The artificial intelligence industry (AI) is one of the technologies that is driving the industrial revolution right now. AI is not a technology that has been evolving for a long time, but what has changed in recent years is the power of computing, cloud-based service options and the applicability of AI in marketing as well. of marketing AI is growing, with a forecast to reach nearly $ 40 billion by 2025. Most CMOs are applying in AI knowledge, but many are still uncertain and unaware of the magnitude of benefits and how they can use artificial intelligence to improve marketing.
Marketing managers have promptly engaged with data analysis, benefiting (and most likely suffering) from the mountains of data at their fingertips. This includes everything from user tracking data on apps and websites, to newsletter conversion rates and online advertising click-throughs, to CRM data analysis. Data mining provides large amounts of data, often unstructured. Marketers are more familiar with data interaction through dashboards that structure data to provide analysis of common elements such as averages, ratios, and percentages. The goal is to aggregate the data in order to report a result, look for a model and find relationships between the variables. Strategies are structured and data are interrogated to attest to this relationship. Data analysis is descriptive because it is based on past events. It does not provide the impact of a change in a variable.
Data Analysis naturally leads to predictive analysis using data collected to make predictions, based on historical data and human interaction to query data, validate patterns, create and then test hypotheses. The hypotheses drawn from past experiences assume that the future follows the same patterns. The “What / if” hypotheses are informed by the human understanding of the past and the predictive ability is limited by the volume, time and cost constraints of human data analysts. The predictive information derived from Data Analysis is extremely useful for professionals of the marketing: help predict the effectiveness of the campaign, to influence the decision-making process on collateral, geographic markets and demographic data related to the chosen target.
AI Machine Learning
Machine learning is a continuation of the concepts related to predictive analysis, with a fundamental difference: the AI system is able to formulate hypotheses, test and learn independently. AI is a combination of technologies and machine learning is one of the most important techniques used for hyper-personalized marketing. Machine learning of artificial machines makes assumptions, reevaluates the model and reevaluates data, all without human intervention. This changes everything. Just as artificial intelligence means that a human engineer does not need to code for every possible action / reaction, machine learning is able to test and retest data to predict every possible product-customer correspondence, at a speed and capacity that no human being could get. This allows the human user to be able to devote himself to specific cases, and build customized campaigns on precise and continuously updated data. This learning can provide insights into microtargets that could not realistically be done by human analysts on a large population. These results can drastically improve conversion rates, return on investment in marketing and customer loyalty.
Data Analysis refers to the revision of data of past events for models.
Predictive Analysis is making assumptions and tests based on past data to predict what / ifs future.
Machine learning analyzes data, makes hypotheses, learns and provides forecasts on scale and depth of details impossible for individual human analysts.
A solution like Microsoft Dynamics 365 for Marketing contains all the technological opportunities mentioned above to better support the complex and strategic work of marketers
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