It is “a time between times.”

That’s what Bruce Avolio at the University of Washington Foster School of Business said recently in a discussion about how broken the US healthcare system is.  The system used to be great – and it will probably be great again at some point, but right now it’s broken because the old way is mixed up with a taste of the new way with technology, privacy, and big data in a way that makes 1+1 seem like less than zero.   It’s going to be as awkward as a newborn giraffe for some time, I fear, but it will get better.  The auto industry is in a similar spot – they ended up with some terrible products, but people still need that category of product, so companies like Ford got their act together and made a diving catch and, for now, have that company back on its feet.

But not every category is like cars and healthcare.

Big data and cloud computing are changing a lot of businesses, business categories, and customer behaviors, and in many respects that’s all the tip of the iceberg.

Big data is great and there will be loads of amazing things unlocked from all of that – as described in this TEDx talk, but we are not there yet.

Banking is in a category that’s another story.  While people still need to keep their money somewhere and pay bills, and save and borrow, the old model of retail banking is already irrelevant.   Companies like Paypal didn’t help – but more importantly, it’s companies like Amazon (and jet.com) that have really highlighted the shift in relevance.

Many of us buy lots of things from the likes of Amazon and Jet.com.  It’s one stop shopping for everything from paper towels to birthday presents.  Our credit card has become an extension of our bank account and once a month we move money from the bank to the credit card.  Many of our other bills are monthly subscriptions that also go on our credit card – so the number of times we actually think about our bank account is much less than it was just five years ago.

Buggy whips became irrelevant when people shifted from horses to cars.  Just like a lot of book stores went out of business when Amazon got big, and that’s part of the reason print newspapers are going away.  Banks – and others- are fast becoming the next buggy whip maker.

Should they just give up?

Not at all.

I was heartened when I saw a recent case study from West Monroe Partners about Barclaycard because I think it’s a step in the right direction.

Before the internet and big data came along, the big advertising inserts in the Sunday paper were enough because business was all still local.  Today, less and less is local – it’s global.  That means competition is global.  That’s not news.  But what companies have been slow to do to respond to that is really understand who their customers are, what they value, and what they don’t.  The value stream mapping that West Monroe refers to is a great label for that.  Break down the value stream, both on the customer experience level, as well as on the operational level, so you can “see” where your high and low cost areas are, in the context of value creation.  That doesn’t mean you cut all of the low value things – it simply means that you invest in them differently (cut cost out of low value, non differentiators and figure out how to improve or protect the things that differentiate).  Decisions like this need to be data driven, and while they don’t all need to be made in real time – they need to be pretty responsive.

The key is to see when demand models change and to test new ones – constantly.

Customers are comfortable switching brands very quickly – so do lots of A/B testing of different offers for different segments of customers within the value streams so you can be sure you are making the right decisions at the right time for the right customers with clear understanding of operational impact (if you push a lot of people online for new services – expect a lot of need for online help . . .).

Do have all of the right answers for all of the retail banks right now.  I don’t.  But if we know that lots of people use social networking that involves photos – think about extending the notion of a bank to be a photo bank – because you might always be happy with Facebook – but you ought to always trust your bank and banks are really good at helping you change banks if you ever decide to do that – because they are required to by law – Facebook doesn’t have to do that.  Similarly – if you know that your banking customers are in office buildings that have retail shopping space and food courts – think about how to engage them in that setting so you can be relevant there as well.

Testing demand models requires a handful of key deliverables/data and here’s a list:

-Do the value stream mapping – see where value is, and is not created

-Map customers, by segment, to the value stream to see where different customers value different things

-Break down the full customer experience journey from discovery through enrollment, and so forth (see other blogs about that)

-Map the operational cost/effort that goes along with the customer experience to see where you are over/under spending on operations relative to value creation

-Create heat maps of the capabilities that are over/under performing by customer segment

-Establish future state target maps to help all members of the team be clear about the path you are on

 

The org charts, process work flows and scorecards of old are probably looking in the rearview mirror and not forward looking enough.  Even if some of the terms above sound like new labels for things you are already doing – give this a try.

