Artificial intelligence: areas of application in e-commerce
5.4.2023
Artificial intelligence: areas of application in e-commerce
Artificial intelligence: areas of application in e-commerce
In e-commerce, but also in other industries, the term "artificial intelligence" is often used misleadingly because in reality no real artificial intelligence is used. Often, machine learning or deep learning is actually behind the promises. After all, machine learning is a type of artificial intelligence that enables computers to learn from data and make decisions without having to be explicitly programmed. Artificial intelligence is thus to be understood as a generic term for technologies that enable computers to imitate human thinking and behavior. Machine learning is thus a method of achieving artificial intelligence. In e-commerce, artificial intelligence (or machine learning) can be used, for example, to create personalized recommendations for customers or to detect fraud. But more on the specific examples later. First, let's differentiate the terms a bit more in order to avoid confusion.
Artificial Intelligence, Machine Learning and Deep Learning: What's the difference?
Artificial intelligence can be understood as an umbrella term that stands above machine learning and deep learning. All of these areas have different characteristics that distinguish them. The following illustration should help you to better distinguish between these areas in the future.
Machine Learning
Machine learning deals with pattern recognition from data. Machines are not explicitly programmed, but use statistical models and data to recognize patterns that occur. With statistical learning methods and neural networks, algorithms are trained, for example, to recognize faces in pictures or even handwritten numbers. A characteristic of machine learning algorithms is that their performance improves progressively as more data is added.
Deep Learning
Deep Learning is a subterm of Machine Learning and this term in turn is subject to that of artificial intelligence. Deep Learning is a learning method that works with artificial neural networks. These neural networks, which are similar to the human brain, are able to record and pass on information from a large amount of data (Big Data).
Artificial intelligence
The beginnings of artificial intelligence go back a long way. As early as the 1950s, the so-called Turing test was used to determine whether or not traits of artificial intelligence existed. In the Turing test, a human tester communicates with two unknown interlocutors via a keyboard and a screen, without seeing or hearing. One of them is a human, the other a machine. If, after extensive questioning, the tester cannot determine which of the two is the machine, the machine has passed the Turing test and is assumed to have reasoning ability equivalent to humans. Over the years, other and more complex tests have been added and can be read about here . In any case, artificial intelligence is spoken of when a program can grasp, reason, act independently and adapt.
Artificial intelligence: 9 areas of application in e-commerce
The application areas of artificial intelligence (or machine learning) in e-commerce are diverse and growing. Here is an excerpt from the most common areas of application at present:
1. chatbots and voice assistants in customer service
You can significantly improve your customer service by using chatbots or voice assistants that can support your customers in real time. For example, you can answer standardized questions, solve problems, take orders, or collect feedback. You can also use chatbots to advise your customers, recommend suitable products or offer them special deals.
2. image processing and content creation
Another area of application is product images. Here, AI-based tools are suitable to improve, edit or generate. For example, you can increase the quality or resolution of the images, remove unwanted elements or apply filters. You can also generate new images of products that don't exist yet or that are shown from different perspectives or in different colors.
3. predictions & analyses
Artificial intelligence (or in this case machine learning) is a great way to predict different aspects of your online store. For example, the demand for certain products, the buying behavior of customers, your sales or even your profit. Smart algorithms are often used to predict which products will sell best at which time of year or on which occasion. This involves analyzing historical data using an algorithm. Based on historical time series, the algorithm can identify how sales and purchases will develop in the future, for example. The prediction of necessary stock levels at certain times of the year is also useful for the optimal use of warehouses. AI-based forecasts help to identify trends, seasonalities and complex patterns. Companies that have in-depth knowledge of demand for their products can make important decisions about marketing spend and identify potential sales fluctuations in advance.
4. A/B tests
You can use machine learning to test different versions of your website or ad campaigns and choose the one that performs best. For example, you can try different colors, fonts, images or texts and see which ones have the highest click-through rate, conversion rate or customer engagement.
