I recently clicked on a link to a Wall Street Journal (WSJ) article behind a paywall. I was shown a $277 annual subscription price and, in an attempt to see if there was a free alternative source that covered the article’s contents, I closed the page and performed a Google News search. I clicked on an article on the same topic but it turned out to be just a brief summary of the WSJ article I was looking to access, and it linked to the WSJ article.
On my second visit to the WSJ, I had already made up my mind to buy the annual subscription to read the article. However, to my surprise, this time I was quoted a $177 annual subscription price rather than the original $277. Since I had not purchased the subscription on my first visit, the WSJ offered me a lower price to convert me into a customer on my second visit.
In this case, the WSJ lost money. This is because I was already prepared to pay the $277 annual subscription price on the second visit independent of the additional discount. However, I imagine that the second visit price is optimized across all of WSJ’s potential subscribers. In most cases, the reduced price helps the WSJ convert a visitor who otherwise wouldn’t have paid to access the WSJ. So, in the aggregate, the WSJ wins. It’s an example of customer segment-based pricing.
However, in the future, as these algorithms gather more data points about individual visitor behavior, they will grow smarter. And this will let them move from customer segment-based pricing to customer-based pricing. At the limit, companies who use data to inform their strategy (pricing or other) will not only win in aggregate, they’ll win every time.
Also published on Medium.