Viewing posts categorised under: Blog

How We Learned (Almost) Everything That’s Wrong with U.S. Census Data

by admin in Blog


The U.S. government is constantly sweeping up vast amounts of data on the details of the retail sector — buying and selling, getting and spending — just as it tracks census information and data on economic indicators such as GDP, employment and unemployment, and inflation.

As economists who’ve been using lots of government data for a long time, we’ve always been delighted that the federal government had systematic, professionally managed programs for collecting data and publishing important information. But a recent experience has shaken our confidence in that belief.

Last summer we wanted data on how the online revolution was affecting brick-and-mortar retail. Our field research showed that some traditional physical stores were rapidly blending the best of physical, web, and mobile to serve their customers, while others were simply being hammered by the growth of e-commerce. We thought getting data would be pretty easy. The Census Bureau has reports galore on e-commerce, broken down by industry. Since the early 1990s, it’s surveyed businesses every year to ask them about e-commerce sales, then compiled the data into a report.

Our first disappointment was that the most recent data for retail industries such as electronics and appliance stores was two years old. These days, for studying anything related to online, 2013 is ancient history. The most plausible defense is that budgets are tight, but surely it is more important to devote resources to measuring in a timely fashion the parts of the economy that are changing rapidly than to keep current on sectors that move glacially.

Worse, though, was when we looked at the published statistics. Scratching our heads, we saw the Census tables showed that general merchandise stores — which is where they code Wal-Mart — had only $88 million in online sales in 2013. That couldn’t be right, since was a multibillion-dollar operation that year.

So we started digging. We pored over the surveys and documentation. We emailed the Census official in charge of the retail trade data. We published our results in an online working paper to help other users of those data and to describe how we tried to use other sources to improve the Census estimates. Then, finally, the Census public information office contacted us, but not to offer congratulations or thanks. They demanded that we retract our paper, and we learned that the Census official with whom we had been emailing had been directed not to directly communicate with us anymore. The public information office retracted, in writing and in a long telephone conversation, what the Census official had told us in multiple emails. They said the process the Bureau followed wasn’t really the process described in the documentation that we had been able to find online.

Oh, and by the way, the data on online sales they report for general merchandise stores — well, that’s not really online sales for general merchandise stores. We were told that their procedures called for online sales of general merchandise stores such as Wal-Mart to be included under non-store retailers along with the sales of food trucks and the like. You may be as surprised as we were to learn that Wal-Mart is a non-store retailer. You might be even more surprised, as we were, to learn that online sales of manufacturers such as Apple and Nike are also supposed to be included under non-store retailers. Think about a food truck selling iPads out of one window.

What, then, is the $88 million reported as online sales of general merchandise stores in 2013? As nearly as we can figure out, that’s the total online sales of small general merchandise stores that didn’t separately report they also operated a non-store retail operation. That is, it’s junk data containing no valid information. If you took it at face value, you would be surprised that giant companies such as Wal-Mart seem to think e-commerce is worth their attention at all.

We retracted our paper, of course, since it had been based on the assumption that the Census e-commerce statistics by industry were biased only because, as the Census official told us, some large companies didn’t report those sales, not because, as we subsequently learned, the industry-specific numbers were essentially meaningless. Census public information did not retract the Census official’s statement that e-commerce estimates were understated.

We think we understand all this better now, though it doesn’t make a lot of sense. But it is hard to be sure. No one audits the work, and the Census people themselves seem to have different views on what was done. Because the rise of e-commerce is such an important development for so many businesses, and because reliable statistics are thus so important to businesses and researchers alike, we’ve called for an independent review of the Census’s retail sales data.

This rather painful episode — which started with us trying to get sensible numbers for one paragraph in a 262-page book — woke us from our slumber when it comes to federal data. We have always had great respect for the professionalism of the federal statistical agencies, and we recognize their chronic budget problems, but the government does suck up massive amounts of time from businesses and consumers collecting data. Relative to that effort, the costs of turning all that data into information useful to businesses and researchers and providing it in a timely fashion must be tiny. The agencies clearly do exactly this in some cases, but we have learned that they don’t do it in all cases. And the public has no idea which published, official data sets are as meaningless and potentially misleading as the $88 million figure that first caught our eyes.

Let’s go back to the question we raised. Brick-and-mortar retailers are now rapidly trying to adapt to a world in which people want to be able to shop where they want, when they want — sometimes in-store, sometimes online, sometimes using their mobile device. A massive part of the economy is undergoing stress as some retailers are being cut down by a buzz saw, while others are making a lot of progress transforming how they do business. Reliable, current information on what’s going on would be valuable, and it could be available but is not. So entrepreneurs, investors, and analysts don’t have much to go on. The federal government has, or could get, the right data and could tabulate it sensibly and report it quickly without adding to the third digit of the deficit.

Given today’s technology, there is no reasonable explanation for why the government needs two years to process data and report results on retail sales. If any data geek in private business needed two months, never mind two years, to go from survey data in hand to final results, they’d be tossed into the unemployment line.

Sadly, the only way to learn which statistics are as reliable as GDP and which are as useless as reported e-commerce sales by general merchandise stores may be to give federal data collection and reporting a top-to-bottom overhaul, perhaps along the following lines:

  • Government statistical agencies need to design coding and classification systems to deliver useful information. Why can’t we be told how much general merchandise stores and other retail industries sell online and how much they sell offline? How about firms that are mainly manufacturers? Census already has the necessary data, after all.
  • Government statistical agencies need oversight from the research and business communities for everything from what they ask to what they report — that’s why we recommended an independent review of the Census retail data.
  • Government statistical agencies need to move quickly to get data out in weeks or months, not years.
  • Government statistical agencies need to make more raw data available to researchers in ways that maintain confidentiality. Census and other agencies already do this in some areas; they could do it across the board. If we had access to the raw data on retailing, we could unscramble some of the eggs that the current classification system has so thoroughly scrambled.

