My long-since-retired parents shared with me the other week their ire over a massive price increase for home-delivery of their local newspaper. The new price of $199/month was more than an order of magnitude higher than the $19/month for the online subscription. Always the problem-solver I told them to cancel their subscription and order the paper to be delivered to their new next-door neighbors. They did and now pay $93 for a 1/2 year which is 80% less than what they paid before. You can guess what they’ll be doing in 1/2 a year. Until then they’ll be talking about the paper as the butt of many a joke at all of their dinner parties whom are also print subscribers.
Humor me as I speculate on what’s happening behind the scenes. A pricing model at the paper recognized that their longest remaining print subscribers had a virtually perfect retention rate at virtually any price-point. What did the pricing model probably include or not include?
- Segment customers correctly by age and media-type.
- Either
- Didn’t have retention rate or customer willingness-to-pay data at that level of price-increase OR
- Did have retention rate or customer willingness-to-pay data by zip code.
- Take into account availability and ease-of-access of new-subscriber offers
- Have additional goals or guardrails for damage to reputation
- Recognized that retirees may already have a subscription to next-best-alternative papers but regard those as a step-down
- Have a goal to migrate customers to less-profitable online subscriptions.
- Experienced pressure to over-promise higher revenues and used this as the primary or sole price model goal.
As the Monday-morning quarterback I can comfortably say that the newspaper could have better modeled for the shortcomings in 2-4 and made more money from this segment in #1. If they were already modelling by zip-code retention rates then hats off: my parents were an outlier. Regardless, a goal or guardrail on reputational harm per #4 from over-pricing could have also addressed mistakes from #2 and #3.
The concept of an additional goal as referenced within #3 brings to mind an interesting, under-utilized tool to price-modellers. A “Linear Program”, best recognized as the “Solver” add-in within MS Excel, allows for balancing and optimizing across multiple linear goals. Using this capability, price modellers can create multiple (linear) models that each individually optimize a price by a singular goal that can then be consolidated into an overall goal, linearly. Give it a try.
