One of the major applications of big data is personalized recommendations. It can be used on Web sites and, in the store setting, offer targeted offers and discounts. Recommendations are based on a customer’s purchasing history or the histories of other consumers just like them. The computerized system predicts what the customer is likely to buy next and makes the recommendation to them. Analysts suggest that Amazon recommendations generate a revenue uplift of between 10 and 30 percent. Some retailers use third-party recommendation engines, but they will increasingly develop their own. Analyze your own purchasing habits. What data would a third-party need to predict your next purchase?

Lipman auto parts, a family-owned auto parts store, began january with $10,300.00 in cash. management forecasts that collections from credit customers will be $11,400.00 in january and $14,800.00 in february. the store is scheduled to receive $5,000.00 in cash on a business note receivable in january. projected cash payments include inventory purchases ($13,000.00 in january and $13,600.00 in february) and operating expenses ($2,700.00 each month). lipman auto parts' bank requires a $10,000.00 minimum balance in the store's checking account. at the end of any month when the account balance dips below $10,000.00, the bank automatically extends credit to the store in multiples of $1,000.00. lipman auto parts borrows as little as possible and pays back loans in quarterly installments of $2,000.00, plus 4 percent interest on the entire unpaid principal. the first payment occurs three months after the loan.