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Easy Ways to Use POS Data to Sell More and Avoid Inventory Problems with Alloy.ai's Manfred Reiche

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Manage episode 419458392 series 3514811
Content provided by Alloy.ai. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alloy.ai or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

What comes to mind when you hear the words ‘sales data’?
Alloy.ai’s resident expert on all things related to retail data, Manfred Reiche, says there’s a whole realm of information you’re likely missing out on when it comes to the sales data bucket. In this episode, we delve into the intricacies of point-of-sale data and its transformative impact on the consumer goods industry.
Listen in as we explore the critical role of data analysis in uncovering regional problems, managing inventory, and enhancing decision-making across various company sizes. From the importance of granularity in data to the necessity of breaking down silos within organizations, this discussion promises to shed light on how businesses can leverage vast data sets to drive success.
Three Key Takeaways:

  1. Accumulating data isn't enough, the key is to convert it into insights that drive action and that’s where granularity and AI play pivotal roles
  2. Encourage cooperation between different business units - breaking down silos can lead to more synchronized decision-making and better use of POS data
  3. Transition from traditional methods to using real-time POS data for forecasting to better predict market behaviors and enhance supply chain decisions
  continue reading

Chapters

1. Easy Ways to Use POS Data to Sell More and Avoid Inventory Problems with Alloy.ai's Manfred Reiche (00:00:00)

2. Common Data Mistakes in Consumer Goods (00:00:02)

3. Maximizing Point of Sale Data Analysis (00:10:00)

4. Maximizing Value Through Collaborative Data Deployment (00:23:04)

5. Leveraging Point of Sale Data (00:26:38)

13 episodes

Artwork
iconShare
 
Manage episode 419458392 series 3514811
Content provided by Alloy.ai. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alloy.ai or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

What comes to mind when you hear the words ‘sales data’?
Alloy.ai’s resident expert on all things related to retail data, Manfred Reiche, says there’s a whole realm of information you’re likely missing out on when it comes to the sales data bucket. In this episode, we delve into the intricacies of point-of-sale data and its transformative impact on the consumer goods industry.
Listen in as we explore the critical role of data analysis in uncovering regional problems, managing inventory, and enhancing decision-making across various company sizes. From the importance of granularity in data to the necessity of breaking down silos within organizations, this discussion promises to shed light on how businesses can leverage vast data sets to drive success.
Three Key Takeaways:

  1. Accumulating data isn't enough, the key is to convert it into insights that drive action and that’s where granularity and AI play pivotal roles
  2. Encourage cooperation between different business units - breaking down silos can lead to more synchronized decision-making and better use of POS data
  3. Transition from traditional methods to using real-time POS data for forecasting to better predict market behaviors and enhance supply chain decisions
  continue reading

Chapters

1. Easy Ways to Use POS Data to Sell More and Avoid Inventory Problems with Alloy.ai's Manfred Reiche (00:00:00)

2. Common Data Mistakes in Consumer Goods (00:00:02)

3. Maximizing Point of Sale Data Analysis (00:10:00)

4. Maximizing Value Through Collaborative Data Deployment (00:23:04)

5. Leveraging Point of Sale Data (00:26:38)

13 episodes

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