Table of contents
- What is Prescriptive Analytics
- Key Components
- Major Features
- 5 Benefits
- 5 Key Applications
- Industry Examples
- Conclusion
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With global data projected to reach 181 zettabytes by 2025 (1 zb = 1 trillion gb), businesses need to leverage advanced analytics to gain actionable insights from big data. While descriptive and predictive analytics summarize what happened and what could happen, prescriptive analytics goes further – recommending the best course of action to capitalize on future opportunities.
Adoption of prescriptive analytics is still low compared to other types of business analytics. However, analysts forecast the prescriptive analytics market to grow from $1.7 billion in 2022 to $12.35 billion by 2027, at a CAGR of 48.4%.
In this comprehensive guide, we demystify prescriptive analytics, explain its key capabilities, use cases, and demonstrate how it enables data-driven decision making to drive business impact.
What is Prescriptive Analytics?
Prescriptive analytics is a type of advanced analytics that provides specific recommendations to capitalize on future opportunities or mitigate future risks. It analyzes current data and trends to determine the optimal path forward to achieve business goals.
Components of a Prescriptive Analytics Architecture
A prescriptive analytics architecture consists of key components working together:
- Data Collection & Analysis: Gather, clean, normalize relevant business data
- Modeling: Apply statistical and machine learning algorithms to find patterns and relationships among data variables, forecast likely future outcomes
- Optimization: Leverage mathematical optimization algorithms to identify the best course of action within specified objectives and constraints
- Recommendations: Suggest actions aligned to business goals to maximize desired outcomes based on predictive modeling and optimization
Comparison to Other Types of Analytics
| Category | Definition | Focus |
| Descriptive | Summarizes historical data to understand past performance | Hindsight |
| Diagnostic | Analyzes data to determine the root cause behind issues | Investigative |
| Predictive | Uses statistical models and machine learning to forecast future outcomes | Foresight |
| Prescriptive | Recommends data-driven actions to achieve desired business objectives | Insight to Impact |
While descriptive and predictive analytics provide rearview mirror and windshield perspectives, prescriptive analytics assists with steering decisions to drive business impact.
Key Components of a Prescriptive Analytics Capability
Let‘s explore some of the key features that enable prescriptive analytics to deliver business value:
1. Advanced Algorithms & Machine Learning
Prescriptive analytics utilizes modern techniques like artificial intelligence, machine learning, simulation, and optimization algorithms to analyze data. For instance, an e-commerce firm can apply ML algorithms to discover patterns in visitor behavior data to determine effective promotion strategies.
2. Big Data Analytics
By leveraging next-gen big data platforms and computational tools, prescriptive analytics can rapidly uncover insights from massive, high-velocity data streams. This allows uncovering granular insights. For example, analyzing millions of transaction records to identify the highest value customer segments for targeted campaigns.
3. Actionable Recommendations
Prescriptive analytics moves beyond just data reporting or visualization to actually suggest executable actions tailored to business goals. For a retailer, it could recommend optimizing inventory levels, balancing pricing to maximize profit margins, or the ideal locations for new store openings based on demand forecasting.
4. Continuous Intelligence
With real-time data pipelines, prescriptive analytics enables acting upon insights quickly to adapt to changing market dynamics. For instance, a bank can recalibrate its automated credit approval risk model when fluctuations in leading financial indicators are detected to minimize defaults.
5. Flexible Business Rules
Domain experts can configure prescriptive systems with business rules and constraints to align recommendations with organizational policy. Eg. supply chain analysts specifying safety stock buffers or product margins for inventory optimization algorithms.
5 Key Benefits of Prescriptive Analytics
Let‘s review 5 compelling benefits driving adoption of prescriptive analytics:
1. Data-driven Decisions
By suggesting actions tied to business KPIs based on data insights, prescriptive analytics enables smarter decision making. According to an Accenture survey, 61% of executives agree analytics has helped them make better decisions.
2. Increased Profitability
With insights to optimize supply chain, pricing, logistics and other business areas, prescriptive analytics unlocks significant cost savings and revenue upside. According to McKinsey, optimization algorithms can boost R&D productivity by 10 to 20 percent.
3. Competitive Differentiation
By enabling data-driven adaption to market changes, prescriptive analytics provides a competitive edge. In a survey, 65% of senior executives said their prescriptive analytics capabilities help differentiate their firm.