5. image recognition & intelligent search functions
Analyze and categorize product images to make searching and browsing easier. For example, you can automatically generate tags or descriptions for your products based on their visual characteristics. You can also provide visual search features that allow customers to find products that are similar to an image.
6. dynamic pricing
Based on factors such as supply and demand, competition, season and customer profile, you can determine optimal prices for your products. It would also be possible to continuously adjust prices to generate more sales when demand is high or to attract more customers with lower prices when demand is low. Also possible are personalized prices. Depending on how likely a customer is to buy a product or not.
7. personalization of the content on the website
Machine learning can also adapt the content of your website, or mobile app, to visitors' preferences and behavior. For example, you can test different versions of your website and choose the one that has the best conversion rate. You can also display different content depending on what industry they belong to, what they are looking for, or how often they visit your website.
8. recommendation systems
Another application of artificial intelligence is the recommendation of products based on customers' interests, behavior and profile. For example, you can analyze which products customers have viewed, added to their shopping cart, or purchased, and suggest similar or complementary products to them. You can also use the experiences of other customers and recommend products bought by customers with similar preferences or from the same region.
9. personalized advertising
Personalizing online advertising and delivering the right offers at the right time are two important aspects that every marketer takes into account. The "Next Best Offer" method makes it possible to address the individual needs of potential customers. This is an analytical approach that can be used to determine customer needs. This requires big data and machine learning. In e-commerce in particular, it can be advantageous for advertisers if they only display those products that customers are more likely to buy. These personalized product recommendations increase the likelihood of a purchase, which is why sales growth and increased profits are the result.
Conclusion
Artificial intelligence, although in many cases it should be called machine learning, is a very useful technology for e-commerce. It helps you to better understand your customers, offer them a better experience in your online store and thus increase your sales. The applications of artificial intelligence in e-commerce range from personalizing your online store, to recommending certain products, optimizing/adjusting your prices, improving or creating your images, improving your predictions, your inventory management, and improving your customer service. You can definitely use artificial intelligence and machine learning to give yourself a competitive edge and stand out from other online stores or even increase your sales. If you want more tips on how to increase your sales, our blog"Increase online store sales: 10 tips for you" is probably for you. If you are now even more interested in the topic of artificial intelligence, then simply follow the link below to our webinar"Artificial Intelligence in Online Retail":
Sources (last accessed 04/05/2023):
- https://datasolut.com/machine-learning-im-e-commerce/
- https://ecommerceinstitut.de/machine-learning-im-e-commerce/
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In e-commerce, but also in other industries, the term "artificial intelligence" is often used misleadingly because in reality no real artificial intelligence is used. Often, machine learning or deep learning is actually behind the promises. After all, machine learning is a type of artificial intelligence that enables computers to learn from data and make decisions without having to be explicitly programmed. Artificial intelligence is thus to be understood as a generic term for technologies that enable computers to imitate human thinking and behavior. Machine learning is thus a method of achieving artificial intelligence. In e-commerce, artificial intelligence (or machine learning) can be used, for example, to create personalized recommendations for customers or to detect fraud. But more on the specific examples later. First, let's differentiate the terms a bit more in order to avoid confusion.
Artificial Intelligence, Machine Learning and Deep Learning: What's the difference?
Artificial intelligence can be understood as an umbrella term that stands above machine learning and deep learning. All of these areas have different characteristics that distinguish them. The following illustration should help you to better distinguish between these areas in the future.
Machine Learning
Machine learning deals with pattern recognition from data. Machines are not explicitly programmed, but use statistical models and data to recognize patterns that occur. With statistical learning methods and neural networks, algorithms are trained, for example, to recognize faces in pictures or even handwritten numbers. A characteristic of machine learning algorithms is that their performance improves progressively as more data is added.
Deep Learning
Deep Learning is a subterm of Machine Learning and this term in turn is subject to that of artificial intelligence. Deep Learning is a learning method that works with artificial neural networks. These neural networks, which are similar to the human brain, are able to record and pass on information from a large amount of data (Big Data).
