Information Asymmetry, Pricing Analytics, and Price Strategy

You may notice periodic sessions in the web analytics data, people visiting the website, looking at a few pages, and leaving, never to return. Or returning frequently, always checking the same pages.

These are usually discount seekers, and there’s a data science niche building around them. Price interception is one of the biggest trends in big data science that we’re not telling you about.

What’s driving it? Discount seekers exist in any market, but this segment grows when consumer sentiment is low. And there a whole bunch of new technologies designed to cause people feel good about bargain hunting, contributing to the growth, and likely establishment, of the sector.

Many are building up an information advantage against firms, and using it to their advantage. Some are visiting the sites periodically, like clock-work, lying in wait for a good price. Others are visiting websites like camelcamelcamel.

Camelcamelcamel is a rather clever application that lets people check out price trends. There’s a browser extension that enables price monitoring, and, in return, the camels likely get great affiliate fees.

Check out the price history of a yodeling pickle below.

 

Alright. So what. The segment exists and consumers are using more tools to become a lot smarter.

Pricing analytics is well established, especially the leisure travel industry, and it’s a key way that some firms manage yield and derive profit. Price analytics, to a certain extent, underpins huge swaths of the financial services industry.

Price interception technology is coming, and it’s interesting. It will assist some firms, those that compete principally on price, to thrive.

Which brings me to price strategy.

Strategy means choice. Great executives are able to select choices among alternatives that complement each other. If they do it really well, they’re hard to copy. You sometimes get non-strategies, like “we’re going to offer the best service and the best prices!”, that end up delivering neither to consumers. They almost always fail. So, you have to be really clear when it comes to price strategy.

I selected a yodeling pickle to demonstrate a point about that.

Vlasic had a nice business in the 1990’s. Basically, they took cucumbers, sliced them, and put them into spiced vinegar. That’s a marketing business in my mind. They don’t really sell vinegar-cucumbers, they sell something that you consume in your mind. I used to compare them to the french cut butter pickle variety that had been standard in my childhood. There was something premium about Vlasic pickles.

Wal-Mart wanted statement products. Vlasic went ahead with the idea of selling a gallon of pickles for $2.97. A year’s worth of pickles, a gallon of pickles, for $2.97. This thing weighed 12 pounds.

Vlasic would make 3 cents margin on each gallon sold. You can imagine the conversation though. Wal-Mart had 3000 stores. They would move 80 jars of pickles a week. That’s 240,000 gallons of pickles a week.

This is a big reason why I hate the words ‘We’ll make it up on volume.”

No. You won’t. That’s at most $7,200 a week. You’re not making anything up on volume. And people aren’t going to start bathing in pickles because they’re so abundant.

They cannibalized their other channels. They failed to project their weakened bargaining position with Wal-Mart.

And then they filed for bankruptcy in 2001.

This story is usually used to demonize Wal-Mart. I don’t see it that way.

This was a strategic decision. Marketing has four P’s, Price, Placement, Procurement and Promotion. Strategy has short term, medium term, and long term consequences. For whatever reason, Vlasic chose to pull on the Price and Placement levers to cause a very short-term ramp in volume, which ultimately ended in very poor long-term consequences.

There are important implications for data scientists and marketers looking to compete on price analytics:

• Pricing analytics without solid price strategy isn’t worth it.
• Price not only affects the firms supply curve, it also has effects on the demand curve (and that secondary effect should be modeled as well.)
• Volume over time translates into power, pay attention to those dynamics.