About the Client​

The Client is a leader in IT asset value recovery. They aim to be the best destination for enterprises, institutes, and OEMs to perform ITAD (IT assets disposition). But the company also goes a step beyond safe retirement of assets. Their refurbishment process recovers the highest possible value from the end-of-life assets. This value recovery is typically achieved by reselling the recertified assets through multiple channels (D2C, D2B, e-commerce marketplaces, and retailers). These affordable assets are given a second life as they are purchased by families otherwise caught in the digital divide.

Business Challenge

At the core of their operations lies a customer portal that allows enterprises to submit their asset details (serial numbers, model, condition) and accordingly receive pricing quotes. However, the client’s customers typically experienced delays (1-2 days) to receive an ideal quote for their end-of-life assets. This was because the pricing team manually computed prices relying on historical patterns and pricing information from competitors’ websites. The effort was significant given the large number of SKUs. Added to this, achieving pricing accuracy while factoring in many variables posed a significant challenge for the pricing team. Taken together, these factors contributed to operational inefficiencies and hindered their ability to scale their ITAD and value recovery business.

Trigent’s Solution

  • Empower the pricing team with a Gen AI-powered Pricing engine.
  • Use a case-based pricing strategy to arrive at the optimal price.
  • A real-time dashboard for internal teams to have strategic control over pricing decisions.

Solution Implementation

  • A scraping engine that fetches real-time market data (URLs and product details) from 16 different websites
  • A search catalog that categorizes the scraped data
  • ML+NLP-based similarity engine to arrive at a price match for end-of-life assets

Case-based pricing for arriving at a fair-market value.

When the user submits the asset details, the engine employs four scenarios to calculate the most optimal quote.

Case 1: There is no match.

The dynamic pricing engine searches on external websites to identify a similar product priced elsewhere. If it doesn’t find a suitable match, the Gen AI model trained on historical data and contextual prompts predicts a fair market price.

Case 2: There is a match but the price is out of bounds

In certain situations, there would be a product match but the price would be unreliable. In such cases, the system discards it and asks the Gen AI to predict a better price.

Case 3: There is a match, but the grades differ.

If the products match but the grades differ, then the Gen AI model applies a suitable discounted or marked-up price. For example, if the customer’s product is grade 2, while the matched external product is grade 1, then a 30% discount is applied to the market price.

Case 4: The product and the grades match.

In this case, the price is applied as is.
The interactive dashboard displays resale values for used products across different platforms such as Amazon, Costco, and Dell. Every SKU has a historical pricing chart, which helps the internal teams to visualize price evolution, decide when to apply markdowns, or price higher. The dashboard also enables the managers to override AI decisions based on their expert understanding of the market.

Conclusion

The case study highlights the ability of a ‘bolt-on’ Gen AI solution to unlock massive savings not just for the business, but also for its customers by recovering highest value from their retired assets. Giving the retired assets a new lease of life is a critical step in conquering the digital divide. The sections of populations rarely touched by digital transformation have been impacted through the affordable technology program. The client steers ahead to ignite a billion dreams through their new-life assets.

 

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