Excitement of Launch

CategoryHealth & Beauty
Business TypeOnline Retail
Study GroupPhase 2
StatusCompleted
Company NameTuccini
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Having implemented ArgoMetrix e-commerce strategy – Phase 1, Long Island, New York based eTailer Tuccini was ready to roll out its operations. Having set up a near real-time sourcing system, they were ready to tap into a vast amount of inventory through strong supplier relationships. Their newly established Amazon Seller account to sell on Amazon Marketplace positioned them to increase Amazon sales and jump start revenues.

While the Company was well positioned to source products, adding new items to Tuccini’s catalog and selecting the profitable large demand items was a complete unknown. Being a startup company meant that there was no historical data to analyze what to sell on Amazon (picking the winning items).

In addition, while they were equipped to identify the right source for such items, the item listings also needed to be efficiently listed on Amazon with correct information to display the fulfillment service level with the available quantity. Such data was essential to both satisfying the Amazon seller performance metrics and ensuring the continuity of sales.

For example, if there were 100 pcs available to purchase for an item, listing only 30 pcs would cause Amazon to stop taking further orders after 30 were sold and cause interruption in order flow. Equally, displaying a generous quantity would jeopardize fulfillment of all the orders should the item become unavailable from the vendor, thus risking Amazon suspension of the listing or the entire Amazon Seller account.

A sophisticated algorithm was needed that would adjust the available quantity on each item dynamically based on demand and supply availability.

The next challenge came when the order flow increased. The company’s fulfillment capabilities were put to the test. Tuccini had to quickly achieve high productivity in receiving purchase into stock and then efficiently picking them as orders were received. They would then need to be packed and shipped as fast on the same day. Such an operation needed to streamline the creation of purchase orders and align them with the incoming sales orders.

Ordering excessively from vendors would result in building up inventory thus tying up capital. Equally if they ordered very little shipment of sales orders would be delayed and compromise the Amazon seller performance ratings.

Also, Amazon customers expected high level customer service in responding to their pre and post ordering inquiries. Tuccini had to prioritize such customer requests and respond promptly to maintain high customer satisfaction to meet the Amazon seller rating benchmarks.

As Tuccini’s revenues started to build up we put in place the algorithms to estimate, not only the expected order volume, but also estimate the price points for each SKU. This was essential due to dynamic price assignment and fluctuating discount levels. For example, it was not enough to just project how many pieces could be sold on each item. It was essential to estimate how much demand would be available at price points considering the minimum price level Tuccini could afford to sell and still make a profit. Our analytics-based algorithms delivered actionable information dynamically on over 5,000 SKUs, thus enabling the Company to streamline its purchasing with fluctuating demand for each item.

This capability provided efficient cash flow management while minimizing the inventory the Company had to carry in stock. In addition, it optimized the fulfillment operations.

After building up a steady stream of orders, the Company faced another challenge when they had a sharp drop in their order flow. This was caused by a change in Amazon policies (which they do often), where they changed how they select a Seller to win the Amazon Buy Box. We had to quickly analyze the new requirement and adjust our pricing algorithms accordingly. After modification of the pricing engine, order flow not only jumped back but it exceeded the previous levels as all the other Amazon sellers were caught by the same change. Tuccini was able to leverage the knowhow and agility we provided to increase Amazon sales even more.

As the operation grew, we optimized the customer service procedures, installed a CRM system and automated response management. This enabled Tuccini to service 2,000 orders daily with a team of only six people.

Our actions ultimately resulted in:

  • Full implementation of the e-commerce strategy (entirely automated for all company operations) and rapidly scaling revenues.
  • More than 2,000 orders daily with annual sales exceeding $10 Million in Year 3 from startup, and a growth rate of over 30% per year.
  • A robust technology infrastructure to scale the product lines across multiple categories
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