What Is Prescriptive Analytics?

Prescriptive analytics is a software methodology powered by artificial intelligence and machine learning which integrates multiple data sources and uses a series of algorithms to identify and tell you, based on the data behaviors:

  • What is happening
  • Why it happened
  • How much it would cost not to act
  • What to do to optimize the outcome
  • Who should take action

Prescriptive analytics can provide data analysis that helps companies make better decisions by using data to guide the best course of action. With prescriptive analytics, a company can see what might happen in the future and take steps to prevent it, or take advantage of opportunities. For example, a retail store might use prescriptive analytics to figure out which products to stock and where to put them in the store to maximize sales and increase profits.

What Is the Purpose of Prescriptive Analytics?

Prescriptive analytics is a means of quickly identifying problems within your organization and quickly alerting the right people by informing them exactly what to do next. It can identify problems faster and more accurately than traditional analytics platforms, which often require a human to analyze and interpret the data, identify any issues, determine a fix, and assign that corrective action to the best stakeholder.

Prescriptive analytics is important for enterprises because it helps them make smarter, more informed decisions. This can lead to better outcomes, improved efficiency, and increased profits. For example, a manufacturing company can use prescriptive analytics to optimize its supply chain. By analyzing data on inventory levels, transportation costs, and lead times, the company can make better decisions on when to order materials, how much to order, and from which suppliers. This can lead to more efficient operations, reduced waste, and cost savings. Overall, prescriptive analytics can help businesses navigate through complex data and make informed judgment calls in many different types of industries.

What Is an Example of Prescriptive Analytics in Action?

Prescriptive Analytics can help employees analyze a large dataset and helps them to identify any anomalies enabling them to act quickly. It can also help to analyze complex data to provide actionable data for associates and managers to act upon.  For instance, prescriptive analytics can help employees identify potential risks and provide recommendations on how to mitigate them. In addition, prescriptive analytics can help managers allocate resources more effectively by identifying which resources are most needed and where they should be allocated. For customer experience, prescriptive analytics can also help employees provide better customer service by analyzing customer data and providing recommendations on how to improve the overall impression that customers have of the brand or store.

What Are the Benefits of Prescriptive Analytics for Business Functions?

Prescriptive analytics can benefit different industries by optimizing operations, improving decision-making, helping reduce costs, enhancing customer satisfaction, and increasing revenue. Prescriptive Analytics can benefit businesses across a variety of their functions namely :


  • Supports the planning and allocation by identifying high-selling products which are out-of-stock, which therefore could attribute to missing sales. It provides an action to fix allocation parameters helping to potentially Increase sales, and reduce costs through improved plan execution, forecast accuracy, and allocation
  • Increases vendor and quality compliance along with better productivity and negotiating power by enabling merchandisers to contact vendors for explanation and credit if there is an increase in damage rates for a particular product

Warehousing and Distribution

  • In the automation process to help increase Return On Investment  through analysis and leveraging of robot-sourced data along with improving collaboration between human labor, systems of record, and machines (“cobots”)
  • Supports compliance to reduce the risk associated with mishandling, safety violations, and fraud by providing better knowledge retention through routine policy reminders delivered to enabled devices
  • Improves benchmark turnarounds/lead times per Distribution Coordinator/Worker
  • Identifies and resolves slotting/picking issues causing inefficiencies and bottlenecks along with reducing complexity and downtime associated with inconsistent Stock Keeping Unit velocities
  • Improves synchronization between loading and unloading activities within shipping/receiving and optimizes cross-docking via reduced sort/reload time
  • Decreases downtime for labor driving efficiency and effectiveness via better visibility of factors impacting critical Key Performance Indicators. It helps to accelerate the onboarding and upskilling of staff via real-time, on-the-job training

