Predictive Analytics vs Machine Learning: What To Use for Your Business?

Artificial intelligence is probably the most mystical technology out there. Everyone knows it exists, but struggles to tell exactly how it works. The same goes for the numerous subsets of this discipline. 

Computer vision, machine learning, deep learning, or neural networks – they all sound similar. Around 37% of executives don’t know how these work or the benefits they entail for the company.

So which one should your company use, and how do we deploy it?

Although we won’t cover each one of the dozens of AI applications, we’ll have a closer look at machine learning vs predictive analytics.

machine learning vs predictive analytics - featured image

Image by @ikukevk

What Is Machine Learning?

Both predictive analytics and machine learning can predict future outcomes by analyzing current and past data. Therefore, they assist companies in making informed decisions based on accurate forecasts. But what sets machine learning apart?

Let’s start with the basics. Machine learning is a form of artificial intelligence that automates the process of building an analytical model and allows computers to adapt to new scenarios with no manual input.

At its core, a machine learning model is a computer that reads data and identifies patterns. The latter is then used to perform the assigned task. The only prerequisite you need is enough training data for the algorithm. 

Based on the specific task, machine learning algorithms operate with varying degrees of human intervention. There are three basic models of machine learning: supervised learning, unsupervised machine learning, and reinforcement learning:

  • Supervised machine learning requires the use of labeled data to help the machine perform a task. This is the simplest model that attempts to recreate the human learning process.
  • Unsupervised machine learning uses unlabeled data for identifying hidden patterns and strategic data. 
  • In reinforcement learning, the computer observes the environment and uses the data to determine the ideal behavior that will minimize risk and/or maximize results. 

How Machine Learning Benefits Your Business?

Machine learning uncovers strategic insights from raw data. The insights then lay the ground for solving complex, data-intensive business tasks.

In the case of unsupervised learning, algorithms are even able to identify hidden relationships even if they’re not explicitly programmed to do so. 

Let’s have a closer look at the benefits of machine learning that The App Solutions helps businesses reap.

1. Rapid data processing and real-time forecasting

The data processing speed of ML algorithms allows them to keep up with the ever-evolving market situation. Therefore, you can stay on top of business trends and the performance of your business efforts. These include:

  • Churn analysis and customer churn prediction – it’s a health indicator of your business that has to be as minimum as possible. By quickly identifying customers at the risk of leaving you can target personalized marketing campaigns or revisit your services and enhance them to meet customer expectations.
  • Leads – machine learning is used as a tech-savvy weapon for customer acquisition that discovers patterns in your lead data and uncovers new lookalike leads at scale. 

2. Real-time business decisions

Better data equates to forward-looking business decisions. The latter is usually based on the insights that business analysts gather from the available data.

However, pulling out the right information is not easy. Machine Learning allows companies to quickly analyze data and tap into the relevant customer behavior.

3. Simplifies documentation process

Intelligent process automation or IPA is the application of machine learning that spans a variety of administrative operations.

From invoicing the client to risk assessment, intelligent algorithms allow companies to handle daily routine tasks with no human input. Intelligent systems can also automate data entry regardless of data formats.

Thus, you can classify invoices from multiple suppliers or documents with erroneous data. 

4. Personalized customer experience

Smart algorithms also help companies to tap into the minds of customers to deliver personalized marketing collateral.

Thus, instead of basing your campaigns on standard info like demographics, ML analyzes a whole range of user data such as previous purchases or browsing history.

Companies use these insights to provide special deals, promos, or products that might be of interest to their customers.

5. Proactive cybersecurity 

Although AI is not immune to cyberattacks, ML algorithms and especially NLP can be effective in keeping hackers at bay.

With ML-enabled software, cybersecurity systems can analyze patterns and learn from historical data to avert similar attacks and respond proactively to suspicious behavior.

Therefore, your cybersecurity teams can react to incoming attacks in real-time. Moreover, ML patterns can even identify the smallest fluctuations in traditional processes and destroy a cyberattack at early stages. 

What Is Predictive Analytics?

Predictive analytics is a scientific discipline and a branch of Industry 4.0 that studies methods and techniques for making predictions.

Based on data about the past and current state of an object, it calculates possible outcomes and provides optimal decisions based on the outcomes. 

According to the SAS Institute, predictive analytics leverages the combination of big data, statistical algorithms, and machine learning techniques to estimate the probability of future trends based on past data.

Examples of predictive analysis include sales forecasting, credit scores, retailers’ suggestions, and others.

The main advantage of predictive analytics is that it gives you a competitive advantage in the market. Imagine that you always have a look into the future when launching a new service or a product or devising a full-scale marketing campaign. 

How Predictive Analytics Benefits Your Business?

Predictive analytics offers business owners the following benefits:

  • Increased accuracy in market segmentation – PA methods allow companies to establish a more holistic image of their target customer.
  • Increased conversions – the data about previous sales enables more effective discovery of new leads.
  • Better sales forecasting – companies can more effectively plan production loads and profits during the fiscal year.
  • Customer segmentation – PA mechanisms allow dividing multiple customers into groups (segments) to identify similar needs and offer them the most relevant goods and services.
  • Discovering hidden potential – predictive analytics helps companies create fertile ground for future business growth. 

Moreover, predictive analytics techniques can be used in a variety of industries, both commercial and public sectors, including:

  • Healthcare – automated predictive modeling helps prevent chronic disease and injury recurrence based on collected medical histories and new data entry.
  • Manufacturing – predictive analytics fuels predictive maintenance systems that help prevent asset downtime
  • Marketing – it identifies the most effective combination of marketing material, product design, and others.
  • Finance – PA averts card frauds, finds inaccurate card applications, and prevents identity thefts.

These are just a sliver of all areas that can benefit from predictive analytics.

By this time, it must seem to you that machine learning and predictive analytics are the same since they have similar use cases and target similar business pain points.

But the interior is deceptive.

predictive analytics

Image by @charlesdeluvio

What Is the Difference Between Predictive Analytics and Machine Learning?

Let’s start our comparison by identifying similarities first. These include:

  • Both machine learning and predictive analytics analyze patterns to predict potential results.
  • Both need large amounts of data to make it work.
  • Predictive modeling is often the main reason why both are used.

Indeed, both disciplines overlap greatly and take advantage of each other to yield more accurate results. Nevertheless, it’s not the same science.

First of all, machine learning is solely a computational science, whereas PA is a statistical one.

Also, predictive analytics systems may be based on machine-learning algorithms, while machine learning doesn’t need the former to produce an accurate result.

These differences may seem subtle but they distinguish the two disciplines.

Although you can develop a predictive data model with machine learning, it won’t make sense without an in-depth analytics process.

Also, machine learning is just one of the many technologies that can be used to build predictive models.

The Bottom Line

Due to the neck-breaking competition, modern businesses are now investing in innovative software that helps automate supplementary processes and yield more profit.

Machine learning and predictive analytics are among the most popular options used for a company’s growth. 

Although both can produce forward-looking models, these two notions bode well for slightly different outcomes.

Thus, if your company needs automation at scale, it’s better to implement ML-enabled systems.

At the same time, if you’re looking for strictly analytical capabilities, predictive analytics will add the missing block into your daily operations and deliver meaningful insights for future business decisions.

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