The money was there.
The data showed where.
Orders that never became sales. Purchases that inflated storage and logistics costs. Fraud hidden behind bonus cards. Three real situations where analysis revealed what the business needed to change.
Anonymized cases from BI Forge’s work. These reported outcomes describe individual cases, not forecasts for another business.
Customers placed orders.
But the sales never happened.
For one phone brand alone, the company was losing $100,000 in sales every month. The problem appeared after checkout: banks were rejecting customers’ financing applications.
An order was not a completed sale.
Customers wanted the phones and submitted orders. But the purchase depended on credit approval, and bank refusals stopped those orders from turning into revenue.
The gap came after the order.
Data analysis exposed the connection between lost sales and rejected financing. The business could see why buying intent was failing to become a completed purchase.
It offered its own installments.
With the cause identified, the company began providing installment plans directly to buyers, giving it another way to complete these purchases.
From addressing this one opportunity. The $100,000 figure describes monthly lost sales before the change; the reported result has no specified measurement period.
The cost problem started
before the goods reached the warehouse.
The company was spending heavily on logistics and warehousing. Data analysis helped it improve purchasing plans around seasonality and other relevant factors.
High logistics and storage costs.
Moving and holding goods was expensive. The opportunity was to improve the purchasing decisions that shaped those costs.
Purchasing needed better context.
Analyzing the data made it possible to account for seasonality and other factors when planning purchases, rather than treating purchasing and operating costs as separate questions.
It planned purchases more effectively.
The company used those findings to improve procurement planning and optimize the associated logistics and warehouse costs.
Through better purchasing plans. A specific savings amount or percentage is not disclosed for this case.
Bonus cards were meant
to build loyalty. Someone was abusing them.
Analysis uncovered a fraudulent scheme connected to bonus cards. Identifying it saved the company tens of thousands of dollars.
A loyalty tool concealed fraud.
A scheme linked to bonus cards was causing financial losses. The issue needed to be detected before the business could address it.
The scheme became visible in the data.
Analyzing the data exposed the bonus-card fraud and gave the company a concrete problem to act on.
The business could address the loss.
The discovery enabled the company to tackle the scheme. Details of the mechanism and the company’s internal response are not disclosed here.
By uncovering the fraudulent scheme. The exact amount and measurement period are not disclosed.
Three different problems.
One reason they became solvable.
Each opportunity was discovered through data analysis. The value came from connecting the finding to a business decision: offer installments, improve purchasing plans or address fraud.
Client names and identifying details are omitted. Figures are limited to the reported outcomes.
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