Applications of AI-Driven Marketing for Mobile Apps

Applications of AI-Driven Marketing for Mobile Apps

Applications of AI-Driven Marketing for Mobile Apps

Last Edited

Last Edited

November 6, 2023

Nov 6, 2023

Nov 6, 2023

Buse KARA

Marketing and Analytics

Marketing and Analytics

7

7

min reading

min reading

APPLICATIONS OF AI-DRIVEN MARKETING FOR MOBILE APPS

Table of Content 

  1. What is AI?

  2. Applications of AI in Marketing

  3. Machine Learning Marketing Actions in Mobile Apps

  4. How is it used in B2Metric IQ Analytics?

  5. Summary

Today, artificial intelligence technologies are being used in marketing to make automated interpretations based on data gathering, data analysis, and further observations of audience or economic trends that may impact marketing activities. In digital marketing campaigns where speed is crucial, AI is frequently deployed. To ensure optimal efficiency, AI marketing solutions analyze data and customer profiles to learn how to best engage with clients. They then give them personalized messages at the appropriate moment without help from marketing team employees.

 

 What is AI?

In its simplest terms, AI, which stands for artificial intelligence, refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

 

Artificial Intelligence is more about super-powered thinking and data analysis capability and processes than any form or function. When it comes to artificial intelligence, although multifunctional, human-like robots that take over the world come to life in our minds, artificial intelligence is not designed to replace humans. It is designed to significantly enhance human capabilities and contributions. Therefore, it is a highly valuable commercial asset.

                                  

 

 

Applications of AI in Marketing

 

AI-Generated Content

 AI can't write a political column or a blog post on industry-specific best practice advice, but there are certain areas where AI-generated content can be useful and help attract visitors to your site.

For some functions, AI content authoring programs can select items from a dataset and create a 'human voice' article. An AI authoring program called 'WordSmith' produced 1.5 billion pieces of content in 2016 and is expected to grow in popularity in the coming years.

AI writers are useful for reporting on regular, data-driven events. Examples include quarterly earnings reports, sporting events, and market data. If you work in a related field, such as financial services, AI-generated content can be a useful component of your content marketing strategy. The good news is that AutoInsights, the firm behind Wordsmith, has announced a free beta of its AI writing app, so you can try the technology and see if it will come in handy for your brand.

 

Intelligent Content Curation

AI-powered content curation helps you drive better visitors to your site by showing them relevant content. This technique is mostly found in the 'Customers who bought X bought Y' section on many sites but can also be applied to blog content and more widely to the personalization of site messaging. It's also a great technique because the more people in subscriber businesses are using the service, the more data the machine learning algorithm should use, and the better the content's recommendations. Imagine if Netflix's recommendation system could constantly recommend you because it shows you're interested in it.

 

Predictive Analysis

Bias modeling can be applied to a number of different areas, such as predicting the probability that a particular customer will convert, predicting at what price the customer will convert, or predicting customers who are most likely to buy again. This practice is called intelligent analytics because it uses analytical data to predict how the customer behaves. The most important thing to remember is that a trend model is only as good as the data provided to generate the data; therefore, if your data has errors or a high level of randomness, they cannot make accurate predictions.

 

Lead Scoring

Machine learning-generated trending models can be trained to score leads based on specific criteria so your sales team can determine how hot a particular lead is and whether it's worth your time. This is especially important in B2B businesses with consultative sales processes, where each sales team's sale takes a significant amount of time. By communicating with the sales points that attract the most attention, the sales team can save time and focus their energies where they are most effective. A sales trend understanding can be used to target sales and where the discount is most effective.

 

Dynamic Content Emails

Highly effective dynamic emails can be created by applying insights generated from machine learning. Predictive analytics using a trend model can tend a subscriber to purchase certain categories, sizes, and colors due to their previous behavior and display the most relevant products in newsletters. Product stocks, deals, and pricing are all correct when you open the email.

 

MACHINE LEARNING MARKETING ACTIONS IN MOBILE APPS

 

App Personalization

The practice of customizing a mobile app or web application to meet the unique requirements and preferences of a particular user is known as "app customization." This can be accomplished in several ways, such as by displaying tailored material or suggestions based on the user's prior experiences with the app or by enabling the user to modify different elements of the app's functionality or user interface.

 

Customer Segmentation

Customer segmentation is the process of dividing a customer base into smaller groups based on shared characteristics or behaviors. The goal of customer segmentation is to better understand and serve different segments of a customer base and to tailor marketing and sales efforts to specific groups of customers.

 

Personalized Push Notifications 

Push notifications are messages that are sent to a user's device, typically through a mobile app, and are designed to get the user's attention and provide them with information or action they may find relevant or useful. Personalized push notifications are targeted specifically at individual users based on their interests, preferences, or other characteristics.

