By Greg Silverman, Jun 29, 2020
Analytics help businesses quantify the effects of their decisions every day. From marketing strategies to financial decisions, the ability to find meaningful patterns in data helps organizations make decisions to maximize their investments, gain shares, and appeal to the market. With the rise of machine learning and AI, businesses are able to quickly transform big data into accurate insights to drive their decisions.
While we are familiar with analytics in this overarching sense, it’s important to note that not all types of analysis are created equal. In fact, each analytic method serves a unique benefit to businesses and ultimately build upon each other to achieve the peak of forecasting: prescriptive analytics.
Gartner’s Analytics Ascendancy Model outlines the linear relationship between different kinds of analysis. As you see, the more difficult the method is to achieve, the more valuable it is to the business in terms of the insight it provides.
The model begins with descriptive analytics, which are most beneficial for combing through historical data to provide information about what happened. Diagnostic analytics, like its title suggests, explains why these things happened. Next up the ladder is predictive analytics, which provides insight as to what could happen in the future.
What are prescriptive analytics then? Often misnomered as prescription analytics or confused with predictive modeling, prescriptive analysis tells businesses how their goals could be achieved better or faster if they took different actions. The foundation of the previous types of analysis is necessary for understanding historical data, but prescriptive analytics technologies are capable of understanding additional information about the current market and the people in it to accurately forecast how and why they may respond a certain way to a change. This knowledge of what drives consumer purchasing decisions and what conditions change them is crucial for businesses needing to make better, faster decisions in the moment.
Advancements in market simulation are making prescriptive data analysis more precise and accessible to business users by recreating how a consumer market behaves in a mock environment. Here are the other key benefits of using prescriptive analytics:
1. Create a repeatable, scalable process: To accurately model complex scenarios for prescription analytics to be possible, it’s necessary to create an accurate twin of the market. The simulated environment mimics current market conditions and consumer behavior for business users to run what-if scenarios in a matter of minutes. Prescriptive analytics acknowledges that the market is fluid, so a flexible, scalable approach to modeling is necessary.
By building off descriptive, diagnostic, and predictive analytics, prescriptive analytics applications take into consideration historical data and forecasting to give insight businesses need. After running multiple scenarios and comparing their results to what occurred in the market to validate the model, businesses have a repeatable process that they trust to help with their decision making.
2. Optimize business actions: The reason Forbes predicts that the future of data analytics is prescriptive analytics is because of its ability to go beyond forecasting what will happen in an organization, but how it could happen better by making certain strategic decisions. Whether it be a new marketing strategy or change in pricing, business users are able to see which levers affect their outcomes in a simulation to choose the best course of prescriptive action to achieve their goals. By not only understanding what could happen in the future but actually being presented with recommendations for how to get there, businesses are able to optimize their actions in stable economic conditions or in times of uncertainty.
3. Use near-time decision-making: With this agile and accurate model in place, business users at all levels of the organization are able to run scenarios in mere minutes. Since the model has been tested and validated multiple times, users trust that when they need answers to their complex questions quickly they receive accurate results without sacrificing the quality of the analysis. Some prescriptive analytics examples in action are if a business decision-maker needs to determine what will happen to revenue if they change the marketing mix or the most effective way to launch a new product. By running a scenario with their trusted model, they will receive insight into their actions in near-time as opposed to weeks or months later when market conditions have likely changed.
4. Experience cost efficiencies with in-house capabilities: With self-service analytics tools such as a simulation platform that accommodates prescriptive analysis, businesses save money and also make cost-effective decisions. As opposed to outsourcing their analytics operations, businesses that invest in in-house solutions are able to keep profits within their organization while optimizing their decision making. Additionally, these solutions enable internal departments who may not be well-versed in analytics to be involved in a collaborative process and learn about data and the impact of their decisions through a user-friendly platform.
5. Improve productivity: It may go without saying, but the ability to achieve better, faster and cost-efficient decision-making made possible with prescriptive analytics benefits the entire business. The process brings teams together to collaborate who may not converse on business matters otherwise but it also allows departments to focus their efforts on their expertise. Self-service analytics are designed to be user-friendly so businesses users are empowered to run scenarios to help them determine what to do next. This means IT departments and data scientists continue to dive into the data as business users don’t need their direct support to run scenarios. This process also effectively reduces data silos and communication blocks so every department improves their productivity.
6. Answer complex questions: Finally, prescriptive analytics are best used to solve complex problems with many fluid parts that business experience daily. An example of prescriptive analytics solving a complex problem would be if a business user needed to forecast demand to make a more efficient supply chain. By seeing how the market would react to such a shock, they are able to put plans in place to mitigate risk and make strategic decisions.
The Concentric model utilizes prescriptive data analytics to transform the way businesses make decisions. Contact us today to learn more about harnessing the power of forecasting.