Context with predictive analytics is the key differentiator for any successful recommendation. It is not only the quality, availability or price of the product, but the ‘context’ (which is real time) that helps make the most appropriate recommendation to its users. A consumer can be put into different profiles for different purchases, and so, the real time context, in which the consumer is carrying out the purchase, is very important to recommend correctly. In this article, I will talk about the importance of ‘context’ integration with predictive analytics and its success in making recommendations.
What is context?
The world is becoming more smart and interconnected with every passing day. Now, due to regular use of the Internet, a large amount of data is being produced every day, which is ever growing. Often, when we think about the Big Data, we think about its huge size and the problems that happen in its management. But that is not all, as this data can be used for the improving the sales of different firms with the use of the contextual data created from huge amounts of big data.
Context is actually a piece of historic data about a certain object. The object can be anything, starting from different physical locations to people themselves. The data is extremely important as it can be used for analysing different situations and then making relevant decisions. Context is essential for business as, without it, the decisions will go awry. By using such information along with Big Data, the businesses can learn more about the historic patterns and current trends. Thus, this type of data is useful for companies who want to make important decisions based on facts, and not fiction.
Why is ‘context’ so important?
Contextual data is extremely important as its correct analysis can heighten the productivity of many organizations and businesses. It can give important information necessary to guide the plans of these organizations. Modern Big Data processing techniques can be used to process large amounts of information from either the Internet or the real world. Such data can be used for the betterment of society by better prediction methods, which will allow more profits for businesses and smart solutions to consumers.
Such data can be made even more useful with their integration with machine learning techniques and artificial intelligence. In this way, the data can be even used for the proper prediction of natural disasters like earthquakes, or by forecasting weather accurately. Businesses must continuously analyze new data in order to process new contextual information, in order to provide effective services to their customers. For this, they need to extract data from emails, smartphones and social media. Also, they will have to process all of this data in real time.
How ‘context’ can be integrated with predictive analytics?
Predictive analysis is not a very recent advancement and it was discovered many years ago. However, the newer techniques, utilizing the latest technology, is driving the movement forward more quickly than imagined and is providing near accurate predictions almost every time. The recent advancements in the field of information technology and artificial intelligence have made many businesses surpass their estimated profits, but even more is possible to achieve.
This can happen by knowing the fact that data cannot be useful from only just one angle. It has to be viewed through multiple angles, which can be done by creating an improvised profile of consumers as well. Here’s where contextual data comes in. The contextual data can be used to priorities a particular aspect which can result in more profit. Normal records like the transactional logs may not give very important information related to a subject, contextual data like behaviour logs can give essential insights used for making accurate predictions.
How contextual integration helps successful prediction?
Many organizations analyze the Big Data resources to find out more about the target entities and also use this information to make their business plans. For understanding this, we can take the simple example to social networking sites, on which the users generate a lot of information about their preference and dislikes. These places can be kept in check regularly, for some important behavioural data, which can be utilized to make real-time context analytics. Also, more effective pattern detection methods can be used in such places where a large amount of data is being generated regularly.
Big Data has a huge potential in helping predictive analytics. The information derived from contextual data is also very important for successful predictive analytics. However, for it to be truly effective, the organizations will need the knowledge, so as to properly apply a context to the Big Data. This will reduce the chances of an error.
The combination of Big Data and context analytics can be a powerful one which can help in the prediction of different outcomes and other factors. Some more advantages of using context analytics is that it enables the organisation to use contexts for correctly modelling a solution for users and that it helps in making correct behavioural observations from such data.
Some practical implementations
There are many practical applications of contextual information. For example, recently, an online computer parts seller called ReplaceDirect started using this service to effectively manage its budget while getting the maximum views and customers. This company used contextual information for the prediction of many items, like the most desirable keywords which would be used to search for their site and the best bidding prices on the most searched according to the data.
Also, some Video on Demand services incorporates the use of such contextual information for predicting the most desirable movies to be shown to the customers and the best time slots for maximum views.
Future of contextual integration
Contextual integration is very important for those businesses which want to get the maximum profit with the use of predictive analytics. With the advent of more and more devices, more data will be generated which could be mined with the help of advanced data mining software. The data can then be quickly processed into useful contextual information.
The advanced data mining and processing techniques, which will be fully deployed in the near future, will be able to make better sense of the data and process large amounts of contextual data in near real-time. Also, precise modelling can be done through this data. In the future, this data may also find application in many different areas other than business sectors, like finding the pattern of an earthquake to predict its next outburst, or modelling an epidemic map easily.
The effective analysis of contextual information is an important trait that the organizations will need to adapt and improve for successful deployment of any service and also for the prediction of an outcome. Also, the data can be integrated with a model to make it even more accurate. Contexts can also help in visualization modelling. Contextual information, if processed in real-time, can reveal very much about an entity, like whether it has become popular or unpopular.
Contextual integration can also help the customers to easily and quickly navigate to his/her desired place and get the desired service. In a similar way, the organizations can navigate to the desired information more easily. This can help businesses achieve huge profits and result in customer satisfaction too.