PREDICTION & PRESCRIPTION
Small firms encounter a challenge when it comes to marketing. Spaces are increasingly competitive, and boutique agencies are expected to attain all the amazing results of large, resource-rich firms. At the same time, they are expected to achieve these amazing results without the power and products large firms throw at the problem of growing consumer data and competitive marketing spaces.
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Big data has become a key driver for enterprises to enhance their sustainability in a competitive world. With more data being produced and stored than ever before, the need for more efficient, effective, and precise processes has grown too. Predictive analytics is one such powerful process.
Many know that AI is not a recent discovery, but it is making recent strides in its ability and applicability. One expert, Paul Greenberg from Tech Target, says, “AI has been around for many years, it’s just now reachig a level of maturity to be of value for more businesses.” And, the value for businesses, especially small firms, promises to be that companies and CMOs can do more with fewer resources.
Predictive analytics is the process of using data mining, statistics, and modelling to make predictions. The software mines and analyses historical data patterns to predict future outcomes by extracting information from data sets to determine patterns and trends
Simply put, it is used on a set of data that defines a range of parameters such as the previous order history of a customer, their interests, pages they view most, products that can benefit them, and products they might need along with their existing order. It can bring you insights and accelerate customer understanding.
In the marketing environment that generates more data points than ever (no matter your firm’s size), companies using
AI and algorithms to present data to their creatives have found a way out of the data jungle. Small and large firms using AI feel confident in the creative of their teams as AI points them in the right direction with prescriptive and predictive insights for their campaigns.
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures. This paper demonstrates how evolutionary AI could be used to facilitate the next step, i.e. determining most effective intervention strategies automatically. Through evolutionary surrogate-assisted prescription (ESP), it is possible to generate a large number of candidate strategies and evaluate them with predictive models. In principle, strategies can be customized for different countries and locales, and balance the need to contain the pandemic and the need to minimize their economic impact. While still limited by available data, early experiments suggest that workplace and school restrictions are the most important and need to be designed carefully. It also demonstrates that results of lifting restrictions can be unreliable, and suggests creative ways in which restrictions can be implemented softly, e.g. by alternating them over time. As more data becomes available, the approach can be increasingly useful in dealing with COVID-19 as well as possible future pandemics.