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Big Data Inspirator

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The “Big Data Inspirator” has been designed to help you grasp the full spectrum of business value that can be unleashed through data. Start by identifying for which business purpose you would like to leverage data and get inspired by the 40 real life examples
that explain each one pattern how to derive value from data.

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Big Data Inspirator

  1. 1. © 2017 Orange HillsTM GmbH. All rights reserved. The “Big Data Inspirator” has been designed to help you understand all the ways in which data can help add value to your business. Start by identifying why you want to collect data and the purpose it will serve within your business. Be inspired by 40 real life examples illustrating different ways of deriving value from data. Once you have understood what you would like to accomplish, use our ”Data-To-Value Builder” to design how to achieve your business objectives with data. AT&T Targeted up-selling The telecommunications company AT&T uses multiple indicators such as billing and sentiment analysis in order to identify customers that can be upgraded to higher quality products. USAA Targeted cross-selling The insurer USAA analyses data from multiple sources to spot key events in customers’ lives. This enables USAA to approach customers with just the right cross-selling offer: car insurance when a customer’s daughter is just about to get her driving license, for example. Jeanswest Retention vs. acquisition The apparel chain Jeanswest figured it was in a far better position to retain the customers it already had than to acquire new ones. By tracking purchases, Jeanswest predicts when its customers repurchase products and is the first to send them an offer. Avis Customer lifetime value The car rental company Avis identifies customers with the greatest lifetime value by predicting their rental frequency and profitability. This has enabled Avis to focus its customer acquisition on handpicked customers of potentially high value. Lavazza Clustering vs. segmenting When it launched Fair Trade Coffee, Lavazza used analytics to group custom- ers instead of segmenting them. Lavazza discovered that 11.6% of customers had been overlooked so far, as no one thought about advertising the self-made rather than the altruistic aspect of fair trade. Dollar General Cross selling partnerships Discount retailer Dollar General analyses which products from different suppliers tend to end up in the same shopping basket of customers. This enables suppliers to work out cross-promotional agree- ments with other suppliers to share their customer bases for mutual benefit. Sotheby’s Leads identification The real estate company Sotheby’s identifies potential home sellers by approaching wealthy households whose children are leaving for college. Sotheby’s identifies new “empty nesters” by tracking shifts in a household’s buying patterns. Walmart Personalised online shopping Walmart has increased the number of purchases made in its online shop by 10-15% by making search results more relevant. Walmart uses text analysis and synonym mining to understand the searcher’s intentions and the contex- tual meaning of the terms he or she has used. John Deere Insights enabled services John Deere places sensors on the tractors it sells. Combining the data collected by these sensors with data on soil conditions, weather and crop features, John Deere helps farmers identify where and when to plant to get the highest yield and how to reduce fuel costs for their tractors. Sephora Personalised shopping The cosmetics chain Sephora has installed beacons at its stores. This technology enables Sephora to send persona- lised offers and recommen- dations to its customers’ phones, tailored to their specific interests and position in the shop. Kayak Insight as service Kayak is a travel search engine that allows users to compare prices of flights. To make itself even more attractive, Kayak has used big data analytics to design a new service which predicts whether the price for a particular flight will go up or down within the next week. Hagleitner Monitising insights Hagleitner Hygiene fits sensors in the toilets of fast food restaurants to ensure that paper towels do not run out. In addition, Hagleitner analyses the data it collects about how frequently toilets are used to help its customers decide how best to deploy their cleaning staff. Express Scripts Insights enhanced products Express Scripts, which processes pharmaceutical claims, realised that the patients who most needed to take their medication were also the most likely to forget to take it - so, the company introduced beeping medicine caps to remind these patients to take their pills. BMW Information enhanced products Like many other automobile manufacturers, BMW has transformed its cars into mobile data generators. Drivers benefit from relevant information such as measurements of distance and environmental conditions. Delta Information as service Lost baggage is one of the biggest inconveniences faced by airline passen- gers. Delta Air Lines uses big data to enable its customers to track their baggage from their mobile devices. This gives travel- lers a lot more peace of mind, and 11 million downloaded the app. Thyssen Krupp Information enabled services The elevator manufacturer Thyssen Krupp collects data about the elevators it has sold, enabling it to offer its customers remote diagnostics and predictive maintenance. The sensors integrated in the elevators have a strong lock-in effect and can be used for new services in the future. Marriott Real-time pricing The hotel chain Marriott uses weather reports and local events schedules to forecast demand and to determine a value for each room throughout the year. Optimising pricing efficiency is vital for Marriott, since its custom- ers often use price comparison services. Verizon Verizon Wireless, the largest U.S. carrier with over 98 million retail customers, sells aggre- gated and anonymous subscriber data to third parties. Details such as gender and geo-localisation allow for targeted market- ing campaigns. Monetising information JE Dunn Clients of the construction company JE Dunn often ask for buildings they cannot afford. For this reason, JE Dunn allows its clients to tailor buildings to their needs in a modular way, calculating the price in real time using data on material and construction costs. Modularised pricing Ryanair Ryanair uses a dynamic price calculator to capture the willingness to pay of travellers depending on factors such as time of purchase and weather at the purchaser’s location. Ryanair analyses a huge amount of data that influences the perceived value of a flight. Capture willingness to pay DM The drugstore DM is a retailer with high levels of fluctuation in customer volume. By