Alfred Stieglitz and His the Steerage Essay Example | Topics and Well Written Essays - 1000 words

Alfred Stieglitz and His the Steerage - Essay Example he depictions a rich shading and pleasantness, despite the fact that Stieglitz stated: â€Å"I don't protest correcting, avoiding or emphasis as long as they don't meddle with the normal characteristics of photographic technique† (refered to in Whelan, 1995). This photograph evolutional pattern had been called â€Å"the pictorializm†. Previews, so much venerated by the devotee of this stream, were made by the hand camera or the fixed gadget, however in the main unrepeatable second, and, because of a meticulous imaginative work they were turning into the remarkable photograph pictures, the workmanship magnum opuses. Be that as it may, this energy didn’t keep going for quite a while and later it got illogical, why the objectives presented by the pictorializm can be fulfilled uniquely by the photograph innovation. Alfred Stieglitz, who for about 15 years was arranging the displays of youthful growing picture takers and specialists, from the start, in the 1900-s was influenced a functioning blend of photography and workmanship, and later, following ten years, his exhibition started to give more consideration on the pictorial craftsmanship itself. New occasions had come, and pictorializm as a pattern turned into the creation of nostalgic pictures. So Alfred Stieglitz profoundly changes his way of taking photographs. In the 1890-s he was strolling the roads with his â€Å"detective† camera and making the reportage photographs, not modifying or enhancing them by any stretch of the imagination. In the later years he discussed the explores of the obscure and said he was searching for the subjects for his works in the sixty yards of his home entryway. In those days such contemplations were innovatory, that was the hour of wistful, type, compositional and â€Å"highart† photographs , scrutinized by Emerson, the creator of the â€Å"Naturalistic photography†, yet well known in the New-York photograph clubs, and these wire-drawn pic tures were hanged... The article Alfred Stieglitz and his the Steerage gives point by point data about a celebrated picture taker and investigates the narrative of his most noteworthy photos ever. He was the main who familiar well known by its preservationist sees American culture with crafted by such virtuoso of that time as Picasso, Braque, Rodin, Brancusi, Matisse, Dyushan, Cezanne, Americans Max Lieberman and James McNeill. Craftsmen and litterateurs focused their consideration on the scans of new techniques for the portrayal of the real world, and, along these lines. In the principal many years of 20-th century the innovation showed up as the complex of masterful patterns (futurism, expressionism, cubism, constructivism, oddity, dynamic craftsmanship and so on.), which were progressing till the Second World War. Alfred Stieglitz is a foremen of piktorializm, the editorial manager of a relationship of beginner photography fans. Yet, when inside the gathering the division had begun, and individuals fr om the Camera Club started to contradict his prohibitive publication arrangements, Stieglitz and a few of his companions picture takers burst with the Club and set up the Photo-Secession group†. Photograph pictures Made by Stieglitz were clear and significant, capitally indicating their airs. In the 1925 every one of his works were exhibited in the display of Mitchell Kennerly. Be that as it may, the most celebrated his work is The Steerage. It was caught in 1907 on the lower deck of perhaps the biggest boat on the planet around then in light of the fact that the depiction had pursued the lower class passenger’s zone, referred to on most ships as the steerage.

