Pricing is one of the most powerful value-creation levers in business. So it’s understandable that you may be resistant to the idea of turning pricing decisions over to a “black box” algorithm, no matter how effective the underlying technology may be. While the opportunity to enhance the use of data and science in your pricing strategy is intriguing, you may be concerned that an algorithm will overlook critical factors such as consumer behavioral insights, competitive factors or planned changes to the your product/service that are difficult to quantify. And you’re not wrong to feel that way. While we at Digonex clearly believe in the power of data analytics, we have found that the best pricing strategy is one that blends the best available data science with the art of pricing — that is, with management experience and judgment.
The question is, how do you put in place a process for blending the best available “art” and “science”? There are three steps we take to help our clients achieve the optimal balance:
Digonex illustration; Photo credit : T. Sathapornnanont
1. Customized solution design
The first step is to incorporate a client’s key pricing rules directly into the algorithm, to the extent possible. All of our solutions are customized to each client’s unique goals and strategies. Clients can choose to embed their budget goals into the solution. Clients can control the rate at which prices change each day and establish firm minimum and maximum prices. Many clients also have strategic objectives that may run counter to short-term revenue maximization. For example, some attractions may want to cap attendance on certain days to avoid over-crowding. Some event producers may price tickets to produce a sell-out even if doing so leave some money on the table. Retailers may want to focus on driving turn rate to clear an inventory backlog even at the expense of optimizing margins. We believe that spending a little more time up front to ensure that each solution is aligned with a client’s goals will always pay dividends down the road.
2. Simple price review process
The second step is to ensure that clients have a simple way to review and modify our dynamic price recommendations. We accomplish this through our web-based SEATS portal through which clients review all of our updated price recommendations and either approve, reject or modify them with the click of a button. This is where the art of pricing can be practiced daily by modifying any recommended prices that may not feel right. Also, a project manager is assigned to each client so there is always a person available to address questions and help in the review process. New prices are never implemented without the client’s review and consent.
3. Continuous algorithm improvement
The third step is to continuously update the solution as new data becomes available. Over time, it is often possible to embed new factors into a dynamic pricing solution for which inadequate data exists today. For example, establishing the admission price for a newly opened attraction may lean on judgment and experience initially but after several months of data have been gathered it is possible to more fully automate those decisions. At Digonex, ongoing algorithm refinement is a core component of each dynamic pricing solution.
Whether to focus on the art of pricing versus focusing on the science of pricing doesn’t have to be an either/or choice. As you evaluate dynamic pricing, don’t view it as a substitute for experience and judgment, but rather as a powerful supplement.