Artificial intelligence
The beginnings of artificial intelligence go back a long way. As early as the 1950s, the so-called Turing test was used to determine whether or not traits of artificial intelligence existed. In the Turing test, a human tester communicates with two unknown interlocutors via a keyboard and a screen, without seeing or hearing. One of them is a human, the other a machine. If, after extensive questioning, the tester cannot determine which of the two is the machine, the machine has passed the Turing test and is assumed to have reasoning ability equivalent to humans. Over the years, other and more complex tests have been added and can be read about here . In any case, artificial intelligence is spoken of when a program can grasp, reason, act independently and adapt.
Artificial intelligence: 9 areas of application in e-commerce
The application areas of artificial intelligence (or machine learning) in e-commerce are diverse and growing. Here is an excerpt from the most common areas of application at present:
1. chatbots and voice assistants in customer service
You can significantly improve your customer service by using chatbots or voice assistants that can support your customers in real time. For example, you can answer standardized questions, solve problems, take orders, or collect feedback. You can also use chatbots to advise your customers, recommend suitable products or offer them special deals.
2. image processing and content creation
Another area of application is product images. Here, AI-based tools are suitable to improve, edit or generate. For example, you can increase the quality or resolution of the images, remove unwanted elements or apply filters. You can also generate new images of products that don't exist yet or that are shown from different perspectives or in different colors.
3. predictions & analyses
Artificial intelligence (or in this case machine learning) is a great way to predict different aspects of your online store. For example, the demand for certain products, the buying behavior of customers, your sales or even your profit. Smart algorithms are often used to predict which products will sell best at which time of year or on which occasion. This involves analyzing historical data using an algorithm. Based on historical time series, the algorithm can identify how sales and purchases will develop in the future, for example. The prediction of necessary stock levels at certain times of the year is also useful for the optimal use of warehouses. AI-based forecasts help to identify trends, seasonalities and complex patterns. Companies that have in-depth knowledge of demand for their products can make important decisions about marketing spend and identify potential sales fluctuations in advance.
4. A/B tests
You can use machine learning to test different versions of your website or ad campaigns and choose the one that performs best. For example, you can try different colors, fonts, images or texts and see which ones have the highest click-through rate, conversion rate or customer engagement.
5. image recognition & intelligent search functions
Analyze and categorize product images to make searching and browsing easier. For example, you can automatically generate tags or descriptions for your products based on their visual characteristics. You can also provide visual search features that allow customers to find products that are similar to an image.
6. dynamic pricing
Based on factors such as supply and demand, competition, season and customer profile, you can determine optimal prices for your products. It would also be possible to continuously adjust prices to generate more sales when demand is high or to attract more customers with lower prices when demand is low. Also possible are personalized prices. Depending on how likely a customer is to buy a product or not.
7. personalization of the content on the website
Machine learning can also adapt the content of your website, or mobile app, to visitors' preferences and behavior. For example, you can test different versions of your website and choose the one that has the best conversion rate. You can also display different content depending on what industry they belong to, what they are looking for, or how often they visit your website.
8. recommendation systems
Another application of artificial intelligence is the recommendation of products based on customers' interests, behavior and profile. For example, you can analyze which products customers have viewed, added to their shopping cart, or purchased, and suggest similar or complementary products to them. You can also use the experiences of other customers and recommend products bought by customers with similar preferences or from the same region.
9. personalized advertising
Personalizing online advertising and delivering the right offers at the right time are two important aspects that every marketer takes into account. The "Next Best Offer" method makes it possible to address the individual needs of potential customers. This is an analytical approach that can be used to determine customer needs. This requires big data and machine learning. In e-commerce in particular, it can be advantageous for advertisers if they only display those products that customers are more likely to buy. These personalized product recommendations increase the likelihood of a purchase, which is why sales growth and increased profits are the result.
