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PREVIEW CHAPTER ONE
First, some perspective on technology and its advancement. As a species, humans have always turned to tools to enrich their lives and make their time more productive. From early-man with spears and fire to ever-increasingly complex machines that led to the industrial revolution and ushered in the first age to automate our most mundane tasks. Since then, we humans have been innovating at an ever-accelerating rate, taking what tasks were done by paper and pen and digitizing them. Using email over snail mail, spreadsheets over ledgers, and social media over print ads to communicate and sell more effectively to vast numbers of prospective customers around the world.
”“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence”Virginia (Ginni) RomettyIBM Chief Executive
The exponential growth in the amount of data and corresponding economic viability of cloud computing to capture, store, and process this information is ushering in a new era – the era of predictive analytics. The rise of predictive analytics also relies on what is commonly referred to as Artificial Intelligence or AI. While the words artificial intelligence conjures up notions of iRobot, the likelihood of a thinking entity that exhibits judgment and what might be called consciousness is not close to becoming reality anytime soon.
In this e-Book, we will focus on how predictive analytics empowers retailers to plan more effectively. How predictive analytics enable a myriad of data points and interactions to be evaluated and optimized in real-time so that planners and buyers focus their expertise in making judgment calls based on real-world factors that are outside of the predictive planning algorithms model.
At its simplest, predictive plannning is the practice of using the data you have, to make a prediction about the most likely futureor outcome. In the case of retail planning, on which this guide focuses, it is using data to make predictions as to how much product is required where and when to meet future demand. The art of this forecasting is using this knowledge to optimize both the customer experience and business financial objectives.
Leveraging historical and real time data, this type of technology is generally based on sophisticated statistical models and machine learning algorithms. These algorithms are “tuned” using vast amounts of historical product, transactional, inventory, and geo-location data.
External data sources such as public and local events, weather, social media trends, consumer sentiment indexes, etc., can also be used to empower the machine learning algorithms. After deploying machine learning models, and monitoring the results in the next iteration of the model tuning process, the machine learning algorithms identify gaps and
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