Differential Industrial Research Project

Question: Talk about the Differential Industrial Research Project. Answer: Presentation: Recommended report title Mechanical Research Project Industry accomplice Mercure Sydney Hotel is the business accomplice that is this specific lodging has a place with the top degree of the Australian inn industry. Mercure Sydney Hotel is a 4 Star Hotel and it is arranged at an area where an individual can without much of a stretch reach by 7 minutes walk just from light rail and train station for example Focal Station. In other manner round, it can likewise be said this predefined urban inn is 1.9 Km away from both the Darling Harbor and Central Business District (Mercuresydney.com.au 2016). By and large, it very well may be said that the lodging business in Australia is continually becoming because of the expansion in the complete quantities of guests to the nation. It has been discovered that the quantities of inland guests have expanded in Australia because of extension of business and for higher investigations. Therefore, the expanded quantities of sightseers have helped the nation to bring its income up in the inn business. Larger issue The association Mercure Sydney Hotel is a 4 Star Hotel and is all around situated in the focal point of the city. In addition, it tends to be said that the area of the specific inn is likewise very much associated with the train and light rail stations. All things considered, it has been discovered that in the current days, the deals or the appearance pace of the quantities of visitors to the inn has been decreased relatively to its center rivals. The point of this exploration venture is to determine the issues that are the primary driver because of which the absolute deals income of the firm Mercure Sydney Hotel diminished with the progression of time. It is imperative to explain the specific issue of Mercure Sydney Hotel on the grounds that the reduction in the appearance of pace of the visitors has unfavorably influenced the business income just as the productivity proportion of the firm. All-encompassing exploration point The point of this venture is to recognize the variables and causes because of which the deals and the appearance pace of the visitors of the firm Mercure Sydney Hotel diminished with the progression of time. The specific research expects to explore the reasons that are obligated for ceaseless reduction in the business income of the firm Mercure Sydney Hotel. Research questions(s) What was the business income of the firm over the most recent 3 years and in the present year? What are the causes that have made a hole in the business esteem? What are the requests of the clients in present days? Information needs Information and data for the essential research will be accumulated through both online poll study of 100 clients and telephonic meeting of 5 chiefs of comparative lodgings. Writing audit: Keywords utilized for writing search The watchwords that will be utilized in this exploration for important writing audit are consumer loyalty level, strategies to expand the business income, techniques to build level of consumer loyalty, contender investigation and upper hands. Writing audit: Name 2 significant articles Ghose, An., Ipeirotis, P.G. what's more, Li, B., 2014. Analyzing the effect of positioning on customer conduct and web index revenue.Management Science,60(7), pp.1632-1654. Blal, I. what's more, Sturman, M.C., 2014. The differential impacts of the quality and amount of online audits on lodging sales.Cornell Hospitality Quarterly,55(4), pp.365-375. References Blal, I. what's more, Sturman, M.C., 2014. The differential impacts of the quality and amount of online audits on lodging sales.Cornell Hospitality Quarterly,55(4), pp.365-375. Ghose, An., Ipeirotis, P.G. what's more, Li, B., 2014. Analyzing the effect of positioning on customer conduct and web index revenue.Management Science,60(7), pp.1632-1654. Ingram, T.N., LaForge, R.W., Avila, R.A., Schwepker Jr, C.H. what's more, Williams, M.R., 2012.Sales administration: Analysis and dynamic. ME Sharpe. Mercuresydney.com.au 2016.Mercure Sydney | Hotel In Sydney CBD.Mercuresydney.com.au. Recovered 26 November 2016, from https://www.mercuresydney.com.au/.

Innovative Business Models Using Predictive Analytics

Innovative Business Models Using Predictive Analytics The Digital Era has brought the world of industry many challenges and opportunities. The irrational exuberance of Nineties technologists and tech investors led to Internet businesses that were unwieldy, unmanageable, and unprofitable.In the early 2000s after the dotcom bubble burst, many spectators wondered whether the marriage of Internet and business was merely a fad, while many speculators, wondered whether the markets would or could regain confidence in digital businesses.However, in the decade and a half since the Tech bubble, the world has since seen the creation, growth, and maturation of digital industries, as well as innovative new digital approaches to traditional brick and mortar industries.Few could have predicted the rise of the online-only business or the virtual company, or that the most successful Internet firms could be quite so successful with market caps upwards of $400 billion. The Web 2.0. Era has even seen the creation of profitable offline businesses housed in online video games!One major driver of competitive advantage is predictive analytics â€" the collection of statistical and computing techniques that allow firms to use historic and dynamic data, aggregated digitally, to create probabilistic models of the likelihood of future events. Predictive analytics has a range of applications, though it is used most commonly to optimize decision-making and to determine consumer preferences. This powerful field has become a cornerstone of the business strategies of many a household name. Indeed, it has even led to the creation of many innovative â€" and profitable, new business models. © Shutterstock.com | ImageFlowHere, we will 1) briefly cover traditional business models; and then address 2) how brick-and-mortar firms are enhancing their businesses with predictive analytics; as well as illustrate 3) examples of new business models that incorporate predictive analytics.TRADITIONAL BUSINESS MODELSFundamentally, there are several basic business models: owners/landlords, manufacturers, distributors, and sellers. Within these broad categories are a number of more specific models â€" for example, sellers include