Post by account_disabled on Mar 5, 2024 13:26:45 GMT 8
Since business intelligence reached its peak, predictive analysis has been gaining interest for investors and entrepreneurs. Much more so if you take into account its close relationship with big data, another of the trend topics of recent years. But what is true in everything that is promised? How does it really benefit the business? How much can be expected from this type of analysis? The essence of predictive analytics Predictive analysis contributes to the generation of models that predict certain information as a result of the influence of multiple factors that are evaluated in a contrasting manner. It is true that forecasts have existed in the business field for a long time, but never in the way they are presented today, thanks to their interaction with big data. It is now possible to model things that could not have been imagined until recently. Among the main business applications derived from predictive analysis , two stand out, extrapolated to any sector: The prediction of failure rates related to an initiative or project: whose function is to reduce them to a minimum , eliminating the risk almost completely and generating savings much more important than one can think ( avoidable failures are a source of incalculable costs throughout the world economy). Individual recommendations based on objective and consistent data: predictive analysis helps create new business opportunities in traditional industries and optimize resources by directing actions towards the most promising leads.
However, we must be honest and realize that predictive analysis can be useless in some circumstances, for example: If it is aimed at a very small number of clients, whose volume does not require a strategy of this type. When it is not possible to collect sufficient data to carry out the analyses. In Chile Mobile Number List cases where sufficient resources are not available to direct actions along the lines proposed by the conclusions drawn from the analysis. The most effective predictive analytics strategies Based on the knowledge extracted from predictive analysis, there are many companies that see their business volume increase, their objectives achieved and their income increased thanks to the application of strategies such as: The creation of durable inventories. Establishing the right prices at the right time, in order to maximize profit. Synchronization of supply with demand. Fraud prevention. Customer segmentation. Customer retention and increased loyalty. Planning of resource needs. All of them are based on flexibility and visibility, of course, with the support of sufficient capabilities to meet the needs and objectives that predictive analytics reveals. Predictive analytics techniques make it possible to make better decisions, take more consistent actions, and reduce costs. It is difficult to do without all these benefits when you have the data that allows you to achieve them and you are in a position to obtain the appropriate tools to turn them into reality. Where the biggest advantages of predictive analytics lie Its benefits are many and important, but there are certain business processes where the benefits of predictive analysis increase, multiplying its positive effects.
These are the following: Processes that require a large number of similar or applicable decisions according to the same pattern. Actions whose results have a significant impact, in purely economic terms or business sustainability. Procedures that support the automation of decision-making or support traditional decision-making based on the insertion of ad hoc calculation models. Processes for which a large volume of data is available, which is already in electronic format, ready for analytical consumption. Predictive analysis achieves more from the information, generates value from data and does so with a clear practical orientation, which only poses one challenge: the need for expert profiles, capable of knowing how to read the conclusions drawn, with the necessary expertise to Dive into the analysis and decide what is the next step to take, what action to take or how to implement an innovative initiative. Precisely, it is notable that in recent years, the use of predictive analysis techniques has tripled, which poses a new paradigm. It is no longer a matter of deciding whether to choose a predictive (or prescriptive) strategy or not, but rather the question is whether the risk of not doing so can be assumed, taking into account that: Competitors already have that advantage. The dynamism of the market is increasing. Data storage costs are still there and performance needs to be obtained in return. The retail, finance and telecommunications sectors are where the incorporation of predictive analysis into business routine has spread most strongly, but they are not the only ones. The automotive, pharmaceutical, insurance and medical and biological sciences industries apply, with increasing enthusiasm and better results, the knowledge extracted from their predictive power, relying on increasingly powerful and increasingly safer tools. and that do not disappoint in terms of their level of precision.
However, we must be honest and realize that predictive analysis can be useless in some circumstances, for example: If it is aimed at a very small number of clients, whose volume does not require a strategy of this type. When it is not possible to collect sufficient data to carry out the analyses. In Chile Mobile Number List cases where sufficient resources are not available to direct actions along the lines proposed by the conclusions drawn from the analysis. The most effective predictive analytics strategies Based on the knowledge extracted from predictive analysis, there are many companies that see their business volume increase, their objectives achieved and their income increased thanks to the application of strategies such as: The creation of durable inventories. Establishing the right prices at the right time, in order to maximize profit. Synchronization of supply with demand. Fraud prevention. Customer segmentation. Customer retention and increased loyalty. Planning of resource needs. All of them are based on flexibility and visibility, of course, with the support of sufficient capabilities to meet the needs and objectives that predictive analytics reveals. Predictive analytics techniques make it possible to make better decisions, take more consistent actions, and reduce costs. It is difficult to do without all these benefits when you have the data that allows you to achieve them and you are in a position to obtain the appropriate tools to turn them into reality. Where the biggest advantages of predictive analytics lie Its benefits are many and important, but there are certain business processes where the benefits of predictive analysis increase, multiplying its positive effects.
These are the following: Processes that require a large number of similar or applicable decisions according to the same pattern. Actions whose results have a significant impact, in purely economic terms or business sustainability. Procedures that support the automation of decision-making or support traditional decision-making based on the insertion of ad hoc calculation models. Processes for which a large volume of data is available, which is already in electronic format, ready for analytical consumption. Predictive analysis achieves more from the information, generates value from data and does so with a clear practical orientation, which only poses one challenge: the need for expert profiles, capable of knowing how to read the conclusions drawn, with the necessary expertise to Dive into the analysis and decide what is the next step to take, what action to take or how to implement an innovative initiative. Precisely, it is notable that in recent years, the use of predictive analysis techniques has tripled, which poses a new paradigm. It is no longer a matter of deciding whether to choose a predictive (or prescriptive) strategy or not, but rather the question is whether the risk of not doing so can be assumed, taking into account that: Competitors already have that advantage. The dynamism of the market is increasing. Data storage costs are still there and performance needs to be obtained in return. The retail, finance and telecommunications sectors are where the incorporation of predictive analysis into business routine has spread most strongly, but they are not the only ones. The automotive, pharmaceutical, insurance and medical and biological sciences industries apply, with increasing enthusiasm and better results, the knowledge extracted from their predictive power, relying on increasingly powerful and increasingly safer tools. and that do not disappoint in terms of their level of precision.