New York City, New York Jan 10, 2022 (Issuewire.com) - The Predictive Analytics market is growing at nearly 25% a year as more companies begin to understand the competitive advantages inherent in big data. Predictive analytics tools are used to drive information-driven personalized advertising and marketing campaigns, understand buyer behavior, measure key performance indicators (the metrics used to create powerful advertising campaigns) and ultimately maximize campaign ROI. Predictive analytics applications are also instrumental for figuring out the customer’s reluctance to purchase a product, the various components which prevent a buyer from making choices, and discovering methods to reduce attrition.
Is not easy to make the best predictive analytics decisions, both strategic (like the redesign of the supply chain), or operative (to deliver goods at minimal cost, or to define prices and design specifics). Even more, sometimes it is not even clear what "the best" really means. Often the outcomes depend on the sum of multiple decisions and "multiple-bests" from different people within the organization.
As shown by ever-increasing evidence, an appropriate, data-driven, and analytical decision-making approach enables better performance and a more robust vision of organizational goals. On the other hand, for large teams, it is not easy to fully introduce such techniques into their day-to-day processes, but it can be an exciting journey. The competencies of the team members make the difference and details matter. AI, Data-driven Predictive, and Prescriptive Analytics can truly help organizations fully understand their markets and take advantage of opportunities provided by abundant data and in-depth technological analysis. The following points give you a conceptual sequence of actions (Updated - Mike Brassil 2018):
1) Focus on the problem: The techniques are not the starting point but the actual issues, the goals the organization wants to reach (for example cut logistics costs or reduce stock-outs, improve web-sales performances);
2) Identify the potential levers you can use: Variables and elements the organization can put in place (for example review supply chain, improve picking productivity, dynamic pricing to better shape the demand curve).
3) Evaluate if the optimal solution will require changes in process and management and determine the scale and scope of the roll-out;
4) Depending on the level of confidence you have that the levers you identified will be corrected, implemented the solution, or pass through a validation phase. What to do depends on the specific situation.
5) Remember: it is not only a question of software and features. Models behind such software have dramatic importance as well as the quality of the available data, and how the decision-making process is re-engineered to lever the power of #Algorithms, #AI, #Simulation, and #PredictiveAnalytics.
6) Analytical experts should support the above steps, preferably since the earliest stage, that is how to have the best experience with the application of models (math-optimization, AI, etc.) to real, often complex, cases. The view they add has a high value and reduces the risk of failure. Finally, the vast benefits of organizational Data-driven Predictive and Prescriptive Analytics far outweigh the costs of implementation and can help drastically improve revenue, decrease costs and ultimately allow the team to operate at optimal efficiency.
Predictive Analytics programs are the logical next step for incremental revenue generation via business analysis, net analytics, advertising, enterprise intelligence, knowledge warehousing, and information mining. Numerous analytical tools are also being used in pattern discovery in relation to fraudulent transactions in the financial industry, preventing criminal actions by applying behavioral analytics to large data sets. This works to reduce or eliminate fraudulent transactions, prevent zero-day vulnerabilities and eliminate the dangers of advanced fraudulent schemes.
“A point of view can be a dangerous luxury when substituted for insight and understanding.” - Marshall McLuhan
Highly successful businesses know that the fundamentals of their client data have evolved. Now they can’t rely solely on their product or service; they must leverage their information (monetary, buyer support, internet interactions, etc.) to better understand their prospects and to learn from their collective experiences as an organization. Clients today want to use predictive analytics to optimize enterprise performance at a variety of levels in a variety of industries. Predictive analytics provides obvious, real-time executable initiatives based on current firm data and is a pure extension of related corporate initiatives in areas akin to net analytics, business evaluation, and knowledge mining. In closing, Predictive Analytics tools are quickly becoming adopted and present the ability for a very strong competitive advantage in highly competitive industries.
"Information is the oil of the 21st century, and analytics is the combustion engine” (Peter Sondergaard, Senior Vice President, Gartner)
July 10th, Mike Brassil NY is an Analytics Executive focused on shaping strategy, driving profitability, and optimizing user experience. He provides product updates for the Marketing organization as well as facilitates code deployment and acts as the advocate/ evangelist and point of contact for the entire solution team. Links:
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