Posted on : 2023-06-11 20:22:36
Article : Good morning, Monday Management solution for TASK 245- There is a constant need to bring more innovations and improvements to handle the big competition and grow. That brought us to the situation where AI is no longer “this technology to try someday” – it’s already happening.
Fast Moving Consumer Goods and services are multi-million-dollar industries. There is a constant need to bring more innovations and improvements to handle the big competition and grow. That brought us to the situation where AI is no longer “this technology to try someday” – it’s already happening. And it’s not only a matter of growth anymore. More and more often, it is about surviving on the market where everyone else adopts it. It applies especially in the FMCG industry, where there is a lot of data to process, operations to optimize, and decisions to make. The potential of AI technology in the FMCG area is big and it’s growing month by month. By 2025, at least 90% of new enterprise apps will embed artificial intelligence. Most of these will be AI-enabled apps, delivering incremental improvements to make applications “smarter” and more dynamic.
The bigger the company, the higher the number of challenges in all kinds of operations. With many departments, transactions, and documents, it looks like running a company without repetitive and tedious work is unavoidable. However, in every area, where there is a lot of data, there is also a place for optimization, which leads to higher revenue and more effective decision-making. Let us see some examples of where AI can bring the highest value for the all-sectors companies. Here below we will discuss on different brand marketers and how they have been utilising AI in different vertical functions in their respective industries for multiple benefits.
Netflix uses AI to personalize its content recommendations for users. The platform's algorithms analyse viewing habits, such as the shows and movies users have watched and the genres they have been interested in, to suggest new content they may enjoy. This helps keep users engaged and increase the likelihood they will continue to subscribe to the service.
Coca-Cola uses AI to analyse large amounts of customer data and create more targeted marketing campaigns. The company can use this data to identify consumer preferences and behaviours, such as the types of drinks they prefer and when they are most likely to purchase and to create more effective advertisements.
Sephora uses AI-powered chatbots to provide customers with product information and recommendations. Customers can ask the chatbots questions about specific products, and the AI will use its knowledge of the Sephora product catalogue to provide accurate answers and suggest products that may interest the customer.
H&M uses AI to analyse customer data and create more targeted marketing campaigns. The company can use this data to understand consumer preferences and behaviours, such as the types of clothing they are interested in and when and where they are most likely to make a purchase, to
create more effective advertisements.
McDonald's uses AI-powered chatbots to help customers place their orders and answer their questions. The chatbots can answer customer questions about menu items, provide nutritional information, and assist with placing an order. This helps streamline the ordering process and increase customer satisfaction.
For demand prediction A great real-life example of how to benefit from AI is the case of 7-Eleven. This FMCG company decided to improve in “out of stock” situations by using machine learning techniques to estimate the demand for different goods. Based on historical sales, out-of-stock conditions, weather, store demographics, and more data, or additional information specific to the chosen location, AI suggests what to order. The company has already processed a billion transactions to train the models and it will be 100 times more processed once fully rolled out.
Drawing good conclusions and making the right business decisions require a lot of data analysis behind them. And at a certain scale, it’s simply impossible to perform it manually.
End point- AI technology can use data from multiple sources and build a store of knowledge, which brings us to having more accurate predictions about the whole business and customers. Machine learning and deep learning use data – including demographic and behavioural patterns, purchase history, etc to learn and generate new rules for future business analytics. Intelligent machine learning systems can help data scientists with providing business solutions and predictions, e.g. more efficient marketing strategies and customer service improvements – both based on predicted customer behaviour and better customer insights.
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