Can We Trust The Trust Numbers?

Each year, there seems to be no shortage of well-publicized surveys showing how much trust consumers have in banks, and how that trust has changed since the previous survey. What is it about the banking industry that makes bankers obsess over whether or not their customers trust them? You’d think that brain surgeons would be as concerned with their patients’ level of trust in them, but noooo.

A recent Harris poll (as reported in The Financial Brand) found that:

“Local credit unions and local/community banks are the most trusted institutions, with over three-quarters of Americans having some or a great deal of trust in them. Big national banks rank second to last, having the trust of only 50% of Americans….while 42% state they have no trust at all or very little trust in these institutions. Online-only banks are seen as the least trustworthy, with only 39% of Americans having at least some trust and 47% having no or very little trust in them.”

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With these data points in hand, credit unions go into full-on Sally Field mode: “They like us! They really like us!”

A lot of good it does, though. According the BusinessWeek’s estimates, the top 3 banks in the country have 33% market share, with the top 10 banks holding nearly 50%. And those market shares appear to have grown over the past few years. Despite low levels of consumer trust.

One community bank CMO used the trust data to support his contention, in an editorial in American Banker, that community banks and credit unions should go “negative” against big banks in marketing campaigns. Like community banks and credit unions haven’t already been doing that for the past five years, and to practically no avail.

Here’s what I have trouble reconciling: On one hand (as reported by Harris), credit unions are the most trusted financial institutions. On the other hand, it would be appear to be common knowledge that credit unions are the industry’s “best kept secret” (you can peruse the 16.2 million results to a Google search for “credit unions best kept secret”).

How can credit unions be the most trusted if nobody knows about them?

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Harris also found that online-only banks are the least trustworthy. REALLY? How would consumers know that? What percentage of the population has actually done business with an online-only bank? (I don’t know. It’s a serious question).

I can’t imagine that it’s a particular large percentage. Yet some researcher thinks it’s OK to ask consumers about their trust in something those consumers have no experience with, and to publish the results as if it were gospel.

Interestingly, a day or so after publishing the results of the Harris trust survey, Jim Marous published a piece in The Financial Brand titled Is It Time For Digital-Only Banking? citing a Javelin Research study which found that “71% of those who use mobile banking say that online and mobile banking is sufficient for their needs.”

Marous is asking a great question, and I don’t dispute Javelin’s findings in any way. But how do you reconcile that with Harris’ findings that consumers have little (or no) trust in online-only banks?

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According to the Harris poll, half of US adults say their trust in banks has declined over the past few years.

Edelman would beg to differ. Or, at least, they could beg to differ.

According to Edelman’s trust survey, which they conduct annually, 38% of Americans trusted banks in 2009. In 2014, that percentage increased to 46%. By the way, as a point of comparison, 48% of Americans’ said they trusted “businesses in general,” so the level of trust in banks isn’t too far out of line.

So, is trust in banks increasing or declining? The answer is: Whatever you want it to be.

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I might not be comparing apples to apples with the Harris and Edelman studies.

Harris appears to capture the “change in a consumer’s level of trust” while Edelman is reporting the change in the “overall percentage of consumers” that trust banks. It’s conceivable that there are a lot of people who a few years ago said they had “very little” trust in banks, and today said they have “no” trust, which, of course, would be a declining level of trust.

This raises questions I don’t hear a lot of people asking: What does it really mean to have a “great deal,” “some,” “very little,” and “no” level of trust?  Is your definition of “great deal” of trust the same as mine? Is trust a “bucket” which we can accurately measure how filled it is?

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Bottom line: If the trust survey data tell a story that supports your financial institution, please don’t let my comments–or any modicum of common sense–get in your way of using the data to your advantage. That’s what quantipulation is all about!

But please don’t deceive yourself into thinking that the findings from these studies have any correlation to who consumers bank with, or how they make their decisions about who to do business with.

 

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Federal Reserve Mobile Financial Services 2014: Great Data, Lousy Graphics

Your objective when presenting data, whether it’s in a presentation or a paper, is to convey meaning.

The reason we use graphs in presentations and papers is that a graphical representation of data is often easier for the audience to process than the written or spoken number. This doesn’t stop a whole lot of people from screwing it up, however.

