The terms predictive modeling and machine learning are sometimes used interchangeably, especially as these tools rapidly grow and evolve to incorporate more advanced capabilities. That’s because they both have similar goals. However, they differ in how they achieve those goals.
Both are powerful options in the digital marketer’s toolbox.
If you plan on deploying these tools, it’s important to understand the differences between predictive modeling vs. machine learning. This way, you’ll be sure to use them most efficiently.
Predictive Modeling Explained
The predictive modeling definition is:
A commonly used statistical technique for predicting the future.
However, you probably want to dive a bit deeper than that. Predictive modeling, also known as predictive behavior modeling, works by using mathematical equations and prediction algorithms on sets of data to determine future events. For example, you may use view, click, and conversion data from a previous marketing campaign to determine the likely outcome of a future marketing campaign.
These models form the basis of predictive analytics, one of three core types of analytics used by digital marketers. The other two are descriptive analytics and diagnostic analytics, but those are topics for another day.
Types of Predictive Models
When you delve into the world of predictive data modeling, you’ll learn several different types of statistical techniques. As with beauty, data is in the eye of the beholder, and how you process that data significantly impacts the outcomes it produces.
As a result, marketers and statisticians have worked to develop seven key types of predictive models. These models look at data in different ways to make the most accurate predictions possible. It’s important to understand how each of them works to form a better understanding of how and when to use them in your marketing efforts.
Regression Models
Regression modeling is a form of statistical modeling that predicts relationships between one dependent variable and one or more independent variables. For example, a regression model may help you understand the relationship between the price of a product and sales. This can help you find the optimal price without significant in-market A/B testing. This type of regression model is known as linear regression.
Logistic regression, on the other hand, is used to calculate the probability of an event. For example, you can use logistic regression to determine what your advertising’s conversion rate is likely to be or what percentage of visitors are likely to make a purchase due to your advertising.
Classification Models
Classification models are prediction models that help you classify items, audiences, and more. In fact, you likely use a classification model in your day-to-day life. If you have a spam filter, you do.
Spam filters and email inboxes classify messages as valuable messages that are sent to your inbox or junk messages that are sent to spam. These same classification models can help you improve your marketing effectiveness.
For example, you can classify specific subsets of your audience, making it easier to target them with highly personalized ads. This technology can also be used to digitally determine the difference between a qualified and unqualified lead.
Clustering Models
Clustering models are predictive analytics models that work to find groups or clusters of data among a given data set. When it finds these clusters, it reports them back to you so you can take action.
For example, a clustering model may look at the demographic data of your recent customers. When it does, it finds a cluster of customers that are males between 30 and 35. This could mean a couple of things:
- Your product is geared toward males between the ages of 30 and 35. In this case, you should adjust your marketing to address this audience more effectively.
- Your current advertising, rather than the product, is attractive to males between 30 and 35. In this case, you may want to adjust your marketing to attract a larger, more diverse audience.
This is just one example of how clustering models can help you make informed marketing decisions.
Time Series Models
Time series models are statistical models in which all data is ordered chronologically. These models are especially useful in forecasting seasonality in sales.
For example, imagine you owned a pumpkin patch. A time series model would likely show that your most profitable month is October. Sure, that’s a very basic (and obvious) example, but time series models can be quite complex, and help gain insight into industries that are a little more challenging to decipher.
You could use time series models to determine which times of day, days of the week, and times of the year are the most effective times to show ads. This could greatly increase your ROI by showing ads when the cost to show them is lower and the conversion rates tend to be higher.
Decision Tree Models
Decision tree models use a series of tests to produce their statistical analysis. In a decision tree model, the technology uses the result of each decision or test to inform and determine the next steps.
In the world of marketing, making decisions can feel risky and uneasy. However, decision tree models make it possible to make well-thought-out decisions with the help of a visual aid.
You can also plug data into a decision tree model and allow it to use multiple variables and tests to show the best possible route to your marketing goals.
Neural Network Models
The human brain is an amazing feat of natural engineering. So, it only makes sense that the world’s best data scientists have used it as a guide to assessing data and making accurate predictions. That’s where neural network models come in.
These are predictive models that are designed based on the human nervous system. These models are particularly useful in understanding how your audience engages with your business online. It answers questions such as what factors make a consumer want to engage with your business, and when they do engage, what factors make them want to make a purchase. This type of model can rapidly spot multiple connections and come to conclusions based on a rapid, intersectional analysis of vast amounts of data.
When you understand these interconnected factors better, you can make better and more informed marketing and customer experience decisions.
Ensemble Models
An ensemble model is any model that combines multiple predictive models to make the most accurate predictions possible. Keep in mind that multiple models may use the same data but process it in different ways. When they do, they tend to produce different results.
Ensemble models process data in multiple ways to find the most likely result, regardless of the processing method. These models are highly intuitive and give you a comprehensive response in relation to the problems they attempt to solve.
