What would happen if suddenly you became capable of reading the minds, needs and secret desires of an entire market?

And if also you acquired the knowledge of how to build the product or service they are craving for, at the lowest cost possible?

I’ll tell you what will happen: you’ll become RICH.

And that is exactly what the Voice of the Customer methodology does: it gives you the means of methodically knowing what your customers want.

Now you only need to pair that knowledge with an efficient production process, and you’ll have a profitable product your customers will be flocking to take from your hands.

What you will learn today

Today we will explore:

• What is the Voice of the Customer (VOC)

• Why it is a good idea to implement a VOC program in your business

• How can you use it to identify new ways to provide Customer Value through your products and services

• What do you need to have your own VOC program

• How to make it happen!

What is the Voice of the Customer (VOC)

In a nutshell, Voice of the Customer is the term used to define the stated and unstated customer needs and requirements1.

Let’s stop here for a minute and analyze the meaning of this.

As a business, we want to know what is in our customers’ minds, their needs, their problems, etc.

Then we can try to create products and services that will cater to those needs, in a profitable way.

But as we cannot read minds (yet), we need to recourse to other means for collecting this valuable information.

The most obvious way to do this is to ask our customers directly.

That’s why we have:

• Customer Interviews

• Focus Groups

• Customer Surveys

• etc.

But here we will face a problem: customers not always know how to verbalize or communicate their needs.

And even worse: most of the time, customers are so used to living with their problems they no longer are conscious of them, so they don’t even try to verbalize them when asked.

This is where Social Sciences come to the rescue: enter the Ethnographic Studies.

When an Anthropologist needs to understand a society, but cannot rely on the ability for their members to explain their own behaviors, they choose to live amongst them for a while.

That way he is capable of observing things people do without even realizing they are doing them, out of habit and unspoken rules and customs.

This is only one example of how an Ethnographic Study can be performed, but you get the idea: finding out the customers’ unstated needs.

Of course, we don’t need to be a Sociologist, nor develop a formal Ethnographic Study to be able to collect unstated/unspoken needs from our customers.

But we must be aware that those needs exist, and if we go the extra mile to find them, we will have an edge over our competition.

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Mining the Voice of the Customer to capture Customer Value

Now that we know what is the Voice of the Customer, we need to understand how to capture it.

Capturing the Voice of the Customer, and making it work for our business looks a lot like mining gold:

• You need to explore and prospect different market segments, to find where your target customers might be.

• Once you find a potential ore vein (customers with underserved needs), you decide to focus all of your efforts there.

• Then you begin extracting the “mineral” using different techniques (collecting VOC using surveys, interviews, ethnographic studies, etc.)

• But the ore you’ll pick up will not only bring your valuable gold, but also rocks and other minerals. The same will happen with the VOC you collect.

• So you need a process to refine it, separating what is useful from what isn’t.

• Finally, you’ll use your refined VOC data to create products with unprecedented Customer Value that your markets will think are nothing short of magic, and become rich.

To understand how the Voice of the Customer can be turned into Customer Value, we first need to understand Customer Value itself.

Voice of the Customer and Customer Value

Let’s begin with this necessary statement:

Your products’ value depends entirely on the opinion of your customers about it. Not on your processes, your raw materials, even the money spent on branding.

It’s in their heads.

No matter how excellent your product might be, if your potential customers think otherwise, they won’t buy it. Period.

There are two ways of creating Customer Value:

• Value Creation by Technology Push: Generate new ideas and connect them to market needs

• Value Creation by Market Pull: Discover hidden markets with unmet customer needs

So, your business will usually have to focus on one of two types of Innovation model:

• Technology-driven Innovation, or

• Customer-centric Innovation

Customer Value

Let’s go deeper into our Customer Value definition:

When customers compare products or services, they consciously or unconsciously compare the benefits and liabilities they get from each of them.

