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Exceedra Byte: Artificial Intelligence & Machine Learning

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On this week’s episode of Exceedra Byte,  Andres Jejen, Exceedra VP of Solutions Engineering, breaks down the concepts of Artificial Intelligence (AI) and Machine Learning (ML) and their application in the consumer goods industry.

Probably, you remember the terms ‘Artificial Intelligence’ and ‘Machine Learning’ from Star Wars, Star Trek, Battlestar Galactica, and other stars. But those are things that no longer belong to the realm of science fiction nor are located into the deep space. Nowadays, there are hundreds of applications of such technologies in our everyday lives, from autonomous cars to loan approvals.

The consumer goods space is not a stranger anymore to this type of technology. Very often, manufacturers and software vendors resort to it, to help with the automatization of data analysis that, made manually, will consume a tremendous amount of human power to achieve the same results.

One of the most common uses of Artificial Intelligence and Machine Learning in the consumer goods space is Image Recognition. Through this technology, we can teach the systems to see and identify the products in a shelf based on a photo captured on the spot, and to produce insights about factors of presence, distribution, compliance, among others. This replaces the typical manual counts that occur at the store, saving up to 80% of the visit time, while obtaining a measurement accuracy above 97%.

But it does not stop there. Artificial Intelligence is also widely used to perform forecasting of production, especially in those cases in which there are upcoming promotions that will increase the demand of products. And in fact, for the very exercise of planning a promotion, Artificial intelligence becomes very handy in analyzing previous promotions, include other sources of information and prescribe what will be the best way to use the trade dollars to maximize the outcomes.

Image Recognition replaces the typical manual counts that occur at the store, saving up to 80% of the visit time, while obtaining a measurement accuracy above 97%.

The success of any strategy lies on using the right amount of information. The more data available, the better the outcomes are. And the technology is called ‘Machine Learning’ for a reason. 

 

The more data is fed into the algorithms, the more the systems learn to produce more and better insights, and ultimately, to improve the accuracy of the prescriptions made. 

 

Imagine, for instance a ‘Perfect Order’ scenario in which you want to suggest your field personnel a list of products and quantities to be placed on an order, at a very granular level. If you have previous sales information, you can use it to predict what the next best sell can be. However, if you combine more data, such as product availability, seasonality, running campaigns, social information, and others, you can train your system to understand this data, and issue prescriptions that will be applicable to the context of the store being visited.

Exceedra has been a pioneer in the application of Artificial Intelligence and Machine Learning in the consumer goods technological space. And we are here, ready to support you through this journey to the stars.

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Exceedra Byte is a weekly vlog series where we take complex trade and revenue growth management topics and break them down into byte-sized pieces. Stay tuned every Thursday for new episodes.

Watch the previous episode of Exceedra Byte – TPM & RE Alignment

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