wholesalers, brokers, traders, retailers, and multi-level marketers. There is also a variety of conventional pricing, supply chain, marketing, and other business strategies, which are often confused with business models and help differentiate firms from each other. Predictive analytics has upended many of these traditional models and strategies of doing business in myriad ways.In short, predictive analytics allows firms to create models of consumer behavior th at are correlated positively with historical data, and use these models to forecast future results. Because, in our Digital Era, data comes in in real-time and because we have developed highly sophisticated and robust hardware and software systems for processing this “Big Data,” firms can feed data into these models in real-time and adjust their business decisions automatically.For example, firms can use predictive analytics to drive dynamic pricing, a strategy that predates predictive analytics, but has been enhanced by it significantly since. A hypothetical hotel might determine pricing during a holiday season by building a model involving prior year data, prior month data, and even prior hour data, by automatically feeding all purchase transaction information into a model designed to optimize revenue and changing pricing based on real-time performance. To the consumer, this might look like a pricing schedule that varies as vacancy rates rise or fall. Predictive analytics has greatly enhanced the performance of many brick and mortar establishments.ENHANCING BRICK AND MORTAR BUSINESSES WITH PREDICTIVE ANALYTICSAs digital enterprise began to flourish in the early 2000s, many brick and mortar retailers lost market share and went out of business. Those that did not, largely, developed successful online strategies to strengthen their existing competitive advantage, or create one. Some used digital technologies and predictive analytics to enhance their marketing; others used it to enhance their supply-chain management, among other strategies.Innovations in marketingPredictive analytics has allowed marketers to increase their estimations of consumer likelihood to purchase. This has allowed firms a to optimize their marketing mix, allowing them a greater ability to target consumers. It has been a boon to traditional retailers such as Macy’s. In 2014, the retailer partners with SAP, a leading provider of software and services, to improve its existing predictive analytics software. The new solution allows it to build multiple predictive models that aid it in targeted email marketing and digital marketing campaigns. In its first three months using the solution, Macys saw, on average, a ten percent increase in sales.Other retailers use the data they receive to build a personalized shopping experience for the consumer online, and/or optimize the overall shopping experience in a retail location. By parsing consumer data and paring it with predictive analytics models, firms can create targeted online messages, unique promotional opportunities, and other incentives to drive them to a brick and mortar retail location.Beyond driving traffic, predictive analytics’ real promise for marketers involves increasing customer lifetime value. Cost-per acquisition can be exceedingly costly, especially for non-essential items and/or in crowded markets. Personalized experiences can deepen customer engagement and brand loyalty, increasing the value of their lifetime purchases and decreasing your retention cost. When you determine the return on investment for particular customer segments, you can more effectively determine the optimal ones to target.Analysis of sales data and sales analytics can also yield significant, positive implications for supply chain management.Supply chain managementSupply chain management is another strategic business area that has been transformed by predictive analytics. Prior to the Digital Era, sourcing decisions were made based on annual evaluations of sales data, personal relationships with suppliers, regional distribution chains, and past practices. Now, predictive analytics provides brick and mortar chains the insights to be able to shift sourcing based on real-time data, determine whether new suppliers of a particular product or skew will increase or decrease revenue, and determine ideal wholesale and retail pricing.One such firm is brick-and-mortar retailer Walmart, whose online storefront is a viable competitor to other market leading online retailers. Walmart, whose suppliers are located in more than 70 different countries, and whose stores stock an average of over 175,000 products, is immensely profitable because it aggregates data on every aspect of its retail operation and analyzes them to forecast demand and consumer purchase behavior. Each day, Walmart feeds the reams of data it receives from in-store and online sales-tracking and inventory management systems and feeds that information back into its supply and distribution systems. By coupling this with sales forecasting data from local demand forecasting models, Walmart stores can minimize all product shortages significantly.Predictive analytics is also used to optimize sourcing and shelving decisions. Through simulation and analysis of historical data, Walmart is able to use predictive analytics to determine the product mix that will allow it to achieve the highest sales revenue at the lowest wholesale cost in the least amount of time. Part of this equation is determining where products should be located in the store to stimulate the most sales growth. All store variables are tracked vigorously and assessed in real-time to ensure the firm’s success. EXAMPLES OF NEW BUSINESS MODELS USING PREDICTIVE ANALYTICSWhether a firm is choosing what products to manufacture or distribute, a retailer is figuring out what to source, or a franchise is seeking the ideal owner-operators, predictive analytics can yield tremendous results for every business type. However, for some firms, predictive analytics has created such a significant competitive advantage that it has yielded entirely new ways of doing business.Online-only distributors and retailersDuring the early days of the Digital Era, the thought of an online-only distributors and retailers was hard to grasp. True, the reduced operating expenses could serve as a source of competitive advantage, but established brick and mortar businesses had the benefits o f human capital, strong brand recognition, advertising dollars, facilities, and relationships.However, as time has passed, digital