What is Prescriptive Analytics?
Prescriptive analytics is one of the types of data analytics aimed at addressing the query: “What steps should be taken to accomplish this goal?” This method uses technology to help businesses in improving decision-making by analysing raw data.
In particular, prescriptive analytics integrates data on potential scenarios, resource availability, historical performance, and present conditions, recommending a specific action plan or strategy. It’s applicable across various timeframes, from immediate decisions to long-term planning. It’s unlike descriptive analytics, which assesses decisions and outcomes after an event has taken place.
How Prescriptive Analytics Works
Prescriptive analytics relies on artificial intelligence (AI) methodologies, particularly machine learning, which enables computer programs to understand and advance from acquired data while adapting continuously.
The power of machine learning lies in its ability to handle vast amounts of available data. As new data emerges, these programs autonomously adjust, surpassing human capacity in speed and comprehensiveness.
Prescriptive analytics works in tandem with predictive analytics, which uses statistical methods and modelling to forecast future performance based on historical and current data. However, it extends further: using predictive analytics’ projections of likely outcomes, it recommends the best future course of action.
Advantages of Prescriptive Analytics
Prescriptive analytics serves as a powerful tool for uncertainty and changing conditions. It plays a pivotal role in preventing fraud, mitigating risks, enhancing efficiency, achieving business objectives, and fostering customer loyalty. This approach helps organisations to base decisions on meticulously analysed data rather than impulsive, ill-informed judgments driven by instinct.
By simulating potential outcomes and their probabilities, prescriptive analytics enables organisations to understand the risk and uncertainty far beyond what averages alone can convey. Using this technology allows businesses to grasp the likelihood of worst-case scenarios, thereby enabling more strategic planning and preparation.
Disadvantages of Prescriptive Analytics
Prescriptive analytics, while powerful, is not foolproof. Its effectiveness hinges on posing the right questions and being adept at interpreting and acting on the insights it offers. The accuracy of its outcomes relies heavily on the validity of its inputs; inaccurate assumptions lead to flawed results.
Primarily suitable for short-term solutions, prescriptive analytics may not be ideal for making long-term business decisions due to its unreliability with prolonged timeframes.
Not all prescriptive analytics providers are created equal. Therefore, it’s important for businesses to evaluate both the technology and its provider. While some deliver tangible, reliable results, others might overpromise using big data without fulfilling their commitments.
Types of Data Analytics
Data analytics involves automated processes using algorithms to analyse raw data, enabling users to draw conclusions from the information. Prescriptive analytics is just one type among several others, which are explained below.
Descriptive Analytics
Descriptive analytics assesses historical data to gain insights into changes within a business. Commonly used data include fluctuations in price, sales growth patterns, user data, and revenue linked to subscribers.
This set of big data seeks to answer the question, “What happened?” Business leaders can use this information to identify strengths and weaknesses, facilitate better decision-making and improve business strategies.
When paired with other forms of analytics, such as prescriptive analytics, descriptive analytics can serve as a valuable tool for businesses.
Diagnostic Analytics
This type of data analytics aims to uncover the reasons behind occurrences, seeking to answer the question, “Why did this happen?” It necessitates a broader range of data inputs. Although it involves some speculation, it is instrumental for businesses aiming to understand the origins of emerging trends. For example, it tries to determine the connections between specific market forces and sales figures or to evaluate the impact of a particular advertising campaign on the sales performance of a specific product.
Predictive Analytics
Predictive analytics tries to anticipate imminent occurrences by analysing historical data and generating projections about future events. Businesses use this data analysis technique to identify avenues for growth and improvement, while also identifying and addressing potential risks.
Examples of Prescriptive Analytics
In marketing, the use of prescriptive analytics holds immense significance. This analytical approach allows marketers to anticipate and capitalise on changing consumer trends. By using historical data and performance insights, both internal and external marketing teams can gain a distinct competitive advantage.
Through the application of prescriptive analytics, marketers can create highly targeted campaigns aimed at specific customers at certain times, such as advertisements for a demographic during events like the Super Bowl. Also, companies can identify effective strategies to engage diverse customers and determine pricing and discount strategies for their products and services.
Summary
Businesses can use various strategies to ensure their success and improve decision-making processes. Among these tools, data analytics stands out as a crucial asset.
Prescriptive analytics, a type of data analytics, uses past performance and trends to determine what can be done to achieve future objectives. Despite its advantages, business leaders should also acknowledge that prescriptive analytics also has limitations. Identifying where to start and choosing the right company or software to help in achieving goals can significantly contribute to long-term success.