Here’s an example of the failure to use good data visualization principles to facilitate the communication of data. Take a look at these two charts from the Federal Reserve’s recently released study on consumers’ mobile financial services habits. The content of data of the questions and answers isn’t important here. It’s the percentages shown.

20140331 FedReserveIn the first chart, the most-frequently cited response was mentioned by 53% of survey respondents. In the second chart, the most-frequently cited response was mentioned by 28% of respondents.

Yet, the length of both bars in the two graphs is nearly identical. Your anal-retentive snarketer here actually measured them. The 28% bar is a little more than 3.5″, the 53% bar is 3.9″. But because they both take up the entire space allotted for the graph bars, you’d be hard pressed to notice this difference.

I don’t have a PhD in Math or anything (just an MBA in Finance and Statistics), but it seems to me that the 28% bar should be just a little more than half as long as the 53% bar. If they were on the same graph, I bet that would be the case. So why would a bar representing 28% on one graph be any different length on other graphs in the report?

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Stop doing this, people. Stop stretching the top bar in a graph all the way across the graph. If you have six inches of space, only a bar representing 100% should take up the 6″. If the top bar in a graph is just 50%, it should only be 3″ long.

You better convey meaning by keeping graph dimensions as similar as possible across graphs in a report or deck. You’re making readers and audiences work too hard to interpret the graphs when the top bars are stretched all the way. And wasn’t “ease of comprehension” the real reason you chose to display data graphically, instead of textually, in the first place?

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Of course, there is another–more sinister–reason why this happens as often as it does: The presenter/writer is intentionally trying to deceive us.

That’s right. They draw that top bar, which might represent just 23%, all the way across the graph, in order to make it appear larger than it really is. And, of course, in a graph where the 23% bar goes all the way to the edge, the difference between 23% and 19% looks huge. In a proportionately drawn graph, that difference looks miniscule.

Of course, when considering the margin of error,  the difference between 23% and 19% might not even be statistically significant. But let’s work on one problem at a time, here.

Why Those Holiday Spending Forecasts Are Total BS

Consider the following claims and forecasts regarding 2012 holiday retail spending:

  • The NRF forecasts that “American shoppers will spend just under $750 on average on their holiday purchases this year, up slightly from the nearly $741 spent last year.”
  • Nielsen forecasts 2012 holiday spending to hit $98.3 billion.
  • Comscore expects 2012 online holiday spending to come in at $43.4 billon.
  • A survey from the National Foundation for Credit Counseling (NFCC) revealed that “50% of consumers intend to spend less on holiday purchases this year than last, while 37% plan to spend nothing at all. “

My take: Something doesn’t add up here.

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The first challenge is provided by the NRF forecast. When it says “American shopper” does that mean individual or household? If individual, what age range are we talking about? Just adults, or does it include teens?

If it’s households, then the NRF forecast comes out to about $86 billion, a bit lower than the Nielsen forecast, but in the same ballpark.

If we’re talking individuals (and just adults), then at $750/person for the 221 million adults, the NRF forecast comes to roughly $166 billion, 69% higher than the Nielsen forecast.

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So I guess we’re talking about households.

But hold on.

Comscore’s forecast of online holiday spending — $43 billion — represents half of the NRF forecast. I know a lot of people of buying online these days, but is the online channel really going to account for 50% of holiday sales? Seems a bit too high for me.

So maybe the NRF “shopper” estimate is for individuals, and Nielsen has its head in the sand.

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That could make sense looking at the input from NFCC.

It says 37% of “consumers” won’t spend anything at all.

Damn. Is “consumers” households or people?

If it’s individuals, total spending would be $104 billion, a little higher — but in line — with the Nielsen forecast. If it’s households, though, then at $750/shopper (or consumer), total spending will only reach $54 billion. Which is nowhere close to anybody’s forecast except for Comscore’s, which only includes online purchases. 

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The NRF says that the $750 that the “average” shopper will spend this year is up slightly from the $741 they spent last year.

But the NFCC found that 50% plan to cut back on spending, 11% will stay the same, and just 3% said they would spend more.

That would mean that those 3% are planning to spend a HELLUVA lot more for the $741 number to rise to $750.

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So how much are Americans going to spend during the holiday season this year?

Don’t ask me.

Maybe Nate Silver’s got a forecast.