technologies have emphasized data  â€" bringing it on par with, if not making it more important than, relationships as the basis for B2B sales and strategic partnerships in many industries. It has allowed us to have a distributed workforce, eliminating the need for central facilities. It has allowed us to build brand recognition in new ways, some for a fraction of traditional advertising costs (indeed, it has eroded them). Finally, it has allowed the online only distributor or retailer a significant source of competitive advantage in the form of Big Data and predictive analytics.Take human capital â€" particularly in sales. Firms can use predictive analytics to predict consumer preferences through recommendation systems. They can then immediately upsell. A human salesperson requires training and a professional demeanor at all times to upsell, whereas a few lines of code are all that is needed for a consumer to receive a personalized suggestion. Netflix is a strong example of an online-only firm that use recommendation engines to drive sales. Netflix’s competitive advantage placed significant pressure on its early rival Blockbuster, which pre-2010, was a household name and market leader in video sales. But with 60,000 employees at its peak, compared to Netflix’s 2,000, as well as an inability to efficiently forecast demand and supply of its products, Blockbuster lost significant share to Netflix and eventually filed for bankruptcy. And while the recommendation engine reduces labor costs, it also drives sales (or in Netflix’s case brand loyalty, as they charge a flat fee) automatically and in real-time.Human labor reductions are not the only use of predictive analytics. Indeed, many firms have used predictive analytics to forecast talent shortages, predict the likelihood of employee retention, and forecast an employment candid ate’s probable performance with their firm. Google is well-known for using what is known as people analytics â€" the application of predictive analytics to human resources to forecast an employee’s future career trajectory inside the firm. It also uses people analytics to design workspaces and collaborative opportunities that optimize employee innovation. And Google has long been known as a firm whose talent has been a source of significant competitive advantage.Online Auction-Based BusinessesPredictive analytics also can be used to drive auction-based businesses. For firms where price optimization is critical, like eBay, which derives revenue from fees placed on user auctions, predictive analytics can be used to determine the price. For firms that auction their inventory, and whose fees vary based on the total transaction amount, predictive analytics can help forecast demand and scarcity, and allow these firms to set initial bids efficiently.Firms can use predictive analytics c oupled with software purchasing applications to purchase goods or services automatically when those goods or services hit certain price targets or other thresholds. This is seen in high-frequency trading, where speculators bid on certain equity or debt instruments meeting certain conditions. Some programs use models and inputs developed entirely by humans. Others have paired predictive analytics with machine learning â€" the process by which computers process increasingly complex information by constructing learning algorithms. This paring allows programs to make decisions without human input concerning bid behavior based not just on the results of the model (that may include thousands of variables), but their own past purchase behavior.eBay takes predictive analytics a step further than its online auctions. Given its scope (an e-commerce portfolio with approximately $300 billion in transactions), eBay uses predictive analytics to optimize decision-making. Their SAP-designed  syste m is used to predict problems through daily forecasting, and run simulations on the effect of decisions on the entire portfolio. Other firms, particularly those in financial and legal services use predictive analytics to forecast potential issues as such issues can be extremely costly for their clients and themselves.Online /Mobile Advertising and Ad ExchangesDigital marketing, which accounts not just for business strategy, but also comprises a healthy industry of digital marketing agencies, is driven by data and analysis. The fields of search engine optimization, for example, involves leveraging a firm’s digital and non-digital assets to drive traffic to a landing page. To do so, a firm must build models of what has worked to inform its strategies going forward. Since digital data can be obtained in real-time, and most web analytics programs now have considerable analytic and testing tools, digital marketers can quickly test and implement some strategies based on their models. So me firms implement programmatic buying â€" buying using the results of predictive analytic models and machine learning, to make and serve ads and marketing content to users.The field of search marketing is also dependent on predictive analytics as it requires firms to forecast the search terms consumers will use to find their firms as well as firms in their industries, as well as related industries. Using web analytics programs allows digital marketers to determine the ideal keywords and key phrases on which to bid. Predictive analytics models can be used by firms to aid in the accuracy of the bidding process.Search itself is driven by predictive analytics. Though the algorithms are proprietary, firms like Google and Microsoft create lists of relevant website links based on user inputs that are not just limited to what one types into the search bar. These firms spend hundreds of millions of dollars working to refine these lists, “predicting” that they will be exactly what you ar e looking for. And by transforming the way firms do business, innovative search engine firms, such as Google, Baidu, and Yahoo have revolutionized industry.Digital advertising is driven by predictive analytics. Many online and mobile search ad-serving platforms create models of target customers using a client firms data. They then serve ads to online users who fit that profile, using cookies to track their movements. A user could visit a partisan political website and a food website, for example, and see the exact same ad on each. This is known as retargeting and is used by many firms, such as apparel retailer Levis. A properly implemented retargeting campaign can increase click-through rates and conversions.

