Big data solutions are receiving a lot of interest from businesses worldwide. Analytics of your data can provide previously unknown information about your company. But, to reap the full benefits of those insights, you need to test source data before applying it to your business strategy. Analytics solutions make it easy to take advantage of business data. But the market has a lot of solutions, and many seem to cover different types of analytics. How can businesses figure it all out? You can start by learning about the different kinds of analytics. Such as predictive and prescriptive analytics.
Table of contents
What is Predictive Analytics?
Predictive analytics takes data from the past and puts it into a machine-learning model that looks at key trends and patterns. The model is then used to predict what will happen next by putting the data from the present into it. For example, in marketing, a lot of information about customers can be used to make better content, ads, and strategies. This is to reach potential customers effectively. Predictive analytics is the process of looking at past behavior data and using it to guess what will happen in the future. Predictive analytics can be used in marketing to determine what will happen with sales at different times of the year. Once companies can predict this, they can plan their campaigns better to benefit from it.
Examples of Predictive Analytics
Prevention From Malfunction
Predictive analytics can also be used to stop bad or unwanted things from happening. For example, in manufacturing, algorithms can be taught to predict things accurately. For example, when a piece of machinery is likely to break down using past data. When the conditions for a future breakdown are met, the algorithm sends a message to an employee. The employee can then stop the machine, saving the company thousands or even millions of dollars in damaged goods and repair costs. This analysis predicts how things will go wrong right now, not months or years from now.
Financial Record
Every business needs to keep financial records, and predictive analytics can help. Especially when it comes to predicting how well your business will do in the future. Predictive analytics do that by using information from past financial statements. Also, by using information about the industry as a whole. You can make predictions about sales, income, and costs to build a picture of the future and make decisions.
No matter what business you’re in, predictive analytics can help you figure out what to do next. Building up your analytical skills can be helpful in many ways. Whether you’re making financial decisions, coming up with marketing plans, or changing your plans.
Sports
The field of sports analytics is also becoming increasingly interested in predictive analytics. Data analysts are employed by professional teams in all three major sports. These are basketball, baseball, and football. This is to evaluate the players’ game and assist the general managers in making the best possible contract decisions. The experts in analytics take into account both on-field and off-field data and can forecast each player’s value and regression. On-field metrics include players’ scoring speed, time, and health conditions.
Other examples of on-field metrics include tactics and health. Off-field measurements pertain to the business aspect of sports and provide insight into how much profit a player can generate for a team. Off-field metrics can also be used interchangeably with financial analytics. This involves engaging fans, selling tickets, merchandise, and other similar activities. Off-field statistics are compiled from various sources, including online social media platforms.
What is Prescriptive Analytics?
Prescriptive analytics considers future scenarios, but it takes a more technological approach. It examines a prospective future outcome’s “what” and “why .”This happens using complex mathematical algorithms, artificial intelligence, and machine learning. Prescriptive analytics can also assist a business in seeing various possibilities and possible consequences. It can adjust its predictions and recommendations as more data becomes available. Prescriptive analytics can also be used to guide product development and enhancements.Â
Examples of Prescriptive Analytics
Lead Scoring
Prescriptive analytics, often known as lead ranking, plays an important part in sales. Lead scoring is the process of giving each step in the sales funnel a point value. This lets you or an algorithm rank leads based on how likely they are to become customers. Views on the pages, interactions via email, and site Exploration. Attending webinars, downloading e-books, or viewing videos are examples of content engagement.
Email Marketing
Email automation is a clear example of the use of prescriptive analytics. Marketers use email automation to sort leads into groups based on their goals, mindsets, and plans. And then send them emails based on those groups. If a lead responds to an email, it can put them in a different group, which will cause a different set of messages to be sent. A person should plan and create automation flows. It does not matter if this is pure algorithmic prescriptive analysis. Companies can use email automation to send personalized messages to many people. So they can increase the chance of turning a lead into a customer by sending them content relevant to their wants and needs.
Behavioral Data
Product managers can get user data by surveying customers and testing beta versions of products. Also gathering behavioral data as existing users interact with each other. All of this data can be evaluated. Either manually or algorithmically, to find trends and discover the causes of those trends. It can also be used to tell whether or not those trends will repeat. Prescriptive analytics can assist in determining which features to add or exclude from a product as well as what has to be changed to ensure an optimal user experience.
Prescriptive analytics doesn’t have to be intimidating. If you build the correct basis for it, it can be a powerful tool. A tool that helps optimize processes, establish strategies and achieve corporate goals.
Predictive vs Prescriptive Analytics: The Difference
Now that we know how to use predictive and prescriptive analytics, what are the main differences between them? There are a lot of resources that try to explain the difference between predictive analytics and prescriptive analytics. In truth, they’re more like different parts of the same orange. In other words, they are not two separate things, with one being more powerful or “better” than the other. Instead, they are both parts of the same whole. But there are some differences between predictive analytics and prescriptive analytics.
By using data from the past, predictive analytics tries to guess what might happen in the future. Prescriptive analytics uses a wide range of data to make specific suggestions for these predictions. Often, historical data is used in predictive analytics, e.g., credit histories and transactional data. Most of the time, prescriptive analytics uses hybrid data, a mix of structured and unstructured data, e.g., videos, pictures, and documents.
Using the values of known independent variables, predictive analytics tries to guess the value of a variable. Prescriptive analytics, on the other hand, involves figuring out the best value for a decision variable to optimize performance metrics. Based on the same data, the same predictive analytics model will always make the exact predictions. Prescriptive analytics models need to be updated with new data all the time to make sure that their recommendations are still helpful. Because of this, it can be hard to try to put them into a single category. A lot of people dwell into this conversation about predictive vs prescriptive analytics before understanding these two in their individual capacity. Any smart business person would deep dive into how to utilize the two to have more benefits.
Predictive vs Prescriptive Analytics: What to Choose?
Businesses need to understand the differences between the two analytics. This is to discuss the problems these types of analytics can solve. Predictive analytics helps companies to understand what’s going on in the short term. In contrast, prescriptive analytics gives answers for making decisions in the long term. As a business grows, algorithms must be fine-tuned with the most up-to-date data to get current and valuable insights.
Both predictive and prescriptive analytics are powerful tools for business, but their combined use yields the greatest level of effectiveness. Both prescriptive and predictive analytics have a wide range of potential applications, some of which include the retail industry, the green sector, the financial industry, healthcare, and the green sector.
