The use of business intelligence and marketing information systems has expanded in recent years. Through advancements in technologies, marketers can extract value from very large data sets. Very often, companies can benefit if they use and reuse the same data to extract added value from it. Sometimes, it would also make sense for these companies to acquire data that they do not own (or data that was not collected).
All individuals leave a “digital trail” of data as they move about in the virtual and physical worlds. This phenomenon is called, “data exhaust”. Initially, this term was used to describe how Amazon.com used predictive analytics in order to suggest items for its customers. Predictive analytics could quantify the likelihood that a particular person will do something — whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job. Such data anticipates human behaviours that have not happened as yet. For instance, Fedex has predicted which customers were most likely to defect to competitors. Even, Hewlett-Packard made a good use of suitable data to identify employees that were on the brink to leave the company. The latter corporation took remedial decisions in anticipation of staff turnover.
Predictive data is usually based on large amounts of cur¬rent and past indicative information that may have been collected from multiple sources. Such data could also provide additional details of customer personas, segments and prospects. Quantitative techniques can be deployed to find valuable patterns in data, enabling companies to predict the likely behaviour of customers, employees and others. First Tennessee Bank had used predictive analytics to increase its marketing response rate by better targeting its offers to high-value customers (IBM, 2015). Through predictive analytics businesses’ could quantify how many consumers will buy their products after receiving electronic mail. They may also measure how effective their personal mailing was.
Nowadays there are fewer inaccuracies in the measurement of big data. In addition, many applications of data can arise far from the purposes for which the data was originally intended. However, big data and predictive analytics could raise a number of concerns. Minor increases in the data accuracy of predictions can often lead substantial savings in the long term. There many companies that have saved significant financial resources by using predictive analytics. For instance, “Chickasaw Nation has used predictive and patron analytics to reduce its month-end close processes by 50%. This way it has also improved customer experience. In a similar vein, predictive tools and smart cards enabled Singapore Land Transit Authority to provide a more convenient transportation system.
Although, individuals tend to regularly repeat their habitual behaviours, pre¬dictive analytics cannot determine when and why they may decide to change their future preferences. The possibility of “one off” events must never be discounted. Many customers may be wary of giving their data due to privacy issues. The underlying question is; when does personalisation become an issue of consumer protection? In 2012, consumers learned that Target was using quantitative methods to predict which customers were pregnant. Very often, advances in technology are faster than legislation and its deployment. These issues could advance economic and privacy concerns that regulators will find themselves hard-pressed to ignore. It may appear that digital market manipulation is pushing the limits of consumer protection law.
Evidently, society has built up a body of rules that are aimed to protect personal information. Another contentious issue is figuring out the value of data and its worth in monetary terms. In the past, companies could have struggled to determine the value of their business; including patents, trade secrets and other intellectual property.
Despite its numerous pitfalls, the market is responding to the emerging demands for corporate IT solutions. Extant relational databases are capable of handling a wide variety of big data sources. Statistical analytical packages are similarly evolving and are working in conjunction with these new data platforms, data types and algorithms. Furthermore, big data is also being modified for those clients that may require cloud-based services. Cloud-based service providers offer on-demand pricing with a fast reconfiguration facility.
This short contribution suggests that in the foreseeable future many corporations would require bespoken software that is relevant for their particular line of business. Customised business intelligence software and big data systems allow organisations to load, store and query massive data sets in short time periods. Business could make good use of structured data (such as demographics) and unstructured information (including text and images) to improve their operational performance and customer service levels.