It is time to break the government’s stranglehold over all the data that we, as citizens and businesses, hand over to them. Let’s take a small step first: support our call for an independent review of how the Census Bureau collects, analyzes, and reports retail trade data.

Read more

The Good, The Bad And The Missing Online Checkout Strategy

by admin in Blog


After nine months and 10,000 data points examining data on the 650 merchants that drive 70 percent of online spend in the U.S., one thing is abundantly clear: big merchants lack an online checkout strategy.

They have plenty of other strategies — and executives — focused on perfecting the process of getting consumers to their virtual front doors. Where things totally fall apart is how to get them out the back door with a purchase. That’s where their lack of a checkout strategy is apparent.

It’s counterintuitive since the big guys have more resources than their smaller merchant counterparts – and this one big oversight is costing them big time. A merchant that clocks $100M in annual sales from their online channel may be losing up to $40M every year thanks to the friction associated with converting shoppers to buyers online – a problem made more acute when those journeys start on a mobile device.

To unpack what goes into a checkout strategy and why they could help boost the bottom lines of merchants big and small, Karen Webster spoke with BlueSnap CEO Ralph Dangelmaier. Here’s what they talked about.

Checkout Strategy? What Checkout Strategy? 

“Most merchants’ checkout strategy is all about reducing the cost of acceptance,” Dangelmaier said. “That’s often the first question we get [from merchants].”

A starting point Dangelmaier said he uses to shift the conversation more toward friction from the consumer’s perspective.

“What we then quickly shift to is a conversation about ‘how do I create less friction?’ – which is the essence of a good checkout strategy,” Dangelmaier emphasized.

Dangelmaier said that the optimal checkout strategy eliminates friction in three key areas: getting a consumer to fill the shopping cart efficiently, getting that consumer from the shopping cart to the checkout page, then making the payment – and all of the parts in between.

Hint: It’s All About The Conversion Data

Webster mentioned that merchants often don’t entirely understand where their conversion “blind spots” are since they don’t have all of the data they need to diagnose how bad the problem is. Or maybe they have data, but not all of the right types of data. As a result, it’s difficult for them to assemble the full story.

“Lot of retailers have data on their costs, but don’t have data on their conversions. Many times, we’ll work with merchants and we’ll ask them how many declines, from where, at what price point, from what issuing bank – and we’re met with blank stares. They just don’t have access to the data – and without the data it’s very hard to actually devise a checkout strategy that optimizes conversions,” Dangelmaier said.

The only way to solve this problem is for merchants need to get more data in their hands. But all too often, Dangelmaier says, merchants are unable to tap into data that’s available so they never see at what point customers abandon checkout or where the customer drops off during their shopping experience.

Big Isn’t Better, Necessarily

The data shows that there is no real difference in performance between the big retailers and the small ones.

“The big guys don’t perform any better than the smaller merchants,” Webster pointed out. “After nine months of looking at this, I found this surprising. We now believe that this is not just an aberration, but a reality.”

Dangelmaier agreed, pointing out one key distinction between large and small merchants.

Smaller merchants, he says, are able to move more quickly and address conversion problems – they have fewer organizational layers to navigate and perhaps feel more pressure to perform.

“The bigger merchants are more entangled or engrained into many more platforms,” Dangelmaier observed. “So to unwind those systems and optimize online/mobile checkout is a lot more work for a bigger merchant than it is for a smaller merchant.”

“I think a lot of merchants want to try these things but they just don’t have a program in place to really get them out into market,” Dangelmaier said.

Tackling Those Problems

Webster points out that the whole process of accommodating online and mobile checkout often boils down to one other major problem: like anything trying to tackle everything at once often means nothing gets done. Breaking issues down into smaller pieces can make the process easier on the merchant’s side – and at least get them on the road to solving their checkout conversion problems.

Dangelmaier agrees, and suggests that merchants evaluate each strategy one by one instead of looking at online checkout conversion as just one big problem itself.

“The only way to really tackle these things are to carve them into bite-size pieces and make progress over a scheduled timeline. I think that’s where merchants could probably use a hand is trying to put those timelines and schedules together and try to come up with what options are best for them,” he said.

Only then can they tackle that one big problem: having a checkout strategy that drives sales, not prevents them.

Read more

A Deep Look Inside Apple Pay’s Matchmaker Economics

by admin in Blog, Sin categoría

David S. Evans and Richard Schmalensee

blog3Standing on stage on September 9, 2014 at Apple’s Worldwide Developer’s Conference (WWDC), Tim Cook announced, “We’ve created an entirely new payment process, and we called it Apple Pay.” Cook displayed a video of a woman who held her iPhone 6, the company’s upcoming upgrade, near a payment terminal. She paid in the blink of any eye. “That’s it,” Cook said, exclaiming twice over “just how fast and just how easy” the new payment method was. An Apple press release claimed the new service would “transform mobile payments.”
Executives, investors, entrepreneurs, analysts, and the media had to assess whether this would be another Apple juggernaut. Would Apple Pay do to financial services what iTunes had done to music? Was the game over for the many other players—big ones like Google, and small ones like LevelUp—that had mobile payment solutions? Should banks agree to pay Apple 15 basis points per transaction for allowing their customers to use their debit and credit cards with the new service? And should retailers agree to take the new payment method?

The tech press called Apple Pay a revolution. So did the Financial Times. PayPal went on deathwatch. In an article called “Will Apple Pay Kill PayPal?” CNN reported, “Apple wants us to ditch our wallet and credit cards. Wall Street is nervous that consumers may dump online payment service PayPal too.” eBay’s shares—it owned PayPal then–fell 6 percent.

We weren’t so sure.