So in this time between times – keep your finger on the pulse of customer demand – and that usually means getting a new set of tools and measures to have the confidence you are doing that correctly.

I don’t intend to pick on retail banking – it’s just such a familiar industry that touches all of us – and it’s really in trouble right now.

Hollywood Ending

Blunder

I am sure there will be even more clever labels for what happened at the end of the Oscar ceremony on February 26th.  Sure you could say anyone can make a mistake – but let’s put that in perspective.

Just going off this list, 24 awards were given out during the show.  So over the course of ~100 years of Oscars, that’s one error in about 2,400 awards.  If you watch this TEDx talk about the amount of information that’s out there about each of us – to the tune of about 150,000 pieces of information about us every day, one error per 2,400 is a horrible track record.

The good news is that companies are getting really smart about data analytics.   Companies like Uptake, Loop (Litmus Automation), and now Concentric IoT (stealth mode, I am told) are helping to collect, and understand data.  And when you align those solutions with the identity management solutions of a company like AuthenticID – breaches like what happened at the Oscars will be all but impossible.

My guess is that next year there will be a different process in place as they hand out the next set of 24 awards.

If you have questions about big data, IoT, machine learned, and identity management – let me know.

 

 

If you have ever been on Facebook and seen a survey that some of your friends have done, like a personality test, or an intelligence test and you completed it – thinking it would be fun to see how you score – you are among many millions of others.  But you might be more than a little surprised how accessible that information is – and how that very same data was used to influence the outcome of the presidential election.  No laws were broken – no Russian spies or anything along those lines – that’s quite frankly what makes it all so scary.

But I am getting ahead of myself.

The term Big Data is often used as one of the most positive things to come along since penicillin.  Finally, with enough data, and the right software, computers can expose patterns the human eye cannot see at a speed few could have even imagined just a few short years ago.   This TED talk highlights some of what’s possible and what’s coming.  Incredible power in unlocking these insights.

But with any power worth having, there are good uses, and bad uses.  And lately, some bad, and scary uses have emerged – and they are likely to create some severe backlash in how people think about Big Data.  More importantly – it is going to lead to some big shifts in what we think is harmless in terms of what we share about ourselves.

This is one of the most amazing articles I have read in recent memory.  It explains how simple surveys on Facebook enabled Donald Trump to win the election using the OCEAN method of classifying personality types so that they could be very targeted in their marketing and spending.  Trump spent a tiny amount of money compared with Clinton, and won the states he needed to win the election.

In short, OCEAN is an acronym for five different personality traits of openness, conscientiousness, extraversion, agreeableness, and neuroticism, and simple surveys completed by millions on Facebook allowed those people to be “bucketed” into one of 32 different categories.  From that, anyone trying to influence one of those people to do anything different, whether it’s buy a Big Mac or vote for Donald Trump could be tailored to their specific fears, motivators, and weaknesses.

It makes perfect sense – but it gets pretty creepy pretty fast.

Almost ten years ago – Louise Story wrote of the coming shift from massive spending on untargeted marketing, to much smarter spending on much more targeted marketing.  Untargeted marketing was all there was until there was better data about the buyer, and a better vehicle to connect in a more intimate way with that buyer.  That day is here and Cambridge Analytica is the tip of the spear in wielding this information – this new superpower.  The fact that Clinton won the popular election by over three million votes, but Trump got the votes where he needed them most only amplifies the message of just how successful Cambridge Analytica was in their efforts using the OCEAN method.  Talk about winning the battle and losing the war.

Last week this article appeared in The New York Times – getting to how a single grade in high school can be an accurate predictor for both success, and likelihood to graduate.  Forget grade point averages and SAT scores – one grade in one class is the #1 most accurate predictor with high percentages on large volumes of data.

So sure, there are good and evil ways to wield this new power.   But that’s not the only problem.  The other problem, one that could be even bigger, is that it can make us lazy.  If one grade in high school is a more accurate predictor than grades and SAT scores – are we really going to hide behind the percentages and use them to draw conclusions about whether my own son, George, is going to graduate from college in four years? Whether he is going to succeed in his chosen field of work?

