Putting The Fizz In Apple Pay

In an article titled Why Apple Pay Is Fizzling and What It Means for the Future of Mobile Payments, MPD founder David Evans writes:

“Apple Pay is fizzling. And unless it drastically changes course Apple Pay will follow the hundreds of other attempts, made around the world in the last seven years, that have sputtered along at low levels of use or, much more frequently, have just flat-out died. The evidence from Black Friday confirmed my fears. InfoScout did a survey of 400+ people who (a) had IPhone 6s and therefore could have had Apple Pay on their phones who (b) were paying at stores that had NFC terminals that accepted Apple Pay. So these are 400+ people who could have paid with Apple Pay. Less than 5 percent did.”

My take: It’s way too soon to be calling the death of Apple Pay.


Take a look at the following charts showing iPod sales.

Pronouncing the failure of Apple Pay in December 2014 is akin to proclaiming in Q1 2002 that the iPod would be a failure.

And there were plenty of reasons–two-side reasons as Mr. Evans might call it–to support an iPod death notice in Q1 2002: Consumers didn’t know what it was, there was no music no buy online, anyway, they were perfectly happy buying CDs (and certainly wouldn’t want to have to throw away all those CDs they had amassed). On the other side of the coin, music publishers and the stores that sold those CDs certainly had no vested interest in supporting the sale of iPods.

I would imagine that a survey of 400+ people who bought music back in Q1 2002 might show little interest in something called an iPod.


The InfoScout survey may very well be a representative sample of iPhone6 owners, but are existing iPhone6 owners representative of the overall public?

In addition, it seems very unlikely to me that a primary reason for those people who rushed out to get an iPhone6 was the Apple Pay feature. So what should we expect from the 400+ iPhone6 owners who were surveyed?


The point (put forth by Mr. Evans) that Apple Pay can’t be used at 98% of merchant locations isn’t a compelling death knell, either. Last I checked, both American Express and Discover were not accepted everywhere. They’re not dead.

It doesn’t matter how many places accept Apple Pay–it matters which places accept it.


Payments isn’t really a two-sided market. It’s more of a three-sided market: consumers, banks, and merchants (and I can’t help but wonder if, when talking about mobile payments, I need to throw in the telcos, and make it a four-sided market). All of which just makes any change in the market harder–and slower–to come by.

The “hundreds” of other attempts that have sputtered or died had, or have had, insufficient support from the constituents in the market. Many have tried to simply garner consumer support without sufficient support from the banks or merchants. Or have presented delusions of interchange reduction to the merchants, without any game plan for winning over consumers, and figuring the banks would roll over and play dead.

Sure, Apple Pay hardly has the support of many merchants–at the moment. But the lack of merchant support isn’t the same across the board.

A large percentage of the 98% that don’t currently accept Apple Pay may never accept any form of mobile payment, and who really cares, since a large of percentage of that 98% represent maybe 0.1% of all sales.

The “merchants that matter” fall into two (overlapping) categories: 1) Those that represent a good percentage of retail sales, and 2) Those where current and future iPhone6 owners shop at and buy from.

It’s the latter category that I don’t hear a lot of the pundits talk about. Everybody wants to focus on the MCX merchants not accepting Apple Pay. But Apple Pay can drive payment volume by following the Amex strategy–not accepted everywhere, but by enough of the places where Amex cardholders shop at. Case in point: Whole Foods.


Bottom line: I hate bad analogies, but I won’t let that stop me from giving one. Apple Pay isn’t a carbonated beverage losing its fizz. It’s more like a wine that needs to be aged.

The “aging” process for Apple Pay does involve a lot of moving parts: iPhone6 adoption, changing consumer behavior, bank (and credit union) support, and merchant acceptance (reluctant or not). In a three-sided market, strong support from two of the sides will probably be enough to bring along the third (even if it is kicking and screaming).

Which is why MCX is DOA. It’s all about one side of the market–the merchants. It can talk all it wants about how it’s supposed to be good for consumers, but that message has little meat to it.

Apple Pay, on the other hand, will provide additional convenience to some consumers and may provide other benefits in terms of yet-to-be developed features (for examples, see The Mobile Moments of Opportunity), gives a huge boost to banks (i.e., keeps them in the game), and is promised to benefit all parties in terms of reduced fraud.