Conclusion
Artificial intelligence, although in many cases it should be called machine learning, is a very useful technology for e-commerce. It helps you to better understand your customers, offer them a better experience in your online store and thus increase your sales. The applications of artificial intelligence in e-commerce range from personalizing your online store, to recommending certain products, optimizing/adjusting your prices, improving or creating your images, improving your predictions, your inventory management, and improving your customer service. You can definitely use artificial intelligence and machine learning to give yourself a competitive edge and stand out from other online stores or even increase your sales. If you want more tips on how to increase your sales, our blog"Increase online store sales: 10 tips for you" is probably for you. If you are now even more interested in the topic of artificial intelligence, then simply follow the link below to our webinar"Artificial Intelligence in Online Retail":
Sources (last accessed 04/05/2023):
- https://datasolut.com/machine-learning-im-e-commerce/
- https://ecommerceinstitut.de/machine-learning-im-e-commerce/
In e-commerce, but also in other industries, the term "artificial intelligence" is often used misleadingly because in reality no real artificial intelligence is used. Often, machine learning or deep learning is actually behind the promises. After all, machine learning is a type of artificial intelligence that enables computers to learn from data and make decisions without having to be explicitly programmed. Artificial intelligence is thus to be understood as a generic term for technologies that enable computers to imitate human thinking and behavior. Machine learning is thus a method of achieving artificial intelligence. In e-commerce, artificial intelligence (or machine learning) can be used, for example, to create personalized recommendations for customers or to detect fraud. But more on the specific examples later. First, let's differentiate the terms a bit more in order to avoid confusion.
Artificial Intelligence, Machine Learning and Deep Learning: What's the difference?
Artificial intelligence can be understood as an umbrella term that stands above machine learning and deep learning. All of these areas have different characteristics that distinguish them. The following illustration should help you to better distinguish between these areas in the future.
Machine Learning
Machine learning deals with pattern recognition from data. Machines are not explicitly programmed, but use statistical models and data to recognize patterns that occur. With statistical learning methods and neural networks, algorithms are trained, for example, to recognize faces in pictures or even handwritten numbers. A characteristic of machine learning algorithms is that their performance improves progressively as more data is added.
Deep Learning
Deep Learning is a subterm of Machine Learning and this term in turn is subject to that of artificial intelligence. Deep Learning is a learning method that works with artificial neural networks. These neural networks, which are similar to the human brain, are able to record and pass on information from a large amount of data (Big Data).
Artificial intelligence
The beginnings of artificial intelligence go back a long way. As early as the 1950s, the so-called Turing test was used to determine whether or not traits of artificial intelligence existed. In the Turing test, a human tester communicates with two unknown interlocutors via a keyboard and a screen, without seeing or hearing. One of them is a human, the other a machine. If, after extensive questioning, the tester cannot determine which of the two is the machine, the machine has passed the Turing test and is assumed to have reasoning ability equivalent to humans. Over the years, other and more complex tests have been added and can be read about here . In any case, artificial intelligence is spoken of when a program can grasp, reason, act independently and adapt.
Artificial intelligence: 9 areas of application in e-commerce
The application areas of artificial intelligence (or machine learning) in e-commerce are diverse and growing. Here is an excerpt from the most common areas of application at present:
1. chatbots and voice assistants in customer service
You can significantly improve your customer service by using chatbots or voice assistants that can support your customers in real time. For example, you can answer standardized questions, solve problems, take orders, or collect feedback. You can also use chatbots to advise your customers, recommend suitable products or offer them special deals.
2. image processing and content creation
Another area of application is product images. Here, AI-based tools are suitable to improve, edit or generate. For example, you can increase the quality or resolution of the images, remove unwanted elements or apply filters. You can also generate new images of products that don't exist yet or that are shown from different perspectives or in different colors.