Not so fast Big Data.

If you haven’t seen the movie Sully, with Tom Hanks, you should. It’s one of the best movies I have seen in a while – and I see a lot.  This movie is a true story about a pilot who landed a plane on a river in New York City in 2009 and everyone survived.  But the computers at the National Transportation Safety Board, NTSB, looked at all of the flight data, and through repeated simulations, found that he had plenty of time to actually land safely in not one but two nearby airports – which meant his employer could fire him without a pension – instead of giving him a ticker-tape parade for saving the lives of all 155 on board.  Sully came very close to criminal prosecution (the movie implies) until he asked a couple of questions – questions only a pilot of 40 years would have had the confidence to ask.  He asked if the big data and their computer simulations had taken the “human element” into consideration.   It turned out the data of the NTSB made a couple of big assumptions about how much time was needed to determine whether or not it was safe to try to make it back to an airport – and once they understood the flaws in their assumptions – it was revealed that the plane would have crashed if the pilot had actually tried to make it back to an airport.  The pilot in Sully had 40 years of experience and made the right decision.  Let’s be really careful about what assumptions we roll into Big Data and not overlook the human element.  After all – my son isn’t going to have 40 years of experience in high school when it’s time for him to apply to college – so he won’t be able to defend himself if the school of his choice makes a wrong assumption about him – just because of one grade.

Another, shorter way to emphasize the importance of the human element is something my father says “it’s not the size of the dog in the fight – it’s the size of the fight in the dog.”

Big Data is incredibly powerful – let’s be smart about how we use it – and let’s not make too many assumptions about what the data means.

We have heard a lot about the gig economy and the sharing economy.  It strikes me that there’s something bigger going on and I think it’s safe to call it the “Nothing is Sacred” economy.  There are no more unbreakable rules – so the notion of something being disruptive is becoming increasingly rare.   Nothing is Sacred.

 

I’ll admit that I was raised in a household with a lot of rules, but I think there is a list most of you will agree were accepted as fact not so long ago, and I have broken it into personal and business items – and these aren’t intended to be exhaustive – merely to orient you on the point.

 

Personal Rules

-Keep your money in a bank

-Own your own car

-Get married and stay married to that person forever

-The husband is the Dad and the mother is the Mom

-You’re born a boy or a girl and that was the end of it

-Never talk about the bathroom

-Never talk about the bedroom

-Eat meals at the table with your family, especially meals like Thanksgiving

-Go to work for your job at the same job for the rest of your life

-To get something you want or need, you have to go to a store

 

 

Business Rules

-Everyone works in an office at a desk

-The business has to make a profit

-You have a strategy

-You have a software team that release software in releases from software projects

 

There are now countless examples illustrating that none of these are rules anymore (to the extent they ever were in the first place).  And individually none of them is especially shocking except maybe that there have been over 28 million views of a product called Squatty Potty that uses animated pooping unicorns to make its point (really – https://www.youtube.com/watch?v=YbYWhdLO43Q) and over 38 million people have watched a video about a product that masks bathroom oders called poo pourri (again, really – https://www.youtube.com/watch?v=YbYWhdLO43Q).  Both of those videos were created by a marketing team called the Harmon Brothers (http://harmonbrothers.com) and, of course, they have another one about the bedroom – and they seem particularly adept at figuring how to push the envelope.

 

How did we get there? As is often the case with something like this, Rome was not built in a day.  Some of us are old enough to remember some firsts that people still talk about where something that had never happened before happened.  Several of these that I can think of happened right in our very own dens as we watched TV.  Star Trek’s Captain Kirk with the first interracial kiss.  The sit-com Soap that had the first gay character (played by Billy Crystal).  Corporal Klinger was the cross dressing army guy on M*A*S*H.  Ally McBeal had two women kissing – not sure that was the first same sex kiss on TV but it was memorable.  Orange juice pitch-woman Anita Bryant was publicly shamed for her homophobia.  On the show Friends, the wife of Ross left him for another woman.  So there’s a long list of how we got to the new normal – where none of the old rules apply to our personal lives.