Of course, this comparison is not mathematically perfect, and is often subject to personal biases and etc., but it’s our marketing job to deliver a message that magnifies the Benefits we provide, while minimizing any Liabilities.

And by innovating on any of the items we are about to explore, you’ll be increasing the Customer Value your product or service provides, making it more appealing to your market.

Benefits

The benefits we can provide to our customers aren’t limited to the product itself, nor are exclusively related to their functions.

Very often we neglect opportunities to provide extra value that can even make commodities more competitive and attractive to our customers.

Functional Benefits

Those are benefits directly related to the intended function of the product or service.

• Product functions. In disciplines like Six Sigma those are directly related to the performance levels of the product or service. This is what the product or service does.

• Economic benefits. How our product or service directly impact our customers’ revenues, making us preferable to our competition.

• Reliability and durability. If our products cause less problems, then we introduce less uncertainty and risks in our customers’ processes, making us a better choice.

Psychological Benefits

As human beings, we don’t coldly evaluate functional benefits in the products we choose, but also benefits that affect our wellbeing, self esteem and state of mind.

Those are the Psychological Benefits a product or service can provide, and many times are a powerful deciding factor, even greater than function itself.

• Prestige and emotional factors. For example, brand name reputation (think of Apple and their overpriced but highly sought after products and services).

• Perceived dependability. Think of Volvo and their perceived extra safety.

• Social and Ethical reasons. For example, products that come from environmentally friendly businesses, or built using ethically sourced materials.

• Psychological awe. Just remember the first iPhone launch, and how it single-handedly created a whole new industry.

• Psychological effect of competition. If there aren’t many competitors yet, the product won’t be seen as a commodity.

Service and Convenience Benefits

Beyond the functional and psychological benefits your product can provide, you can make your customers’ lives easier in many ways, reducing friction for the acquisition, use and disposal of your products.

Again, those benefits are often neglected, and are a great source of differentiation that can make your business preferable to your competition.

• Availability. Is your product or service hard to get? Do you distribute it close enough to your customers?

• Service. If your product isn’t a consumable, and can be subject to repairs or support services, you must make sure those are of easy access.

• Disposal. Some products can be hard to get rid of, or be subject to heavy regulations regarding disposal procedures. You can have an edge over your competition if you provide disposal as part of your services. Think of “Circular Economies”.

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Liabilities

The liabilities that come with the consumption of our product or service must be minimized, to increase the perceived Customer Value.

Economic Liabilities

These are probably the most obvious ones, and the first thing a customer evaluate against the list of benefits.

• Price. The price can make or break a product or service, becoming a strong anchor in the positioning it takes in our customers’ minds.

• Acquisition costs. Transportation, shipping or any additional costs we must incur to access the product or service.

• Usage costs. Costs that come naturally with the usage of the product, like installation fees, subscriptions, insurance, etc.

• Maintenance costs. If the product requires constant maintenance, it can become a nuisance for the customer.

• Ownership costs. Think of the yearly renewal of software licenses, taxes derived from the possession of the product, etc.

• Disposal costs. If the disposal of the product is costly and strongly regulated, it can become a liability to be addressed.

Psychological Liabilities

As with the psychological benefits, the psychological liabilities can be a strong decision factor when choosing a product or service.

Even if the product or service is a B2B one, being purchased for an organization, the buyers are human beings.

Have you heard the old adage: “Nobody gets fired for buying IBM”?

This was an example of a superbly executed branding strategy for reducing a common psychological liability: getting fired because of a bad decision.

• Uncertainty about the dependability of the product or service. Nobody gets fired for buying IBM, remember? Make sure your products are know for their reliability.

• Self-esteem liability for using an unknown branded product. Driving a Toyota is not the same as driving a Mercedes.

• Psychological liability of poor performance of the product. If the product performs poorly, you will feel bad about your decision, beyond any other repercussions.

Service or Convenience Liabilities

• Liabilities due to lack of service. If you don’t provide easy access to services or support, the customers might choose not to purchase from you, even if your product is functionally superior.

