First Order Controls and Fine Tuning In Pricing

I have only been to a handful of major league baseball games. Every time I went,  someone else was paying for it.  But for every seat that was occupied in the stadium there was someone who paid for it, be it purchased individually or as a bundle. Every seat in the stadium whether or not occupied has an expected value for its profit. The profit comes from the price paid for the ticket and additional margin from what the fan spends in the stadium.

The notion of expected value means there is a probability associated with whether or not a given seat will be sold. In fact it is not a single value, it is a probability distribution for different price points. The distribution is not only different for different teams but also is  different for  different opponents, different game in the season, weather etc.  Even more complicated is the fact the probability distribution is dynamic for the same game, it changes from the time the game schedule is announced to just before the Umpire calls  Play Ball. After that the expected value falls to zero, at lest after first two innings.

But most if not all ballparks do not go to this level of pricing. They practice multi-version pricing, but pricing is static or fixed. That is not true however for the secondary market where the price fluctuates and is determined by customer’s willingness to pay. One company is making it easier for ballparks and other event managers with limited capacity that is not inventoriable (that is it expires after certain period). That company is, QCue,

Qcue is the world’s only dynamic pricing engine for live entertainment events. Using a scientific approach to pricing, Qcue combines computational analysis and external data sources to allow organizations to adjust pricing multiple times per day.

Solutions like the one from QCue break down the barriers to adopting complex probabilistic models and multiple regression based predictive models in pricing. But I still want to point out that application of analytics in pricing is still a fine-tuning knob, the one you employ after you have achieved larger first order control.

The  first order controls involve the basic tenets of marketing – segmentation and targeting. Marketing strategy is about understanding the different customer segments, the value to the different segments and producing multiple versions that add value to them at price points that maximize your profit. If your first order controls are not correct or optimized, any additional benefit you achieve through other optimization scheme will only result in sub-optimal profits.

You stand to gain lot more profit from analytics engines like QCue, but only if your marketing strategy is correct to start with.

Pricing For Profit Maximization

I want to discuss a great example of profit maximization pricing strategy I saw. There is a main parking lot right in front of the Boardwalk, Santa Cruz that charges $10 for an all day parking. Parking right in front of Boardwalk and crossing the street to enter the beaches is a great convenience to customers. Is $10 the price for the convenience? I am not fully sure, it could be more. Definitely it is not less than $10 judging by the occupancy rate of this lot.

As you drive into the town and drive towards Boardwalk, you see several signs advertising $5 for  all day parking. Those parking lot owners know their lot is of less value to a customer because of the walk (however short) from the lot to the beach. So they price their offering to reflect the “negative differentiation value”. Those with lower willingness to pay and do not mind the walk will pick this option.

But the most interesting pricing is the one practiced by a hotel on the street that parallels Beach st and on the other side of the $10 parking lot. Clearly their lot is no way near the size of the commercial lot that charged $10. They only had handful of spots. They advertise a price of $30 per day and clearly one can see that the sign they had outside is not hard-coded, it allowed changing the price figure at their will. They are practicing dynamic pricing, based on the demand.

Their advantage is, by 2 PM the main parking lot is all full and the hundreds  of cars are routed through the street the hotel is located. As one can judge from the traffic backed up miles away from the exit to Santa Cruz, there was practically unlimited supply of cars coming in. The traffic on the Beach street was inching its way around the block, as people kept driving in with the hope of finding a nearby spot.  After spending 30 -60 minutes inching around the Beach st, some of the drivers are bound to feel their time and convenience is worth the additional  $20.

Clearly the hotel’s pricing is based on the observation of years of traffic pattern  and pricing based on customer demand. Note that the marginal cost for each spot is $0 and if they had priced the spot for $15 almost everyone would have taken it. But they only have limited supply of parking spaces. Clearly they did not want to reach the wider market and targeting only those with a high willingness to pay so they can spend time on the beach instead of driving around for a $10 spot. The dynamic pricing option allows them to drop prices as they see traffic slow or when they still have empty spots near the end of the day.

That is pricing for profit maximization!