Wayne Gretzky once said that one should skate to where the puck is going to be. Clay Christiansen used that as a hook for an HBR article and a management cliché was born. Now it seems that Amazon wants to apply that logic to shipping retail orders (Amazon Wants to Ship Your Package Before You Buy It, Wall Street Journal, Jan 17).
Amazon.com knows you so well it wants to ship your next package before you order it.
The Seattle retailer in December gained a patent for what it calls “anticipatory shipping,” a method to start delivering packages even before customers click “buy.”
The technique could cut delivery time and discourage consumers from visiting physical stores. In the patent document, Amazon says delays between ordering and receiving purchases “may dissuade customers from buying items from online merchants.”
So Amazon says it may box and ship products it expects customers in a specific area will want – based on previous orders and other factors — but haven’t yet ordered. According to the patent, the packages could wait at the shippers’ hubs or on trucks until an order arrives.
The high production value diagram above (from the patent application) shows the various moving parts to be coordinated.
There is, of course, only one question to ask about this: Is anticipatory shipping crazier than planning to deliver packages via drones? (more…)
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Big data is, of course, one of the business world’s most in vogue buzz words. It may even be having an impact on how various industries function. Case in point, today’s Wall Street Journal reports that several firms are selling data and services to fashion brands and retailers (Fashion Industry Meets Big Data, Sep 9).
The forecasting companies offer analysis of fashion shows, data on the current market offerings and—for an added fee—bespoke research and consultancy services. The data are generated by teams of staff employed to trawl art exhibitions, events, restaurants and even scientific journals.
Fashion companies use the data to plan their latest collection or catwalk show, with the online services replacing the bulky and intermittent style books that designers and merchandisers used to receive. …
“[Fashion forecasters] have always been used but they’re more accessible now because of the technology,” says Marks & Spencer creative director Belinda Earl, who has just launched her first collection for the U.K. high street bellwether. “They are important, not always to lead but to re-evaluate and help confirm you’re on the right track.” …
Retailers are also turning to number crunchers to improve execution. U.K. start-up EDITD trawls the Internet to gather data on who’s selling what, how many products are flying off the virtual shelves and how much are they going for to guide companies in their merchandising decisions.
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Posted in Big Data, Call centers, Computers and high tech, Forecasting, Human resources, Services, Technology, Uncategorized, tagged Big Data, Human resources on April 22, 2013 |
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We have already written in the past about the use of data analytics to best route customers to agents based on demographics and other characteristics. The NY Times has an interesting article on the use of data analytics to improve retention and employee-employer relationships (“Big Data, Trying to Build Better Workers“)
The article discusses the broader appeal of these ideas, but focuses on applications to call centers. Why call centers? In contact centers, customer service agents, that are hourly workers handle a steady stream of calls under challenging conditions, yet their communication skills and learning capabilities play a crucial role in determining both the employee’s tenure and performance. The article discusses a new startup, Evolv, which helps firms find better-matched employees by using predictive analytics.
Transcom, a global operator of customer-service call centers, conducted a pilot project in the second half of 2012, using Evolv’s data analysis technology. To look for a trait like honesty, candidates might be asked how comfortable they are working on a personal computer and whether they know simple keyboard shortcuts for a cut-and-paste task. If they answer yes, the applicants will later be asked to perform that task.
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Back in the early 90s, I was in graduate school in the Bay Area and playing in a regular faculty-PhD student basketball game. At the time, the Golden State Warriors held a high draft pick and while waiting for everyone to show up for our game, there was an animated discussion about what the Warriors would. Our conclusion was that they would screw it up. After all, they had recently traded away Mitch Richmond and had previously had traded away Robert Parish so they could get their hands on Joe Barry Carroll. Twenty years on, the Warriors basketball performance hasn’t gotten a lot better. Sure, they are currently sixth in the West and would make the playoffs if the season ended today. But that would only be their second playoff appearance since I left the Bay Area many years ago.
That long history of mediocrity makes the team’s business performance all the more remarkable. In each of the last eight seasons, they their average home attendance has been over 18,000 — giving them one of the highest average attendance in the NBA. How have they managed to do this? They used lots of data in order to make sure that seats don’t go unsold while also making sure that they don’t give out unnecessary discounts (Warriors Go On Offense to Fill Seats, Wall Street Journal, Feb 6).
