In stages 1-3 of the Gartner pricing maturity chart, businesses set price points using manual or Excel-based techniques. Stage 4 introduces rule-based pricing, where advanced techniques are involved in determining optimal price points. However, it is Stage 5 and the introduction of increased automation, artificial intelligence and machine learning where prices are truly optimized, and businesses realize the full potential of rapid price changes in real time.
These dynamic prices use different algorithms to help organizations reach their profit and sales targets. The algorithms account for historical data, customer clickstreams, user data, product views, revenue goals, elasticity and conversion rates to pinpoint the price level needed to meet goals.
The result helps businesses gain better insights on their pricing strategy, as the system ensures that the strategy was applied uniformly. Manual intervention is almost completely removed from the process, allowing for more time by pricing managers to finetune their strategy rather than get bogged down in prices for thousands of items.
There are numerous strategies that businesses can employ at this stage. Inventory turnover and profit optimization are two examples of these strategies.
When your system understands product turnover targets or product expiration dates, it can get highly creative in finding the right price for your goods. For instance, an electronics company has 100 televisions in its inventory. Their goal is to sell all 100 over the next two months, with an average of 12.5 sales per week.
The system finds the optimal price point to achieve those sales and sells 13 units within 5 days. Since the sales team has reached its target, they can afford to increase the price over the next two days, for higher margin sales. At the beginning of the following week, the dynamic pricing system will learn from the results of the first week, and set its price accordingly to help the sales team reach its goals.
Another use case would involve merchandise that expires over a specified time period. As the calendar moves closer to the expiration date, the pricing engine would lower prices, within established rules, to push the merchandise out the door before it needs to be discarded.
One key area where dynamic pricing can play a big role is profit optimization. In this scenario, companies aren’t necessarily looking for the highest profit on every unit sold. Instead, they want the price point where they will have the highest overall levels of profit. Simply put, they want the system to determine if they are better off selling fewer items at higher price points, or more items at lower price points.
One example of this would be online platforms, where consumers purchase a service such as airline tickets, taxi rides, or temporary apartment home rentals. The dynamic pricing engine needs to calculate the ideal commission level for highest profits. Using all the data at its disposal, the system can determine the right commission to maximize profits.
Attaining Pricing Perfection
The Gartner pricing maturity chart provides guidance and direction for businesses of all sizes looking to improve their pricing performance. QuickLizard supports those goals, with a dynamic pricing engine designed to help your business complete its digital transformation. Schedule a QuickLizard demo today, and see how we can transform the way you price your services and merchandise.