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Due-To-Buy

Updated

Summary

StockIQ's Due-To-Buy screen is an AI-powered screen to evaluate customer buying patterns for different products and alert you to when a customer is due, or overdue to buy.

At the core of the screen is an AI model trained on your customer's purchasing patterns, so it can determine when an item should be purchased by a customer, including effects such as seasonal buying patterns and overall volumes.

This screen is intended to be used by salespeople to know when they need to give their customers a call, or by operations/planners to call out for their salespeople what accounts may be at risk by providing them a report with overdue buys.

Column Description

  • Customer - the code for the customer being evaluated.
  • Customer Name - the name of the customer being evaluated.
  • Item - the item being evaluated for the given customer.
  • Description - the item description.
  • Usage Pattern - similar to item-site usage patterns, this describes the pattern of purchasing for this customer-item combination - New, Recurring, Sporadic, Slow, or Dead.
  • Status - What is the status of purchase expectation on this customer-item combination?
    • Single Buy - this is a special case, at the moment, there is a single purchase against this customer and item combination.
    • OK - the customer has recently purchased and is still in the window where you would not expect a new order.
    • Due To Buy - the customer is in the window where you would expect an order to have come in by now, meaning they are over their normal days-between orders.
    • Overdue To Buy - the customer is far beyond when the model expects that they would have purchased with greater than 90% certainty; this customer may be at risk for no longer purchasing this item.
  • Days b/t Orders - this customer-item combination's typical days between orders.
  • Max Allowed Days - the maximum number of days the model considers normal between orders, beyond this number, the customer would be considered overdue-to-buy.
  • Days Since Last Buy - the number of days since the customer last purchased this item.
  • Purchase Prob % - based on the model's calculation, this is the probability that they should have purchased this item by now.
  • Exp. Next Buy - the date on which we do (or did, if in the past) expect the customer's next buy; this corresponds with the day's between orders value.
  • Latest Exp Next - the latest date that we would expect a buy of this item for the customer; this corresponds with the max allowed days between orders.
  • First Buy - the first date the customer purchased this item.
  • Last Buy - the most recent date the customer purchased this item.
  • Last Order # - the last Sales Order number for this customer-item pair.
  • Typical Order Quantity - the calculated typical order quantity that this customer purchases of this item.
  • Typical Order Value - the value of the typical order quantity of the item, valued at standard cost for the item.
  • Item On Hand Order Quantity - the on-hand order quantity.
  • Item Last In Stock Date - the last date this item was in Stock. 
  • Lifetime Values - the lifetime quantities for that customer-item combination, such as quantity, hits, COGS, etc.
  • Year-To-Date Values - similar to the lifetime values but only based on year-to-date quantities.
  • Lost - denotes that this customer-item pair is considered "lost", meaning that we no longer expect them to purchase these items. 

Detail Tabs

The tabs at the bottom of this screen, provide some additional, row-specific insight for each customer-item pair.

Order Intervals:

  • Due to Buy - this graph is key to understanding the Due to Buy screen. The green section is the "OK" range, indicating we do not expect a buy yet, orange is the "Due" range, and red is the "Overdue" range. The black vertical bar indicates where the customer-item pair is currently, the days-since-last-purchase. The blue line shows the probability over time that they should have placed an order already.
  • Order Seasonality - since the model takes into account seasonality of ordering, a seasonality chart by week is shown to indicate the model's awareness that purchase intervals expand and contract over time:

Histogram: 

The Histogram tab displays a chart of typical order intervals for this customer-item pair, to help you visualize their typical behavior.

Trend Chart:

The trend chart tab shows a forecast-manager like chart that shows historical demand for this customer-item combination and a projection using StockIQ's forecasting model. This visual provides an idea of what their buying behavior will be like in the future, possibly capturing seasonality or peaks that you have noted in the past.

Context Menu

When you right-click on a row in the grid view, you will see this menu pop-up: 

  • Add Lost Item Customer - this will add a Lost Item Customer for the customer-item pair in the row. This allows you to tell StockIQ that this customer will not be purchasing this item anymore, to account for the expected loss in demand for the future forecast. For more information on what this does, see the Lost Item Customer article.

 

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