3. predictions & analyses
Artificial intelligence (or in this case machine learning) is a great way to predict different aspects of your online store. For example, the demand for certain products, the buying behavior of customers, your sales or even your profit. Smart algorithms are often used to predict which products will sell best at which time of year or on which occasion. This involves analyzing historical data using an algorithm. Based on historical time series, the algorithm can identify how sales and purchases will develop in the future, for example. The prediction of necessary stock levels at certain times of the year is also useful for the optimal use of warehouses. AI-based forecasts help to identify trends, seasonalities and complex patterns. Companies that have in-depth knowledge of demand for their products can make important decisions about marketing spend and identify potential sales fluctuations in advance.
4. A/B tests
You can use machine learning to test different versions of your website or ad campaigns and choose the one that performs best. For example, you can try different colors, fonts, images or texts and see which ones have the highest click-through rate, conversion rate or customer engagement.
5. image recognition & intelligent search functions
Analyze and categorize product images to make searching and browsing easier. For example, you can automatically generate tags or descriptions for your products based on their visual characteristics. You can also provide visual search features that allow customers to find products that are similar to an image.
6. dynamic pricing
Based on factors such as supply and demand, competition, season and customer profile, you can determine optimal prices for your products. It would also be possible to continuously adjust prices to generate more sales when demand is high or to attract more customers with lower prices when demand is low. Also possible are personalized prices. Depending on how likely a customer is to buy a product or not.
7. personalization of the content on the website
Machine learning can also adapt the content of your website, or mobile app, to visitors' preferences and behavior. For example, you can test different versions of your website and choose the one that has the best conversion rate. You can also display different content depending on what industry they belong to, what they are looking for, or how often they visit your website.
8. recommendation systems
Another application of artificial intelligence is the recommendation of products based on customers' interests, behavior and profile. For example, you can analyze which products customers have viewed, added to their shopping cart, or purchased, and suggest similar or complementary products to them. You can also use the experiences of other customers and recommend products bought by customers with similar preferences or from the same region.
9. personalized advertising
Personalizing online advertising and delivering the right offers at the right time are two important aspects that every marketer takes into account. The "Next Best Offer" method makes it possible to address the individual needs of potential customers. This is an analytical approach that can be used to determine customer needs. This requires big data and machine learning. In e-commerce in particular, it can be advantageous for advertisers if they only display those products that customers are more likely to buy. These personalized product recommendations increase the likelihood of a purchase, which is why sales growth and increased profits are the result.
Conclusion
Artificial intelligence, although in many cases it should be called machine learning, is a very useful technology for e-commerce. It helps you to better understand your customers, offer them a better experience in your online store and thus increase your sales. The applications of artificial intelligence in e-commerce range from personalizing your online store, to recommending certain products, optimizing/adjusting your prices, improving or creating your images, improving your predictions, your inventory management, and improving your customer service. You can definitely use artificial intelligence and machine learning to give yourself a competitive edge and stand out from other online stores or even increase your sales. If you want more tips on how to increase your sales, our blog"Increase online store sales: 10 tips for you" is probably for you. If you are now even more interested in the topic of artificial intelligence, then simply follow the link below to our webinar"Artificial Intelligence in Online Retail":
Sources (last accessed 04/05/2023):
- https://datasolut.com/machine-learning-im-e-commerce/
- https://ecommerceinstitut.de/machine-learning-im-e-commerce/
In e-commerce, but also in other industries, the term "artificial intelligence" is often used misleadingly because in reality no real artificial intelligence is used. Often, machine learning or deep learning is actually behind the promises. After all, machine learning is a type of artificial intelligence that enables computers to learn from data and make decisions without having to be explicitly programmed. Artificial intelligence is thus to be understood as a generic term for technologies that enable computers to imitate human thinking and behavior. Machine learning is thus a method of achieving artificial intelligence. In e-commerce, artificial intelligence (or machine learning) can be used, for example, to create personalized recommendations for customers or to detect fraud. But more on the specific examples later. First, let's differentiate the terms a bit more in order to avoid confusion.
Artificial Intelligence, Machine Learning and Deep Learning: What's the difference?
