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Descriptive or predictable analytics?

Collecting and analyzing data is essential to evaluate business performance. As we all know, metrics such as sales revenue, product cost, and profit margin are necessary to measure the success of results, making predictable analytics more relevant for each business decision. However, many companies still follow ‘Descriptive Analytics’ based on past reports, which makes the right decision-making less effective.

Descriptive Analytics is no longer powerful enough

Many food retailers still use descriptive analytics to make decisions, even though this old method is stuck in the past because of its focus on past actions and results. For this, it is assumed that future events will follow the same pattern as a result. For example, some operational actions considered as part of descriptive analysis are click, bounce, and conversion rates. click-through, bounce-rate, and conversion rates. Even though descriptive analytics are still useful, they have little abilities to predict future results.

Many companies are still following Descriptive Analytics

According to the University of Cambridge Service Alliance paper on Data and Analytics, 80% of business analytics nowadays are basically descriptive, and this statistic supports the fact that most companies are looking forward through a rear-view mirror, forgetting that in the world of digitization the only option is to predict the future.  

Predictable Analytics as a business-building tool

As descriptive analytics have shown low credibility when it comes to making decisions, predictable analytics rapidly gained importance by using a variety of techniques from data mining, predictive modelling, and machine learning, that predict and measure future behavior with higher levels of assurance.

Predictive Analytics features allow marketers to look forward and not only get predictable results but also the probability of those results even if some variables change. 

Anticipating the future comes to suggest decision options

By anticipating the future, Predictive Analytics can also suggest decision options to have a higher potential in outcomes. For food retailers, the future of business intelligence is directly related to their capability of being more profitable by using the right tools, which help them create the right offers and deliver these to the right people at the right times, ensuring the best results. 

In the end, using both predictive analytics and descriptive analytics is highly useful to better decision-making by providing an overview of both past and future performance.

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