• Liabilities due to poor service. The same might happen with a product with poor service. Word about it will spread, and fast. Customers will avoid purchasing a good product with a poor service.

• Liabilities due to poor availability. Customers won’t go far to get your product if it isn’t easily available, unless you are tremendously unique. And that is quite uncommon.

OK, I want my own Voice of the Customer program. What do I need?

I’m glad you are already convinced of the powerful tool for your business that a properly implemented Voice of the Customer program can be.

Now let’s talk about what you need to have, as an organization, to deploy one.

First of all, lets be clear about how are you expected to use the Voice of the Customer data you will collect and process:

• You need the right kind and sufficient amount of accurate VOC data to feed into all the stages of your product and service development.

• Your VOC data will allow you to discover and understand your customers’ value position and key customer value factors (like pricing and functions).

• If you ever want to change your current customer value proposition into another one (like you do when applying Blue Ocean Strategy), VOC data will be invaluable.

• Incremental product improvements will have a higher probability of success if guided by VOC data.

With that in mind, let’s talk about the core elements for a successful VOC implementation.

The Team

It is common (and a common mistake) to throw this responsibility to the Marketing team.

Or the Product Development team.

Or the Engineering team.

For a Voice of the Customer initiative to be successful, it needs to be multidisciplinary.

The reason is very simple: no single department or profile has all the necessary elements to collect the VOC data completely.

And we are going to see why.

The VOC data collection

Not every type of data that can be provided by the customer is useful as a VOC source, if we are focusing on creating Customer Value through product or service development or improvement.

Let’s review the kinds of VOC data that can be collected.

VOC data not suitable for a product development process

This qualitative and quantitative data can provide us with a deeper understanding of our customers and their needs, but is insufficient for fueling a product development process.

• Solutions. Even if customers know their problems, they are not engineers, scientists nor product experts. So the solutions they might request or suggest are probably not good ones, and might even interfere with other product requirements.

• Specifications. Customers often make requests like “bigger screens” or “lighter weight”. But if you give them a bigger screen, the laptop will become larger and heavier. A better solution might be just “higher resolution”. But the lack of technical knowledge makes the customer provide specifications that probably won’t be useful.

• Needs. If a customer asks you for a more reliable product (a need), how do you define “reliable”? And how much is “more reliable” compared to the current reliability? Customer needs tend to be expressed in vague language that isn’t useful for product development.

• Benefits. Again, expressions as “faster” or “better” aren’t precise enough and don’t bring measurements that allow to use them for a product development process.

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VOC data suitable for a product development process

On the other hand, this type of data can successfully drive our product development process, because it contains the all the necessary elements.

• Jobs to be done. These are the functions that the product must perform to be useful. A complete list of the “jobs to be done” will allow us to have a product or service that fully satisfies the customer.

• Desired outcomes. These must be as specific as possible, describing the results of using the product or service. For example, a laundry detergent might have as a desired outcome: “be able to fully wash 7 kg. of laundry with only 10cc of detergent”.

• Constraints. Constraints are physical, regulatory and environmental restrictions that must the product or service must forcibly comply. For example, if a gun needs to be able to be operated with either hand, left or right, it must provide a safety lever on both sides.

The Multidisciplinary Team

As you can see, from the types of VOC data that can be collected, most of them require being familiar with the customer language and way of thinking.

This is the primary function of the Marketing team, and they are perfectly suited for the task.

But at the same time, measures, specifications, constraints and functions are an important part of the VOC data to be collected.

And those are in the domain of the Engineering and Product Development teams.

So you need members from both teams on your VOC collection program, or you risk getting useless data.

Make it happen!

The Voice of the Customer concept and methodology are part of the Six Sigma’s Quality Function Deployment (QFD) framework for developing outstanding products and services.

We will not cover the QFD framework itself now, because it is quite extensive and deserves its own article in the future.