Artificial intelligence can be understood as an umbrella term that stands above machine learning and deep learning. All of these areas have different characteristics that distinguish them. The following illustration should help you to better distinguish between these areas in the future.
Machine Learning
Machine learning deals with pattern recognition from data. Machines are not explicitly programmed, but use statistical models and data to recognize patterns that occur. With statistical learning methods and neural networks, algorithms are trained, for example, to recognize faces in pictures or even handwritten numbers. A characteristic of machine learning algorithms is that their performance improves progressively as more data is added.
Deep Learning
Deep Learning is a subterm of Machine Learning and this term in turn is subject to that of artificial intelligence. Deep Learning is a learning method that works with artificial neural networks. These neural networks, which are similar to the human brain, are able to record and pass on information from a large amount of data (Big Data).
Artificial intelligence
The beginnings of artificial intelligence go back a long way. As early as the 1950s, the so-called Turing test was used to determine whether or not traits of artificial intelligence existed. In the Turing test, a human tester communicates with two unknown interlocutors via a keyboard and a screen, without seeing or hearing. One of them is a human, the other a machine. If, after extensive questioning, the tester cannot determine which of the two is the machine, the machine has passed the Turing test and is assumed to have reasoning ability equivalent to humans. Over the years, other and more complex tests have been added and can be read about here . In any case, artificial intelligence is spoken of when a program can grasp, reason, act independently and adapt.
Artificial intelligence: 9 areas of application in e-commerce
The application areas of artificial intelligence (or machine learning) in e-commerce are diverse and growing. Here is an excerpt from the most common areas of application at present:
1. chatbots and voice assistants in customer service
You can significantly improve your customer service by using chatbots or voice assistants that can support your customers in real time. For example, you can answer standardized questions, solve problems, take orders, or collect feedback. You can also use chatbots to advise your customers, recommend suitable products or offer them special deals.
2. image processing and content creation
Another area of application is product images. Here, AI-based tools are suitable to improve, edit or generate. For example, you can increase the quality or resolution of the images, remove unwanted elements or apply filters. You can also generate new images of products that don't exist yet or that are shown from different perspectives or in different colors.
3. predictions & analyses
Artificial intelligence (or in this case machine learning) is a great way to predict different aspects of your online store. For example, the demand for certain products, the buying behavior of customers, your sales or even your profit. Smart algorithms are often used to predict which products will sell best at which time of year or on which occasion. This involves analyzing historical data using an algorithm. Based on historical time series, the algorithm can identify how sales and purchases will develop in the future, for example. The prediction of necessary stock levels at certain times of the year is also useful for the optimal use of warehouses. AI-based forecasts help to identify trends, seasonalities and complex patterns. Companies that have in-depth knowledge of demand for their products can make important decisions about marketing spend and identify potential sales fluctuations in advance.
4. A/B tests
You can use machine learning to test different versions of your website or ad campaigns and choose the one that performs best. For example, you can try different colors, fonts, images or texts and see which ones have the highest click-through rate, conversion rate or customer engagement.
5. image recognition & intelligent search functions
Analyze and categorize product images to make searching and browsing easier. For example, you can automatically generate tags or descriptions for your products based on their visual characteristics. You can also provide visual search features that allow customers to find products that are similar to an image.
6. dynamic pricing
Based on factors such as supply and demand, competition, season and customer profile, you can determine optimal prices for your products. It would also be possible to continuously adjust prices to generate more sales when demand is high or to attract more customers with lower prices when demand is low. Also possible are personalized prices. Depending on how likely a customer is to buy a product or not.
7. personalization of the content on the website
Machine learning can also adapt the content of your website, or mobile app, to visitors' preferences and behavior. For example, you can test different versions of your website and choose the one that has the best conversion rate. You can also display different content depending on what industry they belong to, what they are looking for, or how often they visit your website.