That said, just because you let a wine age a few years doesn’t necessarily make it a good wine. But it’s simply too soon to say Apple Pay is “fizzling.”

NOTE: Thank you for reading this post. If you work at a financial institution, please help me out and take just a few more minutes of your time to complete the 2015 Financial Brand Marketing survey. For your time, you’ll receive at least two reports that I know you’ll find interesting and helpful. And you’ll get a discount on the registration fee to the 2015 Financial Brand Forum. Thanks!


Why Consumers Should Fear Mobile Banking

The Financial Brand recently reported on a study conducted by GOBankingRates which found that a little more than half of consumers–56%–indicated that they have a “main concern” about mobile banking.

My take: The survey really goes to show how clueless people really are.


Let’s take a deeper look into some of peoples’ “concerns” with mobile banking.

Less than a handful (3%) of respondents cited “no paper documentation.” I’m sure these people don’t buy anything online, either, because there’s no paper documentation with those transactions. If there are 3% of people in the world who will only transact face-to-face so they can get a paper receipt, so be it. I think banks can live without having these folks as mobile banking customers.

Seven percent of respondents listed “misuse of personal info” as their main concern. Apparently, these people haven’t heard about any of the data breaches that have hit Target, Home Depot, and the gazillion other merchants who have been hit. Bet these 7% still have no problem using their debit and credit cards when they make those face-to-face purchases.

Nine percent said “technical errors” were their main concern with mobile banking. These people are actually on to something. Fear of technical errors is my biggest concern with banking–not “mobile” banking, but banking altogether. Of course, the last time I had a problem with my bank, it was a matter of “human” error–not technical error–and I was the human who made the error.

Far and away, the largest percentage of respondents with a main concern regarding mobile banking was the 37% who cited identify theft as their concern.  Identity theft? How’s that going to happen? These people clearly don’t have a clue what the most common causes of ID theft are. And I can’t help but wonder how many of these 37% are banking online. Checking your account balance and moving funds between accounts is OK to do on a PC, but not a smartphone or tablet? 


The reasons people are giving for “fearing” mobile banking are baseless. If you need a reason to fear mobile banking, I’ll give you some good reasons:

3) Snakes will come squirming out of your smartphone when you use a mobile banking app. I’m not saying this has ever happened before, but it could. And that would be a helluva lot scarier than not having paper documentation of the transaction.

2) Your mobile banking app will access the naked pictures of yourself you keep on your phone, and post them on Facebook. And I’m sorry to tell you this, but that’s scarier for the rest of us than it is for you.

And the #1 best reason for fearing mobile banking….

1) You will give your ID and password to a phisher and try to use a mobile banking app to change that password before the hacker gets into your account–but the mobile banking app won’t let you do it. Sadly, this is true, and I learned it the hard way.

Of course, it hasn’t stopped me from continuing to use my bank’s mobile banking app. But at least my fears of mobile banking are grounded in reality.


The Financial Brand 2015 Marketing Survey

For the third straight year, I am collaborating with The Financial Brand to produce a series of reports on the state of bank and credit union marketing for 2015.

Respondents who complete the survey by December 31, 2014 will receive two reports, one from The Financial Brand on marketing trends in the retail banking sector, and one from Aite Group on mobile marketing trends in banking or marketing analytics in banking.

In addition, if you complete the survey, you will also receive a special discount code entitling you to a $150 discount on the registration fee for The Financial Brand Forum 2015, April 30-May 1 in Las Vegas.

I’ll make another offer to Snarketing 2.0 readers: Complete the survey by December 10 and I’ll send you all three reports.

The survey contains ~30 questions and should take less than 10 minutes to complete. I would be very grateful if you could take a few minutes and fill out the survey–the information is critical to what I do. Thanks.

Click here for the survey: The Financial Brand 2015 Marketing Survey

Round Round, Shop Around, We Shop Around

According to pymnts.com:

“Two years ago showrooming was something of a boogey-man for brick-and-mortar retailers. But shopping has changed since 2014, and while showrooming didn’t go away, it’s changed its form. Consumers are getting savier, and are now increasingly likely to browse for items online for price, and actually make the purchase in the store. The practice is called reverse showrooming, or webrooming, and some have been calling it the brick-and-mortar retailers’ secret weapon in 2014.”

My take: Wait, hold on…I’m still laughing too hard at this ridiculousness to write. OK, there, that’s better. Slapping a new word on an old behavior does not make it something new.