Retail Store Operations

  • Supports quality and returns through increased customer satisfaction through faster identification of mass returns and protecting margins through better Return To Vendor compliance
  • Helps to limit markdowns and pricing through better rotation, and delivery accuracy helping to improve profits, compliance, and margins via faster identification of abuse and pricing errors
  • Improves inventory accuracy to help increase customer satisfaction through better product availability, quality, and freshness while optimizing inventory through closer analysis of product behaviors
  • Helps reduce sales shrink by minimizing losses from expirations, damages, and waste weight on reverse logistics versus expectations
  • Helps execute and increase the efficiency of audits, labor, and cycle counts alongside improving the accuracy of Direct Store Deliveries, assumed-receipt pallets
  • Identify low product movement which can then send an action to a store manager to check the planogram and the shelf. This helps to increase sales, margins, on-shelf availability, and conversion rates
  • For the retail supply chain, it provides actions for receiving managers to verify/check scanning compliance at the store level where there is an increase in cartons marked “missing” which reduces labor costs, increases Distribution Coordinator efficiency, and improves productivity and inventory turns

Planning and buying

  • Prescriptive analytics helps a manufacturer to increase vendor compliance and one-time shipment completion and improve the sales and operations planning process
  • In the warehouse, it improves cross-docking operations/efficacy with better demand sensing providing better collaboration and synchronization between warehousing and other supply chain nodes
  • For home delivery, it improves customer satisfaction through increased accuracy, quality, and availability. It can also optimize cost-to-serve through closer analysis of variables and factors affecting expenses
  • In stores, it can optimize shelf space and layout with improved demand sensing which can help to maximize sales and customer satisfaction via improved realogram-to-planogram compliance


  • Helps increase loyalty by capitalizing on cross-selling opportunities and increasing basket size through the identification of upselling best practices
  • Helps to protect margins by ensuring discount compliance and reducing risk through closer analysis of discount activity
  • In a sales audit, it helps to mitigate risk via closet monitoring of deposits, pickups, and audits
  • Increases efficiency & accuracy of cash processing
  • Supports sales refunds to protect profits & margins through better visibility to voids and returns along with mitigating risk through faster alerts to non-compliance
  • Helps with asset protection where a cashier could be refunding coupons without receipt where CCTV can be checked which helps to minimize total retail loss by reducing internal, external, and ORC fraud through faster case identification and resolution
  • In a corporate office, it is used to improve forecast accuracy by sensing the demand for both brick-and-mortar and online. It can also be used to optimize the cost of transportation via detailed allocation and increasing waterfall forecasting and planning accuracy through root-cause analysis

What Are the Key Differentiators to Using Prescriptive Analytics?

Prescriptive actions

Machine learning and AI engines analyze and interpret data. Any findings are automatically sent to an appropriate stakeholder as a prescriptive action, telling them in simple, easy-to-understand language what is happening and what action to take in response to it, to increase sales and revenue.

Easy prioritization

Before sending out an opportunity, machine learning automatically calculates how much the finding is costing the business. This makes it easy to prioritize responses based on the potential losses an issue may cause if it is not acted on promptly.

Fewer reports

Many retailers rely on report-based analytics systems. Reports only display data - a human needs to interpret it and decide if and how to act on it. Prescriptive Analytics does all the interpretation and delegates tasks to the right person via prescriptive actions. Managers can spend less time reading reports in the back office and more time on the sales floor.

Plain-text directives

Reports can be interpreted in many ways. For example, a report that shows many shipments are missing items upon delivery to stores can spark finger-pointing between Shipping (“Warehousing forgot to pack the items!”) and Warehousing (“Shipping’s delivery drivers are stealing!”). Prescriptive Analytics dispenses prescriptive actions in plain text (“To Asset Protection Manager: Trucks #3, 8, and 11 made unauthorized stops en route to destination and arrived with items missing. Interview drivers.”). This means there is only one version of the truth - exactly what happened and exactly what to do about it. 

Real-time alerts

Many reports are released on a daily, weekly, or even monthly cadence. By the time you actually get that report and interpret it, any problems you find may no longer be actionable and/or have already racked up enormous losses. Prescriptive analytics analyzes data in near-real time, helping catch problems at their earliest stages.

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