8. recommendation systems
Another application of artificial intelligence is the recommendation of products based on customers' interests, behavior and profile. For example, you can analyze which products customers have viewed, added to their shopping cart, or purchased, and suggest similar or complementary products to them. You can also use the experiences of other customers and recommend products bought by customers with similar preferences or from the same region.
9. personalized advertising
Personalizing online advertising and delivering the right offers at the right time are two important aspects that every marketer takes into account. The "Next Best Offer" method makes it possible to address the individual needs of potential customers. This is an analytical approach that can be used to determine customer needs. This requires big data and machine learning. In e-commerce in particular, it can be advantageous for advertisers if they only display those products that customers are more likely to buy. These personalized product recommendations increase the likelihood of a purchase, which is why sales growth and increased profits are the result.
Conclusion
Artificial intelligence, although in many cases it should be called machine learning, is a very useful technology for e-commerce. It helps you to better understand your customers, offer them a better experience in your online store and thus increase your sales. The applications of artificial intelligence in e-commerce range from personalizing your online store, to recommending certain products, optimizing/adjusting your prices, improving or creating your images, improving your predictions, your inventory management, and improving your customer service. You can definitely use artificial intelligence and machine learning to give yourself a competitive edge and stand out from other online stores or even increase your sales. If you want more tips on how to increase your sales, our blog"Increase online store sales: 10 tips for you" is probably for you. If you are now even more interested in the topic of artificial intelligence, then simply follow the link below to our webinar"Artificial Intelligence in Online Retail":
Sources (last accessed 04/05/2023):
- https://datasolut.com/machine-learning-im-e-commerce/
- https://ecommerceinstitut.de/machine-learning-im-e-commerce/
In e-commerce, but also in other industries, the term "artificial intelligence" is often used misleadingly because in reality no real artificial intelligence is used. Often, machine learning or deep learning is actually behind the promises. After all, machine learning is a type of artificial intelligence that enables computers to learn from data and make decisions without having to be explicitly programmed. Artificial intelligence is thus to be understood as a generic term for technologies that enable computers to imitate human thinking and behavior. Machine learning is thus a method of achieving artificial intelligence. In e-commerce, artificial intelligence (or machine learning) can be used, for example, to create personalized recommendations for customers or to detect fraud. But more on the specific examples later. First, let's differentiate the terms a bit more in order to avoid confusion.
Artificial Intelligence, Machine Learning and Deep Learning: What's the difference?
Artificial intelligence can be understood as an umbrella term that stands above machine learning and deep learning. All of these areas have different characteristics that distinguish them. The following illustration should help you to better distinguish between these areas in the future.
Machine Learning
Machine learning deals with pattern recognition from data. Machines are not explicitly programmed, but use statistical models and data to recognize patterns that occur. With statistical learning methods and neural networks, algorithms are trained, for example, to recognize faces in pictures or even handwritten numbers. A characteristic of machine learning algorithms is that their performance improves progressively as more data is added.
Deep Learning
Deep Learning is a subterm of Machine Learning and this term in turn is subject to that of artificial intelligence. Deep Learning is a learning method that works with artificial neural networks. These neural networks, which are similar to the human brain, are able to record and pass on information from a large amount of data (Big Data).
Artificial intelligence
The beginnings of artificial intelligence go back a long way. As early as the 1950s, the so-called Turing test was used to determine whether or not traits of artificial intelligence existed. In the Turing test, a human tester communicates with two unknown interlocutors via a keyboard and a screen, without seeing or hearing. One of them is a human, the other a machine. If, after extensive questioning, the tester cannot determine which of the two is the machine, the machine has passed the Turing test and is assumed to have reasoning ability equivalent to humans. Over the years, other and more complex tests have been added and can be read about here . In any case, artificial intelligence is spoken of when a program can grasp, reason, act independently and adapt.