Quantipulation: Fat People Are The Cause Of Inefficient Fuel Use

A major insurance company (whom I won’t mention by name in a feeble attempt to stay out of trouble, but which you can easily identify with a Google search or two) has concluded (as quoted by the Chicago Tribune, which clearly had nothing better to report on):

“Obese Americans are hurting the fuel efficiency of vehicles, contributing to more than 1 billion gallons of fuel wasted each year.”

According to the infographic on the insurer’s blog (gotta have an infographic if you’re going to disseminate statistics!), 39 million gallons of fuel are used per year for every pound added on in average passenger weight.

My take: The study is an example of quantipulation at its finest. 

While obesity is a real, and serious, issue, tying fuel consumption to increasing obesity rates conveniently leaves out a significant contributing factor: Demographic trends. 

The baby boom between 1946 and 1964 produced a generation of roughly 77 million people. 

So what happened was this: People born between 1950 and 1960 increased in age from newborns to 10 years old to 10 to 20 years old in the 1960 to 1970 period (the first ten years of the study), and so on for each of the next three decades of the study. 

Not surprisingly, as people go from childhood to adulthood (and sadly, through adulthood), their weight naturally increases. 

In addition to individual people’s weight gains, with more kids in the car, the overall passenger weight in the car increased.

Bottom line: The increase in passenger weight is attributable to not just obesity, but to the natural weight gains of people as they age, and to the increase in the number of passengers in the vehicle which increased throughout the study. 

But please don’t let this stop you from blaming fat people for higher gas prices, and for ruining the environment. 

The Snarketing Perspective On Bank Branch Closings

If you haven’t figured it out already, I have an issue with some of the bloggers on Forbes.com. It boggles my mind that Forbes lets some of them publish under the Forbes banner. The quality of some posts is nowhere near Forbes’ (usually) high standards.

The latest offender is an article titled ATMs Not The Only Things Disappearing At Bank Of America, It’s Closed The Most Branches Too. In it, the author writes:

“Over the last year BofA has shut down 163 branches. That’s the greatest number of closings for any U.S. bank. It’s opened just six in the same period for a net of 157 branches closed. BofA’s ATM and bank branch closures shouldn’t come as big surprise as its CEO Brian Moynihan looks for ways to cut expenses. Last year the he announced Project New BAC which he said would slash 30,000 jobs and cut costs by $5 billion by the end of 2012. Still, even with the greatest number of branch closures BofA is still #2 in the country with 5,858 branches nationwide in front of JPMorgan Chase‘s 5,442.”

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OK, first the nit-picky stuff.

The word “it’s” is a contraction for “it is.” So, in the title of the article, saying “it’s closed the most branches” is incorrect. It should be “it has closed the most branches.” Same mistake as in the third sentence in the quote.

Second, when discussing numbers, you say “that’s the largest number…” not “that’s the greatest number…” Great is an inappropriate word to use in that context.

Third, the lack of proof-reading on Forbes’ blog posts kills me.. “Last year the he announced…”? Oh wait, is “the he” the new way of referring to Mr. Moynihan? (Did I ever tell you about the time I met him? Maybe in a future blog post). I might point out that it should be “shouldn’t come as a big surprise…” but I’m afraid someone will accuse me of going overboard here.

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Now for the more substantive comments.

When you’re the biggest or largest (but not necessarily the “greatest”, right?) player in a space, having the most [fill-in-the-blanks] is never a surprise. What’s news is not absolute numbers, but percentages.

But, of course — here comes the Snarketing slam — when your objective is to simply write a puff piece jumping on the BofA bashing bandwagon, you ignore that, and try to make news out of the absolute number.

The real story in the SNL data isn’t Bank of America. Even if you’re a bank basher.

Despite closing the largest number of branches, BofA only closed 3% of its branches (betcha the Forbes blogger would have written that as “it’s”).

Five other banks, however, closed 10% or more of their branches, including International Bancshares, which closed one in four of its branches, and Old National, which closed one in five in the past 12 months.

Granted, these five banks aren’t as high profile as BofA. But combined, the five of them closed 173 of the 1,093 branches they started off with at the beginning of Q3 2011.  That’s 16%, or more than five times larger than the percentage of branches that BofA closed (163 divided by 5,858 = 3% in case the Forbes blogger’s math is as bad as her grammar).

Here’s something else more newsworthy coming out of the SNL report: JP Morgan Chase opened 243 new branches in the past year. In other words, of the 1,234 new branches opened in the past 12 months, one bank (JP Morgan Chase, for those of you having trouble following this) accounted for 20% of the openings.

