Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Gartner
When most people think of advertising and marketing, an image of the “Mad Men” era agency comes to mind. But with surprising speed, the rise of digital–and the accompanying explosion of customer data–has revolutionized marketing.
Using technology and data, marketers today can better understand their customers, deliver personalized one-to-one experiences, and drive significant bottom-line results. To achieve these goals, they now spend over $20 billion annually on marketing technology, a market that has grown by over 67 percent in just two years. In addition, spending on big data hardware, software and infrastructure is forecast to grow to a total market size of $114 billion by 2018.
As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the…
Chinese search engine giant Baidu says it has developed a speech recognition system, called Deep Speech, the likes of which has never been seen, especially in noisy environments. In restaurant settings and other loud places where other commercial speech recognition systems fail, the deep learning model proved accurate nearly 81 percent of the time.
That might not sound too great, but consider the alternative: commercial speech-recognition APIs against which Deep Speech was tested, including those for [company]Microsoft[/company] Bing, [company]Google[/company] and Wit.AI, topped out at nearly 65 percent accuracy in noisy environments. Those results probably underestimate the difference in accuracy, said [company]Baidu[/company] Chief Scientist Andrew Ng, who worked on Deep Speech along with colleagues at the company’s artificial intelligence lab in Palo Alto, California, because his team could only compare accuracy where the other systems all returned results rather than empty strings.
Ng said that while the research is still just research…
According to co-founder and CEO Joshua Tetrick, Hampton Creek is based around a simple premise. “Ninety-nine point nine-nine percent of the food we eat is totally shitty for our bodies and for the environment,” he said, and the only way…
Museums are mining detailed information from visitors, raising questions about the use of Big Data in the arts
One morning last week, a team of experts at New York’s Solomon R. Guggenheim Museum searched for hidden spots in the rotunda to conceal tiny electronic transmitters. The devices will enable the museum to send messages about artworks to visitors via their smartphones while at the same time collect details about the comings and goings of those guests.
At today’s museums, all eyes aren’t just on the art. They’re on the visitors.
Across the country, museums are mining increasingly detailed layers of information about their guests, employing some of the same strategies that companies like Macy’s, Netflix and Wal-Mart have used in recent years to boost sales by tracking customer…
We are marketing people; we want to know how big data effect our marketing? Big Data & Analytics is enabling companies to deliver the right message, to the right person, at the right time, for the right price. Leading marketers are using this advantage to deliver greater value and relevance to their customers. Learn how in this video from IBM.
Data plus cloud computing can do a lot more than we can imagine now!
Someone Said BigData is dangerous; they think BigData is a way to expose and invasion of privacy; however, I believe that big data is good. I found an interview with Fox Business: A CEO’s Analysis. The CEO said: “Big Data is good. Good for our economy; good for people and good for society. I know this defies the newspaper headlines and I am not diminishing some real concerns out there. However, the good that data are doing now – and the promise it holds for the future – is remarkable”
If you’ve been searching through developer job postings and been unsure of why “Knowledge of Agile development methodologies” was listed as a requirement on almost every single job, I’m here to shed some light. But before you can understand Agile, you need to know the method that preceded it, which is Waterfall development. Knowing the difference will give you an edge in learning effective software design principles that are used all over the world.
All development goes through a few distinct stages: Conception, Initiation, Analysis, Design, Construction, Testing, Implementation, and Maintenance. The difference between the two types of development is how they approach these various stages.
The Waterfall methodology is a sequential design process, in which you have various product, technical, and project managers draft up all of the requirements for a system up front, hand it off to the developers and expect their product to meet those requirements. This…
Wherever we travel in our brave new digital world, we leave behind a rich and fertile trail of breadcrumbs of data. Our mobile phones continually tell the phone company where we are, our credit cards reveal how much we spent, and where, and on what — and so on.
Welcome to the world of ‘big data’, where the chain store Target has data-mined its way into my favourite organ, the uterus — and can now accurately predict when your baby will be born.
Back in the old days, a bookstore might slowly build up a base of loyal customers by offering discounts, and mailing out a brochure every month or so. But ‘big data’ is very different. So let’s look at the three ‘V’s: volume, velocity, and variety.
First, volume. In the USA, Walmart, the giant chain store, harvests some 2.5 million gigabytes of data each hour — just from customer transactions.
Second, velocity. Today, we can analyse the data in real time. For example, you can quite accurately predict the sales figures of big shopping centres by monitoring the activity of mobile phones in the parking lot — even before customers have opened their wallets.
And thirdly, variety. The data can be GPS location data from your mobile phone, or it can be the entire contents of your address book with the phone numbers and birthdays of all your friends and their families. Big data also includes everything you have ever done on Facebook, Twitter and all the other social media — and let’s not forget the good old credit card.
Back in 2002, Target in the USA wanted to know which of its shoppers had just become pregnant — because it wanted more customers. You see, shoppers tend to shop at the same place that they’ve always gone to — and it’s hard to get them to shift. But when women get pregnant, it’s a whole new world — and the pregnant woman might be lured into changing to a new shop.
So Target hired Andrew Pole (who has master’s degrees in both statistics and economics) to see if he could data-mine his way into freshly pregnant uteruses. His starting point was the so-called ‘guest ID number’. This is a number that Target assigns to each customer, and is linked to their name or email address or credit card.
It soon became clear that women (who had voluntarily put themselves on the Target Baby Registry) were buying some 25 products around the beginning of their second trimester. These included mineral supplements such as calcium, magnesium and zinc, large quantities of unscented lotions, and so on. And if they bought a little bright blue rug, the new baby was probably a boy.
And when they started buying scent-free soap, hand sanitisers, washcloths, a handbag large enough to carry a few nappies and extra-large bags of cotton balls — well obviously, they were getting close to their delivery date. Indeed, Target found it could predict their delivery date with some 83 per cent accuracy — just from their shopping habits.
So Target began sending discount coupons for various baby items to customers, according to how they rated on Target’s own “pregnancy prediction algorithm”.
But then it got messy.
An angry father walked into a Target store just outside of Minneapolis, clutching a brochure that had just come through the mail, demanding to speak to the manager. Angrily he said: “My daughter got this in the mail! She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”
Sure enough, the brochure did carry the daughter’s name and address, and had advertisements only for maternity clothing, nursery furniture, and so on. The manager apologised.
A few days later the manager called the father to apologise again. But this time, the father was a little embarrassed. He said: “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”
Now some people like their privacy. Some Target customers were annoyed by receiving brochures packed only with baby merchandise, especially when they had not told anyone they were pregnant. So Target produced new brochures that had some non-baby merchandise sprinkled around the baby goodies, so the newly pregnant mothers didn’t realise they had been data-mined.
Target is happy. Their revenue jumped from $44 billion in 2002, to $67 billion in 2010 — helped mightily by the fact that with regard to pregnancy, Target knows, before it shows.
Now it’s early days for big data. But one step might be to have more transparency and openness about what we (the customer) are giving away — and what we get back in return.
People often talk about Thanksgiving weekend as one of the biggest travel weekends of the year, but thanks to big data it’s now possible to actually see how accurate that is in different places– at least, in terms of driving.
The popular navigation app, Waze, which lets any users report traffic and accidents that can then be viewed by any other Waze users and used to find the fastest route, looked at the data from last years Thanksgiving weekend to see what travel trends showed.
It turns out that in New York, people (at least the ones who use the Waze app) actually did more driving on Thanksgiving day than on the Wednesday before Thanksgiving, contrary to popular ideas that Wednesday is the biggest travel day. This isn’t necessarily true in every city, of course. They also found that the traffic data from Black Friday, which is normally considered a…