The team looks at data generated by Ticketmaster as well as resale-market sites like StubHub, and pores over weather forecasts and ticket sales from competing entertainment in the Bay Area.
The Warriors examine, for example, how much ticket buyers historically paid for a Tuesday night contest against the Houston Rockets versus a Friday night game against the Rockets. This helps the team predict how much it can charge for tickets without curbing demand. In some scenarios, price-points can change hourly, part of a growing sports-business practice adapted from airline bookings known as “dynamic pricing.”
The Warriors also use data when planning to move the last unsold seats for a game with an online offer. The team will take its 200,000-person email list and break it into chunks, testing different times, different subject lines and different links in the body of the message to gauge what brings the quickest response.
“We’ll send 100,000 messages 10 minutes after a victory, and 100,000 the next day,” says Mr. Schneider, 33 years old, now in his 11th season with the Warriors. Data on response time, tickets sold and the price point for each ticket tier will determine how the team tailors future pitches.
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Posted in Big Data, Demand management, Pricing, Priority queues, tagged Big Data, congestion pricing, Demand management, Dynamic Pricing, Pricing, Queues on January 3, 2013 |
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Slate has an article that asks an intriguing question: Who Really Benefits From “Big Data”? (Dec 27). That’s clearly something of a loaded question. Big Data is currently everyone’s favorite answer for everything. The ability to leverage vast amounts of data for new insights and improved decisions holds a lot of promise. There are also many success stories of firms creating new markets or improving profitability or providing great value to customers to back up claims and bolster expectations. Big Data has remade baseball with an emphasis on new statistical measures and has allowed Netflix to suggest the perfect next movie to watch.
Those examples sound great. Of course, consumers may be less enthusiastic about one of the longer standing examples of Big Data, airline revenue management systems. While these have been around for a couple of decades,they bear the hallmarks of Big Data applications. They are built on careful data analysis to forecast how systems will evolve and seek to replace intuition with frequent, reasoned decisions. These decisions may not necessarily be optimal but they clearly balance costs and benefits and can be improved over time. I’m not sure that customers love revenue management systems the way they love Netflix recommendations. Although revenue management systems are just as responsible for some sweet deals as they are over for extravagantly priced tickets, people tend to focus on the latter. Consequently, if you ask who benefits from Big Data and lead with revenue management systems as an example, I would venture that many customers would be leery of embracing Big Data.
So what example does the Slate article go with in thinking about Big Data? Lexus Lanes on the DC Beltway!
Advances in real-time data acquisition, processing, and display technologies means that it is possible to design a toll road that can continually change prices to control how many cars are on the road and how fast they are going. These “hot lanes“ have just been opened along a part of the Washington, D.C., Beltway, the 10-lane, traffic-infested artery that to normal humans is a metaphorical boundary between the real, outside-the-Beltway world and the weird, political one on the inside. (For those of us who live around Washington and must drive on it, however, the Beltway is very concrete indeed, a daily flirtation with delay and frustration, homicidal instincts, and death itself.)
At a cost of $2 billion, a private sector partnership (which gets to keep the tolls) has built a 14-mile-long, four-laned section of highway, parallel to the main lanes of the toll-free Beltway, and has guaranteed to the state of Virginia that it will always keep traffic moving at no less that 45 mph along its length. They do this by continuously monitoring the number of cars (which must be equipped with EZ-Pass transponders) and their speed, and by raising toll prices as necessary to keep the number of cars on the road at a level that will allow the speed to stay at or above the guaranteed minimum. The dynamic toll prices are displayed on huge signs near the entrances to the smart-highway lanes, so drivers get to decide at the last minute whether they want to spend the money to go faster or not. As the traffic on the toll-free Beltway lanes gets worse, some drivers will be willing to spend more to go faster. The worse the traffic is, the more they’ll have to spend. (In the early days of this new technology, numerous accidents were caused by drivers trying to decide how much they were willing to pay, but no doubt this initial problem will sort itself out as people get used to driving-while-economically-rational.
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