I hear you saying, “You’re violating your own rule, Snarketing boy. It’s not how many branches Chase opened, but what percentage of its branches are new!” How quickly you learn, grasshoppers!

Yes, just 4% of Chase’s branches are newly opened in the past year. But of the banks on SNL’s list, only two have a higher percentage: Huntington with 8% of its branches opened in the past 12 months. and First Community, where one in five branches have been opened in the past year. And I have no idea who or what First Community is, so they don’t count. So sit back down, junior statisticians, the Chase number is still meaningful.

That concludes today’s Quantipulation lesson.

Oh, and if you think I’m being too tough on the Forbes blogger’s grammar, read I Won’t Hire People Who Use Poor Grammar. Here’s Why.

How To Quantipulate Using Graphics

To refresh your memory, quantipulation is:

The art and act of using unverifiable math and statistics to convince people of what you believe to be true.

Examples of quantipulation abound in marketing and politics. Today, I’ll show you how to quantipulate with graphics, using a real-life example pulled from a very reputable firm’s blog.

In a post titled What Social Networking Websites Do Consumers Access Within the Course of a Typical Month? the Raddon Group enclosed a Powerpoint deck that reported the results of a recent survey the firm conducted. The graphic below was pulled from the deck, and was altered to hide the numbers on the vertical axis. The chart shows the adoption of PFM (personal financial management) tools from Fall 2010 through Spring 2012.

Based on the chart, what would you guess the growth in PFM adoption has been? From about 30% to 90%+? Sounds reasonable to me. But here’s the chart with the numbers, as displayed in the presentation deck.

Although the two bars representing Fall ’11 and Spring ’12 are twice the size of the Fall ’10 bar, the difference between the bars is just 2%, or 20% of the original bar’s total. 

Call me silly, but if one bar is twice the size of another bar, how can that bar be only 20% greater than the other one?

Bottom line: While it’s tempting to manipulate graphics to make changes look more pronounced than they really are, it’s not a good management or presentation practice. Axes should start at zero (unless the numbers reported go into the negative range). And the size of bars should be proportional to the space allotted for them. 

In other words, if your chart takes up 5 inches of space, a bar representing 20% should be about 1 inch long (or high, depending on the orientation). 

Oh, and one more thing: If you do try to quantipulate, I will find it and call you out on it. 

Quantipulation: Financial Advisors’ Use Of LinkedIn

LinkedIn recently published an infographic depicting financial advisors’ use of social media. Advisors’ use of LI exceeded their use of other networks like Facebook and Twitter. As Gomer Pyle would say, “Soo-prise, soo-prise!”

But seriously, LI’s findings on advisors’ prefered networks are consistent with my research. What caught my eye, though, was the following stat:

Advisors who prospected on LinkedIn achieved a 62% success rate.

My take: Quantipulation at its finest.

To refresh your memory, quantipulation is:

The art and act of using unverifiable math and statistics to convince people of what you believe to be true.

What exactly does 62% success rate mean?  Succeeded at what? Is it implying that 62% of the time that advisors used LinkedIn they “succeeded”? Does it mean that 62% of the prospects they found on LI became clients?

My guess is that it means that 62% of advisors said that they had success with LinkedIn, not that they had a 62% success rate.

If that’s the case, it’s hardly a remarkable finding. Advisors — and all marketers — generally find some success with every marketing channel they use.

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The infographic also says that, of the advisors who had success with LI,  32% gained $1m in new assets.

Oh really? Was that $1m in assets from just the LI prospects, or $1m in new assets overall? How much in new assets did the other 68% generate? It’s entirely possible that those 68% grew their book of business more from other sources that the LI-successful group.

Here’s my contention: Advisors who are good at marketing use lots of different channels to prospect, and are more aggressive in prospecting than advisors who aren’t as good at marketing.

The channel doesn’t make the marketer, the marketer makes the channel.

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Another interesting data point states that 52% of investors would interact with advisors on LI, but that just 4% do.

Hey, LinkedIn: If you want advisors to be successful using your network, you have to answer these questions:

  • Why the gap?
  • Why don’t more investors interact with advisors on LI?
  • What’s the secret to engagement on LI??

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Bottom line: You can’t take statistics at face value. But you knew that already, right? RIGHT?