Let me ask you something: How do you think Google got to be worth a gazillion dollars? In large part, it’s because of one word: SEARCH. People discovered that finding stuff on the Internet was a lot easier if they used a search engine than if they…oh, I don’t know…tried typing out different URLs to see what worked and what didn’t.

Of the many things they searched for was…maybe you should sit down for this in case it comes as a shock…products they intended to purchase. People wanted information about products to help them make their decisions. One of the pieces of information they wanted was price.

I shouldn’t have to explain this, but apparently there are people out there (not readers of this blog, of course) who think webrooming is something new.


Although it doesn’t actually prove the prevalence of webrooming–because it didn’t capture channel behavior–a recent study from Insurance.com quantifies the amount of product research consumers do for a range of products/services, and the savings they achieve from their research. Or from their “webrooming” if you were born yesterday.

According to the study, consumers spend, on average, 10 minutes when shopping for car insurance. I imagine that number is the result of the half of people who took 15 minutes to shop at Geico.com and the half of people who took 7.5 minutes shopping at eSurance.com. These were 10 valuable minutes, however, as the average savings produced by these 10 minutes totaled $540, or $54 per minute.

In contrast, consumers spent an average of 97 minutes shopping for cellphone plans, which resulted in savings of $179 (or just $1.86 per minutes. And when shopping for cable TV or other programming, consumers spent 144 minutes, and saved $248.

My take: It’s not very surprising that an insurance shopping site would find that shopping around for insurance would produce the highest savings per minute. But some of the findings regarding the amount of time consumers spend researching by product area are interesting–if they’re reliable, that is. The savings part of the equation is a bit suspect, in my opinion.


It’s a fairly straightforward process to calculate the annual savings on an annual car insurance policy. You paid $500 last year. You found a new policy for $450. You saved $50.

But how did consumers determine that they saved $1,054 when shopping for a new car? Is that $1,054 saved on the sticker price of the car? People aren’t that stupid, are they?

And how did people figure out that they saved $119 per year by shopping around for gas? It’s entirely possible that I’m the only idiot on the planet who can’t tell you how much I spend on gas in a year. But even if I did know, I’m not sure how I’d figure out how much I saved by shopping around. And how did consumers determine that they 5 and a half hours per year shopping around for gas? And why does “waiting time” figure into this equation?


What I find more interesting in the study is consumers’ estimates of the time they say they’re willing to spend to find a better price.

Apparently, consumers are willing to spend  63 minutes to find better car insurance rates.

Let’s do a little math here (just a little, because I know how it much it hurts for some of you to do this). According to Insurance.com, the average consumer who researches car insurance saves $54 for each of the 10 minutes they shop around. But consumers are willing to spend another 53 minutes to find better rates. That means consumers are leaving more than $2,800 on the table (53 times 54 equals 2862, in case you were wondering).

In other words–according to Insurance.com’s math–if consumers spent the full amount of time they were willing to invest in researching rates, they could save more than the cost of the policy.


The study also found that, in order to find better prices, consumers were willing to spend the following amount of time (in minutes) researching other product categories:

68 Airfare
68 Laptops
53 Hotel rooms
41 Clothing
34 Prescription drugs
17 Beer/alcohol

Unfortunately (for my area of focus), the study didn’t ask about checking accounts or credit cards. I really don’t know what consumers would have said. One part of me says they’d say they were willing to spend closer to an hour, and another part of me says that number would’ve come out closer to the time they’re willing to spend researching beer/alcohol prices.

I’m also torn on how much time consumers would say they actually spent researching their checking account or credit card decisions. 

Bankers’ Data-Driven Delusions

An American Banker article titled Bank CEOs Fear the Data-Driven Decision reported that:

“A recent study found that analytics are underused at banks and that senior executives are cold to the technology: a scant 20% said that if it were up to them their organization would be highly data driven.”

My take: As Paul Newman might have said, what we have here is a failure to communicate.


If you don’t think banks (in general, or the one that you work for) aren’t “data-driven,” then try the following:

1. Ask for a mortgage, but refuse to provide any information that would enable the bank to figure out your credit score or credit history. Ask the bank to decide on your loan-worthiness based on their “gut” reaction. Do this especially if you belong to a group considered to be a “minority.”

