The Value Of Predictive Analytics

So many users of the internet blindly browse and post on these sites without any thought to the online identity they are creating for themselves. Shares, tweets, hashtags, likes, and comments all combine to make up an amalgamation of marketable information. In November of 2016, an average of six thousand tweets were made per second, and ninety-five million Instagram posts were made per day (Sayce; Parker). Facebook “has become the largest database of personal information ever collected,” says Richard, and Facebook takes advantage of this. With almost two billion users, Facebook has no shortage of information to gather (Sparks). They do this mainly for targeted advertising. There is no small profit to be made in this. In 2011 Lori Andrews wrote, “Facebook made $3.2 billion in advertising revenue last year, 85% of it's total revenue.” However, Facebook and other internet corporations also relay data gathered on users to the

“Predictive analytics uses technology to predict the future and influence it.” [27] It is predominantly being used to improve business processes, which is a great opportunity for entrepreneurs to achieve positive business outcomes [26]. The goal of this white paper is to discuss the impact of predictive analytics in today’s world and the various concerns that come along with it. The paper addresses key research questions like what are the legal and ethical concerns that rise from predictive analytics? And where can we use predictive analytics to get positive results? We have tried to analyze the current market situation in order to answer these questions, focusing on the key areas where predictive analytics has had positive and negative impact. After intense scrutiny of the facts and details encountered by us, we have come up with some recommendations and solutions to address the issues caused by the use of predictive analytics and how their effects can be balanced by organizations.

Rotating Facebook ads send patients to your site and the homepage messaging + pages that mat-ter most to them. We run the ads when your audience is most likely online, and select the cam-paign-specific geographical area, age/gender and income to target. Catalyst HCM watches the response and adjusts accordingly, replacing the ad with an alternative to boost performance if necessary. [We cannot use the word ‘you’ or ‘yours’ in ads.]

In today’s companies, the analytics software plays the important role and guides the future activities to a great extent.

Facebook relies on ad advertisements in their business model. Facebook platform empowers developers to build applications and web sites that integrate with Facebook to reach its global network of users and to build personalized and social products.

I find this highly invasive and disturbing. A recent rumor holds that Mark Zuckerberg intends to reduce the Ad content on Facebook and this has resulted in a drop in the value of FB stock. I, for one, would be happy to see this rumor become fact. But, this is only a small part of the problem which Harris points out to us. It’s not just Ads, it is the way our personal data is used to manipulate everything we do. We may think we are in control of our time, but by following every “ding” and every tangent presented to us, we are not, actually, in control of our time, at

First is the advertising. According to the case, we know that the major revenue of Facebook is advertising, which took up 98 percent in 2009, 95 percent in 2010 and 85 percent in 2011. Facebook uses all information uploaded by users to become the property of the firm. By analyzing database, Facebook provides advertisers target customized services and products based on users’ preferences and connections. In its view, the advertising which based on social

Facebook has a specific page of helping the customer to refine your advertising to reach the people who matter most to your business. This indicates that most of advertisement appears on Facebook are well refined based on user’s trait.

Predictive Analytics is an important requirement for our Industry and LOB applications, and BI solutions. It is a significant market - $2.0B 2014 (IDC), in which we have a minor presence.

The problem started becoming increasingly important when online retailers like Amazon made their way into the lives of many consumers. As more people became proficient with technology and the internet, the popularity of online shopping increased. Online shopping giants like Amazon and eBay are destroying the economy by enticing shoppers to spend their money online, rather than at an actual retail store. The problem has deep roots in people’s desire to be informed before making purchases. As seen in the article, “Bricks or Clicks,” 44 percent of consumers do some type of research about a product before purchasing it in a store (Sunil, 2013). Much of this

Retail stores often store large varieties of products, which may become an issue for them. It is very costly for them to store large varieties of products that are sold seldom, while further this can also create consumers to land in a situation of information overload due to being exposed to an increasing variety of products and information (Walter et al., 2011). According to Walter et al., (2011) recommendation systems provide a “scalable technology to establish a personalized communication channel between customers and retail stores.” Due to this new technology, retail stores can more easily identify the preferences of their customers and thus offer them products that best match the customers’ need. The customers benefit by reduced search costs and reduced information overload. Furthermore, the retail store can bring forth more appropriate goods for the consumer making best use of their assortment

Data analysis and predictive modeling with all its scope, has already reached to the very center of our operative organization. Currently, to improve the efficiency of operations, all companies, government bodies, institutions or industries are making decisions guided by predictive stats. In addition, with more data, enhanced hardware, increased knowledge, and analytical advancement, we see extensive promise towards predictive analytics. For example, predictive analytics based on our biometric values differentiate from an imposter while starting engine of our car, Music app plays song depending upon your mood level, etc. Of course, these specifics are just speculations; predictive analytics yet need to be explored fully. However, we are confident to see growth and improvement in the field.

Being the first who enter the Chinese online search market, it has gained majority of the market share in China. Many people are used to the platform and competitors find it too costly to imitate and roll out equivalent products and services to serve customers’ needs.

• Building new products and services supplemented with Big Data analytics and privacy by design, developing products adapted to European privacy standards . The power and opportunity of big data applications used well, big data analysis can improve economic productivity, drive improved consumer and government services, prevent terrorists, and save lives. Examples include:

In general, we people often make decisions based on suggestions from our friends, family members or even from the people who have better knowledge and experience with the things that we want to make decision on. And moreover, in the today’s world, things that we want to buy or make decision on, are coming with a vast number of alternative solutions which is throwing us into the difficulty to make a decision. For example, when we want to buy a television for the first time, it will be very difficult for us to choose the right one from the list with a large number of alternative solutions ranging from different makers to different models from a maker. Because of this confusion, we will consult people who have better experience and knowledge on these things and ask for their valuable suggestions or recommendations that will help us in making decision. This type of suggestions and recommendations can also be provided by using software tools and techniques which are termed as Recommender Systems or Recommendation Systems (RSs). A recommender system, often recommends or suggests the items which may be kind of useful to the user. And these recommender systems are mainly employed by the e-commerce industries who sells millions of items to the people through internet. Here, these RSs are employed to suggest or recommend items (which may be of useful) to the users based on their past purchases and also at the same time to increase their sales.