In recent decades, business intelligence tools have become the norm for companies that want to stay ahead of the competition. Predictive Analysis is the natural evolution and the next step towards a deeper understanding of future business models. It is based on historical data and statistical models and constantly updated algorithms.
This is why it is good to keep these measures in mind when approaching Predictive Analysis:
Project definition phase
It is preferable to elaborate a specific strategy on what we hope to achieve by implementing the Predictive Analytics methodology. Better prepare a sort of business plan that includes the expected results, the actual results and the input that will be used. Before you begin, ensure that all data sources are available, up-to-date, and in the format expected for analysis. Some critical issues that should be included: a forecast of customer abandonment, marketing campaign improvements (customer segmentation, optimization, customer recommendations), cash flow forecast and revenue and dynamic pricing.
Collection and analysis of data
Because Predictive Analysis is based on the use of large volumes of data to obtain information on trends and anticipate strategies, the phase of data collection is fundamental to the success of the initiative. Most likely this will include information from multiple sources. Data must have a unified approach. Sometimes information will be collected and cross-checked for a complete picture of the underlying phenomenon. With a solution like Microsoft Dynamics 365 in which business intelligence and predictive analytics are integrated, data collection becomes easier, faster and more effective, all in one. Thus, collected Big Data can be transformed into quality data to be shared with the whole company and to structure new, more effective strategies.
Although Predictive Analysis is based more on Big Data, statistics are still an integral part of structuring the strategy. It is used to test and validate hypotheses. Most of the time management has a specific hypothesis about consumer behavior, conditions that indicate fraud and so on. With statistical methods, these are put to the test and decisions are made based on numbers, not on intuition.
Once the data has passed through statistical analysis and the model has been calibrated, the results must be interpreted and integrated into the daily routines. As suggested, once the model has been created and deemed sufficient, it should be used to dictate daily choices and govern processes within the organization. It is not enough to have numbers that show what would be best for society unless it results in feasible steps and measurable outcomes. It is good practice to periodically review models and test them with new data to make sure they have not lost their meaning.
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