2. When trying to decide which bank branches to close, suggest giving each branch a number, then writing that number down on a piece of paper. Put all the pieces of paper together in a bowl, mix them up, and have someone pull out a piece of paper. What ever branch corresponds to the number on the piece of paper gets closed.

3. Ask for $1 million to launch a new marketing campaign. Tell the CEO he or she must make the decision to invest in the campaign without any ROI estimates, and to make the decision based on his or her “gut.”


The notion that banks aren’t data-driven is nonsense. From lending decisions to funding decisions, data is used all the time. In fact, I hear a lot of marketers complain that senior execs rely too much on data (i.e., ROI projections). And come to think of it, my suggestion in #2 above, regarding branch closings, involves numbers, so, in a way, it is data-driven.

Granted, the data banks use to make decisions might suck, but that’s a different issue.


There’s a deeper issue running through the AB article, however. It’s an issue of “definitions.” While analytics relies heavily on the availability of data, not every use of data qualifies as analytics. If you’re an analytics professional, you probably know what I mean. If you’re not, you might have no clue what I’m talking about.

Can I explain it better? No. I’m an analytics person, so I’m not very good at speaking in the language of the quantitatively-challenged. (In reality, I’m ten times better at it than most analytics people. So you can imagine how bad the problem is).


There’s another problem here. The notion that “data-driven” and “gut-driven” are at opposite ends of the spectrum is fallacious. How do experienced executive develop a “gut feel” for the market? Often, it’s after years and years of experience dealing with the data. (I wrote about this a while back).


There are countless opportunities for banks to become more analytical, or analytical-driven (although sometimes “analytics” might be overkill). But being analytically-challenged does not mean “not data-driven.” In fact, as Deva Annamalai (aka @bornonjuly4) points out in the AB article, the bigger challenge to becoming more analytical isn’t necessarily access to data, but organizational barriers and lack of business processes.

Bottom line: I’m not buying that bank CEOs “fear the data-driven decision.”

Are Bankers More Dishonest?

In an article titled Are Some Professions Less Honest Than Others? Bank On It, Researchers Find, the New York Times reported on an academic study which found that:

“______ were about as honest as anyone else—until they were reminded that they were ______.”

What was in the blanks?

What if I told you it was “African-Americans,” “Jews,” or “women”? What would you think? I bet you’d be horrified, disgusted, and outraged–without even bothering to find out how the study came to that conclusion.

What if it was “Democrats”? You probably wouldn’t believe it, but you would read further, and then blast the study for the shortcomings in its design, and lack of scientific rigor. If it was “Republicans,” many of you would chuckle and say “I knew it!”

But the blank wasn’t filled with any of the types of people I just mentioned. It said Bankers.


Apparently, “researchers” (I have to put this in quotes, as their credibility has to be called into question) from a European university ran a test with 128 employees of an ‘international bank.” They found that a subset of them, who were asked what their profession was, were more likely to cheat when reporting the results of a series of coin tosses than those who were not asked what their profession was.

According to the Times, the “researchers” then concluded that if you remind a banker that s/he is a banker, they’ll be more likely to be dishonest. The NYT article went on to say:

“To confirm their findings, the researchers performed the study again with people from other professions. Those people did not become more dishonest when asked about their work.”

Really? No other profession demonstrated any degree of dishonesty? Not a single one? How many professions were studied? Is it not possible that members of other professions are dishonest without having to be reminded of what profession they work in?

The answer to these questions aren’t as easy to find as it should be. The Times’ online article includes a link to Nature where the study was published, but the link goes to an unrelated article. I’m sure that was an honest mistake on the part of a someone at the Times (who didn’t need to reminded that s/he was a journalist who is supposed to check the facts before publishing something).

It only took a little searching to find the right article. Reading it led me to a different interpretation than the Times’, however. The Nature article says:

“The team tried to replicate the pattern in other groups of people — for example, priming students to think about banking. But they did not see the same effect on the participants’ honesty levels.”

So it does not appear that the “researchers” asked car dealers to think about selling cars, and then tested their honesty. They tested whether or not thinking about “banking” made people who weren’t bankers more likely to be dishonest!


If it’s in the New York Times it must be true, right?  I guess that we should assume that because the Times published this article, that the sample of 128 employees of this one bank are representative of the hundreds of thousands of people who work in banks across the globe.

