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The Impact of Analytics and Data on Business in 2016 and Beyond

Analytics and data have been shaking up the multiple industries and the impact of this would be more distinct as acceptance reaches significant mass. 2016 was marked to be an exciting year for the organizations using big data, being able to improve on the real-world solutions with the analytics of big data. 2017 would just be the continuation of the same as technology is making smarter moves with the functioning of deep learning by various organizations like UBER, Starbucks and Amazon (Provost  & Fawcett, 2013).. Organizations are currently boarding the strategy of Data Lake applications that are centralized along with the ones coming on a single fundamental platform. Organizations have started realizing the fact that they have been collecting enormous data for profitable decision-making in business and that they would be able to obtain value from the same through data lakes. This report would be dealing with two organizations that have made the best use of Big Data in their business and have merged ahead of the competition. UBER and Starbucks are those two companies that have strategized better and made effective use of the data to understand the market, the customers and provide better solutions in meeting the expectations of the customers.

Uber is the smartphone-app supported taxi booking facility that has been connecting the users needing to get wherever with the drivers eager to give them a journey. However, the service has been immensely contentious, with the usual taxi drivers asserting that it is obliterating their livelihoods along with the regulation over the drivers of the company. This allegations has not been able to tarnish the success of UBER, since it has been launched in purely serving San Francisco in 2009. This service has now being expanded to other major cities of the continents except for the region of Antarctica.

The business model of UBER is firmly based on the application of Big Data and leveraging the same in an effective manner than the conventional taxi firms, playing a major part in its success (Varian, 2014). The overall model of the business is based on crowd sourcing of the Big Data principle. UBER is blessed with an enormous database of the drivers in all the cities it has been covering, so if a passenger asks for a ride, they have the ability in immediately matching the passenger with the most appropriate drivers. Fares in these cases are being calculated mechanically, using the system of GPS along with the organizations’ own algorithm in making certain adjustments based on the journey time that is likely to obtain. This is one of the major differences from the normal taxi services as customers are charged for the time that the journey would be taking and not for the distance that they would be covering.

UBER: How Algorithm-Based Pricing and Big Data are Reinventing the Taxi Industry

Surge Pricing:

These sort of algorithms monitors the conditions of the traffic and the time required for journeys in real-time, stating that prices can be regulated with the change in the demand for the rides and the traffic conditions implies that the journeys are expected to take longer. Thos factor has been encouraging the drivers in getting behind the wheel when they are being required the most and staying back at homes when the demand is low. The company has functioned in a patent way on this method of Big Data- learned pricing, which is otherwise known as the ‘surge pricing’.

This approach of algorithm-based having little supervision has occasionally been the reason for problems- reports suggesting that the fares were being pressed up sevenfold by the new York traffic conditions on the eve of 2011 New Year, with one mile journey seeing a rise in price from $27 to $135 over the route of the night (Michael & Miller, 2013). This has been an implementation of ‘dynamic pricing’, which is similar to something used by the chain of hotels and the airlines in adjusting the prices for meeting the demands instead of escalating the prices on public holidays or weekends. UBER through the effective use of Big Data makes use of the analytical modelling in estimating the demand in real time.

UBER Pool:

UBER has modified the ways people have been booking taxis over the years. CEO of UBER, Travis Kalanick claimed that the service would be able t cut down the number of private, operated by owner vehicles plying on the roads of some of the most congested cities across the globe (Marz & Warren, 2015). He added that the car-pooling facility of UBER Pool service would be capable enough in cutting the London traffic by a third.

UBER Pool facilitates the users in finding others nearer to them, which as per the data of UBER, frequently makes comparable journeys at same point of time, providing a share ride with them. This service introduction became a no-brainer when the data depicted them the immense majority of having a look-a-like trip- considering a trip starting and ending nearer and is taking place at the identical time as another trip.

Rating Systems:

As per Wu et al., (2014), the UBER service also relies heavily on the comprehensive rating system, with the users being able to rate the drivers and vice versa, a process that helps building of trust and allowing both the parties in making knowledgeable decisions about who they desire to allocate a car with. Drivers, especially needs to be conscious of setting and keeping the standards high, as one of the leaked documents illustrated that those with scores of low rating below a certain brink faces being ‘fired’ and not being presented with anymore work (Provost  & Fawcett, 2013).

Big Data in Action: UBER Pool, Ratings Systems, and the Challenges of Dynamic Pricing

There is another metric that the drivers are worry of, is their ‘acceptance rate’. This states the number of jobs that have been accepted versus the number that has been declined by them. Drivers have been told that their aim should be in keeping this above 80 per cent for providing a reliable available service to the passengers. UBER has been strong enough in providing fitting response to the protests made by the drivers of conventional taxis over its service in attempting to co-opt them through a new segment addition to its fleet (Zysman et al., 2014). UBER Taxi was brought in picking passengers up by a taxi driver who was licensed in a registered private rent vehicle, joining UBERX and UBER Lux as standardized options (Varian, 2014).