But with the information we have already reviewed you have more than enough new tools to deploy your initial Voice of the Customer program, and start increasing the Customer Value you provide.

You can start by checking the list of Benefits and Liabilities, and finding where you can provide extra benefits and reduce or eliminate existing liabilities in your products or services.

Do you sell computers and IT hardware to your customers? Offer them an end-of-life service for collecting the hardware they are going to dispose of, removing that hassle from the product ownership.

Do your country has a new car tax during the first few years of ownership? Offer to cover it, as long as the customer keeps doing their maintenance on authorized dealers during that period of time.

The possibilities to differentiate your business are endless.

To get the information you need, perform Customer Surveys, Customer Interviews, Focus Groups, etc.

But remember: the customers are not always capable of communicating properly their real needs and requirements.

Have you ever thought about “lending” someone from your Engineering or Marketing teams (or one from each) to a customer so they can help them with their everyday operations?

I can assure you the insights you’ll acquire will be unparalleled, and largely surpass the costs of that “hands-on experience”.

Build a multidisciplinary Voice of the Customer team, and make them responsible of crafting the surveys, running the interviews, guiding the focus groups, analyzing the data collected, etc.

Their simultaneous points of view will shed more light on the customers’ behavior and needs than if they were to work separated or sequentially.

Conclusion

Thank you for reading my publication, and if you consider it helpful or inspiring, please share it with your friends and coworkers. I write weekly about Technology, Business and Customer Experience. Don’t hesitate to subscribe for free to this publication, so you can keep informed on these topics and more.

As usual, any comments and suggestions you may have, please leave them in the comments area. Let’s start a nice discussion!

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

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References

You might have witnessed this yourself.

Or maybe not.

But Customer Experience initiatives and projects can have a bad rate of success1, depending on how the organization faces the challenge.

From my experience and research, it depends a lot on where the initiative comes from.

Because that mandates where the organizational focus and control are placed during the project.

For example: let’s say a software vendor with a new and shiny Customer Experience software came to the organization and convinced the CEO to acquire it.

Most probably, the focus will be around the software implementation, the training of the people, integration with existing platforms, showing nice reports, etc.

Now, what if it is a consultant that comes with the latest and shiniest patented methodology for Customer Experience Management?

You can bet it will be all about Customer Journeys, Net Promoter Score reports, coaching for the executives, and some training for the ranks.

Don’t get me wrong: both approaches have merit on themselves and cover vital parts of what is needed to succeed in a Customer Experience initiative.

But they are lacking the most important element, in my opinion.

Managing the Organizational Change.

Customer Experience is probably the most encompassing of the modern functions existing in organizations today.

It permeates everything and everyone.

You can have the most advanced e-commerce site and web page, the best delivery and fulfillment partners, and the most committed Support Specialists.

But if, for some reason, a customer approaches any of your physical locations, and is treated like an intruder by untrained personnel, like an unaware clerk or assistant, then all of your effort went right into the trash.

Like Security, the weakest link in Customer Experience is the measure of the strength of the whole chain.

And, like Security, it is a mindset. An organizational-wide mindset that must permeate everyone’s actions and decisions.

So, when you decide to transform your organization into a Customer-centric one, and begin adopting Customer Experience strategies and techniques, the first thing is thinking about Organizational Change Management.

Your main objective becomes changing the whole Culture of the organization.

This means that everyone, on each role, from the clerks to the accountants, to the sales people, to fulfillment to support, everyone must have clear Customer Experience objectives and instructions.

Like a cashier in a supermarket empowered to provide remediation to customers up to certain amounts of money, without having to ask for approvals.

But, you might be thinking: “OK, Alfredo, but what happens in those situations where the unexpected happens and we don’t have a clear procedure?”

That’s where Policies come in. They are not strictly tied to a situation, and serve more like guardrails that help you decide under uncertainty.

And when Policies fail you, then you have the Organizational Values: your people understand what you stand for as an organization, and use that as a guide to decide what to do.