Artificial intelligence: 9 areas of application in e-commerce
The application areas of artificial intelligence (or machine learning) in e-commerce are diverse and growing. Here is an excerpt from the most common areas of application at present:
1. chatbots and voice assistants in customer service
You can significantly improve your customer service by using chatbots or voice assistants that can support your customers in real time. For example, you can answer standardized questions, solve problems, take orders, or collect feedback. You can also use chatbots to advise your customers, recommend suitable products or offer them special deals.
2. image processing and content creation
Another area of application is product images. Here, AI-based tools are suitable to improve, edit or generate. For example, you can increase the quality or resolution of the images, remove unwanted elements or apply filters. You can also generate new images of products that don't exist yet or that are shown from different perspectives or in different colors.
3. predictions & analyses
Artificial intelligence (or in this case machine learning) is a great way to predict different aspects of your online store. For example, the demand for certain products, the buying behavior of customers, your sales or even your profit. Smart algorithms are often used to predict which products will sell best at which time of year or on which occasion. This involves analyzing historical data using an algorithm. Based on historical time series, the algorithm can identify how sales and purchases will develop in the future, for example. The prediction of necessary stock levels at certain times of the year is also useful for the optimal use of warehouses. AI-based forecasts help to identify trends, seasonalities and complex patterns. Companies that have in-depth knowledge of demand for their products can make important decisions about marketing spend and identify potential sales fluctuations in advance.
4. A/B tests
You can use machine learning to test different versions of your website or ad campaigns and choose the one that performs best. For example, you can try different colors, fonts, images or texts and see which ones have the highest click-through rate, conversion rate or customer engagement.
5. image recognition & intelligent search functions
Analyze and categorize product images to make searching and browsing easier. For example, you can automatically generate tags or descriptions for your products based on their visual characteristics. You can also provide visual search features that allow customers to find products that are similar to an image.
6. dynamic pricing
Based on factors such as supply and demand, competition, season and customer profile, you can determine optimal prices for your products. It would also be possible to continuously adjust prices to generate more sales when demand is high or to attract more customers with lower prices when demand is low. Also possible are personalized prices. Depending on how likely a customer is to buy a product or not.
7. personalization of the content on the website
Machine learning can also adapt the content of your website, or mobile app, to visitors' preferences and behavior. For example, you can test different versions of your website and choose the one that has the best conversion rate. You can also display different content depending on what industry they belong to, what they are looking for, or how often they visit your website.
8. recommendation systems
Another application of artificial intelligence is the recommendation of products based on customers' interests, behavior and profile. For example, you can analyze which products customers have viewed, added to their shopping cart, or purchased, and suggest similar or complementary products to them. You can also use the experiences of other customers and recommend products bought by customers with similar preferences or from the same region.
9. personalized advertising
Personalizing online advertising and delivering the right offers at the right time are two important aspects that every marketer takes into account. The "Next Best Offer" method makes it possible to address the individual needs of potential customers. This is an analytical approach that can be used to determine customer needs. This requires big data and machine learning. In e-commerce in particular, it can be advantageous for advertisers if they only display those products that customers are more likely to buy. These personalized product recommendations increase the likelihood of a purchase, which is why sales growth and increased profits are the result.
Conclusion
Artificial intelligence, although in many cases it should be called machine learning, is a very useful technology for e-commerce. It helps you to better understand your customers, offer them a better experience in your online store and thus increase your sales. The applications of artificial intelligence in e-commerce range from personalizing your online store, to recommending certain products, optimizing/adjusting your prices, improving or creating your images, improving your predictions, your inventory management, and improving your customer service. You can definitely use artificial intelligence and machine learning to give yourself a competitive edge and stand out from other online stores or even increase your sales. If you want more tips on how to increase your sales, our blog"Increase online store sales: 10 tips for you" is probably for you. If you are now even more interested in the topic of artificial intelligence, then simply follow the link below to our webinar"Artificial Intelligence in Online Retail":
Sources (last accessed 04/05/2023):
- https://datasolut.com/machine-learning-im-e-commerce/
- https://ecommerceinstitut.de/machine-learning-im-e-commerce/
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