AI in Retail Excess Inventory Management

Making Off-Price Selling Data Driven: Introducing INTURN Tool’s New inLine Analytics Feature5 min read

January 22, 2024   |   4 min read
Making Off-Price Selling Data Driven: Introducing INTURN Tool’s New Inline Analytics Feature

Making Off-Price Selling Data Driven: Introducing INTURN Tool’s New inLine Analytics Feature5 min read

Reading Time: 4 minutes

Historically, when selling excess inventory, Excel becomes your best friend – and your worst enemy. Excel is a powerful tool but going through the product line by line can be tedious and lead to errors.

On top of that, for anyone who isn’t an Excel Guru, it eats up hours you could have used building strategy, and growing the business (or just simply having fun :)). 

This is where’s INTURN tool comes in – the tool gives users a comprehensive view of their excess inventory and allows you to efficiently sell it to your off-price retailers. Hence, you can sustainably clear the excess inventory without diluting prices for too many buyers. 

As we spoke to more of our customers, we understood that selling excess inventory in off-price can be quite intimidating. When you go too low, you risk losing out on profits and when you go too high, no retailers want to buy it. Finding that sweet spot for pricing that goes out in the offer can be daunting.

The straightforward answer is relying on data – especially the historical kind – to see how similar offers were made in the past, price points picked, quantities sold, and the retailers it was sent to. However, it isn’t that simple when you have to rummage through multiple tabs, each with different documents, invoices, offers, and sheets to put data points together. It gets even tougher when said data lies with different people in different teams and there are hundreds of thousands of lines of it.

INTURN’s latest feature solves exactly that.

Introducing INTURN tool’s inLine Analytics

What is inLine Analytics? 

The inLine Analytics feature provides immediate access to historical sales data right within the line sheet, enabling the creation of more intelligent and personalized offers within minutes. You won’t need to revisit previous offers, emails, or sell sheets to gather historical pricing and sales volume information anymore!

This feature empowers users to leverage data by embedding historical Quantity and Price values within the Offer Linesheet, which can also be applied to your current offer if you so choose. This, in turn (pun intended), also enables users to create smarter offers quicker than ever before. It ties together well with our mission to help you sell through more of your excess inventory, faster, with the best possible recovery.

There are different types of data you can view:

  1.  For price: You can view the default “Highest Sold Unit Price”. Additionally, you can access the “Latest Sold Unit Price”. In the future, there will be options to review the “Margin” and the “Lowest Sold Price.”
  2. For Quantity: You will be able to view the “Highest Sold Quantity” as well as the “Latest Sold Quantity”.
  3. Also, If a retailer has been entered for an Unpublished Offer, Price and Quantity values should be shown for that retailer. If there’s no retailer entered yet, it will display the available data for each metric regardless of retailer. This means showing relevant information, such as the Highest Sold Price or Highest Sold Quantity, irrespective of any retailer association.
Some Specifics
  • Time Frame of Metric Values: Metrics such as quantity and price values are derived from Accepted offers within the past 6 months. If a product hasn’t been part of an Accepted offer within this timeframe, its metrics will display null values.
  • Product Data Matching: Data is matched based on the INTURN ID. If there are new INTURN IDs, they will show null values as there won’t be historical data for them.
  • Data Refresh Frequency: Historical data used for these metrics is refreshed every 2 hours. We’re working to move towards more frequent updates, closer to real-time.
  • Applying Invalid Metrics: When applying metrics to products, there are checks in place. For quantity metrics, if the product has fewer unallocated units than the quantity specified, the platform will update as many unallocated units as possible. For WHL (Wholesale) or MSRP (Manufacturer’s Suggested Retail Price) metrics, if the product doesn’t have a value for these, the price won’t be changed.
  • Retailer-Specific Metrics – These metrics provide specific insights based on the retailer and buyer information in an offer.
    • ​​Metrics labeled “Retailer” will show Price and Quantity values from the Retailer Company of the Buyer you’ve added to your Offer. 
    • Metrics labeled without “Retailer” will show Price and Quantity values from all the historical retailers that you have. 

What all this means for INTURN users

Now with insightful data and analytics, deciding on pricing for new offers has become seamless. By incorporating these findings into pricing strategies or product selections, you can optimize offers to align with market demands, consumer preferences, and competitive pricing. This enriched understanding enables you to craft more compelling and competitive offers that stand out in the market, appealing directly to your target audience and maximizing the potential for sales success. 

Ultimately, integrating this data into curated offers empowers you to make informed decisions that drive better results.

There is more coming up!

This is just the beginning! INTURN’s intelligent layer – AIONA (the AI Offer and Negotiation Assistant) will be launching soon, giving brands the ability to generate smart offers based on historical sales information and market trends at the push of a button (while ensuring users have the final say over the offer). AIONA will add to an extraordinary range of’s INTURN tool that will positively impact the supply chain management and inventory business. 

Sign up or reach out for a personalized demo and embark on a data-driven journey with Inturn tool’s inLine Analytics!


ABOUT THE AUTHOR engineers bespoke AI transformation roadmaps for enterprises across industries. Retailers to resellers, auto-extracting data from files to extrapolating fashion styles, 150+ conglomerates in five continents recruit How can we help yours?