Almost a year ago I wrote about how different the use of the expression “customer experience” is from just a short time ago in this blog post.  The gist of it is that it used to be a vague notion that couldn’t be measured and now it can be measured in all sorts of ways.  There’s a reference in that blog to an HBR piece by my friend Ewan Duncan from McKinsey who highlights how they found that the key elements of customer experience were literally between the touch points the client was actually measuring.  So they were literally measuring the wrong things and thus didn’t know when their customers were extremely unhappy.

I recently heard from my friend David Lancashire and his CTO Rob Pierry at Projekt202 – they do interesting work in customer experience and David told me about this piece that came out just yesterday in FastCompany.  There’s so much room for improvement in the airline industry – and with a firm like Projekt202, I am not surprised to see that Southwest has had success with them.

John Hawthorne is someone who got in touch with me and point out this piece about how to really wow customers and John made me realize that my previous blog post only pointed out the opportunity to define and measure customer experience differently – his piece does a great job of highlighting some not-so-obvious ways to go above and beyond in that department.

You MUST see this TEDx talk on customer experience and big data.

 

We continue to do some really groundbreaking work in the realm of customer experience and we look forward to telling you all about it when we can.  So stay tuned.

My friend Lance Knight wrote this very provocative piece recently and I am including both the text and the link.

I have a really deep background in CRM and I have read The Challenger Sale (I did a summary of that book using a new tool that lets you zoom in and zoom out of detail – message me if you are interested in that) and it struck me that what Lance is suggesting is on the one hand kind of obvious, and on the other hand somewhat shocking that no one has done this yet.  So have a read and let us know what you think.

-Ric (see the TEDx Talk on How Technology is Going to Improve Your Quality of Life – FINALLY)

Backlog, Teams, Closed Sales WHAT?  

People are racing to blockchain for some really good reasons – it reduces risk, creates efficiency, and adds transparency for industries as diverse as logistics, banking, and healthcare.  This article from The New York Times is a good explanation about it and what’s good about it.  And this TED talk by Don Tapscott offers a very clear explanation of what blockchain is.

But there’s an issue I haven’t heard people talking about that seems like a “Houston we have a problem” kind of risk – or if you saw the movie Ex Machina – it feels a bit like the AI issue that movie hints at.

This TED talk gets into the patterns that will emerge from Big Data.

If you haven’t seen Ex Machina – it’s an amazing thriller of a movie that suggests that once artificial intelligence gets smarter than we are (in this case in human form) there won’t be anything we can do to stop it – and the implications are pretty scary.

Yes.

If you have an open source platform that can trace the full chain of custody of everything from radishes on a container ship, to heart valves in a hospital, to every transaction in a financial services company – you have transparency into everything.  Part of the backbone of Wall Street and the global stock market is quarterly and annual forecasts and results.

If there is total transparency in all of these systems in real time and the systems are open sourced – even if companies try to make some or all of this data confidential or private – do you really think it won’t be hacked or exposed (if those two things are different)?  That’s the promise of blockchain.

Hacking the personal data of tens or hundreds of thousands of accounts at Target was big news a few years ago.  But hacking your grandmothers target account information is a bogeyman problem compared with gaining transparency to the global markets in real time.

Now one way to look at this is that if all of a sudden we can use machine learning to get predictive about that shipment of radishes to Whole Foods and what that means to their forecasts, and how many new accounts at Wells Fargo will do for their projections, or how many heart valves today will translate into Kaiser Permanente financial results – that real time global transparency will make the world smarter and we can do away with quarterly projections and that every thing will be run like Amazon (everything managed in real time).

But while that may seem like an interesting and viable end state – what seems like a more realistic outcome is that a small number of people will figure this out ahead of everyone (think The Big Short by Michael Lewis – but on a much bigger scale) and they will be able to game the entire global stock market.

That seems like a very big, very real risk and I don’t think we are ready for that.

So as we race to blockchain everything – let’s think through some of the downstream risks before we open up this new floodgate of Big Data.

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.