Social selling
Just 2 percent of European companies specialized in various areas use the high potential of data driven marketing, showed a study conducted by Google and the Boston Consulting Group. Companies were separated into 4 main groups. The study found out that, whereas most of the companies are playing in the middle field, just a few belong to the best. Let us take a deeper look and find out why.
The reason might be that not all companies have yet realized what powerful asset customer data is and how to implement this change. To compete with the best companies, one needs data driven marketing. To obtain data in the best possible way, one needs to collect data in various ways, regardless whether data is collected through digital cookies or with loyalty cards at the point of sale.
Predictive analytics can help lower your risks and make safer decisions. Every time a company invests in an expensive marketing campaign they run risks because there is always the chance that the campaign fails and a pile of money simply disappears. However, when predictive analytics are in use a company is reducing that risk. The purpose of predictive analytics is to study human behavior and get a sense of how people will respond to certain situations, such as seeing an advertisement. It does that by taking into consideration a wide variety of statistics and human characteristics, all of which are focused on understanding individual as opposed to general behaviors. In other words, you would not use data to determine which advertisement has the broadest appeal, you would use it to determine the likeliest responses of specific people to specific advertisement. More precisely, once you enter all your variables, you are given a predictive score. Now this score does not tell you the future, as much as it tells you how probable certain individual reactions will be. For example, let us say you want to know which online add people in the United States will be most tempted to click on while searching for grants and scholarships. The more variables you supply, such as, age, gender and the e-mail domain, the more precise the predictive score will be. These predictive scores are useful to organizations that want to know the best demographics to target for certain discount offers and advertisements, as well as for organizations that want to know which stocks to buy or people to audit. The predictive model used in predictive analytics is more dynamic than other models since it is based on machine learning, which means it can change grow into debt based on the kind of data is given and it is more accurate than other predictive tools, since it uses back testing, which takes all data to determine how accurate your results will be. So, if you are trying to predict whether the S&P index is going to go up or down in a years’ time, with back testing you can feed it all data from 1990 to see how accurate it is to S&P in 1991.
Making predictions lead to questions of morality, prejudice and responsibility. As our ability to use technology for predictive purposes gets better, an important question arises: How many spoilers you want to be given about your life and more importantly, how many lives are you willing to spoil. But it is more than just the question about knowing about your future a more pressing concern of predictive analytics and data mining goes hand in hand with it is privacy. The press found out that a certain company was using predictive analytics to predict, which customers were more likely to become pregnant. Many thought that, the company had gone too far, while the company said, it just wanted to advertise maternity goods, to the right women. This kind of marketing runs the risk of leaking people’s personal information to their friends, family and co-workers – information that those people may not be ready to share.
Many people think that opens the door for prejudice to enter into PA models. Imagine there are two criminals, who committed the same crime and they are both up for parole. One of them comes from a ZIP code with a higher crime rate. Because of the rate of crime in his ZIP code, the criminal will be deemed more likely to return into a life of crime. This would be clearly a prejudiced prediction. In short, it is just another alteration of racial profiling.
Although you may not be aware of the massive influence predictive analytics has on your daily life it is indeed just about everywhere. It not only influences the way technology interacts with you it also a driving force behind many technological advancements. So, if you want to know what innovations are happening in the world you should be familiar with predictive analytics.