The new economics of multisided platforms provides a theoretical framework and body of empirical knowledge for handicapping “matchmaker” businesses like Apple Pay. Of course, as the saying goes, it’s hard to make predictions, especially about the future. But the new multisided platform economics provides powerful tools for separating new platforms that will likely succeed from those that are going to fail spectacularly.

Most would-be matchmakers don’t get it right, though. In fact, platforms are based on one of the most challenging business models there is.

Economists, including us, have shown that matchmakers face a difficult coordination problem. They have to get a critical mass of customers that want to interact with each other to ignite and grow quickly.

Securing critical mass isn’t just a numbers game. There have to be enough members of both groups that could exchange value to make it worthwhile for either. They need density—what’s known as a “thick market” in trading financial assets—more than just scale. A smaller number of participants who actually want to do business with each other is much more important than a larger number of participants who don’t care so much.

No one had really figured out how common this business model was until a paper by Jean-Charles Rochet and Jean Tirole started circulating in 2000. It provided an organizing framework for collecting empirical evidence on how successful platforms operated and why some matchmaker pioneers succeeded and most didn’t.

Some of our empirical work is described in our new book, Matchmakers: The New Economics of Multisided Platforms, but many other economists have done significant research as well. In our work we’ve paid special attention to payment platforms, particularly on what has led some to ignite and others to implode. The modern payment card industry started with Diners Club, in 1950. It focused on developing a critical mass of cardholders and restaurants in a small geographic area—Manhattan. Once it had a thick market there it moved on to other cities, and eventually other verticals. Other payment networks, in one way or another, also deployed strategies to make sure they had enough cardholders and merchants on board their platforms quickly.

More recently we’ve followed a mobile payment startups in the US. Several of these were very well funded including Google Wallet; Softcard, which was backed by three of the four mobile carriers in the US; and CurrenC which was sponsored by a group of large retailers including Wal-Mart. They all fizzled. Softcard and CurrenC are officially dead. Google Wallet relaunched after a series of missteps. Many other less prominent startups died quieter deaths.

Our analysis of these endeavors during their startup phases found that they made two major mistakes.

They didn’t solve an important problem for consumers. In the US it is very easy and efficient to use payment cards at most merchants. There was no powerful reason for consumers or retailers to embrace alternatives—without more.
They didn’t have a sound strategy for creating the density necessary for critical mass. Each of these mobile payment schemes relied on technology that only worked at merchants that had NFC terminals. Most merchants didn’t have those terminals and didn’t want to spend money to get them. Since consumers couldn’t use the mobile payment schemes at most merchants, they didn’t have much incentive to use them anywhere. And since most consumers weren’t pining to use the mobile payment schemes, merchants had no compelling reason to upgrade their terminals.
It was immediately apparent, based on our previous work, from Tim Cook’s presentation that Apple Pay faced challenges. In fact, one of us predicted, on Sept 25, 2014 and October 31, 2014, that Apple Pay would have a very challenging road ahead to secure critical mass given its scattershot approach to ignition and the fact that it didn’t appear to be solving a significant friction for consumers or merchants.

To begin with, the Apple Pay story was based on a false premise. Cook claimed that it was inconvenient to use a card to pay retailers in the US. He showed a video to prove his point—of a woman fumbling through her purse to find her card, a clerk swiping it a couple of times to get it to work, asking to see her license, and then her signing for it. In fact, for most people it is very easy and quick to use payment cards at retailers in the US. It usually only takes a couple of seconds. While Apple Pay was slick in many ways, its main value proposition for consumers was that it was easy. But so were cards. Unlike Diners Club in Manhattan, it wasn’t clear that Apple Pay was addressing a significant friction.

Apple’s ignition strategy also raised red flags. Consumers could only use Apple Pay at merchants that had NFC terminals. But even in late 2014 most merchants didn’t have them. Also, consumers could only use Apple Pay if they had a new iPhone 6—which cut out most of the existing iPhone user base as well as the many consumers who used Android.

As Apple Pay was designed, it was difficult for Apple to secure the density necessary for critical mass. Few consumers would be able to use Apple Pay, which would reduce the value to merchants, and few merchants would be able to accept Apple Pay, which would reduce the value to consumers.

Working with, we started collecting data on Apple Pay acceptance to see, in comparison with other successful platforms, whether it was getting traction. The first results, which became available in November 2014, confirmed our skepticism. Few iPhone 6 users were installing Apply Pay but, more disturbingly, most of those who had it installed weren’t using it even when they were paying at the few NFC terminals that could accept it. We made an early call, on Dec. 5, 2014, that Apple would struggle unless it changed strategies in the United States.

Eighteen months later, the evidence bears us out. The latest research, as of March 2016, finds that only one in twenty iPhone users who could use Apple Pay—because they have the right phone, and are standing at an NFC terminal that can take Apple Pay—use it. That take-up rate hasn’t budged much over the last 18 months. Our calculations find that Apple Pay’s share of payment card transactions has remained well below 1 percent—ranging from around 0.7 percent in November 2014, to 0.8 percent in June 2015, to 0.6 percent in March 2016. There is no evidence of that Apple Pay is on the road to the exponential growth that matchmakers strive for and get once they build critical mass.

Tellingly, unlike they way it talks about its successes, Apple has been silent on the extent to which people actually use Apple Pay to conduct transactions. Monday, at WWDC, Apple execs were proud to say they had 15 million paid Apple Music subscribers and that Siri got a billion requests a week. They remained mum on how often consumers are using their payment service.

Of course, with its huge base of devoted iPhone users and more than $200 billion in cash reserves we can’t write off Apple like we might a typical startup. The company could change strategies to secure critical mass or play a much longer game than many mobile payment rivals could. In fact, Apple just announced that it is making Apple Pay available for online purchases on websites for mobile and desktop. That sounds promising but still challenging. And very different from the problem they set out to crack. For now Apple Pay is another revolution that wasn’t.