Innovative Business Models Using Predictive Analytics

Innovative Business Models Using Predictive Analytics The Digital Era has brought the world of industry many challenges and opportunities. The irrational exuberance of Nineties technologists and tech investors led to Internet businesses that were unwieldy, unmanageable, and unprofitable.In the early 2000s after the dotcom bubble burst, many spectators wondered whether the marriage of Internet and business was merely a fad, while many speculators, wondered whether the markets would or could regain confidence in digital businesses.However, in the decade and a half since the Tech bubble, the world has since seen the creation, growth, and maturation of digital industries, as well as innovative new digital approaches to traditional brick and mortar industries.Few could have predicted the rise of the online-only business or the virtual company, or that the most successful Internet firms could be quite so successful with market caps upwards of $400 billion. The Web 2.0. Era has even seen the creation of profitable offline businesses housed in online video games!One major driver of competitive advantage is predictive analytics â€" the collection of statistical and computing techniques that allow firms to use historic and dynamic data, aggregated digitally, to create probabilistic models of the likelihood of future events. Predictive analytics has a range of applications, though it is used most commonly to optimize decision-making and to determine consumer preferences. This powerful field has become a cornerstone of the business strategies of many a household name. Indeed, it has even led to the creation of many innovative â€" and profitable, new business models. © Shutterstock.com | ImageFlowHere, we will 1) briefly cover traditional business models; and then address 2) how brick-and-mortar firms are enhancing their businesses with predictive analytics; as well as illustrate 3) examples of new business models that incorporate predictive analytics.TRADITIONAL BUSINESS MODELSFundamentally, there are several basic business models: owners/landlords, manufacturers, distributors, and sellers. Within these broad categories are a number of more specific models â€" for example, sellers include wholesalers, brokers, traders, retailers, and multi-level marketers. There is also a variety of conventional pricing, supply chain, marketing, and other business strategies, which are often confused with business models and help differentiate firms from each other. Predictive analytics has upended many of these traditional models and strategies of doing business in myriad ways.In short, predictive analytics allows firms to create models of consumer behavior th at are correlated positively with historical data, and use these models to forecast future results. Because, in our Digital Era, data comes in in real-time and because we have developed highly sophisticated and robust hardware and software systems for processing this “Big Data,” firms can feed data into these models in real-time and adjust their business decisions automatically.For example, firms can use predictive analytics to drive dynamic pricing, a strategy that predates predictive analytics, but has been enhanced by it significantly since. A hypothetical hotel might determine pricing during a holiday season by building a model involving prior year data, prior month data, and even prior hour data, by automatically feeding all purchase transaction information into a model designed to optimize revenue and changing pricing based on real-time performance. To the consumer, this might look like a pricing schedule that varies as vacancy rates rise or fall. Predictive analytics has greatly enhanced the performance of many brick and mortar establishments.ENHANCING BRICK AND MORTAR BUSINESSES WITH PREDICTIVE ANALYTICSAs digital enterprise began to flourish in the early 2000s, many brick and mortar retailers lost market share and went out of business. Those that did not, largely, developed successful online strategies to strengthen their existing competitive advantage, or create one. Some used digital technologies and predictive analytics to enhance their marketing; others used it to enhance their supply-chain management, among other strategies.Innovations in marketingPredictive analytics has allowed marketers to increase their estimations of consumer likelihood to purchase. This has allowed firms a to optimize their marketing mix, allowing them a greater ability to target consumers. It has been a boon to traditional retailers such as Macy’s. In 2014, the retailer partners with SAP, a leading provider of software and services, to improve its existing predictive analytics software. The new solution allows it to build multiple predictive models that aid it in targeted email marketing and digital marketing campaigns. In its first three months using the solution, Macys saw, on average, a ten percent increase in sales.Other retailers use the data they receive to build a personalized shopping experience for the consumer online, and/or optimize the overall shopping