Over the last decade, companies in the travel industry have slowly introduced dynamic pricing (price differentiation based on constantly updated data). In this industry, there are numerous examples of constantly fluctuating prices, e.g. for flights or hotel stays. But there is more to come now. Dynamic pricing is moving to the next maturity phase. Companies outside the travel industry are starting to adopt dynamic pricing.
Whereas dynamic pricing is still almost nonexistent in the business to business environment, firms in business to consumer environments, such as retail, have started to see that dynamic pricing can help to further exploit the willingness to pay and to introduce more sophisticated price differentiation models. Examples include consumer electronics shop MediaMarkt that has installed electronic price tags to constantly change prices depending on competitor behavior, gas station chain Tanq4you that bases its prices on the actual oil price and the weather condition, or a grocery store that has introduced dynamic pricing based on expiration date. But what should you check before introducing dynamic pricing in your company? Which pitfalls should you avoid?
Run before you can walk
When you are new to the field of dynamic pricing, it may be very tempting to dream about the immediate introduction of a dynamic pricing algorithm with dozens of predictive factors, based on for example customer (age, gender, average expenditure etc.) or situation data (time of day, weather conditions, season etc.). Experience shows this is not the way to go. With regards to dynamic pricing, we advise a step-by-step approach. Start the dynamic pricing dream with a (relatively) simple price differentiation approach, e.g. differentiating prices on weekdays and weekend days. Once your customers and organization are used to price differentiation and volatile prices, you can slowly expand your dynamic pricing approach, e.g. by introducing a predictive algorithm or by experimenting with machine learning.
Let dynamic pricing be a black box
As customer trust is one of the key success factors for dynamic pricing, make sure that price differences are explainable, can be influenced by, for example, early booking and are in line with your company goals. Customer trust can be gained by explaining to customers what determinants drive the pricing and by convincing customers that this is fair. The airline industry has understood this very well. Airlines explain to customers what drives the pricing, e.g. the time of the day (during the week a morning flights is more expensive than a lunch flight) or the number of seats left (higher prices when demand is high). However, not all companies have succeeded in gaining the trust of their customers. Think of travel website Orbitz that used an algorithm to charge Apple users higher prices than Microsoft users or Amazon that used the ZIP code as a determinant for the price. The result? A lot of negative media coverage, angry and disappointed customers and a sharp decline in demand. The lesson? Explain what drives your prices and make sure this is considered fair by customers.
Only rely on your algorithm without any human pricing intervention
Forgetting the human aspect in dynamic pricing is a guarantee for failure. As appealing as it may sound that the dynamic pricing algorithm will determine the price, without any human intervention it will not work. Every company needs a dedicated pricing team to think about and explain the reasoning behind the dynamic pricing, to link specific events to demand peaks (e.g. Champions League final is in Manchester or today is a rainy day) and to monitor the algorithm. If there is, for example, no monitoring policy in place, the result might be the algorithm generating a price of e.g. €10.000 euros for the last flight ticket from Amsterdam to London. Useless to say that this is not a desirable and fair price and that this will result in negative media coverage and, more importantly, mistrust from customers.
With all this in mind, how will you apply dynamic pricing in 2018? Will you base your prices on (expected) demand, e.g. by asking higher prices for leisure activities during the weekend? Or will you use customer data to determine the price that perfectly exploits willingness to pay of the customer? Let us know what your plans are!