We didn’t get the prognosis for Apply Pay right because we have any special talent for making predictions. We simply used a toolkit—the theory of multisided platforms and relevant empirical evidence—that is available to everyone now to look under the hood of platform businesses.

Any executive scared about platform disruption, VCs getting ready to lay money down on the Uber of “X”, CEOs being pitched to start a matchmaker business, and entrepreneurs getting ready to work night and day to get their platform ignited would benefit from using it. None will get certainty, but they’ll improve the odds of separating likely failure from possible success. Most importantly, though, matchmaker-wannabees can use the new matchmaker economics to get their business model and strategy right in the first place.

Original source Harvard Business Review

© 2016 Microsoft Términos Privacidad y cookies Desarrolladores Español

Read more

The Incredible Mobile Payments Myth

by admin in Blog, Sin categoría

INTERNATIONAL The Incredible Mobile Payments Myth

Today, I’m going to tell you how billions of dollars could be going up in smoke in the payments business because many of you are making assumptions based on data that is just flat out wrong.

Happy Monday.

But first let’s talk about jobs. (Stick with me, there’s a point here, I promise.)

It’s 2011.

Harvard University Professor Gary King and several of his colleagues launched a project to see if it was possible to monitor chatter on social networks – Twitter, Facebook, LinkedIn – and predict shifts in the economy more accurately than conventional models did. A big part of that project was keeping close tabs on how often key words like “jobs” and “unemployment” were used in those social conversations.

At the start of 2011, the U.S. unemployment rate was 9.20 percent and was slowly but steadily inching downwards month by month. By the start of the summer, it stood at 9 percent.

In October, King and his team began to see off the charts chatter on social media related to their observable key words – so much so that they were convinced they were on the verge of predicting a huge reversal of that downward trend well before anyone else even noticed.

Until they realized much later that the spike was entirely attributed to the death of Steve “Jobs” on October 5, 2011.

King speaks openly about his team’s failure at first to distinguish the employment “jobs” from the Apple chief “Jobs” as an unfortunate consequence of relying on what he calls “distorted data.” Luckily for him and his team, the error was caught before their “predictions” were circulated and decisions using data which was not properly vetted were made by everyone from employers to retailers to consumer goods manufacturers.

Which brings me to the subject of mobile payments.



Take a look at this chart.

It’s just one of several data points that has perpetuated the ongoing narrative about how positively backwards the U.S. is when it comes to embracing mobile payments, and in particular using mobile phones to pay for things at the retail point of sale. That conversation has been in full swing for at least five years, maybe longer. This particular chart shows the U.S. below the global average and totally bringing up the rear when comparing its use of mobile payments in-store to countries a fraction of its size and economic prowess.

Adding to insult to injury, if you’re a member of the U.S. payments and technology sector anyway, is the never-ending stream of reporting about how mobile payments at the point of sale has “taken off” in the U.K., is surging in Korea and about ready to massively explode in China.

Which of course leads everyone, everywhere to two logical assumptions….

1. That the U.S. is not only late to the mobile payments party, everyone else is practically minting money because of the massive mobile payments opportunity at the retail point of sale; and

2. To both save face and catch up, the U.S. payments and technology innovators better make up for that lost time by throwing money at contactless mobile payments at the retail point of sale initiatives – and they better do it fast.

And business decisions are based on those assumptions.

Over the years, there’ve been massive investments in contactless mobile payments schemes by nearly all of the major players across the payments and technology arenas. North of a billion dollars was invested by dozens and maybe even hundreds of innovators and investors – all in response to the loud and growing drumbeat that everyone but the U.S. is cashing in big time on the mobile- payments-at-the-point-of-sale gold rush.

A narrative that’s been helped by “distorted data” that’s just as bad as mixing up jobs and Jobs.

Almost everything you keep reading about the “success” of mobile payments at the retail point of sale is wrong, despite the continued reporting by journalists and analysts that it is.


Let’s take the U.K.

The U.K. Cards Association keeps impeccable data on payments – volume, usage, transactions, spending volumes – you name it, they track it with precision and have for years. Their 2015 report (which reports 2014 data) says that the total value of transactions using contactless at the point of sale – cards and mobile phones –  increased 331% from the year prior.


That’s, of course, the headline that got reported. Everywhere.

And, it is true that contactless volume is growing in the U.K. But it’s sort of like the fact that my Scottish Terrier grew 300 percent in his first 2 months of life. The little guy was still, well, pretty little despite his growth spurt – and still is years later.

In 2014, total card transactions in the U.K., they report, totaled £600B on some 13B transactions.

Contactless volume – cards and mobile phones – was £2.3B of that £600B and 319M of those 13B transactions.

I’ll do the math for you. That’s .4 percent of card volume and 2.5 percent of transactions.

What’s fueled that “massive” increase in volume?

It wasn’t smartphone-toting fashionistas shopping at Harrods.

The average transaction value on those contactless purchases was £6.37 – which is a little less than $10. That’s lunch at Eat, a favorite London lunch spot. The real thanks goes to the London Transport, which in December 2014 alone, drove 11 percent of the contactless volume. And that volume wasn’t the result of using mobile phones, either. Although more than 60 percent of U.K. adults have smartphones, according to Deloitte, the percentage of those with “NFC-enabled SIM cards and phones” is very low.  Monthly mobile phone usage at any point of sale is reported to be less than 0.5 percent of the 450 million-500 million NFC-phone owners as of mid-2014, the last time data was available.

Then, there’s Korea.

South Korea has been heralded as one of the world’s most advanced mobile payments’ meccas since about 2010. Google “mobile phone payments in Korea” and you’ll find article after article after article extolling the virtues of South Korean consumers using contactless mobile payments at the retail point of sale there.

Except that’s not the way it really is.

