3 books on AI for Retail [PDF]
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These books explore AI solutions in the retail industry that are used for inventory management, demand forecasting, personalized marketing and customer experience enhancement.
1. Revolutionizing Retail Analytics: Harnessing AI and Machine Learning for Business Growth
2025 by Shashank Shekhar Katyayan Dr. Jhankar Moolchandani
This book tells a brief history of retail analytics development. It began long before the advent of AI: in the past, retailers relied on traditional manual methods of collecting data and used simple metrics such as sales volume, customer traffic and basic inventory levels. Information sources were limited to cash register and accounting system data. The main problem was that data was analyzed after the fact - at the end of the day or week, making it difficult to make timely adjustments. When online POS and SaaS inventory management software appeared - companies got the opportunity to track product levels, sales dynamics and customer behavior in real time. However, the real breakthrough came with the widespread adoption of big data and AI. Retailers started collecting huge amounts of data: in-store transactions, online shopping behavior, social media reviews and location data. Machine learning have become key technology to process this big data, as it allows to detect patterns that no human-analitic can find. For example, retailers use ML algorithms to predict future demand based on historical data, customer preferences and influence of external factors such as weather conditions or the economy decline. In addition, ML helps retailers improve pricing strategies. Dynamic pricing models allow retailres to change prices in real time basing on competition, fluctuations in demand and consumer opinions.
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2. ChatGPT AI and E-commerce
2024 by ALESSANDRO HOFFMANN
This book is entirely generated by ChatGPT, so it's only interesting to read the table of contents. First, it talks about what e-commerce is, then it talks about what ChatGPT is and then how ChatGPT is used in e-commerce. Basically, it repeats 100 times that ChatGPT is used for personalized marketing and communication with customers (primarily via chatbots). Well, everything is clear here: you train ChatGPT on your documents and knowledge base and set up a chat on your website or in messengers that automatically answer customer questions. It is also clear that ChatGPT can be used to generate marketing texts and images for social networks. More advanced application is using ChatGPT as an interface to analytics systems. That is, instead of writing some queries to the database in some language, or even instead of looking at visual graphs and understanding something from them, you can simply connect ChatGPT to your analytical system and ask it in natural language how the company is doing now: which products are selling well, which are selling poorly and how to improve sales and increase profits.
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3. AI for Retail: A Practical Guide to Modernize Your Retail Business with AI and Automation
2023 by Francois Chaubard
Unlike many books that repeat the same predictions about how AI will be used in retail, this book shows the other side of the coin - how AI will NOT be used in retail. For example, it tells the story of Amazon Go project that was intended to create a completely cashierless store using hundreds of computer vision cameras that continuously track your movement around the store. Shelves were equipped with weight sensors that record when an item is picked up. The system "links" the product to the person who is closest to the shelf. In 2018, Amazon said it would roll out 3,000 such stores in the US in 3 years, but in 2022, it only had about 42 stores, half of which later closed. According to the author, everything was obvious: the cost of GPU computing alone would have cost more than cashiers' salaries. The costs of equipment, computing, DevOps, a huge number of people who manually review video and correct system errors, thousands of cameras, each with its own GPU for processing images and depth data - all in all, it exceeds $120,000 per year per one checkout. For a store, this is absolutely unprofitable. Even if we assume that costs will fall by half annually (which is very optimistic), the system will still not break even until 2040. And if you compare it to alternatives like self-checkouts or regular cashiers, the situation for Amazon Go looks even worse.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Revolutionizing Retail Analytics: Harnessing AI and Machine Learning for Business Growth
2025 by Shashank Shekhar Katyayan Dr. Jhankar Moolchandani
This book tells a brief history of retail analytics development. It began long before the advent of AI: in the past, retailers relied on traditional manual methods of collecting data and used simple metrics such as sales volume, customer traffic and basic inventory levels. Information sources were limited to cash register and accounting system data. The main problem was that data was analyzed after the fact - at the end of the day or week, making it difficult to make timely adjustments. When online POS and SaaS inventory management software appeared - companies got the opportunity to track product levels, sales dynamics and customer behavior in real time. However, the real breakthrough came with the widespread adoption of big data and AI. Retailers started collecting huge amounts of data: in-store transactions, online shopping behavior, social media reviews and location data. Machine learning have become key technology to process this big data, as it allows to detect patterns that no human-analitic can find. For example, retailers use ML algorithms to predict future demand based on historical data, customer preferences and influence of external factors such as weather conditions or the economy decline. In addition, ML helps retailers improve pricing strategies. Dynamic pricing models allow retailres to change prices in real time basing on competition, fluctuations in demand and consumer opinions.
Download PDF
2. ChatGPT AI and E-commerce
2024 by ALESSANDRO HOFFMANN
This book is entirely generated by ChatGPT, so it's only interesting to read the table of contents. First, it talks about what e-commerce is, then it talks about what ChatGPT is and then how ChatGPT is used in e-commerce. Basically, it repeats 100 times that ChatGPT is used for personalized marketing and communication with customers (primarily via chatbots). Well, everything is clear here: you train ChatGPT on your documents and knowledge base and set up a chat on your website or in messengers that automatically answer customer questions. It is also clear that ChatGPT can be used to generate marketing texts and images for social networks. More advanced application is using ChatGPT as an interface to analytics systems. That is, instead of writing some queries to the database in some language, or even instead of looking at visual graphs and understanding something from them, you can simply connect ChatGPT to your analytical system and ask it in natural language how the company is doing now: which products are selling well, which are selling poorly and how to improve sales and increase profits.
Download PDF
3. AI for Retail: A Practical Guide to Modernize Your Retail Business with AI and Automation
2023 by Francois Chaubard
Unlike many books that repeat the same predictions about how AI will be used in retail, this book shows the other side of the coin - how AI will NOT be used in retail. For example, it tells the story of Amazon Go project that was intended to create a completely cashierless store using hundreds of computer vision cameras that continuously track your movement around the store. Shelves were equipped with weight sensors that record when an item is picked up. The system "links" the product to the person who is closest to the shelf. In 2018, Amazon said it would roll out 3,000 such stores in the US in 3 years, but in 2022, it only had about 42 stores, half of which later closed. According to the author, everything was obvious: the cost of GPU computing alone would have cost more than cashiers' salaries. The costs of equipment, computing, DevOps, a huge number of people who manually review video and correct system errors, thousands of cameras, each with its own GPU for processing images and depth data - all in all, it exceeds $120,000 per year per one checkout. For a store, this is absolutely unprofitable. Even if we assume that costs will fall by half annually (which is very optimistic), the system will still not break even until 2040. And if you compare it to alternatives like self-checkouts or regular cashiers, the situation for Amazon Go looks even worse.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
