Optimized Line Planning

Enhance your retail assortment planning with the Voice of the Customer and Machine Learning.

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What does optimized line planning do for your business?

Retailers and brands can now align product development lifecycle activities with consumer insight, enabling them to deliver more successful assortments; maximizing sales and margins.

 

First Insight extends the value of Product Lifecycle Management beyond design and product development into a smart platform that leverages predictive analytics and machine learning.

 

Retailers and brands can now align product development lifecycle activities with consumer preferences, enabling them to deliver more successful assortments, maximizing sales and margins. 


Why Incorporate OLP into your assortment decisions?

For many retailers and wholesalers, assortment planning relies heavily on the merchant's ability to aggregate disparate data sources to drive decisions for line and merchandise planning. These data are often scattered among many sources, which requires a large investment of time and effort to aggregate and analyze.

 

How does OLP address these issues?

Strengthen data sources by leveraging your existing line and merchandise planning process through our three-stage methodology of real-time consumer data from First Insight and machine learning powered by PTC ThingWorx®.

Three Stages of Value

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1. Understand
Connect disparate data sources

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2. Strategize
Plan your line around your customer and business goals

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3. Optimize
Leverage design recommendations to create placeholder products


1. Understand

  • Connect disparate data sources such as CRM data, historical POS sales data, purchase data, as well as First Insight customer data
  • Leverage continuous machine learning based on product performance

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2. Strategize

  • Set a clear strategy for your product assortment
  • Adjust revenue targets based on business goals
  • Understand ideal styles for each customer persona to maximize sales and gross margin
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3. Optimize

  • Receive recommendations for optimal placeholder characteristics by persona
  • Identify attribute groups that will resonate by persona
  • Create style groups that can be leveraged across personas
  • Identify projected retail price range
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