What Is Prescriptive Analytics? Benefits and Examples

By Indeed Editorial Team

Published 2 August 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many organisations rely on big data analytics to help make informed business decisions and remain competitive. Prescriptive analytics is a valuable tool for this, providing analysis, predictions and explanations to recommend the best course of action for a business to progress. It can be very helpful in driving data-informed decision-making, and learning about this may help users grow a business. In this article, we discuss prescriptive analytics, its benefits and examples of how companies use it.

What is prescriptive analytics?

Prescriptive analytics is the process of analysing data to provide instant recommendations on a decision-making process and validate a course of action before committing to it. By considering known data, this process can analyse business goals and suggest the best steps forward based on complex algorithms and past examples. Solutions to varying outcomes are possible from a combination of artificial intelligence (AI), machine learning, business rules and algorithms. It then suggests the best way to optimise business practices.

For example, it may help a marketing team determine which market segment best suits a product based on past and present consumer patterns and behaviours. It can assist the team on how and where to use marketing budgets and optimise marketing campaigns. Using hard data and not unproven assumptions can significantly reduce risk in organisations.

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Benefits of prescriptive analytics

When a company focuses its potential outcomes based on the data it has, it can then understand which variables to use to control the prescribed effects beneficial to the business through prescriptive analytics. This ultimately allows companies to respond to developing changes while making real-time decisions. They no longer deal with human bias and intuition. Instead, statistical modelling, advanced analytics and a decision engine can solve planning, scheduling, pricing and inventory challenges, along with many other problems beyond the capabilities of a spreadsheet and human mind.

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How do businesses use prescriptive analytics?

Predictive analytics uses historical data to predict future events and answers the question of how to rather than what if. It allows companies to understand what could happen and how to achieve it. Operations research, machine learning and applied statistics can apply to the decision-making process.

This can benefit businesses in terms of the following:

  • Create scalable and repeatable processes. Companies could use a simulation created from in-house data to evaluate multiple data sets effectively. It allows businesses to assess their current situation, adopt the most effective options, and quickly make them repeatable and scalable using historical data and market trends.

  • Optimise business plans to meet return on investment (ROI). The process can recommend the best time and sequence for scheduling and updating marketing, pricing and sales plans to deliver the best ROI. With these actionable insights, a business can speed up its planning cycle and be confident that its investments capitalise on current market conditions while reaching its revenue targets.

  • Remove under-performing workflow. In large organisations, underperforming workflow or assets may go unnoticed for some time and may cut into a company's profit. Prescriptive analytics allows companies to identify these criteria, take the necessary actions and channel the budget to those projects that deliver maximum returns.

  • Decrease human error. Increased use of AI means that companies can eliminate human error in analytics. For example, a company may use AI to consolidate and analyse large data sets, which can help reduce possible human error in statistical calculations.

  • Achieve higher agility. A business can find the best ways to navigate market intricacies according to organisational advantages by simulating and analysing different scenarios of multiple circumstances. Additionally, because of its quick turn-around time, a business has the flexibility to make almost real-time decisions to circumvent issues as they arise.

  • Gain sales lead scoring. Product sales analysis through this process can lead to better lead scoring. Lead scoring or lead ranking is the process of assigning a value to various actions along the sales pipeline, and it can increase factors such as page views, site searches, email interactions and content engagement.

  • Generate consumer insights. A business can gain algorithmic recommendations based on customer engagement patterns on its website with this process. Based on user interaction in an app, weighted recommendations can increase customer engagement rates and customer satisfaction and retarget customers with ads based on their search history.

  • Real-time anomalous transaction detection. With this tool, an organisation can detect suspicious transaction activity in real time. Algorithms scan and analyse transaction data patterns, notify the business and provide a recommended course of action if any anomalies arise.

  • Improve product development. Businesses can better understand customer needs by identifying trends, discovering the reasons behind them and predicting if these trends may happen again. This analysis helps determine which features to include or omit in a product to address unnecessary characteristics and optimise the user experience.

  • Provide effective email automation. Email automation is a powerful marketing tool that sends customers personalised emails to increase sales. This analytical tool allows organisations to provide personalised messaging and improve the conversion rate from leads to deals using content that applies to customer motivations and needs.

  • Predict churn rates. Churn rates are the rate at which customers stop subscribing to a service, and having the ability to predict churn rates allows a business to determine any warning signs of potential customer loss. With this process, a company can reduce attrition rates and respond instantly to consumer changes while actively improving revenues.

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Examples of prescriptive analytics

Various factors make this analytical device an effective choice for companies, such as complex business processes and the ability to compile and understand large amounts of data and make better decisions faster. Although it applies to almost all industries, there are some for which it's particularly beneficial:


Prescriptive analytics uses historical customer data and store performance optics to generate recommendations about pricing, assortment and promotions in each category in each store to boost revenues, profits and customer loyalty. Customer preference can vary by week, by region and even by neighbourhood. Suppliers also have their schedules and agendas. This tool can help marketing managers navigate through these intricacies and provide fresh insights to help them understand product stock-keeping unit (SKU), better communicate with suppliers and set the correct promotions and pricing schemes to best attract consumers.

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It can optimise financial service companies by determining which products and services are best for individual customers. Insurance plans, mortgages and investment platforms all carry some risks that the system can consider and mitigate. For example, if an insurance company intends to change its mortgage qualification criteria, by analysing scenarios such as the risk of foreclosure, customer income and changing economic scenarios, the organisation can determine if relaxing its criteria is worth the potential increase in customers.

Food and beverage industry

The food and beverage industry is constantly changing to meet market demands. Backed by a rising economy and entrants of easy-to-use online-catering companies, this sector is digitising to be more efficient and innovative. Some variables that can be more transparent through this process include:

  • procurement of raw materials and packaging costs

  • inbound and outbound logistic costs, transportation networks, bills of material conversions, scrap factor

  • manufacturing labour costs, production costs, fixed and variable costs and resource scheduling

  • management services organisation, SKU demand, customer demand and market availability


It can positively impact the health industry in evaluating and developing healthcare practitioners, detecting anomalies in X-rays, scans and reports and predicting outbreaks. It can rate doctors by analysing patient data and the necessary training provided to close any possible skill gaps. Machine-learning algorithms may prevent incorrect diagnoses, potentially saving lives. It can also help forecast epidemics and their severity and help healthcare providers plan for eventualities, such as patient bed availability and staffing needs.

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