For example, a news app may send personalized push notifications to users who have expressed an interest in a particular topic, such as sports or politics. An e-commerce app may send personalized push notifications to users who have previously made purchases or shown an interest in a particular product or category. Personalized push notifications can also be used to deliver updates or promotions related to the app or service.

 

Chatbots

Any corporation looking to boost productivity and spur creativity for B2C and B2B businesses, on mobile as well as desktop applications, should consider implementing chatbots. Businesses have been employing chatbots, both internally and externally, due to the numerous advantages they provide to the workplace (HR). These apps offer personal assistants that may respond to frequently asked questions and describe a product's capabilities around the clock. By eliminating the need for a manual agent, chatbots are able to save a ton of time and money while also improving customer satisfaction.

 

Product Recommendations

By using a recommendation engine, consumers can receive content recommendations based on their tastes. These preferences can be plotted based on users' current habits of content consumption or on the ratings they may have given for other types of material; for example, consider what Spotify does for music, YouTube does for videos, and Medium does for blogs.

How is it used in B2Metric IQ Analytics?

To use B2Metric IQ Analytics for personalized push notifications, businesses would first need to integrate B2Metric IQ Analytics with their push notification platform or service. This would allow IQ Analytics to collect data about the push notifications that are being sent and the actions that users take in response to those notifications.

Once the integration is set up, businesses can use B2M IQ Analytics to track a range of metrics related to their push notification campaigns, including the number of notifications sent, the number of notifications opened, the number of clicks on links within the notification, and the conversion rate (the percentage of customers who took the desired action after receiving the notification, such as making a purchase).

B2M IQ Analytics also provides a range of features and capabilities that can be used to analyze and understand this data, such as segmentation, event tracking, and the ability to create custom reports and dashboards.

By using B2M IQ Analytics to track and analyze the performance of personalized push notification campaigns, businesses can gain insights into which types of push notifications are most effective at driving engagement and conversions and can use this information to optimize their push notification strategy.

Conclusion

 The replication of human intelligence functions by machines, particularly computer systems, is known as "artificial intelligence." Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific AI applications.

Large volumes of labeled training data are ingested by AI systems, which then examine the data for correlations and patterns before employing these patterns to forecast future states.

 Read here for an overview of B2Metric IQ and how it can benefit your business. 

If you are interested in more about our blogs, you can find more information here.

Visit our website to learn more about B2M IQ.

APPLICATIONS OF AI-DRIVEN MARKETING FOR MOBILE APPS

Table of Content 

  1. What is AI?

  2. Applications of AI in Marketing

  3. Machine Learning Marketing Actions in Mobile Apps

  4. How is it used in B2Metric IQ Analytics?

  5. Summary

Today, artificial intelligence technologies are being used in marketing to make automated interpretations based on data gathering, data analysis, and further observations of audience or economic trends that may impact marketing activities. In digital marketing campaigns where speed is crucial, AI is frequently deployed. To ensure optimal efficiency, AI marketing solutions analyze data and customer profiles to learn how to best engage with clients. They then give them personalized messages at the appropriate moment without help from marketing team employees.

 

 What is AI?

In its simplest terms, AI, which stands for artificial intelligence, refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect.

 

Artificial Intelligence is more about super-powered thinking and data analysis capability and processes than any form or function. When it comes to artificial intelligence, although multifunctional, human-like robots that take over the world come to life in our minds, artificial intelligence is not designed to replace humans. It is designed to significantly enhance human capabilities and contributions. Therefore, it is a highly valuable commercial asset.

                                  

 

 

Applications of AI in Marketing

 

AI-Generated Content

 AI can't write a political column or a blog post on industry-specific best practice advice, but there are certain areas where AI-generated content can be useful and help attract visitors to your site.

For some functions, AI content authoring programs can select items from a dataset and create a 'human voice' article. An AI authoring program called 'WordSmith' produced 1.5 billion pieces of content in 2016 and is expected to grow in popularity in the coming years.

AI writers are useful for reporting on regular, data-driven events. Examples include quarterly earnings reports, sporting events, and market data. If you work in a related field, such as financial services, AI-generated content can be a useful component of your content marketing strategy. The good news is that AutoInsights, the firm behind Wordsmith, has announced a free beta of its AI writing app, so you can try the technology and see if it will come in handy for your brand.

 

Intelligent Content Curation

AI-powered content curation helps you drive better visitors to your site by showing them relevant content. This technique is mostly found in the 'Customers who bought X bought Y' section on many sites but can also be applied to blog content and more widely to the personalization of site messaging. It's also a great technique because the more people in subscriber businesses are using the service, the more data the machine learning algorithm should use, and the better the content's recommendations. Imagine if Netflix's recommendation system could constantly recommend you because it shows you're interested in it.

