Two terms frequently appear in our conversations with clients and prospects about analytics: predictive  or prescriptive modeling. The meaning of each is fairly evident, but the details tend to get a bit more muddled. These terms do represent fundamentally different approaches to using data for business decision-making—and understanding the distinction can dramatically impact your company's strategy and bottom line.

What are the key types of business analytics?

Think of business analytics as a journey of increasing sophistication:

1. Descriptive Analytics describe what happened. We ran a 2/$5 promotion that achieved a 125% lift, with an ROI index of 110. That is valuable information.
2. Diagnostic Analytics takes it a step further, investigating WHY something happened. This promotion was successful because we pre-empted the competition, for example.
3. Predictive Analytics uses machine learning techniques to project what will happen, typically on a volume or profit basis. If we run a 2/$5 promotion the week before Easter in Giant Foods we project that it will generate a $125% lift.
4. Prescriptive Analytics advises us on what we should do, using more advanced machine learning and AI modeling and comparing different potential outcomes. The best promotion to run before Easter is a 2/$5 because it provides the best balance of volume and profit relative to the alternatives.

Let's focus on that leap from predictive to prescriptive modeling.

What is predictive modeling?

Predictive modeling uses historical data to identify patterns and forecast what might happen in the future. It's like a sophisticated weather forecast for your business.

Key characteristics of predictive modeling:

• Focuses on probability and likelihood
• Identifies trends and patterns
• Projects future outcomes based on historical data
• Answers: "What will happen if current trends continue?"

Common techniques of predictive modeling:

• Linear and non-linear regression analysis - in simple terms this is the statistical process that helps us understand the relationship between variables, it finds the line or curve that best fits the data points. If X happens, Y is the likely result. It is useful for quantifying fairly straight-forward relationships, for example -  if the temperature declines by X degrees, my sales of ice melt will be Y.
• Time series forecasting - uses trends over time to predict the future by identifying patterns over time. Unlike in regression, we know the order of the data so information from prior periods can be utilized to identify and model different periods. Predicting sales based on historical data, seasonality, overall trends etc., is a common application of time series forecasting.
• Machine Learning is a broad category of models that include many different approaches, including those mentioned above. The difference is that ML models are much more advanced and can have many hyperparameters used to train the models, which have an impact on both the output and the accuracy of the models. (Hyperparameters are the configuration settings or variables that govern the training process of machine learning algorithms, not learned from the data, these are inputs.)
  • One sub-type of machine learning is classification models. These help to characterize and group things on their characteristics, like digital sorting systems that learn to put things in different buckets. It starts with training data, the models learn patterns that enable them to classify new data. Your spam filter on your email likely uses classification models to filter out your junk emails. 
  • Another is neural networks, which have the ability to discover complex partners without being explicitly programmed. They can get very sophisticated, with layers (deep learning) and recognize very complex patterns. Voice assistants, face recognition technology and language translators all use neural networks. These powerful models do need more training and computing power, but they achieve bigger results from more complex data.

What is prescriptive modeling?

Prescriptive modeling takes predictive insights and goes a step further by recommending specific actions. It doesn't just tell you what's likely to happen; it suggests what you should do about it.

Key characteristics of prescriptive modeling:

• Focuses on optimization and decision support
• Evaluates multiple possible scenarios
• Recommends specific actions
• Answers: "What should we do to achieve the best outcome?"

Common techniques of prescriptive:

• Optimization algorithms are problem-solving methods that find the best possible solution from many options. Think of them as smart shopping assistants. Imagine you need to buy groceries for $100. An optimization algorithm would help you get the most nutritious and satisfying combination of foods possible within your budget. It evaluates thousands of possible shopping carts to find the perfect one that maximizes your goals (nutrition, taste, etc.) while respecting your constraints (budget, storage space). Companies use these to determine the best production schedules, delivery routes, or investment portfolios that maximize profit while considering all limitations.
• Simulation modeling - is like creating a virtual "sandbox" version of a real system to test different scenarios safely. Think of it as a sophisticated "what-if" tool. For example, before building a new airport terminal, planners can create a computer simulation showing how passengers would move through it. They can test different layouts, staffing levels, and security checkpoint configurations to see which design minimizes wait times and congestion before spending millions on construction. Simulations let you experiment with different decisions and see likely outcomes without real-world risks.
• Decision trees are flowchart-like models that map out possible choices and their consequences. Imagine planning a road trip with a flowchart. At each junction, you ask a yes/no question (Is it raining? Is traffic heavy?) and follow the appropriate branch. Each path leads to different outcomes with associated probabilities and values.

Decision trees help make complex decisions by breaking them down into a series of simpler choices, showing you the likely results of different decision paths. Decision Trees are predictive models that are applied to prescriptive analytics, such as during optimization.