4. Operational Excellence
Prescriptive insights can drive process improvements across manufacturing, marketing, supply chain to reduce costs and environmental impact. Analytics-driven organizations are 4X more likely to substantially improve operational excellence.
5. Proactive Risk Management
By predicting failure scenarios and advising preventative actions, prescriptive analytics enables mitigating issues proactively. Weather analytics has helped airlines prescriptively manage flight disruption risks reducing passenger delays by upto 20%.
Clearly, prescriptive analytics is pivotal for leveraging data for competitive advantage and business performance. Next, let‘s explore some of its powerful applications across domains.
5 Key Application Areas of Prescriptive Analytics
1. Supply Chain Optimization
From manufacturing to transportation and warehousing, prescriptive analytics is invaluable for supply chain excellence. By applying decision optimization algorithms, it can enhance demand forecasting, inventory management, and production scheduling driving operational efficiency, profitability and agility.
2. Marketing Mix Optimization
Prescriptive analytics empowers optimizing marketing ROI by allocating budgets across channels and campaigns aligned to customer targeting strategies. Models can suggest media buying based on response prediction, assisting in bids and pacing for best results within constraints.
3. Financial Planning & Analysis
Prescriptive modeling helps FP&A teams in long term planning as well as quarterly forecasts leveraging regression analysis for projections. Optimizers can prescribe resource allocation decisions across business units to maximize corporate profit goals and adapt projections to market variability.
4. Asset Maintenance & Pricing
Prescriptive maintenance analytics can detect early performance deterioration signals from sensor data to minimize downtime via predictive maintenance. It can also calibrate optimal dynamic pricing of rental equipment or service contracts factoring in demand elasticity, yield optimization and competitive positioning.
5. Healthcare Patient Management
Prescriptive analytics shows high impact in clinical settings and patient engagement. It can prescribe personalized treatment plans, high-risk patient interventions and streamline hospital operations improving care quality while reducing readmissions. Ethics and privacy are paramount here.
Across these domains, prescriptive analytics unlocks tremendous value. Next let‘s review examples of prescriptive analytics use cases driving business impact.
Industry Examples of Prescriptive Analytics Use Cases
Here are some real-world examples of prescriptive analytics in action:
Retail – Optimized Promotion Planning
Leading pharmacy retailer Walgreens applies prescriptive analytics to design product promotion calendars. By analyzing purchase history, customer segments and inventory data, their algorithms prescribe optimized promotion frequency, product selection, sequence and pricing for localized stores to lift sales and profitability.
Logistics – Dynamic Pickup & Delivery Route Planning
UPS leverages prescriptive analytics to dynamically optimize package delivery routes balancing factors like transit time, fuel costs, truck space etc. to improve efficiency. The model adapts in real-time to traffic pattern changes prescribing alternate routes to ensure on-time delivery.
Insurance – Personalized Policy Pricing
Drawing insights from consumer demographics, property, vehicle and climate risk models, insurers like Progressive provide quotes tailored toindividuals‘ coverage needs and risk factors. Customers get policies right-priced for their unique requirements.
Banking – Automated Credit Risk Assessment
Wells Fargo employs prescriptive analytics to evaluate loan applications prescribing approval decisions by assessing risk. Applicants receive faster decisions while risk models refine credit policies protecting against defaults.
Energy – Predictive Grid Resource Optimization
Utilities like Duke Energy optimize power grid management using prescriptive analytics by forecasting electricity demand changes and prescribing the best energy mix to balance wind, solar and gas resources efficiently.
The transformations made possible by prescriptive analytics are compelling. Next let‘s conclude with key takeaways.
Conclusion
Prescriptive analytics is advancing from a niche capability to a mainstream competitive necessity:
- It enables data-driven decisions aligned to business goals leveraging AI/ML, simulation and optimization
- Delivers actionable, targeted recommendations to capitalize on opportunities and mitigate risks
- Unlocks operational excellence, improved planning and accelerated growth across sectors
Leaders are investing to build in-house prescriptive analytics competencies while also partnering with expert analytics consultants to accelerate solution deployment.
With exponential data growth, it is imperative to realize outcomes from analytics investments. As organizations embrace prescriptive intelligence to translate enterprise data into strategic business advantage, it will be a key differentiator for performance, innovation and market leadership.