Machine Learning for User Acquisition
Machine learning (ML) is a growing market that is set to top $106B by 2030. Every day, new applications of machine learning are created, allowing companies to streamline operations, better engage with customers, or drive better results.
But what is machine learning? And how can businesses use machine learning to improve customer acquisition or user acquisition, drive better outcomes, and achieve greater results? Fortunately, predictive models for user acquisition can help make this process faster, more effective, and more impactful on business outcomes.
What is machine learning?
Machine learning is a method of teaching computers to learn. It’s a type of artificial intelligence that allows systems to automatically improve their performance with experience. Just like a student in school studies information and can perform better with additional studying, machine learning models similarly perform better as they gain information from existing data.
ML involves the use of large amounts of data. A model is first trained on a dataset and then used to make predictions or decisions without human intervention.
What are the main types of machine learning?
The core types of machine learning can be broadly grouped into the following categories:
- Supervised learning: Training a model using labeled data to predict outcomes based on new input data. Examples include image classification, speech recognition, and natural language processing.
- Unsupervised learning: Training a model using unlabeled data to identify patterns or relationships in the data. Examples include clustering, anomaly detection, and dimensionality reduction.
- Reinforcement learning: Training a model to make decisions based on feedback from the environment. Examples include game-playing agents, robotics, and decision-making systems.
- Generative learning: Training a model to generate new data that is similar to the training data. Examples include image and text generation, as well as generative models for music and video.
- Transfer learning: Reusing a pre-trained model on a new task, which can save time and resources compared to training a model from scratch.
- Online learning: Training a model using streaming data, which allows the model to adapt to changes in the data distribution over time.
- Deep learning: A subfield of machine learning that uses deep neural networks to model complex patterns in data. Examples include image and speech recognition, natural language processing, and decision-making systems.
These are just some of the main applications of ML, which is a constantly evolving field. As you know if you’ve seen recent news coverage of hot new AI tools, there are constantly new applications and use cases emerging as the technology and research develop.
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How is machine learning used in business today?
Machine learning is currently being used in a wide variety of business applications. Here are some of the top examples of how machine learning is showing up in today’s business world:
- Predictive analytics: Machine learning models are used to analyze data and make predictions about future business outcomes, such as customer behavior, sales trends, and equipment failures.
- Personalization: Machine learning is used to provide personalized recommendations, such as product recommendations for e-commerce sites and personalized content for streaming services.
- Automated customer service: Machine learning is used to power chatbots and virtual assistants that can respond to customer inquiries and provide support.
- Fraud detection: Machine learning models are used to detect and prevent fraudulent activity in areas such as credit card transactions, insurance claims, and tax filings.
- Supply chain optimization: Machine learning is used to optimize logistics, predict demand and inventory management, and improve forecasting.
- Predictive maintenance: Machine learning models are used to predict when equipment is likely to fail and schedule maintenance before failures occur.
- Human resources: Machine learning improves recruitment processes, employee retention and performance tracking, as well as to analyze employee data.
- Healthcare: Machine learning is used to analyze medical images, predict patient outcomes, and identify potential health risks.
- Marketing and advertising: Machine learning is used to optimize ad targeting and personalize ad content, as well as to analyze customer data and improve campaign performance. Many performance marketing ad platforms and user acquisition teams have found great success in adopting machine learning-based tools and technologies into their marketing strategies due to the higher performance and efficiencies they can create.
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How does machine learning support user acquisition initiatives?
As noted earlier, machine learning is achieving a fast adoption rate among performance marketers and user acquisition marketing teams. ML-based tools and technologies have proven their ability to generate higher performance and greater marketing efficiencies.
In a recent Ascend2 survey, over 350 marketers shared how they are testing and utilizing AI/ML. Nearly one-third of the respondents said paid advertising was a key application of AI for their marketing efforts.
From a high-level perspective, machine learning can support performance marketing and user acquisition initiatives in five distinct ways.
- Predictive modeling: ML algorithms can be used to predict which users are most likely to convert and/or will be the highest value customers over time, allowing companies to focus their acquisition efforts on the most promising individuals. Using machine learning in this format usually takes the form of predicting campaign ROAS.
- Personalization: Machine learning can be used to personalize the user experience, which can increase engagement and conversion rates.
- Automated optimization: Machine learning algorithms can be used to optimize the performance of advertising campaigns, such as by identifying which ad placements and targeting criteria lead to the highest conversion rates.
- User segmentation: Machine learning can be used to segment users based on their behavior, demographics, and other characteristics, enabling companies to create targeted marketing campaigns that are more likely to resonate with specific user groups. A very common ML-based segmentation strategy is the creation of look-alike audiences. These are audiences that look like your best-performing customers.
- Fraud detection: Machine learning can be used to detect and prevent fraudulent traffic, which can help companies avoid wasting resources on fake users and increase the ROAS of their user acquisition initiatives.
- Churn reduction: Based on patterns in customer data (purchase history, online engagement, etc) machine learning algorithms are able to identify which customers are about to churn.
Overall, machine learning enables companies to make data-driven decisions, making their customer or user acquisition initiatives more efficient and effective.
What are some examples of brands using machine learning to drive user acquisition initiatives?
Hydrant: Pecan AI worked with consumer wellness brand Hydrant to improve their retention initiatives. Hydrant wanted to understand which customers were more likely to churn and which past customers could be enticed back. Using Pecan’s ML solution, they were able to predict likely customer churn and send personalized messages to retain these customers in order to drive higher revenue.
Customer segmentation based on Pecan’s predictions resulted in a higher winback rate than in the control groups. Customers predicted to have the lowest chance of purchasing again were sent targeted offers. That group had a 2.6x higher conversion rate and a 3.1x higher average revenue per customer than a control group.
Pecan’s predictions informed our marketing efforts, helping us reach out to the right customers and allocate spend in the right places. The models were incredibly accurate in identifying which customers would more likely respond to our offers and make new purchases.
— John Sherwin, CEO, Hydrant
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SciPlay: A mobile gaming company was able to leverage Pecan’s ML-based predictive analytics platform to quickly identify which ad channels were underutilized and which had met saturation points. As a result, they were able to improve their app marketing and UA campaigns, driving higher results with their marketing budgets.
- The most effective channels are underutilized (accounting for <20% of spend)
- The most utilized channels are saturated, and their budgets could be reduced
In a very short time, Pecan’s marketing mix modeling solution granted our team a new way of analyzing our marketing spend and its revenue contribution, while exposing opportunities for budget allocation and channel ROI optimization. We can now leverage both attribution data and marketing mix modeling contribution to have a more holistic understanding of our marketing investments.
– Evyatar Livny, Senior Director of AdTech
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