Computer data analysis in marketing research

This is as easy as that: economics is based on mathematics, and a computer in its turn is an absolutely perfect device for number crunching, here this close connection between them follows.

Extracting information from data helps bring calculations up to a brand new level. In banking, industry, trade, and marketing people get help from reliable digital assistants, which carry out billions of mathematical operations per second. The primary goals are the following: classification, clustering, and forecasting. Neural networks, decision trees, genetic algorithms, fuzzy queries, and analysis are employed here.

We are all “under a microscope”. Big companies store terabytes of data about their clients. This information brings a colossal increase in profit for businesses and user-friendliness for customers. If so, does data mining favor the development of big companies only? Not at all. Everyone can use data mining for their purpose, data availability is the only condition.

Forecasting

As a result of solving the problem of forecasting, it becomes possible to evaluate the target characteristics of the subject by analyzing historical trends. For example, browsing the characteristics of earlier sold houses, apart from the area we can see the influence of such factors as quantity of bathroom units, number of stories, swimming pool, and other features. Each of them contributes to the cost of the house, being irrespective of each other and “blurred” in time. Having enough information concerning house sales in your city at your disposal, you can easily forecast the prices with high accuracy. Thus, the use of API property sale companies may serve as a self-reliant solution for getting information and creating real estate evaluation services in different areas. A classical problem of estimating the borrower’s reliability is also frequently solved in banks.

Classification

Classification is a systematic dividing of studied subjects, phenomena, and processes according to their types or any essential features for ease of research. It is also a grouping of initial concepts and their arrangement in a certain order, which reflects the degree of their similarity. For example, the data on supermarkets’ sales is given, namely the cheques with the lists of sold goods and their prices, time, and ID of a discount card if any. Having counted the share of each product in each cheque, you can conclude that bread, milk, and butter are the most saleable items. Beer, snacks, and yogurts are goods of the second category, spicery, and canned food are the least profitable. In such a way, you can distinguish key goods and shares of each category in a total profit. You can analyze different characteristics getting a wide range of categories to improve data understanding for a person who has a limited calculation resource.

Clustering

Clustering is designed to divide the plurality of objects into groups according to their similarity. If you imagine sampled data as points in featuring space, then the task of clustering is reduced to a “points” concentration. Application of clustering methods to a supermarket’s sampled data helps extract such groups as “young family”, “cheese lovers”, “sweet tooth” and others among the customers. Information about customers is essential for increasing advertising campaign conversation. Personal newsletters bring down the price of information delivery to the customer. Enhances conversation and customer loyalty. The construction of recommendation networks based on users’ similarity also refers to this kind of task.

Figuring out the pairs of goods refers to group search tasks. “Beer and snacks”, “Milk and cookies” and other less noticeable groups of goods, are sold together more frequently than the other. Naturally, market stimulation of either item boosts sales of another one. Data mining is also much used in the transport sphere. A computer system analyzing route data provides the most optimal tracks for carriers and transporters. It can independently give guidance for each car leading and calculate the route. Most given examples would take ages, which would reduce the actuality of information to zero. If at a particular point in time, this branch of science would let a man create sort of a time machine.

Energetics, economics, biology, medicine, agriculture, and many other spheres are successfully using the magic of big data, picking up its secrets. Fast-growing services for data analysis became available for small businesses several years ago, which will undoubtedly encourage the growth of this sector and raise lots of new kinds of activities, changing the world around us.