(that’s also why you must only have 2 or 3 strong Organizational Values, easy to remember, not 10 or 20 like some misguided managers love to plaster their walls with)

That’s Organizational Culture.

Once your Customer Experience strategies become ingrained to that level into your organization, you’ll be certain that everyone will know what to do, regardless the situation.

Everything else: software, dashboards, whiteboards with sticky notes, meetings, reports, documents, etc. is accessory.

Those are only tools.

Tools with the purpose of helping you to change your Organizational Culture into a Customer-centric one, and then manage and nurture it afterwards.

That’s why I usually say, in my courses and consulting projects, that the smaller the organization, the easiest it is to turn it into a Customer Experience powerhouse.

You don’t have to align thousands of minds to your new Culture.

You don’t have to deal with dozens of stores, with different middle and line managers, and petty politics.

Small and Medium Businesses have it easier.

And often they disregard this opportunity because they think it is expensive, difficult, or it isn’t worth the effort.

That’s why I created this publication.

That’s why I created my Customer Experience 101 course, and my free webinars.

To provide those initial insights a Small and Medium Business owner or leader needs to start its path towards a Customer Experience Culture in their organizations.

I hope this post shed some light on the strong asset that can be having a good Customer Experience Culture in your organization

And why starting a Customer Experience Initiative with the wrong objective usually ends in disaster and loss of money.

I’ve written a more in depth article previously on this topic. Check it here2.

Let me know what you think in the comments, and share this post with your friends and coworkers if it was useful to you.

See you soon!

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

Buy me a coffee

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References

Corporate penny-pinchers often find a way to sour the sacred relationship between brands and their valued customers.

But it isn’t often when the customer community manages to fight back the corporate greedy impositions and win.

We have witnessed (or for some of you, even participated) in such event during the last few days.

Sony announced the enforcement of Playstation Network (PSN) accounts for PC gamers1 and it led to the absolute crash in the review ratings and massive refunds.

For those of you who aren’t familiar with how this messy gaming platforms environment works, let’s do a quick recap.

How the Gaming Platforms work

The four main gaming platforms today are PC, Microsoft’s Xbox, Sony’s Playstation and Nintendo.

And their corresponding leading distribution and account services providers are Valve’s Steam for PC, Microsoft’s Xbox Live for Xbox, and Sony’s Playstation Network (PSN) for Playstation.

Gamers are used to live inside their respective walled gardens, according to the platform where they own their games.

These services provide:

• account management

• leaderboards

• software distribution (where you download the games and their patches)

• online communities (with different levels of moderation between platforms)

• online security (anti-cheat mechanisms, hacker reporting protocols, etc.)

• online stores (where you purchase the games, downloadable content, and more)

• and most importantly, hosting the online matchmaking servers where players meet and play

Gamers create a unique account in the service of their preference, depending on which platform they own (PC, Playstation, etc).

If a gamer owns several platforms (a PC and a Playstation, for example) it is common for him to be registered in more than one service.

But what happens when Sony decides to start selling their Playstation games through the PC distributor (Valve’s Steam in this case)?

The gamer already has a Steam account, and is purchasing the software through Steam, so he doesn’t need a PSN account.

Steam already provides the account management, software distribution, communities, security, online store, etc.

But the online matchmaking and game servers are still run by Sony and the game developer, Arrowhead.

What happened then?

Apparently, Sony didn’t like the idea of relinquishing the direct commercial relationship with its customers to a third party (Valve’s Steam on PC).

So, even when it wasn’t technically necessary for the game to run, they enforced onto the developer the requirement of linking the gamer’s Steam account to a valid Sony’s PSN account.

Of course, Sony argued it was because they want to be able to monitor the quality of the online interactions, and provide extra security measures.

But Sony has a really bad track record on security matters, having been breached a few times in the past, losing their customers’ personal and financial data.

Most probably, it just was some clever corporate who wanted to up the numbers of PSN subscribers (the game had already gained 8 million users in under a month) to brag to their shareholders.