Learning from UBER’s Big Data Use:

UBER’s use of Big Data have taught people a great deal about the usage of Big Data and not just idly sitting on the notion. They have taught people in looking for associations in every manner possible of the data. Every time one is gathering any sort of information but nt making use of it, there is the probability of missing the opportunity in growing and improving one’s business. If a certain tool does not exist in crunching the data the way one wants it, that particular person or business needs to take steps in making it work. Even if that leads to the rudimental cobbling of the tools that are existing, similar to what UBER has performed, requiring reworking the existing systems for fitting into a completely new mould (Cohen et al., 2016). This has facilitated UBER in remaining supple and adapting on the important traits that any good digital marketer would be able to practice. It is worth comprehending the fact that, much like UBER records the rhythm of a specified city that not every answer one gleans from the data can be approved over along with making use in blanket-like fashion to a different city. Collecting the data in independent fashion and evaluating it for what it actually is opens up the opportunity that lies within.

As per Walker, (2015)., UBER underway in small fashion, but their San Francisco successes made it realize that such sort of service can be highly beneficial across the whole of U.S. and around the globe, not just with normal cars but also with luxury sedans, SUVs, bike and foods. It is still to be seen whether the other services succeed the way UBER has, but there is no denying the fact that UBER pounces on the opportunities like no other.

What We Can Learn from UBER's Use of Big Data: The Importance of Adaptability and Flexibility

In the present scenario, global companies are dedicating a throng of resources in analyzing and monitoring the behaviour of customers, having the aim of offering better experience along with an augmented bottom line (Paharia, 2013). Starbucks, since its inception has heavily been dependent on the technology factor in assisting itself in preserving its market-leading position. The coffee chain has expanded to more than 24000 stores across the globe, kitted out with intelligent machines for brewing, smart ovens, state-of-art systems for mobile payments and wireless indicting hubs. However, at the heart of its ever-growing success is the evaluation of the big data. It’s much dependence on the data has led Starbucks in viewing itself as a technological company striving in making knowledgeable business decisions that are mainly based on patterns. There is existence of huge amount of data for sifting at the HQ of Starbucks as the firm provides approximately 88000 combinations of drinks, serving 4 billion drinks per year along with a base of 12 plus million customers on its loyalty design.

A Brewing Opportunity:

It would be interesting to find out the exact things happening when a company like Starbucks conducts around 90 million transactions per week spreading across its 2400 plus stores across the globe. The answer to that would be it would continue accumulating countless data. Over the period of last few years, Starbucks has been putting the data into good effect, integrating the data analytics into its efforts of sales and marketing. (Fuchs, 2014).

Mobile and Starbucks Rewards have become the integral factors of all these efforts made by the organization. The mobile app of Starbucks has over 17 million dynamic users with the mobile orders and pay recording over 7 million transactions per month. In USA, Starbucks’ mobile order along with pay makes up to around 27 per cent of the overall transaction of the company. The Starbucks Rewards boasts of around 13 million active members that enable the company in collecting more data and testing and rolling it out through the initiatives of data-driven factors (To?lukdemir, et al., 2016).

Pressing with Data Analytics:

At the time of the annual shareholder meeting, CTO, Martin Flickinger highlighted the goal of personalizing the experience of customer. The company experiences 90 million transactions a week where it gets to know a lot about the things people are buying,; their preferred location for buying, the ways they buy those products which the organization wants to unite the information with inventory, weather and promotions for delivering better personalized customer service. This can even take place at the time of shopping at Starbucks one has never been before. One might make the same Starbucks order most of the days, and that too at same time. The point-of-sales system of the store would be able to identify that person’s smartphone proximity, and offering the barista with that particular information.

Starbucks: Using Big Data to Personalize Customer Experience and Streamline Operations

The CTO also explained the fact that how this data can be promoted further to the up-sell consumers in a more effective and targeted manner. As per Elder,  Lister & Dauvergne, (2014), through the help of the data analytics, the organization would be capable enough in depicting customers their favourite treat in a picture at the same pint of time. This sounds crazy, though Martin Flickinger is optimistic about the fact that in the coming days and tears, customers would be able to experience better delivery of fundamental desire to distribute technology enhancing human association.

New Store Location:

The Director of market planning, Patrick O’Hagan, described about the ways big data helps in driving decisions on the opening of the new stores. Through one of the systems known as ‘Atlas’ Starbucks has been able to associate as many internal and external APIs as achievable, associating the data with R in building cannibalization replicas that establishes influence of existing stores when a new store enters the same arena (Majumdar & Sowa, 2016). O’Hagan’s referenced platform, Atlas is considered to be a mapping and business intelligence device augmented by GIS organization, Esri. The system takes into consideration a massive amount of factors, inclusive of the patterns of traffic, density of population along with the immediacy to the other stores of Starbucks in evaluating the locations of candidates for fresh stores (Khan et al., 2017).