• Machine learning with reinforcement learning is a type of machine learning where an algorithm learns optimal behavior through trial and error, much like how we learn from experience. Think of teaching a dog new tricks with treats. At first, the dog tries random behaviors. When it accidentally does what you want, you give it a treat (positive reinforcement). Over time, the dog learns which actions earn rewards. Similarly, reinforcement learning algorithms try different actions in various situations, receive feedback (rewards or penalties), and gradually discover which strategies work best. This approach powers systems that learn to play games, control robots, or manage complex operations by practicing repeatedly in simulated environments before being deployed in the real world.

The Crucial Difference between Predictive and Prescriptive: An Everyday Example

Imagine you're driving to an important meeting, Predictive modeling is like a GPS that tells you, "Based on current traffic patterns, you'll arrive 15 minutes late." Prescriptive modeling is like an advanced GPS that tells you, "Take the next exit, use the alternative route, and you'll arrive 5 minutes early. Plus, there's a gas station on the way where fuel is 10 cents cheaper per gallon."

How do Predictive and Prescriptive Analytics apply to Trade Spend Management?

Let's apply these concepts to trade spend management in consumer goods:

Predictive Modeling in Trade Spend

Predictive models might:

• Forecast sales lift from different promotion types
• Project ROI for various trade spending scenarios
• Estimate cannibalization effects across product lines
• Predict seasonal demand fluctuations

Example: A predictive model analyzes your historical promotional data and determines that a 20% temporary price reduction for your snack product in July will likely increase sales by 35%, with an estimated ROI index of 180.

This is valuable information, but it leaves you asking, "Is this the best promotion strategy? Should we allocate our budget differently?" The promotion looks good, but we don’t know if there is something even better!

Prescriptive Modeling in Trade Spend

Prescriptive models take this further by:

• Recommending optimal promotional mix
• Suggesting precise timing for promotions
• Allocating trade budgets across retailers, regions, and products
• Creating scenario-based action plans

Example: A prescriptive model evaluates thousands of possible promotional combinations and recommends: "Implement a 15% price reduction combined with an end-cap display at Retailer A during weeks 28-30, while running a BOGO promotion at Retailer B during weeks 29-31. Shift 12% of your budget from Region X to Region Y. This approach will maximize overall ROI by an estimated 22% compared to your current plan."

The prescriptive model doesn't just predict outcomes—it recommends specific, actionable steps to optimize performance.

Real-World Application: Transforming Trade Promotion with a progressive analytics journey

Consider how a CPG manufacturer might apply both approaches:

Using predictive modeling alone: The company forecasts that their Q3 promotions will generate $1.2M in incremental sales with a 1.5 ROI. Marketing and sales teams discuss whether this is satisfactory and debate potential changes based on intuition and experience.

Adding prescriptive modeling: The company's prescriptive system evaluates the entire promotional calendar, and evaluates alternatives. It recommends shifting certain promotions to different weeks, reallocating spend from certain tactics to others, and adjusting discount depths for specific promotional product groups. The system projects these changes will generate $1.6M in incremental sales with a 210 ROI index.

What are some challenges encountered with Prescriptive Analytics?

While prescriptive modeling offers significant advantages, it also presents challenges:

1. Data quality requirements are higher for prescriptive models
2. The data sources that enhance the decisions may not be available
3. Implementation complexity is greater
4. Change management becomes crucial as organizations adapt to algorithm-guided decisions
5. Human judgment remains important for evaluating model recommendations

Finding the Right Balance

The most successful organizations use both approaches:

• Predictive modeling provides insights, forecasts, and analysis
• Prescriptive modeling offers specific recommendations and optimizations
• Human In the Loop evaluates recommendations, considers intangible factors, and makes final decisions

Next Steps for Your Organization

As you consider enhancing your analytics capabilities:

1. Assess your current analytics maturity
2. Identify high-impact business problems where prescriptive approaches could add value
3. Start with focused use cases rather than organization-wide implementation
4. Invest in data sources and data quality improvement alongside modeling sophistication
5. Build cross-functional teams that combine analytics expertise with domain knowledge
6. Employ a platform like CPGvision that enables you to gain actionable value quickly.

Conclusion

The shift from predictive to prescriptive modeling represents a fundamental evolution in business analytics—moving from insight to action. While predictive models tell you what's likely to happen, prescriptive models help you make better decisions about what to do.

For trade spend management specifically, this evolution can transform promotional strategies from educated guesses to optimized investment plans, ultimately driving higher ROI and more efficient use of marketing dollars.

As you continue your analytics journey, remember that the goal isn't simply to predict the future—it's to shape it.

By adopting CPGvision’s best-in-class trade and revenue management software suite, you can harness the power of predictive and prescriptive analytics, working with our data science team.

At CPGvision, we take pride in our commitment to your goals. Our dedicated team consists of CPG industry professionals who are fully equipped to support you with your TPM solution.

To learn more about AI-powered CPGvision, get in touch with us today.