In my last article, I explained how businesses need to differentiate between digital strategy, digitization and digitalization. The piece focused on how everyone uses the terms differently and, ultimately, how digitalization was really about thinking through how companies can best automate processes and practices in their organization.
Case in point: Digitalization is converting an entirely or partially manual process to be entirely digital. This could involve automating workflows or processes. Digitization, on the other hand, is converting analog content to digital format. Simply put, the goal of digitalization is to create efficiency and capture value.
A big part of digitalization is figuring out how to improve processes, not only to cut costs and save time, but also to improve an organization's productivity and the overall customer experience. Machine learning (ML) and artificial intelligence (AI) become an important part of the formula, since both help companies scale.
ML tools can help automate the process of extracting insights from large amounts of data, then classifying and indexing them to create value for the customer. For example, at a consumer packaged goods company, they can analyze their point-of-sale data to create a better product—and more compelling marketing—for their customers. Let’s assume a product like toothpaste and then expand that SKU into different attributes that are associated with toothpaste: contains fluoride, its color, whether it's whitening, flavor, if it’s a combo pack or just a single, etc. When you take a look at the volume of sales of those products over time and analyze the data, helpful insights can be pulled. Perhaps a customer is willing to pay 5 cents more for whitening, or the combo pack always performs better at a specific store.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/06/why-companies-need-to-apply-machine-learning-to-overall-digitalization-efforts/?sh=40483f167171
Case in point: Digitalization is converting an entirely or partially manual process to be entirely digital. This could involve automating workflows or processes. Digitization, on the other hand, is converting analog content to digital format. Simply put, the goal of digitalization is to create efficiency and capture value.
A big part of digitalization is figuring out how to improve processes, not only to cut costs and save time, but also to improve an organization's productivity and the overall customer experience. Machine learning (ML) and artificial intelligence (AI) become an important part of the formula, since both help companies scale.
ML tools can help automate the process of extracting insights from large amounts of data, then classifying and indexing them to create value for the customer. For example, at a consumer packaged goods company, they can analyze their point-of-sale data to create a better product—and more compelling marketing—for their customers. Let’s assume a product like toothpaste and then expand that SKU into different attributes that are associated with toothpaste: contains fluoride, its color, whether it's whitening, flavor, if it’s a combo pack or just a single, etc. When you take a look at the volume of sales of those products over time and analyze the data, helpful insights can be pulled. Perhaps a customer is willing to pay 5 cents more for whitening, or the combo pack always performs better at a specific store.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/06/why-companies-need-to-apply-machine-learning-to-overall-digitalization-efforts/?sh=40483f167171