analysing historical turnover and contextual parameters, DM has been able to automate the shift planning for each of its stores. This has significantly improved staff availability for customers. Shift scheduling Union Pacific Union Pacific Railroad has reduced derailments of its trains by 75%, by proac- tively identifying and carrying out maintenance work on at-risk equipment. Union Pacific uses thermometers, micro- phones and ultrasound to collect data about its engines and rails. Proactive maintenance McDonalds McDonalds has started adapting its franchising model to tailor all of its restaurants to their own markets. McDonalds analyses the preferences of local customers. This data is used when design- ing menus, drive-through restaurants, etc. Smart franchising Intel The chip manufacturer Intel has saved USD 3 million by analysing the data produced by its manufac- turing equipment for one line of chips in order to predict quality issues. This has significantly reduced the number of quality tests that need to be performed. Streamlining production Otto The availability of purchased goods is crucial for customers of online retailer Otto. Using an algorithm based on sales history, current stocks and marketing campaigns, Otto has managed to increase availability while reducing stocks. Predicting stocks Telekom The telecommunications company Telekom scans social media almost in real time to identify potential customer service issues, and proactively contacts the author of the post. Customers appreciate the effort the company has gone to, and feel that Telekom cares about them. Proactive customer service Saarstahl The steel company Saarstahl sorts scrap parts at an early stage of production in order to make better use of its production capacities. It has installed sensors to monitore product and process quality, which allows for real-time adjustments to the production process. Production optimisation Tesco The supermarket chain Tesco has cut energy costs for its refrigerators by 20%, resulting in €20 million in annual savings. Tesco achieved this by collecting and analysing 70 million refrigerator-related data points coming off its units. Asset performance Bristol-Meyers The pharma company Bristol-Meyers has used clinical trial simulation to speed up drug studies from 2.5 to 1.7 years. Applying analytics to historical trial data has enabled the company to reduce the required number of blood samples per subject from 12 to 5. Speeding up R&D Nestlé Social media has caused serious damage to Nestlé’s brand. Today, the company uses social media analytics to actively engage with people that post about Nestlé. This gives Nestlé the opportunity to explain its point of view and thus mitigate brand damage. Social media management Red Roof The hotel chain Red Roof realised that up to 3% of flights are cancelled during harsh winters. By analysing weather conditions and flight cancellations, Red Roof is able to place ads on mobile devices in the areas most affected, increasing hotel occupancy by 10% in these areas. Event based marketing T-Mobile T-Mobile realised that when customers with a lot of social influence switch brand, many of their peers follow. By identifying the so-called “tribe leaders” via social network analytics, and focusing its marketing efforts on them, T-Mobile has managed to increase overall customer loyalty. Tribe leader marketing Kroger The supermarket chain Kroger launched a direct mail campaign with a coupon return rate of over 70% within six weeks, compared to an industry average of 3.7%. Data about customers’ shopping history was used to make the coupons highly relevant to each customer. Personalised loyalty program HDFC Bank HDFC Bank analyses the profiles and user behaviour of its customers in order to tailor the content and channels of its communi- cations to each individual customer. This has created a more personal relation- ship, with less perceived spamming. Smart communication Sport Scheck The retailer Sport Scheck gets its customers to run on a treadmill in its shops to analyse their running style. Using product and running data, Sport Scheck helps customers select the footwear that suits them best. This experience encourages customers to visit physical stores again. Selection support Citibank As soon as people use their Citibank card to make a purchase, Citibank sends them information via a push notification about how to save money on that purchase. The comapny’s services have generated a lot of word-of-mouth recommendations and brand love on social media. Contextual marketing Xerox Xerox has used talent analytics to reduce the attrition rate in its call centres by 20%. In order to do this, Xerox analysed what was causing the high rate of staff turnover. This enabled Xerox to hire the right people and to improve employee motivation. Smart hiring DHL For logistics company DHL, the “last mile” is the most expensive part of the distribution process. Using location data from DHL’s fleet and participating taxi drivers, commuters and students, DHL has deve- loped a low cost, crowd- based delivery service for the last mile. Smart delivery EMI By intentionally leaking music and monitoring reactions to it, the record label EMI can confidently predict demand for albums. Since sales volumes vary for each album, this has enabled EMI to match production to actual demand in a much more accurate way. Forecasting demand “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard One of the challenges faced by Daimler Fleet- Board is assessing the individual risk level of each of the truck drivers it insures. By analysing their truck data, the company is able to price insurance policies based on drivers’ individual driving behaviour. Usage based pricing Daimler Download our template to visualise business models here: http://bit.ly/UHYzra Data Information Insights BIG DATA INSPIRATOROrange HillsTM GmbH | www.orangehills.de | Follow us on Twitter: @orangehillsgmbh Avis Sotheby’s Lavazza DollarGeneral AT&T USAA Jeanswest Citibank T-Mobile Nestlé Red Roof Telekom Kroger HDFCBankSportScheck Sephora Walmart JohnDeere ExpressScripts Kayak DM UnionPacific Otto EMI Tesco Saarstahl Intel Bristol-Meyers Xerox DHL McDonalds Ryanair Marriott Daimler JE Dunn Verizon Delta BMW ThyssenKrupp Hagleitner Innovate your marketing Boostyourrelationships Im prove custom er experience Build new offerings on insights Build new offerings on information Adapt your pricing Big Data Inspirator Whomto serve How tocreate Howtodeliver Whatto offer Acquirenewcustomers Leverage current custom ers Increaseyour efficiency Increase your efficacy Improve planning “The goal is to turn data into information, and information into insights.” Carly Fiorina ...is ”raw”, unorganised and without meaning on its own. ...is interpreted data with contextual meaning. ...are conclusions derived from information.

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