Predictive Modeling
Quick definition
Predictive modeling is a statistical technique and process used to forecast future outcomes. It is most closely associated with predictive analytics, which uses machine learning to predict what might happen next.
Key takeaways:
- Predictive modeling can help a company understand how well the business performs, mitigate potential risks, and build better customer experiences and overall lifetime value.
- Predictive modeling does not require machine learning or artificial intelligence (AI), but they expand the available processing can improve efficiency, and allow for better data evaluation.
- There are seven significant steps in the predictive modeling process: understand the objective, define the modeling goals, gather data, prepare the data, transform the data, develop the model, and activate the model.
Answers to the following questions were discussed in an interview with John Bates, the director of Product Management for Predictive Marketing Solutions at Adobe.
What is predictive modeling in marketing?
How does predictive modeling work?
What are the different classes of predictive modeling?
What is the business strategy of predictive modeling?
How do marketing departments use predictive modeling?
Are AI and machine learning required for predictive modeling?
What tools are required for predictive modeling?
How do you build a simple predictive model?
What is predictive modeling in marketing?
Predictive modeling in marketing uses historical data and statistical techniques to build models that predict future events. Common applications of predictive modeling include customer churn, campaign response, and lifetime value.
Predictive modeling is a powerful tool to help marketers increase sales, improve customer retention, and drive growth.
How does predictive modeling work?
Predictive modeling in marketing uses historical data to predict future events. That can be time-based data or data about the behavior and characteristics of past consumers.
By using historical data to build a predictive model and capture important events or trends, a predictive model then uses current data to predict what will happen next or suggest actions to take for optimal outcomes.
Generally, people will group the process of predictive modeling into either five or seven steps:
Step 1: Understand the business objective
The first step is to understand the business objective. What are you trying to achieve? For example, are you trying to increase sales, reduce costs, or improve customer retention? Once you know the business objective, you can better align the predictive model to help achieve that goal.
Step 2: Define the modeling goals
The second step is to define the modeling goals. What do you want the predictive model to do? For example, do you want it to predict customer churn, identify cross-sell opportunities, or forecast inventory needs? After defining your modeling goals, you can better determine which algorithms and techniques to use.
Step 3: Gather data
Gather any data that will be relevant to making those predictions, including both the historical about to the event you’re trying to predict and the data surrounding characteristics or behaviors. Data mining is a common way to gather the necessary information.
Step 4: Prepare the data
Preparing the data is the longest and most tedious aspect of the entire process. Eighty percent of the total time will be spent cleaning the data, transforming the data, or turning the raw data into meaningful variables that fit better with your choice of predictive model.
Step 5: Analyze the data
At this step, you may perform sampling to test and evaluate. This is when you start to understand the relationships within your data and begin to establish which predictive modeling technique or algorithms will work best.
Step 6: Develop the model
Next, you select and develop the model you’ll use to get the business insights you need from your data. Once you have developed these models and are pleased with the expected accuracy and estimates they are producing, you validate the models.
Step 7: Validate the model
This is when you test, optimize, and deploy the models into the areas of the business in which you may need to take action.
Not all predictive models need to be deployed. Some of them produce an output, and then the business understands that output.
But depending on the business objective and the intended goals, you may need that predictive model to be deployed.
What are the different classes of predictive modeling?
There are many types of predictive models, and each fulfills a specific purpose or addresses a specific need. An organization must consider its business goals and the information they hope to gather when selecting a model to use.
Classification models
Classification models are best at answering yes-or-no questions and guiding decisive actions.
A classification model may provide information on the risk of customer churn, which is how likely a customer is to stop using your product or service, or how likely a customer is to convert.
Clustering models
Clustering models separate data into different buckets or groupings of individuals based on common attributes or similarities.
For example, you might want to separate your customers who churn based on similar behaviors and common traits or characteristics, and then proactively make recommendations to each one of those different clusters.
Forecast models
Forecast models deal in predictions using historical data. They use numeric information to determine the values of new data based on learnings from historical data.
Forecast models answer questions like “How many customers are likely to convert next week?” or “What is the optimal price for this product?” Given a piece of information, they forecast or predict another piece of information.
Anomaly detection models
Anomaly detection models, or outlier models, are used to identify odd behavior.
They calculate things like spikes in revenue that are different from the norm and notice things like fraudulent purchases or transactions because these things are different from what we typically expect.
Time-series models
Time-series models differ slightly from forecast models, even though those words are often used interchangeably. Time-series models use data over time to predict a numerical metric for future periods.
What is the business strategy of predictive modeling?
From a strategic business standpoint, predictive modeling can be used in many ways at many different levels within an organization.
Forecasting performance
At the highest level, you may have executives who depend on the data to understand the performance of the overall business. This data provides executives with insights into the markets in which they can compete.
Minimizing risk
Predictive modeling is also used to minimize future risks. If you're evaluating significant new investments, product lines, ventures, or acquisitions, predictive modeling can help minimize future risk.
Identifying potential problems
Predictive modeling can also help businesses identify potential problems early on and take corrective action before they become serious.
How marketing departments use predictive modeling
The marketing department may use predictive modeling to produce rapid, real-time, personalized, one-on-one experiences for customers. In such cases, you're trying to increase not only overall business performance but also customer experience and lifetime value.
Are AI and machine learning required for predictive modeling?
The terms “predictive modeling” and “machine learning” are often used interchangeably, but they are distinct. Machine learning uses statistical techniques to allow a computer to construct predictive models.
An individual can construct a predictive model on a napkin with a small data set, but machine learning is when a computer constructs those predictive models for you.
Organizations often use machine learning when trying to reach a certain level of scale in computation processing.
Machine learning itself is a branch of artificial intelligence in which the machine displays the intelligence.
The key difference is that AI systems make assumptions and tests autonomously. And AI uses a combination of technologies to get the right processing speed, as well as different inputs of data. Machine learning is one technique that AI uses. Other techniques are closely associated with neural networks, or algorithms designed to work like the human brain. The purpose of AI is to reassess the model and reevaluate the data, all without the intervention of a human, and then make assumptions based on that predictive analysis.
What tools are required for predictive modeling?
You need software for data collection transforming the data, analysis, model building, and for the actual activation of the model.
A company can either use one solution for each piece or find solutions that perform more than one task, depending on their budget and needs.
How do you build a simple predictive model?
If you're new to predictive modeling in marketing, begin with something familiar. It's probably best to choose a smaller sample size that is relatively easy to explore.
If it's your first predictive model, start with a time-series model looking at something like customer churn over time. These are sometimes the easiest to grasp quickly.
Most businesspeople and marketers are familiar with spreadsheet software like Microsoft Excel. If you're building a model for the very first time, take the trended data by time, such as total revenue by day for the last 12 months, and input that data into your spreadsheet.
Then, use the trend function to fit a linear trend using a straight line. It uses the least squares method, a mathematical procedure for finding the best fit of a set of data points, to make a set of known Y’s.
This might be your revenue daily, for example. Your Y’s are the data points you have already collected.
Then, your known X’s are your days or weeks or months, whatever the detail is. It needs to be consistent. The model will return the revenue, or the Y values along that line for the array, for any new X’s, which would be future days. With that, you're projecting out into the future.
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