ML & AI in Marketing: Use Cases

Machine Learning and Artificial Intelligence have various applications that greatly benefit businesses. Depending on how and where AI and ML are applied to achieve a goal, the combination of algorithms and available data has yielded some very impressive results.

AI and ML Applied in Business

1. As Recommendation Engines

Alibaba generated 20% higher conversion rates on personalized landing pages powered by recommendation engines on Singles’ Day in 2017.

More than 80% of TV shows people watch on Netflix are discovered through its recommendation engine.

2. For Forecasting

Move from forecasting using historical data to real-time, ML-assisted forecasting with up-to-the-minute data for accurate predictions.

3. In Addressing Churns

Online retailer Showroomprive.com uses a Machine Learning-powered churn prediction system that identifies churners with 77% accuracy.

4. In Content Generation

The Content Marketing Institute reveals that when pitting content marketing against paid search, content marketing gets 3 times the leads per dollar spent.

5. Hyper-targeted Advertising

Research by SalesForce says 51 percent of consumers expect that by 2020 companies will anticipate their needs and make relevant suggestions before making contact.

6. Pricing Optimization

McKinsey estimates up to 30% of the thousands of pricing decisions that companies make every year fail to deliver the best price.

7. Lead Scoring

An IDC survey says 83% of companies use (or plan to use) sales and marketing predictive lead scoring.

8. Marketing Attribution

A study by Bizible showed that 77% of companies believe they’re not using the right attribution models.

The Impact Of Machine Learning In HR

Machine learning can already efficiently handle the following:

  1. Automation of Workflows – with the help of AI and Machine learning, the most tedious and repetitive tasks can be off-handed in order to allow human capability to focus on other important tasks.
  2. Attracting Top Talent – through relevant data collection set into an algorithm, many companies have increased their chances of filtering and attracting potential recruits.
  3. Less Time, Reduced Bias & Greater Accuracy Recruiting – through machine learning, the human resource process has become more efficient. It becomes easier to set a workflow and process that is virtually error-free and effective.
  4. Applicant Tracking & Assessment – bias and common human errors are greatly decreased by allowing Machine Learning to take over. Through predictive analytics, qualified candidates are screened to see if they are fit to be part of a team, department or company.
  5. Forward Planning & Efficiency Improvement – through machine learning, data is better utilized and analyzed. This results in better assessments, predictions, communication, engagement and project tracking.
  6. Measure and Understand Employee Engagement – data is effectively and efficiently processed with the use of Machine Learning. The insights taken can be used to increase productivity and reduce staff turnover.

Reimagine Your Business!

In order to get the most value from AI, operations need to be redesigned. To do this, companies must first discover and describe an operational area that can be improved. It might be a balky internal process (such as HR’s slowness to fill staff positions), or it could be a previously intractable problem that can now be addressed using AI (such as quickly identifying adverse reactions on new functionality or price rise, or quality of the photo/video content across main customers populations). Moreover, a number of new AI and advanced analytic techniques can help surface previously invisible problems that are amenable to AI solutions.


For more information about New Media Services, contact author Anastasiia Bilous on ab@newmediaservices.com.au