The reality is that mobile payments at the retail point of sale are sputtering in South Korea. According to the GSMA, only 6 percent of terminals there are enabled for contactless payments –mobile or otherwise. Back in 2010 when the mobile payments hoopla started, there was a big arm wrestle over standards that slowed things down. It took Korea Telecom almost two years to get 100k users on board with a QR code based scheme at a handful of merchants. Volume was miniscule and if mobile phones were used in stores, it was to access them for coupons, not make payments, which are still dominated by the use of plastic cards.

Things are starting to turn the corner a little, but only recently.

Last summer, Samsung Pay reported that they had acquired 500k users in the space of a month and racking up millions in transaction volume. Leveraging both existing card accounts and merchant terminals created a ubiquitous payments environment that has expanded the possibilities for merchants, banks and consumers and perhaps broken the mobile payments at the retail point of sale logjam.


Then there’s Japan.

The birthplace of NTT DoCoMo and the mobile contactless scheme that inspired just about every mobile payments strategy in the early 2000s.

It’s pretty much the same story.

Sure, there are 70 million NFC-enabled phones in a country of 123.7 million people. But how they’re used is pretty much a carbon copy of how they are used in the U.K. – to buy transit tickets, items from vending machines and snacks at the convenience stores in the train stations.

Even in Australia, everyone’s favorite poster child for the adoption of contactless payments, contactless terminal penetration is still at only 40 percent. And a lot of the contactless activity that’s happening in stores is via cards – not mobile phones.

And does anyone really believe that mobile contactless payments are exploding in China? Not only is there a lack of contactless terminals, there’s a general lack of cards. No cards and no terminals makes for a tough contactless mobile payments environment at the retail point of sale no matter how you try to cut the mobile payments pie.

Now none of this is intended to throw cold water on any progress that is made. All forward progress is great. But the real progress and the rhetoric and hype describing it, you gotta admit, are greatly mismatched.

But there are some mobile payments successes in the most surprising of places.


Where there’s been mobile payments traction, there’s been a real problem to solve – problems that don’t always fit neatly into the existing “everything, everywhere is taking off like a rocket ship” narrative.

One of the most successful mobile payments schemes – and the most successful in terms of penetration in a country – is M-Pesa.

Eight years since its launch in 2007 in Kenya, it’s now used regularly by nearly three-quarters of its population –and 43 percent of the country’s GDP flows through the M-Pesa mobile money network. M-Pesa has also raised the level of economic prosperity in the country significantly, boosting the ranks of the emerging middle class from 20 percent of the population in 2007 to more than 72 percent by 2011. Now that a critical mass of users is established, M-Pesa is starting to expand into payments at the physical retail point of sale.

Schemes like M-Pesa in Kenya, Nettcash in Zimbabwe, and bKash in Bangladesh are not only emerging – but flourishing. It’s not hard to understand why.

In developing economies, mobile payments solve an enormous pain point – how to move money between people in the country without being robbed, killed, kidnapped or having to take two days off work to deliver it. The habit that consumers needed to change was easy-peasy because the alternatives were, well, let’s just say that friction-filled is an understatement. It wasn’t the struggle of finding a card in a bottomless Gucci handbag. It was getting killed on the long journey across rugged, violent territory.  And, no, I’m not talking about the drive from San Francisco to San Jose, as inconvenient as that can be these days.

In the developed world, it took a coffee purveyor’s quest to overcome one of its customer’s key frictions to grow the most successful in-store mobile payments scheme. That, of course, is Starbucks.

Oh, wait, aren’t they from the backwards U.S.?

In the space of six years, the Starbucks mobile app accounts for 20 percent of its total volume – some 7 million transactions a week. This barcode-enabled payments-plus loyalty scheme was originally conceived to get customers with gift cards to use all of the balances on those cards. Their hypothesis was that customers didn’t want to be embarrassed at the register by presenting a gift card that didn’t have enough money on it to pay for their purchase – so they never used them fully. The team at Starbucks initially set out to create a mobile app that provided only gift card balance information so that users could avoid that potential embarrassment yet still use the cards.

But when the product managers observed that every single person in line at Starbucks was staring at their phones while waiting in line, they decided to add payments. Starbucks overcame one of its own points of friction by tapping into existing customer behavior – consumers staring at their phones while waiting in line. Being able to avoid public humiliation at checkout with impatient caffeine-deprived people standing behind them, is a huge incentive if using Starbucks gift cards is a consumer’s passion.


Today, and just about everywhere, if you’re brave enough to look past the distorted data, with a few exceptions, the introduction of mobile payments at the retail point of sale introduces more frictions than it solves. Most people chalk that up to inconsistent acceptance of mobile payments apps or restrictions on how much can be spent when using them. And so if we only installed more terminals or raised spending limits, all would be right with the mobile payments world – so let’s get busy doing that now and keep the distorted data stories alive and kicking.

Maybe not.

Think for a minute about the customer experience at Starbucks or riding public transit where it’s a fact that a lot of mobile payments apps are used.

Now think about shopping at a grocery store or a department store or the hardware store.

At Starbucks or at the subway station, a consumer is standing in line or approaching the turnstile holding nothing but her phone, as she waits for someone to take her order or board her train. In just about every other retail situation, she’s pushing a shopping cart filled with stuff that she has to unload and stick on a counter or a conveyor belt (and maybe even has to put in bags), or is holding a bunch of stuff in her arms while standing in line (maybe even while holding her kid, too).

Her phone is with her but probably not in her hand.

So, when she gets to the checkout counter, absent a really good reason to reach for her phone in her purse or her pocket and/or with the knowledge that if she does, the method of payment she has on her phone will be accepted everywhere she shops, she’ll just default to reaching for her leather wallet and her plastic card that she knows will.

That’s exactly what the Apple Pay experience has shown, too.


Even the most enthusiastic early adopters are no longer reaching for their phones to pay in the stores. If they still have to stand in line with stuff in their hands or in their carts that they have to unload, and reach for anything when it comes time to pay, it’s a whole lot more natural to reach for their plastic card and not the mobile payments app – all things equal.

