Machine Learning Marketing and Marketing Automation: Dawn of a New Era
Machine learning is a discipline combining science, statistics and computer coding that aims to make predictions based on patterns discovered in data. As opposed to rule-based decision systems, which follow an explicit set of instructions known by the developers in advance, machine learning algorithms are designed to analyze data and discover patterns that people cannot find by themselves. In other words, machine learning leverages the massive power and objectivity of computers to see things in big data that slow and biased humans cannot – and then use those insights to determine how new data can be used to accurately predict results.
The CRM Marketer evolution curve
How does Machine Learning Help Marketers?
Machine learning and pattern recognition can help marketers in a variety of ways. One of the biggest challenges facing marketers is how to personalize messaging to individual prospects and customers so that it most strongly resonates with the recipient. The results of successful, highly relevant marketing include increased customer loyalty, engagement, and spending.
Without machine learning, it is simply too difficult to compile and process the huge amounts of data coming from multiple sources (e.g., purchase behavior, website visit flow, mobile app usage and responses to previous campaigns) required to predict what marketing offers and incentives will be most effective for each individual customer. However, when all of this data is made available to computers programmed to perform data mining and machine learning, very accurate next best action predictions can be made.
Other areas in which a machine learning application can help marketers include:
- Customer segmentation – Machine learning customer segmentation models are very effective at extracting small, homogeneous groups of customers with similar behaviors and preferences. Successful customer segmentation is a critical tool in every marketer’s toolbox.
- Customer churn prediction – By discovering patterns in the data generated by many customers who churned in the past, churn prediction machine learning forecasting can accurately predict which current customers are at a high risk of churning. This allows marketers to engage in proactive churn prevention, an important way to increase revenues.
- Customer lifetime value forecasting – CRM machine learning systems are an excellent way to predict the customer lifetime value (LTV) of existing customers, both new and veteran. LTV is a valuable tool for segmenting customers, and for measuring the future value of a business and predicting growth.
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Implementing Machine Learning in Marketing
Pattern recognition and machine learning software have come a long way since their early days in the 1960s. New algorithms and technologies are constantly emerging, suggesting new possibilities and applications. Despite this, most marketers are not using any form of machine learning in their day-to-day efforts because it remains a complex field, requiring the involvement of data scientists and developers. As a consequence, effective implementations of machine learning algorithms in marketing remain beyond the reach of many small- and medium-sized businesses.
However, specialized applications developed specifically to address marketing challenges – and to be very easy for marketers to use – are now available for smaller businesses with modest budgets. This is a game changer for savvy marketers because machine learning can eliminate the guesswork involved in many of the most challenging – and valuable – aspects of data-driven marketing.
Frequently Asked Questions
What is an example of a content recommendation engine?
One of the most popular examples of a content recommendation engine is Netflix.
Netflix uses multiple filtering methods and machine learning algorithms to make recommendations to users. These recommendations suggest TV shows or movies based on user‘’s watch history or engagement with specific genres. This streaming service aims to provide personalized content that enhances customer experience and differentiates them from competitors.
What are the three types of machine learning models used by recommendation engines?
The three types of recommendation engines are collaborative filtering, content-based filtering, and hybrid filtering.
*Collaborative filtering: Makes individual recommendations based on similar users’ preferences and behavioral data.
*Content-based filtering: Matches features of specific items to certain user preferences.
*Hybrid filtering: Combines collaborative and content-based filtering for improved recommendations.
Start Using Machine Learning in your Marketing Today!
Optimove is the leading customer marketing automation system available today, and machine learning is a big reason why. Much of Optimove’s power comes from the machine learning algorithms that contribute to its highly accurate customer modeling, customer segmentation, LTV predictions and next best action recommendations. The Web-based software is designed to deliver the advantages of advanced machine learning algorithms to marketers, without any need to understand data modeling, statistical analysis or algorithm development.
Contact us today – or request a Web demo – to learn how you can use Optimove to dramatically improve your customer marketing to convert more customers, increase the engagement of existing customers and reduce customer churn.
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