experience in a retail location. By parsing consumer data and paring it with predictive analytics models, firms can create targeted online messages, unique promotional opportunities, and other incentives to drive them to a brick and mortar retail location.Beyond driving traffic, predictive analytics’ real promise for marketers involves increasing customer lifetime value. Cost-per acquisition can be exceedingly costly, especially for non-essential items and/or in crowded markets. Personalized experiences can deepen customer engagement and brand loyalty, increasing the value of their lifetime purchases and decreasing your retention cost. When you determine the return on investment for particular customer segments, you can more effectively determine the optimal ones to target.Analysis of sales data and sales analytics can also yield significant, positive implications for supply chain management.Supply chain managementSupply chain management is another strategic business area that has been transformed by predictive analytics. Prior to the Digital Era, sourcing decisions were made based on annual evaluations of sales data, personal relationships with suppliers, regional distribution chains, and past practices. Now, predictive analytics provides brick and mortar chains the insights to be able to shift sourcing based on real-time data, determine whether new suppliers of a particular product or skew will increase or decrease revenue, and determine ideal wholesale and retail pricing.One such firm is brick-and-mortar retailer Walmart, whose online storefront is a viable competitor to other market leading online retailers. Walmart, whose suppliers are located in more than 70 different countries, and whose stores stock an average of over 175,000 products, is immensely profitable because it aggregates data on every aspect of its retail operation and analyzes them to forecast demand and consumer purchase behavior. Each day, Walmart feeds the reams of data it receives from in-store and online sales-tracking and inventory management systems and feeds that information back into its supply and distribution systems. By coupling this with sales forecasting data from local demand forecasting models, Walmart stores can minimize all product shortages significantly.Predictive analytics is also used to optimize sourcing and shelving decisions. Through simulation and analysis of historical data, Walmart is able to use predictive analytics to determine the product mix that will allow it to achieve the highest sales revenue at the lowest wholesale cost in the least amount of time. Part of this equation is determining where products should be located in the store to stimulate the most sales growth. All store variables are tracked vigorously and assessed in real-time to ensure the firm’s success. EXAMPLES OF NEW BUSINESS MODELS USING PREDICTIVE ANALYTICSWhether a firm is choosing what products to manufacture or distribute, a retailer is figuring out what to source, or a franchise is seeking the ideal owner-operators, predictive analytics can yield tremendous results for every business type. However, for some firms, predictive analytics has created such a significant competitive advantage that it has yielded entirely new ways of doing business.Online-only distributors and retailersDuring the early days of the Digital Era, the thought of an online-only distributors and retailers was hard to grasp. True, the reduced operating expenses could serve as a source of competitive advantage, but established brick and mortar businesses had the benefits o f human capital, strong brand recognition, advertising dollars, facilities, and relationships.However, as time has passed, digital technologies have emphasized data  â€" bringing it on par with, if not making it more important than, relationships as the basis for B2B sales and strategic partnerships in many industries. It has allowed us to have a distributed workforce, eliminating the need for central facilities. It has allowed us to build brand recognition in new ways, some for a fraction of traditional advertising costs (indeed, it has eroded them). Finally, it has allowed the online only distributor or retailer a significant source of competitive advantage in the form of Big Data and predictive analytics.Take human capital â€" particularly in sales. Firms can use predictive analytics to predict consumer preferences through recommendation systems. They can then immediately upsell. A human salesperson requires training and a professional demeanor at all times to upsell, whereas a few lines of code are all that is needed for a consumer to receive a personalized suggestion. Netflix is a strong example of an online-only firm that use recommendation engines to drive sales. Netflix’s competitive advantage placed significant pressure on its early rival Blockbuster, which pre-2010, was a household name and market leader in video sales. But with 60,000 employees at its peak, compared to Netflix’s 2,000, as well as an inability to efficiently forecast demand and supply of its products, Blockbuster lost significant share to Netflix and