Based on what consumers have said, when asked, even adding loyalty options might not be enough to get them to switch. According to the latest Fed’s Survey of Consumers and Mobile Financial Services, it’s just easier to use cards and the vast majority – some 65 percent – say that nothing can get them to change their minds.

Unless of course, someone does something to remove the friction from checking out in the store.

A 2014 study by Accenture shows that, short of actually paying people to use a mobile payments app at checkout, what would get nearly three quarters of them to take mobile payments for a spin is the ability to skip the line completely.


And it makes total sense.

All of the data show that making checkout easier on the mobile phone increases conversions.

So, why wouldn’t the same principle hold true in the physical retail store?

And inspire innovators to use the mobile device and apps to reinvent the in-store shopping and checkout experience?

Which is why the order online and pick up in-store innovations across all categories of merchants are growing like gangbusters. What better way to skip the line than never getting in one?


None of what I’ve written is intended to take a swipe (no pun intended) at the potential for mobile payments at the retail point of sale but to, instead, provide a much-needed reality check on where things stand right now (and actually have for a while). For those of you who haven’t been following me, I’ve been touting the advantages of moving to mobile at the point of sale for about a decade now—but also emphasizing that it won’t get anywhere unless it solves big friction – and a real friction – at the point of sale.

The reality check is that there are mobile payments success stories but just not as many at the retail point of sale as the “distorted data” accounts might have you believe.

And, most important of all, no one anywhere in the world has missed out on a massive in-store mobile payments opportunity. In fact, it’s all really just getting started.

Those that are showing signs of success share a common characteristic that the distorted data stories ignore but you shouldn’t. Innovators have looked beyond the obvious to understand how to make things better for the consumer and the retailer – instead of what we’d like them to adopt because we happen to know how to make it work.

Innovators have looked beyond the obvious to understand how to make things better for the consumer…

For those who are thinking that way, take great comfort in the fact that you’re not missing out on a thing. And, with any luck, you’ll have some great undistorted data to share on how you’re doing soon enough.

Be sure to write to me when you do. We’ll tell the whole world.



Read more

Massive Online Retail Data Error Uncovered

by admin in Blog, Sin categoría

Massive Online Retail Data Error Uncovered

It’s common knowledge that Kodak’s demise came at the hands of digital. But few know the whole story of why that happened. Kodak relied, too much it turns out, on a lot of bad data about how much of a threat digital was to its business.

Kodak didn’t think it had much to worry about – until it did – and by then, it was too late.

Their “Kodak Moment” came in 2003 – seven years after its best year ever – and not long after a board report concluded digital was nothing to really worry about.

In 2016, retail is on the verge of its own “Kodak Moment” – and not because it doesn’t understand the impact of digital to its future.

But because it, too, has been relying on bad data about how much of an impact digital and eCommerce has had on its business.

And making decisions based on that bad data for years.

My colleagues at MPD and PYMNTS found a massive mistake in how retail and Census data is reported – a mistake that has severely underreported the impact of digital sales on physical retail sales.

I’m going to share that story with you now.

But first, a little context, because you know how much I like a good story put in the right context.


1996 was a very good year for Kodak.

It had a commanding market share: two-thirds of the global camera and film market.

It was a beloved brand – in fact, one of the most valuable in the world behind McDonald’s, Coke and Disney.

It was a sales and profit machine with annual revenues of $16 billion that year and profits of more than $1 billion. In fact, a Harvard Business School case study said that Kodak accounted for 90 percent of film sales and 85 percent of camera sales in the U.S. in 1976 – a position that didn’t waver all that much over the next two decades.


Life was grand, but Kodak’s CEO understood that digital was coming and wanted to be ready. Kodak’s CEO knew that digital would be a threat to its business model. Kodak’s “razor and blades” model banked on selling cameras at decent prices and the sales of film for those cameras forever. No film camerasales – no film sales.

Kodak took that threat so seriously that it got to work producing a digital camera – and, in fact, produced the world’s first. And, in the 1990s the company commissioned research to better understand the growth of digital cameras and where the adoption might be toward the end of the decade. Those studies showed the projected growth of digital cameras to be relatively low, at least outside of Japan, something on the order of 400,000 units a year. Against the tens of millions of film cameras projected to be sold, Kodak concluded that digital was coming but not with the force once thought. Digital, they concluded based on that data, was a niche market for “power users” and therefore not much of a threat to Kodak’s core consumer.

The Kodak Board and senior execs must have been pretty happy when that data was presented to them.

But, as it turns out, not for long.

Those conclusions, obviously, turned out to be very wrong – the unfortunate consequence of relying on bad data that drove some very bad decisions by Kodak management.

In 1999, IDC reported that digital camera sales were projected to be 4.7 million that year – a far cry from the 400,000 that Kodak’s research disclosed. And, in 2003, just seven years after one of Kodak’s best years ever, and a few years after the research report, which I’m sure was filled with lots of gorgeous charts and perfectly formatted data, that said, “No problem, don’t worry!” the sales of digital cameras completely swamped the sales of film cameras.

Their “Kodak Moment” had arrived.

That downward decline of Kodak continued — slowly but surely — with the shuttering of plants and film processing labs, and the shrinking of its workforce. In 2012, Kodak filed for Chapter 11 bankruptcy, was delisted from the New York Stock Exchange and sold its patent portfolio for $527M to a group of 15 companies including Apple, Google, Amazon, Adobe and Microsoft.

Although Kodak emerged from bankruptcy in 2013, it struggles today to recover from the digital tsunami that it, and others, seemed not to see coming. Kodak’s fall was really more of a long, slow slide: 36 years after Apple unveiled the Apple I, 16 years after the start of the commercial Internet, and 12 years after the first cellphone with a camera was launched. Today, its CEO is searching through the remnants of Kodak’s storied part to see what can be made of its IP. “We missed enormous opportunities,” says Jeff Clarke, Kodak’s CEO. “We’ll never be able to prosecute the value of our intellectual property with Kodak-branded sales.” Kodak’s market cap today is roughly $800 million.

