Machine Learning Vs Predictive Analytics: What’s the Difference?

In discussions about AI and its impact on business, the terms “machine learning” and “predictive analytics” are sometimes used interchangeably.

This can be misleading.

There is a strong relationship between the two (the first is a technique often used to do the second) but they are distinctly different concepts.

Let’s explore each term, where they diverge, and how they work in synergy within a business context.

Laying the Foundations

Machine learning is an artificial intelligence technique where algorithms are given data and asked to process it without predetermined rules.

Machine learning algorithms use what they learn from their mistakes to improve future performance.

Data feeds machine learning; the results are most accurate when the machine has access to massive amounts of it to refine its algorithm.

There are two general types of machine learning: supervised and unsupervised.

  • Supervised: A training dataset is provided to tell the machine what kind of output is desired. The labelled data gives information on the parameters of the desired categories and lets the algorithm decide how to tell them apart. Supervised learning can be used to teach an algorithm to distinguish spam mail from normal correspondence.
  • Unsupervised: In this type of learning, no training data is provided. The algorithm analyzes a body of data for patterns or common elements. Large amounts of unstructured data can then be sorted and categorized. Unsupervised learning is used in intelligent profiling to find similarities between a company’s most valuable customers.

Predictive analytics is the analysis of historical information (as well as existing external data) to find patterns.

These patterns are used to make informed predictions about future events.

It’s an area of study, not a specific technology, and it existed long before artificial intelligence. Alan Turing applied it to decode encrypted German messages during World War II.

As a general rule, any attempt to quantify the possible future based on past events is encompassed by predictive analytics. A number of alternate techniques are still common in business.

For example, using sophisticated mathematical & statistical models to evaluate data provides excellent results.

Differentiating predictive analytics from some closely related practices offers a better understanding of the field and where it falls on the analytical spectrum.

Descriptive analytics

It describes past activity and the current state of things. It breaks the raw story of “what happened” or “what is happening” down into quantifiable data that can be used to better understand a situation.

Charting a marketing campaign’s performance in real time is an exercise in descriptive analytics.

Diagnostic analytics

It determines why an event happened the way it did, screening out unrelated data and assigning relevance to each component.

It can uncover previously unexpected contributing factors. Principle components analysis is a form of diagnostic analytics.

Predictive analytics

It attempts to forecast the most likely scenarios by comparing current conditions to historical data and placing the results in a modern context.

It’s often used in sales lead scoring, where leads are assigned priority based on the past value of similar customers.

Prescriptive analytics

It provides suggestions for future decisions by evaluating the possible outcome of several courses of action.

While not widely adopted, the healthcare industry has shown interest in using it to manage the treatment of patients with multiple medical conditions.

Sometimes one issue should be addressed before another for the best result. Predictive analytics weighs thousands of factors to recommend an optimal schedule of treatment.

predictive analytics vs descriptive analytics vs diagnostic analytics vs prescriptive analytics

Related, but Not the Same

Because predictive analytics is one of the most common enterprise applications of machine learning, they’re understood by casual users to mean the same thing.

It’s true that machine learning is an excellent means of forming predictions from data.

Classification and regression are strengths of supervised learning, and unsupervised learning can find relationships within enormous databases of unstructured data.

Machine learning is much bigger than predictive analytics, though.

There’s a broad spectrum of business use cases that fall outside the predictive umbrella.

Facial recognition

Supervised algorithms have been distinguishing between humans and animals or picking faces out of larger images for some time.

Now, they can identify specific people regardless of body position or lighting.

This is one of the more mature uses of machine learning, used for everything from password authentication to automated security monitoring.

Natural language processing

Natural language processing, or NLP, processes normal linguistic patterns without demanding specific phrasing or keywords.

It’s the technology driving the meteoric rise of chatbots. Among other uses, chatbots give companies the ability to provide consistent entry-level customer service at all hours, no matter where the user is in the world.

Managing user-generated content

User-generated content is both an asset and a risk. It’s a core piece of the business model for social media platforms, but it’s hard to manage in any useful volume.

Some of it is low quality and should be ranked lower in search results regardless of its associated keywords. Some content violates community standards and shouldn’t be accepted at all.

Sorting, ranking, and labelling unstructured data like forum comments, videos, and social media posts would be incredibly difficult without machine learning algorithms.

Search engines

When a person types a phrase into a search engine, a number of rankings happen between clicking “ok” and receiving a page of links.

The initial results are ranked in terms of properties like technical match, contextual relevance, location, sentiment, and personal search history.

While the average search returns millions of potential matches, only 10% of users will go farther than the first page. Machine learning helps search engines put the most helpful results on that all-important first page.


Also, there are other ways to do predictive analytics. As discussed earlier, it’s more an end goal than a specific technique.

Methods other than machine learning are still in use around the world. Forecasting based on an autoregressive integrated moving average (ARIMA) model is reliable enough to be used in modern logistics.

One recent usage of an ARIMA model was a 2016 study aimed at understanding and streamlining shipping traffic between the Far East and Northern Europe.

Machine learning could have produced similar results, but the ARIMA model gave a sufficiently clear picture for logistical planning.

A Dynamic Pairing

Despite the differences, it makes sense that predictive analytics and machine learning are often found together. Predictive analytics is one of the newest and most exciting applications for machine learning at an enterprise level.

One reason for the interest is the sheer volume of data involved in operating a business.

Sales numbers, production processes, inventory control, website activity, social media- there’s far too much data to process in a timely manner without artificial intelligence strategies.

Data is only useful when it results in actionable insights. Machine learning provides those insights with growing reliability.

Companies who adopt machine learning-powered predictive analytics gain a serious competitive edge on those who aren’t simply by being able to process more of their data. They stay one step ahead of their competition.

These companies create more efficient marketing strategies, are better prepared to act on time-sensitive opportunities, and often see fraud risks and security threats far enough ahead to limit the potential damage.

Even in cases where statistical methods of predictive analytics can be applied, machine learning has advantages.

Other techniques are limited to considering factors the user identifies, but machine learning algorithms don’t need to be told what’s important.

They find patterns that may only be visible in the aggregate. It’s a highly efficient way to do predictive analytics, too.

Fewer humans need to be involved in processing machine learning results, making them less prone to error in general.

Real Word Usage

With more convenient and cost-effective cloud computing on the rise, machine learning is poised to become the business world’s favorite way to do predictive analytics.

The technologies can already be found in many areas of operation.

  • Refining marketing strategies: Which activities have the highest ROI? Which activities don’t produce appreciable results?
  • Customer segmentation: Who are your customers? How are they alike or different?
  • Optimizing inventory/ordering systems: How much inventory should be kept on hand at this specific time? When will demand increase or decrease?
  • Predictive pricing: Where is the “sweet spot” between reasonable profit and customer satisfaction? When should it change in response to external events?
  • Recommendation engines: Based on past activity, which future activities will a specific customer enjoy? What kind of recommendations will inspire increased engagement?

Looking Forward

Artificial intelligence and machine learning have been trending upwards in use for some time now.

Besides the undeniable cool factor, they satisfy the need for personalized service delivered more efficiently.

There will always be a place for other predictive analytics methods, but as business problems grow larger to fit into the global marketplace those other methods become awkwardly labor-intensive or inaccurate.

Machine learning can adjust itself to match a project’s scale. This flexibility makes it a necessary part of an executive’s digital tool box.

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