScore reports that 57 million Americans shopped online on Black Friday, a 26% increase over 2011.

Understanding and winning customers is complicated in today’s ultracompetitive retailing environment. But the problem isn’t a lack of data about who your consumers are and what they’re buying. Data pours in from multiple systems, channels, and regions around the clock. The challenge, rather, is how to extract meaning from the data to inform decision making and enable productivity and agility in the face of multi-faceted market demands. Part of that challenge is consolidating the large data sets your organization amasses from a variety of sources. That’s especially difficult given the many tools to analyze and report on the data, creating islands of information that may not offer the big picture or best decision-making insights. Today’s customers use social and mobile technologies to make more informed decisions.

Big data analytics make it possible for retailers to directly correlate consumer web activity with promotions and marketing campaigns, and track resulting sales transactions. And as a result, retailers can monitor and tweak promotions and campaigns in near real-time to maximize spend, increase profitability and generate revenue during this short, but critical period of time. They do this by quickly slicing and dicing terabytes of data, including millions of daily emails, every click on web sites, and every ecommerce and brick and mortar transaction.

These advanced analytics enable retailers to perform deep, precise customer segmentation by demographics, such as age and income, and psychographics such as interest and lifestyle profiles – segments which are then used to drive highly optimized and personalized offers and campaigns 2. Every day, retailers are taking steps to increase their efficiency, improve their customer experiences, and develop smarter retail. This analytical approach to customer decisions is not limited to the web; some retailers are now using technologies to analyze foot traffic throughout their physical stores. These maps, combined with sales data, make way for new applications focused on optimizing store layout and product placement.

Based on recent news, retail giant Debenhams has launched a new big data initiative to create a more personalized, multi-channeled marketing strategy. The UK-based company – founded in 1778 – had 167 stores as of October this year, and this heritage has seen it build a complex landscape of data. It now plans to use big data to analyze its 40 different databases – which include email lists, mobile users, and customers of its wedding services – in order to understand the individual preferences of its customers and shape its marketing communications accordingly. To provide an outstanding shopping experience while increasing sales and protecting profits is always a balancing act for retailers. Business analytics takes into account data streams from various areas of the retail operation to help decision makers improve the customer shopping experience.

Here are few types of big data analytics that can performed in the retail industry:

  • Customer Analytics and KPIs – Understand your most valuable customers and target  them to maximize profits and loyalty
    • Discover who your customers are
    • Expectation and sentiment tracking
    • Track impact of promotions on basket and provide a holistic view of behavior
    • Tap into the transactional data to connect the dots between customers, stores, products and promotions
    • Move beyond basic segmentation, personalities and list pulls to create targeted micro-segments
  • Merchandising KPIs – Significantly reduce costs, eliminate the expense of stockouts and overstocks, and make powerful, rapid decisions
    • Quickly accelerate shipments by evaluating top-selling products
    • Make markdown decisions based on seasonal sell-through
    • Cancel shipments for bottom-selling products
    • Communicate more effectively with vendors
  • Store Operation Analytics and KPIs – Keep store managers on the selling floor, not behind a desk. Give store operations the right information at the right time to make the right decisions. Addresses the challenges of sales assistance, queues, merchandising/promotions, and stock out.
    • Increase profitability
    • Gain visibility into service levels, operational performance, and customer preferences
    • Optimize staffing, improve service levels, and enhance customer experiences
    • Reduce out-of-stock situations
    • Improve efficiency by facilitating management of compliance across hundreds or thousands of stores
  • Vendor and SKU Management Scorecards and KPIs – Analyze vendor performance, drive improvement, and strengthen negotiations. Improve performance across the supply chain.
    • Increase sales as products reach sales floor faster
    • Increase data accuracy for inventory management and replenishment
    • Reduce costs through elimination of data entry and manual processes
  • Marketing Analytics and KPIs
  • Returns, Fraud and Loss Prevention Analytics

One of the largest U.S. retailers, an early leader in analyzing on-line customer behavior, is a good example of a retailer that is experiencing the blending of e-commerce, mobile apps, and in-store shopping. To stay ahead of the omni-channel shopping revolution, this retailer is capturing and analyzing enormous volumes of customer behavior information gathered across its stores, websites and mobile applications. The company uses this data to manage its entire demand chain. As a result, it is able to anticipate shopper behavior in a way that minimizes out-of-stocks while reducing overall inventory. This retailer also offers a smartphone check-in feature to allow in-store consumers to access and use coupons while in the store.  Another major retailer has deployed a Hadoop-based big data store to more cost effectively capture, store and analyze an exploding volume of customer data. The new structure is allowing the company to personalize marketing campaigns, coupons and offers to the individual customer, with a solution that is cost effective and has timely turnaround. The retailer’s big data store holds more than two petabytes of data about consumer behavior – from point of sales devices, e-commerce web sites, GPS-enabled tablet devices and smart phones, and embedded sensors. With Hadoop’s massively parallel processing power, the company sees little more than one minute’s difference between processing 100 million records and 2 billion records3.

To stay competitive, retailers must understand not only current consumer behavior, but must also be able to predict future consumer behavior. Accurate prediction and an understanding of customer behavior can help retailers keep customers, improve sales, and extend the relationship with their customers. In addition to standard business analytics, retailers need to perform churn analysis to estimate the number of customers in danger of being lost, market analysis to show how customers are distributed between high and low value segments, and market basket analysis to determine those products that customers are more likely to buy together. Data mining within the retail industry can be used for many business objectives. For instance, data mining can be used to better understand the purchasing behaviors of your customers, to help you understand your high- and low-margin customers, to help you understand which customers are most likely to respond to a marketing campaign, or to help you identify which customers are likely to leave. Data mining can enhance and amplify the knowledge of all of your assets, from customers to suppliers to employees, and even the presentation of merchandise within the store.

Advanced analytical applications leverage a range of techniques to enable deeper dives into customer data, as well as layering this customer data with sales and product information to help retailers segment and market to customers in the ways they find most compelling and relevant. Historically, retailers have only scratched the surface when it comes to making use of the piles of customer data they already possess. Add social media sentiment to the mix, and they can access a virtual treasure trove of insights into customer behaviors and intentions. The timing couldn’t be better, because these days’ consumers award their tightly held dollars to retailers that best cater to their need for customized offers and better value. The ability to offer just what customers want, when they want it, in the way they want to buy it requires robust customer analytics. The opportunity is now: It’s critical that retailers step up their customer analytics capabilities as they transition to an all-channel approach to business.

A recent Cognizant study, in association with Forbes Insights, “Innovation Beyond the Four Walls: Breaking Down Innovation Barriers,” shows that 60% of companies surveyed encourage customer input to gather information and ideas for innovation, and another 22% are considering it. To this end, companies have been quick to adopt new structures that involve their customers in their innovation efforts by establishing internal company teams combined with customers. Forty-one percent of companies surveyed for the report already have such teams in place. When their customers come first, companies can thrive even as consumer critiques explode on social media and as the economy stagnates.

The proof lives at corporate culture pioneers, Southwest Airlines and The Walt Disney Co. These highly successful enterprises offer different philosophies, management strategies and lessons that can be adapted and applied to smaller businesses. But the underlying keys are happy customers and, just as important, happy employees. The customer-driven movement is getting stronger and stronger, and it’s about more than service—it’s about the experience you provide. The customer has indeed become the king for retailers, and smart retailers are willing to listen to them and go out of their way to offer customized and personalized services and product.