eventually filed for bankruptcy. And while the recommendation engine reduces labor costs, it also drives sales (or in Netflix’s case brand loyalty, as they charge a flat fee) automatically and in real-time.Human labor reductions are not the only use of predictive analytics. Indeed, many firms have used predictive analytics to forecast talent shortages, predict the likelihood of employee retention, and forecast an employment candid ate’s probable performance with their firm. Google is well-known for using what is known as people analytics â€" the application of predictive analytics to human resources to forecast an employee’s future career trajectory inside the firm. It also uses people analytics to design workspaces and collaborative opportunities that optimize employee innovation. And Google has long been known as a firm whose talent has been a source of significant competitive advantage.Online Auction-Based BusinessesPredictive analytics also can be used to drive auction-based businesses. For firms where price optimization is critical, like eBay, which derives revenue from fees placed on user auctions, predictive analytics can be used to determine the price. For firms that auction their inventory, and whose fees vary based on the total transaction amount, predictive analytics can help forecast demand and scarcity, and allow these firms to set initial bids efficiently.Firms can use predictive analytics c oupled with software purchasing applications to purchase goods or services automatically when those goods or services hit certain price targets or other thresholds. This is seen in high-frequency trading, where speculators bid on certain equity or debt instruments meeting certain conditions. Some programs use models and inputs developed entirely by humans. Others have paired predictive analytics with machine learning â€" the process by which computers process increasingly complex information by constructing learning algorithms. This paring allows programs to make decisions without human input concerning bid behavior based not just on the results of the model (that may include thousands of variables), but their own past purchase behavior.eBay takes predictive analytics a step further than its online auctions. Given its scope (an e-commerce portfolio with approximately $300 billion in transactions), eBay uses predictive analytics to optimize decision-making. Their SAP-designed  syste m is used to predict problems through daily forecasting, and run simulations on the effect of decisions on the entire portfolio. Other firms, particularly those in financial and legal services use predictive analytics to forecast potential issues as such issues can be extremely costly for their clients and themselves.Online /Mobile Advertising and Ad ExchangesDigital marketing, which accounts not just for business strategy, but also comprises a healthy industry of digital marketing agencies, is driven by data and analysis. The fields of search engine optimization, for example, involves leveraging a firm’s digital and non-digital assets to drive traffic to a landing page. To do so, a firm must build models of what has worked to inform its strategies going forward. Since digital data can be obtained in real-time, and most web analytics programs now have considerable analytic and testing tools, digital marketers can quickly test and implement some strategies based on their models. So me firms implement programmatic buying â€" buying using the results of predictive analytic models and machine learning, to make and serve ads and marketing content to users.The field of search marketing is also dependent on predictive analytics as it requires firms to forecast the search terms consumers will use to find their firms as well as firms in their industries, as well as related industries. Using web analytics programs allows digital marketers to determine the ideal keywords and key phrases on which to bid. Predictive analytics models can be used by firms to aid in the accuracy of the bidding process.Search itself is driven by predictive analytics. Though the algorithms are proprietary, firms like Google and Microsoft create lists of relevant website links based on user inputs that are not just limited to what one types into the search bar. These firms spend hundreds of millions of dollars working to refine these lists, “predicting” that they will be exactly what you ar e looking for. And by transforming the way firms do business, innovative search engine firms, such as Google, Baidu, and Yahoo have revolutionized industry.Digital advertising is driven by predictive analytics. Many online and mobile search ad-serving platforms create models of target customers using a client firms data. They then serve ads to online users who fit that profile, using cookies to track their movements. A user could visit a partisan political website and a food website, for example, and see the exact same ad on each. This is known as retargeting and is used by many firms, such as apparel retailer Levis. A properly implemented retargeting campaign can increase click-through rates and conversions.