Notes from the AI frontier: Applications and value of deep learning




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This is an excellent article summarizing the different branches of AI, where they are most applicable, and use cases.

I copied out parts of the article and pasted below that are relevant to consumer products. But it is still recommended to read the entire article.

  • AI-driven logistics optimization can reduce costs through real-time forecasts and behavioral coaching. – shift this to sales demand forecasting, layered with logistics to get to customer.
  • For example, deep learning analysis of audio allows systems to assess a customers’ emotional tone; in the event a customer is responding badly to the system, the call can be rerouted automatically to human operators and managers.
  • In other areas of marketing and sales, AI techniques can also have a significant impact. Combining customer demographic and past transaction data with social media monitoring can help generate individualized product recommendations. “Next product to buy” recommendations that target individual customers—as companies such as Amazon and Netflix have successfully been doing–can lead to a twofold increase in the rate of sales conversions.
  • By one estimate, a supervised deep-learning algorithm will generally achieve acceptable performance with around 5,000 labeled examples per category and will match or exceed human level performance when trained with a data set containing at least 10 million labeled examples.
  • These massive data sets can be difficult to obtain or create for many business use cases, and labeling remains a challenge. Most current AI models are trained through “supervised learning”, which requires humans to label and categorize the underlying data. However promising new techniques are emerging to overcome these data bottlenecks, such as reinforcement learning, generative adversarial networks, transfer learning, and “one-shot learning,” which allows a trained AI model to learn about a subject based on a small number of real-world demonstrations or examples—and sometimes just one.
  • Along with issues around the volume and variety of data, velocity is also a requirement: AI techniques require models to be retrained to match potential changing conditions, so the training data must be refreshed frequently. In one-third of the cases, the model needs to be refreshed at least monthly, and almost one in four cases requires a daily refresh; this is especially the case in marketing and sales and in supply chain management and manufacturing.
  • Significant potential to create value in CPG and retail.
  • Consumer industries such as retail and high tech will tend to see more potential from marketing and sales AI applications because frequent and digital interactions between business and customers generate larger data sets for AI techniques to tap into. E-commerce platforms, in particular, stand to benefit. This is because of the ease with which these platforms collect customer information such as click data or time spent on a web page and can then customize promotions, prices, and products for each customer dynamically and in real time.


I post what I see and do in consumer products. But I am just one person with my own perspective. I want your opinion and observations from your point of view. Please comment below so I and others can learn. Thank you!


2018-05-08T20:19:04+00:00By |Categories: Micro Content|0 Comments

About the Author:

I am a startup and growth company expert: sold 1, built 5, and crashed 2. I develop, launch and grow consumer products through uncommon methods that can lead to more sales – faster – and can make a company and its products more appealing to consumers and resellers, with less risk. More about me here.

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