 

Predictive Analysis

Bias modeling can be applied to a number of different areas, such as predicting the probability that a particular customer will convert, predicting at what price the customer will convert, or predicting customers who are most likely to buy again. This practice is called intelligent analytics because it uses analytical data to predict how the customer behaves. The most important thing to remember is that a trend model is only as good as the data provided to generate the data; therefore, if your data has errors or a high level of randomness, they cannot make accurate predictions.

 

Lead Scoring

Machine learning-generated trending models can be trained to score leads based on specific criteria so your sales team can determine how hot a particular lead is and whether it's worth your time. This is especially important in B2B businesses with consultative sales processes, where each sales team's sale takes a significant amount of time. By communicating with the sales points that attract the most attention, the sales team can save time and focus their energies where they are most effective. A sales trend understanding can be used to target sales and where the discount is most effective.

 

Dynamic Content Emails

Highly effective dynamic emails can be created by applying insights generated from machine learning. Predictive analytics using a trend model can tend a subscriber to purchase certain categories, sizes, and colors due to their previous behavior and display the most relevant products in newsletters. Product stocks, deals, and pricing are all correct when you open the email.

 

MACHINE LEARNING MARKETING ACTIONS IN MOBILE APPS

 

App Personalization

The practice of customizing a mobile app or web application to meet the unique requirements and preferences of a particular user is known as "app customization." This can be accomplished in several ways, such as by displaying tailored material or suggestions based on the user's prior experiences with the app or by enabling the user to modify different elements of the app's functionality or user interface.

 

Customer Segmentation

Customer segmentation is the process of dividing a customer base into smaller groups based on shared characteristics or behaviors. The goal of customer segmentation is to better understand and serve different segments of a customer base and to tailor marketing and sales efforts to specific groups of customers.

 

Personalized Push Notifications 

Push notifications are messages that are sent to a user's device, typically through a mobile app, and are designed to get the user's attention and provide them with information or action they may find relevant or useful. Personalized push notifications are targeted specifically at individual users based on their interests, preferences, or other characteristics.

For example, a news app may send personalized push notifications to users who have expressed an interest in a particular topic, such as sports or politics. An e-commerce app may send personalized push notifications to users who have previously made purchases or shown an interest in a particular product or category. Personalized push notifications can also be used to deliver updates or promotions related to the app or service.

 

Chatbots

Any corporation looking to boost productivity and spur creativity for B2C and B2B businesses, on mobile as well as desktop applications, should consider implementing chatbots. Businesses have been employing chatbots, both internally and externally, due to the numerous advantages they provide to the workplace (HR). These apps offer personal assistants that may respond to frequently asked questions and describe a product's capabilities around the clock. By eliminating the need for a manual agent, chatbots are able to save a ton of time and money while also improving customer satisfaction.

 

Product Recommendations

By using a recommendation engine, consumers can receive content recommendations based on their tastes. These preferences can be plotted based on users' current habits of content consumption or on the ratings they may have given for other types of material; for example, consider what Spotify does for music, YouTube does for videos, and Medium does for blogs.

How is it used in B2Metric IQ Analytics?

To use B2Metric IQ Analytics for personalized push notifications, businesses would first need to integrate B2Metric IQ Analytics with their push notification platform or service. This would allow IQ Analytics to collect data about the push notifications that are being sent and the actions that users take in response to those notifications.

Once the integration is set up, businesses can use B2M IQ Analytics to track a range of metrics related to their push notification campaigns, including the number of notifications sent, the number of notifications opened, the number of clicks on links within the notification, and the conversion rate (the percentage of customers who took the desired action after receiving the notification, such as making a purchase).

B2M IQ Analytics also provides a range of features and capabilities that can be used to analyze and understand this data, such as segmentation, event tracking, and the ability to create custom reports and dashboards.

By using B2M IQ Analytics to track and analyze the performance of personalized push notification campaigns, businesses can gain insights into which types of push notifications are most effective at driving engagement and conversions and can use this information to optimize their push notification strategy.

Conclusion

 The replication of human intelligence functions by machines, particularly computer systems, is known as "artificial intelligence." Expert systems, natural language processing, speech recognition, and machine vision are some examples of specific AI applications.

Large volumes of labeled training data are ingested by AI systems, which then examine the data for correlations and patterns before employing these patterns to forecast future states.

 Read here for an overview of B2Metric IQ and how it can benefit your business. 

If you are interested in more about our blogs, you can find more information here.

Visit our website to learn more about B2M IQ.

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