 

Some of the changes in business rules have been more recent, and more concentrated.  And not on TV in your den or your kitchen.  People have been increasingly changing jobs and working from home or remotely for a long time – especially in high tech jobs.  Amazon was really the first one to prove you can be hugely successful without profits while Microsoft on the other side of Lake Washington was seemingly trying to follow the rules by growing profits, under Steve Ballmer, which went completely unrewarded.  I am told Harvard Business School has had to revamp some classes because of that particular episode.

 

Not having to have a strategy is a newer thing and the companies that have achieved that don’t talk about it much – yet. It relates to the fact that they (Amazon, Netflix, Google, Apple to name some) don’t do software projects any more and they don’t release in cycles anymore.  They have figured out how to organize teams around products and those product teams are constantly (almost literally) releasing new code.  Some call it CICD for continuous integration and continuous delivery. Some refer more to the organizational change – a Matrixed Organization Structure, or MAXOS for short (http://maxos.ai).  Because they are releasing new code all day every day – they are able to measure the success or failure of each release pretty close to real time.  So when something doesn’t perform as expected, they can change it – also in near real time.  People are calling it sense and respond management and because you can manage performance in real time – the need for long term strategies becomes less important for an already established business.  So instead of there being rules for how customers are supposed to behave relative to strategies and prior behaviors, now you can just respond to their behavioral changes and go with the flow.

 

I will admit that the light bulb moment for me on this new reality was this Pillsbury ad that aired around Thanksgiving. http://www.tvcommercialspots.com/food-and-beverage/pillsbury-tv-commercial-happy-friends-giving-from-pillsbury-make-your-own-delicious-crescents-and-toaster-strudels-this-holiday-season-pillsbury-make-dinner-pop-thanksgiving/

.  It has some great messages, one of which is that there’s no wrong way to do Thanksgiving – and in a way it felt like a permission slip to people to do whatever the heck they want to do for Thanksgiving.  And for me – Pillsbury products were gross and not part of our Thanksgiving – but because of the way they wove the products into the message – all of a sudden Pillsbury crescent rolls are OK now.

 

So there it is.  Here we are.  The Nothing is Sacred economy.

In many organizations – the expansion of the role of the QA Engineer, for quality assurance, the people who test the lines of software created by developers, has led to a similar shift of accountability, in effect laying out the welcome mat for risk and sloppiness, that we saw come to a head in banking in 2007.

This dawned on me  when someone was explaining the importance of Agile as a software development model (this being the week of the national Agile2016 conference), and why the old waterfall model many of us knew in the 90s is ill-suited to current needs.

I get it.

But in the description of the journey – they mentioned something that made me realize that part of the massive mess many development organizations are in today (but software companies as well as companies with an IT department that does development), which includes waste and risk and stress, has a lot in common with the 2007 financial crisis in terms of the root cause.  I expect Freakonomics authors Levitt and Dubner would see the connection.

Let me back up and offer a simple explanation of the financial crisis.

The 2007 financial crisis hurt millions of people all over the world, and as people like Michael Lewis told us in The Big Short, and Matt Damon narrated in Inside Job, it was because of some now painfully obvious decisions by a relatively small number of people.  If you haven’t seen the movies or read the books – I can’t recommend them enough – but (spoiler alert) you won’t like the ending. At all.  The short of it, no pun intended, is that banking, specifically loans, used to be extremely simple two party transactions with a borrower and a lender like the image below.

When the borrower was too risky for the lender, they wouldn’t get a loan, which makes perfect sense, and then because banks usually did a pretty good job of assessing risk, most loans were safe for a long time.  If you haven’t gotten a loan, they ask a series of pretty basic (oversimplified here) questions:

1. What is your income level?  If you have a job and make a certain amount of money, that’s an important way to tell whether someone can afford a loan.
2. What are your monthly expenses?  If you make $5,000 a month and your expenses are already $4,500  a month, you probably shouldn’t have a mortgage payment higher than $500 a month
3. What’s your net worth?  If your income changes, is there another way for you to pay the mortgage.
4. Do you pay your taxes?  They don’t really ask the question this way, but part of the test of loaning someone money, in addition to seeing how much income they have told the government they make, is whether they are honest and timely with their tax payments.