And to think, in 1996, Kodak was riding high, even though in retrospect it is clear that its business was collapsing below it. Bad data masked the real threat to the business.

In 2016, I believe that physical retail may be facing its own “Kodak Moment.”

How many times have you heard retail experts assuage the concerns of physical retailers over the impact of online sales to their business by saying:

“Don’t worry – 94 percent of retail sales still happen in a physical store?”

This is from Forbes in July of 2014:

“Indeed, despite the hubbub over digital commerce, 94 percent of total retail sales are still generated at brick-and-mortar stores, according to data from market research firm eMarketer.”

“The buzz given to Amazon, eBay and Alibaba far outweighs their true sway in the marketplace,” Mike Moriarty, co-author of the A.T. Kearney Omnichannel Shopping Preferences Study, told Forbes. “Particularly when you consider that Amazon increased their retail revenue from 2009 to 2013 by $50 billion, but their profits went up zero.”

Yeah, but …

It turns out that Forbes and everyone else in the market that reports retail sales has been relying on data from the U.S. Census Bureau.

And that data is wrong.

Massively wrong.

The Census Bureau is the source for reporting physical and retail sales in the U.S. It’s the gold standard. Companies file reports with the Census Bureau according to strict guidelines. Depending on what the Census is covering, companies send this data in monthly, quarterly, or yearly. It’s why everyone uses them as a source. And, hey, while it’s not the IRS, it’s the main government statistical agency, so companies must take their data obligations pretty seriously, right?

But my colleagues at MPD, economists David Evans and Dick Schmalensee, found a flaw in that reporting, a flaw that was uncovered as they were writing the chapter on retail reinvention for their forthcoming Harvard Business School Press book on platform businesses, Matchmakers.  Scott Murray, Head of Data Analytics at PYMNTS, was working closely with them on their analysis.

As Evans, Murray and Schmalensee started to dig into the data on the impact of online sales to brick-and-mortar sales, something didn’t quite add up, literally. All of their field research showed that physical retail sales were cratering, but then the Census data said not much was happening.

Then they made a big discovery.

One of the biggest online retailers,, wasn’t in the Census figures on online sales for 2013. The Census had clearly missed more than $6 billion of online sales.


And that led them on a journey to find out what else was missing.

Six months of analysis and some email correspondence with the Census folks later, they uncovered a data bombshell: online sales as a percentage of all retail sales has been undercounted – and by a lot.

Online sales as a percentage of physical retail sales has been undercounted by about one-third each and every year for at least the last five.

Evans, Murray and Schmalensee have documented that reporting error back to 2010. They suspect that it’s been underreported for even longer than that – probably ever since the start of the online sales revolution more than two decades ago.

But let me put this in practical context.

This Census Data error means that in 2014, eCommerce as a percent of sales wasn’t 6.4 percent as the Census reported, but 8.2 percent.

But here’s the really big news, we think.

The Census Data for 2015 aren’t all in yet, but we’re pretty confident that they will report something in the neighborhood of about 7.3 percent. Correcting for the one-third mistake, Evans et al. think that right number is more like 9.3 percent.

If we ignore sales by Gas Stations, which currently aren’t selling anything online, online retail sales will hit 10.3% of all retail sales.

Which means that for the first time ever, in the United States, the percent of retail sales that take place online will hit double digits and the percentage of retail sales taking place in a physical store won’t start with a “9.”

Here’s a chart which shows the estimates of the online retail sales for 2010-2015 with and without gasoline, just to show you visually, what we’re talking about.


But these figures just report the average across all retail categories from motor vehicles to health products.

The other big data error these guys discovered was in reporting what physical retail stores themselves are doing online.

If you looked at the Census data, you’d think physical retailers were just sitting out the online revolution. It turns out Census missed a massive amount of online activity by the online divisions of physical retailers themselves, which of course, all physical retailers are doing now – and some more than others.

In some retail sectors, like general merchandise (think Walmart, Kohl’s, Target), clothing (think Macy’s, Nordstrom, Gap and all of those specialty retailers), home furnishings (think Williams-Sonoma and Crate & Barrel), building materials (think Home Depot, Lowe’s) and electronics and appliance stores (think Best Buy and Fry’s) the undercounting of online retail sales is orders of magnitude off – in the hundreds of percent.

For general merchandise, a conservative estimate is that the real amount of online sales by physical retailers is 23 times higher than what the Census reports. For electronic and appliance stores, the real amount of online sales by physical retailers is almost 14 times more.

So, you might ask, why does any of this matter? Everyone already knows that eCommerce is cutting into physical retail sales – what’s the big newsflash? Does it really matter that the Census was off by a third?

That depends on whether you’re the CEO of a massive physical retailer who might have wanted to know six years ago (or more) that not only was the growth of eCommerce accelerating  – which was well-known – but that the base upon which it was growing was a third larger than she thought.

Maybe she might have wanted to know that so that she could have reprioritized her digital initiatives – and moved them closer to the top of the list.

You see, if she’s looking at the Census numbers for her peers, and knows her own numbers, she’s probably thinking one of two things: she’s doing better than anyone else so there’s no real hurry to do more, and everyone’s in the same place so there’s no real threat. Both conclusions would be wrong, now with the benefit of 20/20 data hindsight.

Maybe she might have embraced mobile wallets and cloud-based point of sale solutions a few years earlier. Who knows, maybe we would be a whole lot further down the mobile/digital wallet field by now.

Maybe she would have adjusted her merchandising and pricing and loyalty strategies to get consumers tied closer to her store using the digital shopping media that her consumers clearly preferred and were using more often than she thought.

Maybe she would have known a little earlier where Amazon was cleaning her clock and had the time adjust her strategy.

