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Why Artificial Intelligence Crushes Linear Approaches

Marketing requires numerous complex decisions that have traditionally been made based on a great deal of qualitative and quantitative data analyzed with linear statistical approaches… and, let’s be honest, gut feeling.

That simply doesn’t work in today’s data-driven environment. Artificial Intelligence, however, provides a way to distill the ‘noise’ into actions and match financial goals with the marketing decisions necessary to attain them.

Utilizing Artificial Intelligence confers many advantages over traditional linear approaches when pressure is on to deliver exceptional real-world results.

Put simply: Artificial Intelligence simplifies the marketing executive’s ability to process large volumes of customer data, and obtain accurate and consistent findings that can result in improved sales and real-world financial results. It can separate the ‘the wood from the trees’, enabling marketers to determine the best strategies as well as individual tactics for a brand.

Specific marketing challenges that machine learning algorithms can address include:

Financial Results

Questions to ask include:

  • What is the maximum market share and revenue I can achieve given the market and competitors, and what do I have to do to get there?
  • How do I allocate my marketing budget across channels to realize maximum profit?
Customer Segmentation

Machine learning models can identify groups of customers with similar behaviors, as well as your most valuable customers, enabling targeted marketing for optimal results.

Strategic Direction

Using machine learning analytics to assess existing strategies can objectively identify flawed approaches and, by analyzing the results, provide the necessary information to shift direction.

Personalized Driver Messaging

Machine learning enables marketers to process huge amounts of data coming from multiple sources - such as CRM systems, sales data, distributor data, website visit flow and campaign responses - required to predict the most effective marketing message for small market segments. Targeting messaging to smaller segments of the market can change customer behavior, thus improving revenue.

Rx Switch Prediction

Machine learning enables marketers to see patterns in the data that can identify why healthcare providers who switched brands decided to switch, and more importantly, accurately predict which providers are at a high risk of switching. This allows marketers to engage in targeted, proactive switch prevention approaches.

Resource Allocation

Machine learning analytics accurately identifies the right way to reallocate resources and budget to achieve optimal results, if companies are willing to take a risk (a small risk, given the accuracy of the data) to implement the changes.

So why is Artificial Intelligence creating such a buzz and proving to provide significantly superior results?

Discovers all critical relationships in the data - both linear and non-linear relationships

If the relationships in the data are non-linear, then the linear models either completely miss them or give an erroneous answer. However, machine learning will pick up the true relationships in the data - whether linear or non-linear – to provide critical insights that make all the difference in real-world results.

For example, think about budget allocation. To do this with linear approaches you need multiple time periods and the right data, and even then - with changes in the market, changes in channel effectiveness, and all the non-linear data not being considered - you really are not going to achieve any degree of accuracy using a linear approach. However, using Artificial Intelligence for these questions will allow significantly high levels of accuracy. Data has both linear and non-linear relationships within. Until AI, only the linear relationships could be uncovered. Now, with AI we can find the true relationships (not just correlations) in both the linear and non-linear data to provide highly accurate real-world results.

What about customer segmentation analysis as another example? Using linear approaches you can test your segmentation hypotheses, but you cannot uncover segments you had not already considered or identified. However, by using Artificial Intelligence, you will uncover both physician and patient segments that are high growth segments which linear approaches would have missed.

Deep, accurate insights to create real-world improvements and results

We are now creating billions of data points and combinations. Statistics and other older analytics techniques simply don’t have the power to provide the level of meaningful information that machine learning offers.

Machine learning can churn through hundreds of millions of data points to provide answers you can trust with an accuracy that is in a different league to older approaches. For instance, we’re finding that machine learning analytics are actually better than experienced Pathologists at identifying certain Breast Cancers.

AI is more accurate than linear by a lot. An example we saw on the same data gave an accuracy of 40% using linear and 94% with machine learning.

Sample data

Linear approaches using sample data require robust sizes of data, but AI can provide good insights even when only a sample dataset is available. These limit the results in linear models.

Exponential advances and continually increasing power

We are constantly innovating as the pace of knowledge increases in this domain. For instance, the machine learning algorithms Eularis uses today did not even exist three years ago.

Capacity to exceed human ability

Today’s machine learning algorithms are better than humans at things once thought to be the unique domain of humans. For example, lawyers used to painstakingly read through boxes of legal documents to develop their case. Now, the documents are uploaded and sophisticated algorithms are used to identify useful material.

Improved productivity

How long does your analytics staff spend blearily staring at spreadsheets to identify trends and pull data that, as you know, doesn’t provide the expected return? The kind of computer analytics we’re talking about is capable of identifying trends and patterns in minutes, even seconds, providing more timely business intelligence than even your best analyst.

The capability to continually add data

Data is dynamic, and the amount and type of data available changes on a daily - even hourly - basis. Machine learning can integrate such data in real-time, even incorporating events outside your company such as economic issues, weather and natural disasters, to provide the most accurate, comprehensive results possible.

Solves real problems

AI-based approaches are far superior for solving real-world problems, including impact on revenue of specific marketing actions and their synergistic impact.


Once you plan and implement these appropriately, they learn when new data comes in and adjust their own algorithms to take the new data points into account. So you can keep adding data, and the more data you put in, the more they adapt and learn; millions and billions of data points can be processed quickly and easily.

Gain a competitive edge

If your competitors are all using linear, they will not be getting the richness of the data, nor the accuracy, and you will have access to more information which will allow you to succeed before they do - hence our results.

So, what do you want to achieve? Mediocre results with linear approaches or outstanding results with AI approaches? The choice is yours.

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