Timing is everything: Working out when to buy online
It used to be, as the saying goes, that timing was more an art than a science. But a raft of new companies using big data analytics aims to change this by telling you the best point in time to buy everything from a sofa to a plane ticket.
Nathan Sharp wanted to buy a pair of skis, but it was September and he knew the skis might end up gathering dust and taking up space in his closet until the ski season started in December.
So should he buy now, or wait and risk that the price would rise?
It's a problem that faces many consumers, and it was one that Mr Sharp discovered had no easy solution.
So he and co-founders Greg Kimball and Abe Kurjal built one.
Nifti, which was launched this month, aims to track the prices of consumer goods like clothing and home goods, alerting users who install the bookmark on their browser when the price of the item they're coveting drops below a certain price.
But more than alerting users to price falls, it aims to gather pricing data over time to better understand price fluctuations.
Nifti wants to expose "the secret life of prices," says Mr Sharp.
"As e-commerce platforms have become more sophisticated, and merchants are more experimental with their pricing tactics, prices are changing more than you would expect."Now is the time
Online shopping and more flexible and dynamic pricing have changed the calculations for consumers.
"Maybe two years ago it wouldn't have been as big of a problem. But now - Amazon, Walmart, Best Buy - they're some of the retailers changing prices the most. Intra-day it will change several times on the most popular items," says Shauna Causey, vice-president of research and marketing at Decide.com.
Like Nifti, it focuses on helping consumers to better time purchases on more than three million items using 100 different factors.
"The new question has gone from, 'What should I buy?' to 'When should I buy it?".
End Quote Stefan Weitz Microsoft senior director of search
Airline ticket prices are one of the most perfect examples of chaos theory in the world”
It's only going to get more complicated, as more purchasing takes place on the web. Online commerce is expected to top $1.2 trillion in 2014, according to research firm eMarketer.
The good news is this explosion in online retail and dynamic pricing have correlated with a rise of more and better data.
This in turn has made it possible for new technologies to employ mechanical learning techniques to pinpoint the perfect time to purchase.
"The machine learning technologies we use have been around a long time," says Giorgos Zacharia, Kayak.com's chief scientist, who helped build the travel website's fare price predictor, which looks at airline and hotel prices.
"The ability to get access to the data quickly is what has made the difference."Up in the air
Airline ticket prices were one of the first arenas that data scientists targeted with these improved technologies, partly because of the easy availability of pricing data.
"Airline ticket prices are one of the most perfect examples of chaos theory in the world," says Stefan Weitz, Microsoft's senior director of search.
"Some small variable somewhere has kicked off a chain of events that's kicked off price variation or price variability."
When to buy airline tickets in the US
- The lowest average domestic airfares are found 21 days out
- International airfares are lowest 34 days out
- September is the cheapest month for domestic travel; February and March are cheapest for international flights
- Travel to Toronto now - it was the only major city where fares dropped in 2012
- If you're travelling internationally, leave on a Tuesday and return on a Wednesday - prices are 21% lower than average
Using variables such as historical data, capacity, and what's happening in different areas, Bing Travel - formerly known as Farecast.com, which was acquired by Microsoft in 2008 - claims to tell you with 86% accuracy whether you should buy a plane ticket now or wait, because the price will drop in the near future.
When it gets a prediction wrong, crucially, it aims to show users why the wrong prediction was made - displaying colourful pricing graphs and data points - to maintain trust.
User feedback can be plugged into the algorithm to improve future performance.When timing isn't everything
Beyond the limitations of the algorithms powering price predictions, the reality is that there are certain areas where knowing when to buy is of little use.
Take concert tickets.
SeatGeek aggregates and tracks prices of tickets to events like baseball games and concerts.
When it was launched, it aimed to be the Farecast of event tickets.
However, "the data would indicate that the best time was always to wait a little bit longer because due to the perishability of tickets, the ticket has a finite expiration date," says Will Flaherty, SeatGeek's director of communications.
"What you tend to see is the very best deals in the marketplace are going to show up at the last minute" - which isn't helpful for anyone who wants to make plans more than an hour in advance of the event start time.
Now, the company calculates what it calls a deal score - a rating on a scale from one to 100 which tells users if tickets are being underpriced or overpriced for an event.
End Quote Michael DeSimone CEO, Borderfree
Canadians tend to shop in the evening after they're done with work… Australians tend to shop on their lunch hour, and Russians tend to shop all day long”
Timing isn't just important for consumers, obviously, but for retailers as well.
Big retailers like Amazon and Walmart are constantly monitoring consumer buying habits and competitor pricing, trying to maximise profits throughout the day.
Already, one company - Borderfree, which aims to help retailers expand their online shopping portals globally - provides shopping data to help online retailers better target the timing of their online sales.
"We've looked at the times of day that people shop," says Borderfree boss Michael DeSimone.
"Canadians tend to shop in the evening after they're done with work… Australians tend to shop on their lunch hour, and Russians tend to shop all day long."
It almost goes without saying that technologies that aim to help shoppers find the lowest price aren't exactly in their best interest.
This is why, at least for some companies looking to help consumers time purchases, the cliché "know your enemy" isn't too far off.
"We've built the same technology" as e-retailers like Amazon," says Decide.com's Shauna Causey.
"If they would change the way that they're pricing products, then we can determine those patterns and then predict that as well."
Just, as they say, in the nick of time.