How To Use Predictive Modeling for Advertising
Predictive modeling is typically used to determine future trends in your company and marketing campaigns. There are three common applications of predictive analysis:
- Customer Churn Predictions: Predictive modeling can give you a relatively accurate depiction of your customer churn rates. This is important because it tells you how many customers you’ll need to attract each month to ensure that customer churn doesn’t lead to negative customer growth.
- Campaign Response Predictions: Wouldn’t it be great if you could tell how your audience was likely to respond to an advertising campaign before you spent money on it? That’s exactly the type of information predictive modeling can help you determine.
- Customer Lifetime Value Predictions: It’s important to understand the lifetime value your customers represent. After all, the only way to profit is to ensure your customer acquisition cost is below your customer’s lifetime value. Predictive modeling can give you insights here.
Machine Learning Explained
Unlike predictive modeling, which uses a basic set of data that you provide, machine learning is always combing for new data. Artificial intelligence and machine learning platforms start with a training algorithm. They then process vast amounts of data using that algorithm, and they use the data they process to learn.
This deep learning process makes it possible for machine learning models to find anomalies in data and learn from their mistakes. As a result, machine learning models become more effective with time.
Types of Machine Learning
As is the case with predictive modeling, there are multiple different types of machine learning models. These include supervised learning models, unsupervised learning models, and reinforcement learning models. Each of these processes data differently, so it’s a good idea to form an understanding of them before you deploy machine learning in your marketing efforts.
Supervised Learning
Supervised learning models use labeled data sets to train machine learning algorithms. The goal of these models is to classify or predict outcomes as accurately as possible. As you feed data into a supervised learning model, it adjusts its algorithm and weights based on the new data provided. These models use cross-validation to do so.
Supervised learning models have several real-world applications in the marketing industry. For example, you could train a supervised learning model to determine which groups of potential customers are most likely to make a purchase.
Unsupervised Learning
The defining factor between supervised and unsupervised learning in machine learning models has to do with labeling. While supervised learning models use labeled data sets to learn, unsupervised learning models use unlabeled data sets. As such, they’re blind to the data you’ve found important to classify as a human being.
It’s important to consider unsupervised learning models in your marketing efforts because these models are able to point out patterns deep in the data that your human eye would never see. This technology is particularly useful in terms of customer segmentation and cross-selling strategies, though there are plenty of other uses for unsupervised learning in marketing.
Reinforcement Learning
Reinforcement learning is a type of machine learning model that trains algorithms by punishing undesired outcomes and rewarding desired ones. As the machine learning algorithm makes the right decision, either you or the algorithm lets it know it was correct. When it makes a wrong decision, it’s informed of that, too.
Over time, reinforcement models are able to produce the type of data you want with pinpoint accuracy.
How To Use Machine Learning for Advertising
There are several machine learning applications in advertising. Some of the most exciting applications include the following:
- Creative Element Optimization: Whether they be text, images, or even videos, machine learning models can help you optimize the creative elements of your advertising, leading to higher conversion rates.
- Customer Segmentation: Today’s consumers tend to react better to personalized advertising experiences. That’s why customer segmentation is so important. Machine learning algorithms can help you accurately segment and target customers for a better ROI.
- Improved Budget Decisions: Machine learning can help you determine the optimal budget for clicks and other actions. This can help ensure that your ads are competitive without you overpaying for conversions.
- Cookie Elimination: Third-party cookies are a thing of the past, but you don’t need them anymore. Machine learning models can help you improve conversions without cookies.
Each of these applications of machine learning can significantly benefit your company’s marketing campaigns.
The Role of Predictive Modeling and Machine Learning in Programmatic Ads
There are several differences when you compare predictive analytics vs. machine learning. The biggest is the fact that machine learning learns as it goes. Nonetheless, both of these technologies work hand in hand, and can be most effectively leveraged together, such as in holistic suites of AI-powered solutions such as AUDIENCEX Intelligence (AXi).
Ultimately, all of these technologies can help you better understand your audience, predict the outcomes of your marketing campaigns, determine the value of each customer, ensure your advertising costs are lower than your customers’ lifetime value, and much more.
Leverage Your Marketing Strategy With AI-Powered Programmatic Solutions
If you haven’t started using machine learning and predictive analytics in your marketing efforts, you’re behind the curve. But don’t worry, it’s not difficult to get up to speed.
The team at AUDIENCEX is highly specialized in identifying, integrating, and effectively deploying the latest emergent technology to improve your marketing outcomes. With AXi, our suite of privacy-safe AI-enabled solutions that incorporate both predictive modeling and machine learning, these capabilities are leveraged throughout the digital landscape.Backed by custom holistic strategies, tech-enabled creative capabilities and seamless omnichannel media access, we can help you not only reach but engage and convert your target audiences anywhere they are. If you’d like to learn more about our solutions, please feel free to reach out. Our team would love to connect to discuss your goals and needs, and explore how we can build a custom strategy to strategically leverage advanced solutions and drive meaningful results.