Starbucks boasts of an incredible Business Intelligence team. Making proper use of the achieved insights, it verifies the economic possibility of opening a store in that location. It is therefore one of the impressive examples of converting data into knowledge and the sane knowledge into the business stratagem. Starbucks is gradually becoming the ‘Third Place’ between the work place and home due to the expediency of its locations (Elder,  Lister & Dauvergne, 2014).

Targeted Marketing:

Another major application for the profuse data of Starbucks is targeted marketing. A latest mobile app upgrade has started targeting the customers with discount schemes and rewards on few items based on the history of their purchase (Song, et al., 2014). Moreover, Starbucks did send out emails for re-engaging the dormant customers. The substance of such emails has been targeted towards each of the customers, based on their purchase history. This is stated to be particularly strong, considering the fact that Starbucks provides around 87000 unique combinations of drinks.

One of the major interesting components in the efforts of data analytics of Starbucks is the weather. According to To?lukdemir, et al., (2016), the organization has been immensely trying to recognize the consequences of not only seasonality, but also the regular weather on the order patterns of the customer. This would be allowing in higher level of customization along with more efficient target marketing in driving up the revenue factor.

Learning from Starbucks’ use of Data Analytics:

According to David Dobson, Senior Industry Advisor of Retail and Consumer Goods Industry in EMEA at Intel stated that there is enough to be learned from the advancement of Starbucks. Creating reports is no longer considered to be good enough in the present day business. One needs to invest in employees who have the ability in interpreting the data, predicting the trends likely to happen in future and generating actionable insights for staying ahead in competition. The best news is that there is existence of platforms and tools within markets for any sort of business to influence the strength of analytics. It becomes easy for other businesses to study the ways Starbucks has been able to make use of Big Data in being to a position where it is today through the efficient use of the data (Oh & Doo, 2015),

Conclusion:

The Big Data concept has been around for years, though it has only been recently that the organizations have started realizing the importance of using it. In the present scenario, every industry is making the best possible use of it as proper understanding of consumers and their behaviour would lead to satisfying their needs. With data being constantly flowing in and out of companies like UBER and Starbucks, it is significant in establishing repeatable procedures for building and maintaining the standards for the quality of the data. Once the data is considered reliable, organizations should be able to institute programs of data management in getting the entire organization on the same page. This paper has reflected upon the success of UBER and Starbucks in utilizing Big Data to their advantage, certainly understanding better than other companies in their respective industries on the ways to deal with customer satisfaction.

Reference:

Cohen, P., Hahn, R., Hall, J., Levitt, S., & Metcalfe, R. (2016). Using big data to estimate consumer surplus: The case of uber (No. w22627). National Bureau of Economic Research.

Elder, S. D., Lister, J., & Dauvergne, P. (2014). Big retail and sustainable coffee: A new development studies research agenda. Progress in Development Studies, 14(1), 77-90.

Fuchs, M. (2014). Gamification as twenty-first-century ideology. Journal of Gaming & Virtual Worlds, 6(2), 143-157.

Khan, Z., Khan, Z., Vorley, T., & Vorley, T. (2017). Big data text analytics: an enabler of knowledge management. Journal of Knowledge Management, 21(1), 18-34.

Majumdar, A. K., & Sowa, J. F. (2016). Big Data: Structured and Unstructured. Big Data and Business Analytics, 249.

Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems. Manning Publications Co..

Michael, K., & Miller, K. W. (2013). Big data: New opportunities and new challenges [guest editors' introduction]. Computer, 46(6), 22-24.

Oh, S. J., & Doo, I. C. (2015). Analysis of Relevance of Myth-motif Brand'Nike'using Big Data of Portal Sites, Twitter and Blogs. International Information Institute (Tokyo). Information, 18(5 (A)), 1555.

Paharia, R. (2013). Loyalty 3.0: How to revolutionize customer and employee engagement with big data and gamification. McGraw Hill Professional.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.

Song, G. Y., Cheon, Y., Lee, K., Lim, H., Chung, K. Y., & Rim, H. C. (2014). Multiple categorizations of products: cognitive modeling of customers through social media data mining. Personal and ubiquitous computing, 18(6), 1387-1403.

To?lukdemir, M., Tuygan, E., Ye?il, H. E., & Kayakutlu, G. (2016, September). Evaluating business success through social media strategies using AHP. In Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on (pp. 1397-1401). IEEE.

Varian, H. R. (2014). Beyond big data. Business Economics, 49(1), 27-31.

Walker, R. (2015). From big data to big profits: Success with data and analytics. Oxford University Press.

Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. ieee transactions on knowledge and data engineering, 26(1), 97-107.

Zysman, J., Kenney, M., Hassel, B. J., Kushida, K., Lewin, A., Neilsen, N. C., ... & Seppala, T. (2014). Sustainable Growth and Work in the Era of Cloud and Big Data: Will Escaping the Commodity Trap Be our Undoing? or Where Will Work Come from in the Era of the Cloud and Big Data?. Berkeley Roundtable on the International Economy BRIE Working Paper, 6.

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