These seem like very reasonable questions to ask.

Then things started to get complicated when other organizations like investment banks started buying loans in bulk from the lending banks and then selling shares in those bulk blocks of loans like shares of stock, which are securities, and thus was born the mortgage-backed security.   On paper that’s still sane and on the up-and-up. Then what happened is that the people lumping in all of these mortgages started to put some less valuable, riskier mortgages into the bundles.  But as they stuffed the bundles with worse and worse loans, no one seemed to notice, and they maintained very high safety ratings (there was no motivation for the ratings agencies to downgrade them – that was also part of the problem).   This is what Inside Job describes (in great detail) as the Securitization Food Chain, depicted below.

So while banks used to be rigorous in choosing who they would, and would not, lend to – some banks like Countrywide figured out that someone would buy the mortgages as soon as they were signed, so there was really no risk to them in signing them, so because there was so little risk – they decided to be much less rigorous in choosing who got loans.  In many cases, they didn’t even ask question #1 above.   So people who should not have been approved for loans got approved.  Sloppy.  That’s a big part of why millions of people lost their jobs as a result of the crisis.

As I said at the outset – in many organizations – the expansion of the role of the QA Engineer, for quality assurance, the people who test the lines of software created by developers, has led to a similar shift of accountability, in effect laying out the welcome mat for risk and sloppiness, that we saw come to a head in banking in 2007.

Back in the 90s I was a developer and we wrote software for a machine called an AS/400, and like most developers who were writing software for internal use at a big company (things like order entry and accounts receivable, general ledger, customer relationship management, etc.), you were also expected to test that it did what it was supposed to do (usually captured in a requirements document).  There were several levels of testing:

1. Unit Test.  This is making sure it does what it is supposed to do, all by itself.
2. String Test.  Most software applications, especially complex internal systems, communicate with other applications, which means passing information back and forth in the right format.  String test usually included just the immediate “neighbor” applications with which that application communicated.
3. Integration Test.  Often applications have to communicate with other applications at other companies, in other places, where you have less control and visibility over changes.  Integration test is a way to ensure the software passes information back and forth successfully with those “outside” applications.   A simplistic example would be that many companies process payroll through another company, and that means sending lots of information about people, money, timing, and so forth, to the payments company, in a timeframe that works to complete payroll successfully.
4. System Test.  System Test is where you run all applications in a given ecosystem at the same time – often simulating entire weeks or months of transactions.
5. Stress/Volume/Load/Failover Test.  Once you are confident the software does what it is supposed to do – some organizations process millions of transactions a day now, and that means you have to make sure there is enough hardware and network bandwidth to handle the loads successfully all day every day.

Both of the above lists of questions (loans) and tests are simplified – but they make sense even if you aren’t a developer.  When I was developing, test #5 was usually done by a different group of specialists, but each person was accountable for testing their stuff through System Test, and if it failed, it reflected badly on you, and you felt you let the whole team down.

Fast forward to today, and a lot of developers aren’t even expected to do test #1, Unit Test for the code they write.  Once they finish coding, they hand off the code to these QA testers and it’s their job to see if the code works or if it has bugs, and then there are even more QA people to do the other string, integration, and system tests.  And when they find a bug – of course they send it back to the person who wrote the software to fix it, right?

No they don’t.

They document it as a bug and it’s sent to a different group of people to fix it.

Which is why – when someone was telling me they need to hire more QA engineers to ensure they are producing high quality code, I realized it has gone too far.  Developers aren’t being held accountable for their software – their only motivation is to write hundreds of lines of code, even if it just says “All work and no play” like it did in The Shining.