And actually, since the sample was split, it’s probably more like 64 people who cheated and therefore besmirch all bankers everywhere.

No questioning things like “for how long did these people work in banking?” I mean, isn’t it possible that the 64 people in the group who cheated all worked in used car sales before joining the bank, and that, therefore, it’s really used car salespeople who are the lyin’ stealin’ cheaters?

And were there any ex-bankers among the people from other professions? Do lyin’ cheatin’ stealin’ bankers become good–all of a sudden–upon leaving the industry?

The results of this “study” are laughable. The lack of rigor in its design is plain and clear to anyone with half a brain.

But in its zeal to besmirch the banking industry, and the people who work in the industry (all of whom can be considered “bankers” apparently), the New York Times had no trouble running the article.


All of this is bad enough. But what’s even more disturbing to me is the reaction I got from some people when I tweeted the link to the article, and pointed out the shortcomings of the study.

One person accused me of being a “career bank apologist.”

At the risk of incurring the wrath of the friend who sent me the link in the first place, in an email he said (among other things) “I’m not interested in a conversation that denies the findings out of hand.”

Well, that was the end of our conversation, of course, because I do deny and dismiss the findings out of hand.


I don’t really care if you believe the study’s conclusions or not. You’re free to believe that bankers are generally more dishonest than people in other professions. That’s your prerogative.

But you can’t cite this study as proof of your beliefs. The study is seriously flawed.

And if you do harbor this belief that bankers are generally more dishonest than people in other professions, I don’t see how you can be outraged when other people harbor prejudices or biases against other groups of people. What makes their biases worse than yours? Do you really not see the hypocrisy of this?

If all this makes me a “bank apologist” so be it. I’d rather be an apologist than a hypocrite.

Trouble For Small Credit Unions?

When it comes to blog fodder, at one of the spectrum are sources like Forbes blogs, Fast Company, and Motley Fool, all of which occasionally publish stuff so crappy it just begs for Snarketing treatment.

At the other end of the spectrum is Callahan Associates, who I have a ton of respect for, and who I believe does great work. That view wasn’t changed last week when I sat in on Callahan’s quarterly review of credit union industry performance. Great data, great analysis.

Overall, Callahan painted a very optimistic picture of the credit union landscape–loan volume is growing, and market share is increasing in many areas and markets.

There were, however, a couple of slides that warrant further analysis and questioning–specifically those that related to the performance of credit unions by asset size.


Before we get into those slides, I’d like to state for the record that I’m not here to comment on the viability of small credit unions. I’m simply commenting on some data that Callahan presented and (respectfully) challenging their interpretation of the data.


The first slide that caught my eye contained data regarding credit unions’ 12-month loan growth, broken out by asset category. According to Callahan, the credit union, as a whole, grew loan by 10.3% in the 12 months ending September 2014. I’m assuming the data refers to dollar, and not unit, volume growth.

For the six asset categories of credit unions with less than $1b in assets, loan growth was below the 10.3% average. The largest credit unions–those with more than $1b in assets–grew their loan volume by 10.7%.

20141117 Callahan1I may be missing something here, but I can’t understand how the tail–the small number of credit unions with more than $1b in assets–is wagging the dog. If the–what 150? 200?–$1b+ credit unions grew lending volume by 10.7%, and the thousands of <$500m CUs grew at 5.1% or less, then the $1b+ CUs must have an incredibly large percentage of the overall volume of loans.


Now, if it is true that the largest CUs have an incredibly large share of CU loan volume, then the next slide in Callahan’s deck–which was titled “Smaller credit unions are posting some extraordinary growth rates”–is misleading.

20141117 Callahan2While it may be true that some small credit unions are posting “extraordinary” growth rates–like the one with less than $20m that grew loan volume by 104.7%–the reality is that the actual dollar volume must be incredibly small. And that the credit unions in each of the smaller peer groups with “extraordinary” gains are few and far between.


If I’m reading the Callahan data correctly, then, although the credit union industry as a whole may be doing well (in terms of loan volume and market share), that good health is not evenly distributed across asset groups.

And I just can’t buy in to Callahan’s attempt to spin the results as positive for smaller credit unions.


Is this evidence that small credit unions are doomed, and will disappear in the next few years? I don’t know. That’s for someone else to argue. I’m simply trying to make sense of Callahan’s data.