Predictive Analytics Meets Reinforcement Learning: A Winning Combination for Solving Business Problems
The Power of Reinforcement Learning for Optimizing Predictive Analytics Outcomes
Imagine you are a business owner who wants to make the best decisions for your customers and your bottom line. You have access to a wealth of data and machine learning tools that can help you predict future outcomes and trends. But how do you act on these predictions? How do you choose the best actions that will maximize your long-term objectives?
This is where reinforcement learning comes in. Reinforcement learning is a branch of machine learning that deals with learning from trial and error. It enables agents to learn optimal policies for interacting with dynamic environments by receiving rewards or penalties for their actions. Reinforcement learning can help businesses create prescriptive analytics, which is the process of recommending optimal actions based on predictions.
Predictive analytics is the process of using data and machine learning to forecast future outcomes and trends. It can help businesses make better decisions, optimize processes, reduce risks, and increase customer satisfaction. However, predictive analytics alone is not enough to achieve these goals. Sometimes, businesses need to take actions based on their predictions, such as sending personalized offers, adjusting prices, or scheduling maintenance. How can they choose the best actions that maximize their long-term objectives?
In this article, we will explore how predictive analytics and reinforcement learning can be combined to create powerful solutions for various business problems. We will use examples from different domains such as marketing, manufacturing, and transportation to illustrate how these techniques can be applied in practice.
Marketing: Next Best Action
One of the most common applications of predictive analytics in marketing is next best action (NBA), which is the problem of deciding what action to take next for each customer based on their preferences and behavior. For example, a bank may want to offer a credit card, a loan, or a savings account to its customers depending on their needs and interests.
However, choosing the best action for each customer is not trivial. There are many factors that need to be considered, such as:
The customer’s profile and history
The customer’s current context and intent
The available actions and their costs
The expected outcomes and rewards of each action
The long-term impact of each action on customer loyalty and retention
To address these challenges, some businesses have adopted reinforcement learning as a framework for building NBA models that can learn from data and feedback over time. Reinforcement learning provides a convenient way to model the NBA problem as a Markov decision process (MDP), which consists of:
A set of states that represent the customer’s situation
A set of actions that represent the possible offers or messages
A transition function that describes how the state changes after taking an action
A reward function that measures how good an action is for a given state
The goal of reinforcement learning is to find a policy that maps each state to an action that maximizes the expected cumulative reward over time.
One example of using reinforcement learning for NBA is Adobe’s Project Bon Voyage 1, which aims to optimize email marketing campaigns by dynamically selecting content for each recipient based on their preferences and behavior. The system uses deep neural networks (DNNs) to encode user features into state vectors, and then uses deep Q-learning (DQN) 2, a popular reinforcement learning algorithm, to learn an optimal policy for selecting content from a predefined pool.
Another example is Alibaba’s Deep Interest Evolution Network (DIEN) 3, which aims to optimize online advertising by dynamically selecting ads for each user based on their interests and feedback. The system uses recurrent neural networks (RNNs) with attention mechanisms to model user interest evolution over time, and then uses deep deterministic policy gradient (DDPG) , another popular reinforcement learning algorithm, to learn an optimal policy for selecting ads from a large inventory.
Both examples show how predictive analytics using DNNs can be combined with reinforcement learning using DQN or DDPG to create effective NBA models that can adapt to changing customer behavior and preferences over time.
Manufacturing: Predictive Maintenance
Another common application of predictive analytics in manufacturing is predictive maintenance (PdM), which is the problem of predicting when a machine or component will fail or degrade based on sensor data. For example, a wind turbine may have sensors that measure its temperature, vibration, and power output. By analyzing these data, a PdM model can estimate its remaining useful life (RUL) or probability of failure within a given time window.
However, predicting failure or degradation alone is not enough to optimize maintenance operations. Sometimes, businesses need to take actions based on their predictions, such as scheduling inspections, repairs, or replacements. How can they choose the best actions that minimize maintenance costs while maximizing reliability?
This is where reinforcement learning comes in again. Reinforcement learning can help businesses create prescriptive maintenance policies that can learn from data and feedback over time. Reinforcement learning provides a convenient way to model the PdM problem as a partially observable Markov decision process (POMDP), which extends the MDP framework by adding:
A set of observations that represent the sensor readings
An observation function that describes how the state influences the observations
The goal of reinforcement learning is to find a policy that maps each observation to an action that minimizes the expected cumulative cost over time.
One example of using reinforcement learning for PdM is Skordilis et al.'s approach for real-time sensor-driven decision making and predictive analytics. The system uses particle filtering (PF) , a Bayesian filtering technique, to estimate the RUL of a wind turbine gearbox based on current signals. Then, it uses DQN to learn an optimal policy for deciding when to perform maintenance actions based on the RUL estimates.
Another example is Elbasheer et al.'s framework for developing an intelligent decision support agent (DSA) for integrated PdM and production planning and control (PPC) based on reinforcement learning. The system uses DNNs to encode production and maintenance features into state vectors, and then uses DDPG to learn an optimal policy for selecting production and maintenance actions from a predefined pool.
Both examples show how predictive analytics using PF or DNNs can be combined with reinforcement learning using DQN or DDPG to create effective PdM policies that can adapt to changing machine conditions and production requirements over time.
Transportation: Route Optimization
A final common application of predictive analytics in transportation is route optimization, which is the problem of finding the best route for a vehicle or a fleet of vehicles based on traffic conditions, fuel consumption, travel time, customer demand, etc. For example, a delivery company may want to optimize its routes to minimize costs, maximize profits, and satisfy customers.
However, finding the best route for each vehicle is not easy. There are many factors that need to be considered, such as:
The vehicle’s location, capacity, and fuel level
The customer’s location, demand, and time window
The road network topology and traffic congestion
The weather conditions and road hazards
The stochasticity and uncertainty of all these factors
To address these challenges, some businesses have adopted reinforcement learning as a framework for building route optimization models that can learn from data and feedback over time. Reinforcement learning provides a convenient way to model the route optimization problem as a stochastic shortest path (SSP) problem , which consists of:
A set of states that represent the vehicle’s location and status
A set of actions that represent the possible movements or stops
A transition function that describes how the state changes after taking an action and its probability distribution
A cost function that measures how expensive an action is for a given state
The goal of reinforcement learning is to find a policy that maps each state to an action that minimizes the expected total cost until reaching a terminal state.
One example of using reinforcement learning for route optimization is Li et al.'s approach for real-time ride-sharing dispatching. The system uses graph convolutional networks (GCNs) , which are neural networks designed for graph data , to encode spatial-temporal features into state vectors , and then uses actor-critic methods , which are hybrid algorithms that combine value-based and policy-based methods , to learn an optimal policy for matching drivers and passengers based on their locations , destinations , preferences , etc.
Another example is Casas et al.'s approach for real-time traffic signal control. The system uses convolutional neural networks (CNNs) , which are neural networks designed for image data , to encode traffic images into state vectors , and then uses proximal policy optimization (PPO) [13], which is an advanced policy gradient method , to learn an optimal policy for adjusting traffic signal phases based on traffic flow , congestion , delay , etc.
Both examples show how predictive analytics using GCNs or CNNs can be combined with reinforcement learning using actor-critic methods or PPO to create effective route optimization models that can adapt to changing traffic conditions and customer demand over time.
Bottom line
In this article , we have explored how predictive analytics using deep neural networks can be combined with reinforcement learning using various algorithms to create powerful solutions for various business problems .