So I get the importance of Agile, but if at the same time we don’t hand accountability back to the software developers who write the code, we are no less crazy than Jack in The Shining if we expect to realize all of the risk, efficiency, and cost benefits promised by Agile.  Well maybe not that crazy.

Just as the banking errors that led to the 2007 crisis are now incredibly obvious – this shift of accountability should be equally obvious to people leading development teams, and the steps needed to fix it aren’t too complex.

Next I will point out that many organizations think they are Agile, or have started to try, but haven’t gotten to Agile – and that in part because of the mountains of additional data they are going to have to start collecting very soon – they are running out of runway to become truly Agile before their maintenance costs and bug rate expense spins completely out of control.

There are phrases like Big Data and Cloud Computing that were not in our regular lexicon 20 years ago.  In that sense experience is not literally a new word, but when it comes to the way many people are using it, it might as well be.

As a word, “experience” has been around since at least the 13th century tracing back to the latin word for “try” and since then it seems most languages including Russian (опыт), Chinese (经验), Icelandic (reynsla), to German (Erfahrung), Dutch (revering), Italian (esperienza), and every other language I Googled,  have a word for it.

I considered comparing the transformation the word experience is going through to eclosion – the process by which a caterpillar goes through in transforming into a butterfly. But even that doesn’t work, because the transformation is really one where the word experience was an almost abstract and intangible but deeply personal notion (in ways a caterpillar is not), in much the way many of us use the word love.  Increasingly experience is being used to convey something very concrete, real, and measurable.  Some of this is rooted in the opportunity to have richer and more personal context for experiences of all kinds and Robert Scoble’s book Age of Context was ahead of its time with some of its insights on what people are doing with technology to amplify context and improve experience.

These days I see and meet organizations that have people and teams with the word “experience” in their titles and having been a big part of efforts to create “next generation experiences” for Disney customers and “customer experience transformation” efforts for others – it is clear that organizations see that designing and creating new customer experiences is the next frontier, and data and information are at the core of this.

Customer experience journey maps are an increasingly common tool to inform the prioritization of opportunities related to these customer experience transformations.  This is one place where experience already means much more than “try” in the traditional sense.  Common steps in experience journey mapping include:

-Discover (how they find out the experience is possible or an option for them)

-Enroll (if they need to sign up to participate in the experience this is a step)

-Plan

-Arrive

-Experience (this is when they immerse themselves in whatever it is they want to do, online or physical.  You might put the word “try” here if you prefer)

-Remember (whether looking at photos of their experience, or completing a survey about their doctor visit, all of that fits in this segment)

 

In some cases experience journey mapping is an ideal place to start – in other cases it is both risky and ill-informed.

We are all familiar with the old adage of getting from point A to point B.  Many people talking about experience definition and change are solely focused on point B – where they want to be, not actually stopping to consider that they don’t have much information about point A (where they are right now).  Important questions to ask include:

-Who is the customer?

-What is the customer lifetime value for each customer?

-What are the meaningful customer segments?

-Which segments are growing and shrinking and do we know which we should be more excited or concerned about?

-Why do they choose us when they have an experience with us?   Is it our price, speed of service, product selection, the way we recognize them for their loyalty, or something else?

 

Because the information most organizations have about their customers is transactional – that is, what they have purchased, as one example, there is very little information about the information around the transaction – why they chose that transaction, what other things they considered, how long they thought about it, whether it was meaningful to them, and so on.

Experience data is in many instances the data between the transactions and touch points (this is a good piece in HBR by Alex Rawson, Ewan Duncan, and Conor Jones where they elaborate on case studies about delving into the data between the touch points and what it can reveal in terms of actionable intelligence).  From that data, an organization can create information, and from that information patterns emerge.  That’s when you unmask your point A, and from that point A, it’s much more practical to start defining a point B.

If you are part of this data-driven experience world, I encourage you to start collecting data so you can get information from that data before you get too bold in your experience transformation bets.

The bigger point is to realize that the way people are using the word is in itself transforming, and people who continue to treat experience as an intangible abstract notion like love are missing the change already underway, and clinging to transactional data about customers is not enough.