And then there’s the Board.

My guess is that many CEOs of physical retailers, probably echoing what their management consultants and execs were feeding them in their perfectly crafted and formatted decks, have been telling their boards and investors not to worry. “Online is just a drop in the bucket.” “People love shopping in physical stores.” “We’ve got plenty of time to adjust to online; after all, 94 percent of physical retail still happens in physical stores!”

Wrong, wrong, wrong.

And that brings us to retail’s “Kodak Moment.”

Yes, I’m sure you’re curious about how in the world this could have happened and what makes the MPD team feel confident about the missing Census data. The authors are releasing the technical details of their work in a couple of days — geeks who want to know the details, stay tuned at But if you can’t wait, here’s my best non-economist summary of what they did and the crux of the error at a high level.

The Census Bureau appears to count sales from pure-play etailers just fine. But that’s not the issue, nor the problem.

There are three contributors to the bad data problem at Census.

First, things get hairy — and wrong — when it gets down to counting online sales from physical retailers, which is now just about everyone.

Omnichannel is in full swing as most retailers, even some of the luxury brand holdouts, recognize the importance of having a synergistic online and physical retail presence.

As I mentioned, the Census Bureau relies on data that retailers report to them. MPD examined physical retailers at the 3-digit NAICS Industry code (using the most recent complete data set, which was for 2013). That resulted in the discovery of the undercounting error. Walmart’s more than $6 billion for 2013 was just a tad more than the $88 million reported by Census for general merchandisers, which is where Walmart should have been. A bit more digging and some emails with the Census Bureau pretty much nailed the fact that was missing.

Now, according to Evans, the Census can’t actually say anyone is missing or they’d get their heads handed to them. But everything his team looked at, and the feedback from the Census, made it pretty clear wasn’t anywhere in the Census online data. When they finished their digging and added in other missing physical retailers they found that the Census had missed about $62 billion in online sales in the U.S. in 2013.

The second source of retail’s big data mistake is that the Census isn’t capturing sales of big groups of nontraditional retailers like manufacturers who sell online, like Apple or Nike, in the retail numbers it reports.

I know, hard to believe.

But that turned out to be a big deal, too. That segment alone – retailers like Under Armour, Ralph Lauren, Apple, Kate Spade, Lululemon and many others — accounted for roughly $22 billion in 2013, and a projected $25 billion and $29 billion in 2014 and 2015. Yet all are completely missing in how the Census tabulates things.

There’s a third contributor to retail’s big data problem.

Most researchers, data analysts, and media who rely on the Census data use the average online sales as a percent of total retail sales across all retailers. That’s where the “online is still very tiny” conclusion has come from.

A lot of times the “average” isn’t a very good number to base decisions on. The average annual temperature in Boston is 58.7 degrees Fahrenheit. Last Saturday afternoon, when the Patriots won the AFC East Championship Title here, it was 37 degrees Fahrenheit. So, a fan in the stands dressing for 58.7 degrees would have been pretty cold (happy, but still cold). Generally, dressing for 58.7 degrees is a bad idea in Boston since it’s hardly ever 58.7 degrees. It can be a lot colder, like Saturday’s 37 and last year’s sub-zero temps, and a lot hotter, like over 100 degrees.

Further, reporting an average physical/online break fails to show the impact of online sales growth by retail category – which is important for understanding what sectors will feel the online hit most acutely, and how soon that impact will be felt.

The bottom line? When the Census reports that 94 percent of all retail sales still happen in brick and mortar locations, everyone feels better. When that number is really 89.7 percent now, the storyline turns sour. And for retailers sitting in a category that is a lot lower than the “average,” it’s downright scary.

Now before you start sending hate mail to the Census Bureau, please don’t blame them.

These folks are understaffed and under-resourced, and can only report based on what they’re given. They also operate big, inflexible government databases that can’t account for the differences and nuances of our changing retail environment. Part of it is they are stuck using outdated NAICS codes that are based on treaties the U.S. has signed with other countries. Let’s not go there right now either.

It’s pretty shocking, though, that a government entity tasked with reporting such important information for the largest economy in the world, now some 20 years after the birth of the Internet and eCommerce, isn’t given the resources to do it properly.

But thank goodness we spent $856,000 last year to find out that it takes 3 months to teach a mountain lion to run on a treadmill.

Of course, as the blurring of the online and offline worlds accelerates, accounting for online and physical retail sales will only get harder and hairier. Buy online and pick up in-store may be great for retailers and a sign that omnicommerce is in full swing, but Census Data isn’t set up to count those sales correctly.

And retail will just keep feeling better that physical retail is still such a large part of the retail economy. Yet retailers know instinctively that they must embrace digital and step up their online and omnicommerce games since that’s where the consumer is taking them.

They’re also starting to internalize the hit they take when they don’t.

Our Checkout Conversion Index – a collaboration with BlueSnap – shows that most merchants today fail the most basic of all online tests: how easy it is for consumers to check out on their sites.

When we benchmarked 650 sites in the U.S. – representing about 75 percent of U.S. eCommerce sales – we found that there was so much friction across the board, that most retailers were putting as much as 36 percent of their sales at risk. Of course, those sales aren’t lost forever, but simply lost to merchants who can’t convert those shoppers to buyers to those who can.

Like Amazon, for example.

Now that we’ve discovered this mistake, we’re going to share what we know on a regular basis. Coming soon, we’ll be publishing the “Whole Scoop And Nothing But The Scoop On Online/Offline Retail Sales Tracker” so that you have monthly updates on what’s really going on at the intersection of online and offline retail.

We’re doing to give you averages, of course, but the more valuable data by three-digit NAICS code.

What you do with that data, is your call.

Consider it our contribution to turning retail’s “Kodak Moment” into a picture truly worth framing. And a memory worth remembering – for all of the right reasons.


Read more