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Understanding the 'High Demand' Section on the Back-in-Stock Dashboard

Identify and prioritize restocking of the most popular out-of-stock products based on customer request counts and average waiting times.

Updated this week

Overview:

The "High Demand" section provides valuable insights into which out-of-stock products are generating the most interest among your customers. This table sorts products based on their request counts, allowing you to see which items are in high demand. Additionally, it shows the average request age, giving you an understanding of how long customers have been waiting to be notified about these products.
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How It Works:

  1. Sorting by Request Count:

    • The table sorts products based on the number of requests they have received. The request count is the number of customers who have subscribed to receive notifications when a specific product is back in stock.

    • This helps you quickly identify the most popular items that are currently out of stock.

  2. Average Request Age:

    • The average request age represents the average duration between when customers subscribed to receive notifications for a product and the current date.

    • This metric helps you gauge customer patience and urgency, providing insights into how long customers are willing to wait for certain products.

Table Columns:

  • Product: Displays the name and image of the product.

  • Current Request: Shows the total number of customers who have requested to be notified when the product is back in stock.

  • Avg. Request Age: Indicates the average waiting time for customers who have requested notifications for that product.

Benefits:

  • Inventory Prioritization: Helps you prioritize which products to restock based on customer demand.

  • Customer Satisfaction: By understanding the average waiting time, you can take proactive steps to reduce waiting periods and improve customer satisfaction.

  • Sales Insights: Provides valuable data on customer preferences, enabling you to make informed decisions about future inventory purchases.

Example Use Case:

  • You notice that the "NIKE | CRACKLE PRINT TB TEE" has the highest request count of 6 with an average waiting time of 1510 days. This indicates strong customer demand and a long waiting period.

  • Conversely, the "VANS | AUTHENTIC | (MULTI EYELETS) | GRADIENT/CRIMSON - 6 / red" has a request count of 1 but an average waiting time of 151 days, suggesting that while it is not as highly demand, customers have been waiting for a shorter period of time, for it to be restocked.

  • Using this information, you can prioritize restocking the "NIKE | CRACKLE PRINT TB TEE" promptly to capture immediate sales, while also addressing the lower demand for the "VANS | AUTHENTIC" to improve customer satisfaction.

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