All in all, it was a very messy process, even previously to the game being launched.

Arrowhead, the game developer, stated that they were told by Sony about the PSN account linking 6 months before the game finished development.

And given that the game had been in development for 8 years, it seems to me like a major oversight.

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The storm is brewing

So, the game was launched on the Playstation 5 (PS5) platform and PC.

It naturally used PSN as its account service for PS5, and Steam for the PC release.

But it also brought the (poorly communicated) requirement of linking the Steam account to a PSN one if you were a PC gamer.

If you were a PC gamer, and you didn’t own a Playstation, you probably didn’t have a PSN account.

So you would have to create one from scratch just to be able to play.

This caused several unforeseen problems:

• Helldivers, the first game in the franchise, wasn’t very popular. At least, not at the level its sequel has been.

• That means the developer and Sony didn’t anticipate such a huge inflow of concurrent gamers hitting the servers.

• So, during the first week after the launch, they had it very hard with the server load, and chose to waive as Optional the account linking, until they could fix it.

From the PC gamers’ point of view, most of them weren’t aware that the account linking even existed, and that they would have to eventually comply with it.

But this was just the start of it.

The game was marketed by Steam worldwide.

And a worldwide success it became.

Gamers from all over the world purchased it and spent hundreds of hours playing cooperatively with others.

Then, suddenly, Sony announced they were going to make the PSN account linking mandatory for everyone.

Not a storm, but a hurricane

Of course, this caused outrage in the PC gamer community, as they were being forced into creating an account they didn’t really need.

And for a company that commands a very low level of confidence in their security capabilities, after a few fiascos in that regard2.

The PSN account creation process requires biometric identification and registering a valid government ID.

All of that for playing a game.

Especially if you know that it isn’t technically necessary at all.

But, again, this was only the beginning.

Sony realized that Steam had sold the game worldwide, and Sony’s policies didn’t allow PSN accounts to be created worldwide.

Actually, the restricted countries list is quite large.

So, now you not only had a rightfully outraged community, but you had people that lawfully owned the game, and were going to be unable to continue using it.

To add insult to the injury, the game was delisted from all the restricted countries on the Steam platform, effectively disabling the it for those users.

And Steam’s policy of only refunding a game purchased online if you had played it under 2 hours in total wasn’t cutting it: some people had already hundreds of hours accrued.

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All of this for a game?

You might be asking yourself right now: why all this ruckus for a game?!?!

Well, games are no longer Pac-Man or Space Invaders.

Nor are they created by small teams or even a single person.

Most AAA games today have budgets that rival or surpass Hollywood blockbusters, or even entire franchises.

No more 8-bit beeps in the PC speaker for those games.

Now we have game soundtracks and scores performed and directed by Hans Zimmer, Inon Zur, etc.

Voices and faces motion-captured from renowned Hollywood actors and actresses.

And, if successful, those IPs (Intellectual Properties) can be later turned into merchandising, movies and series.

Like the latest hit from Amazon Prime: Fallout, based on a well beloved PC game franchise.

So, when a game gets 8 million players in a month, you know you are into something.

And you are supposed to take care of that community. It’s worth its weight in very literal gold.

Sony’s misguided handling

Instead trying to find a solution, Sony doubled down on the enforcement, and retired (through Steam) the access to the gamers on restricted countries.

But not happy enough, they modified their website to try to erase any trace of proof that the game was launched with the PSN account linking is Optional notice.

The sum of all of these unfortunate decisions and events caused an upheaval in the ratings and usage of the game.

Refunds were being issued all over the world, even in countries not affected by the PSN restrictions.

People stopped playing.

And the ratings on the Steam platform went from the sky high levels they had deservedly earned in 2 months, to the deepest recesses of Hell, in just a couple of days.

In this ugly situation, an unintended casualty was the game developer company, Arrowhead.

Arrowhead had garnered a lot of goodwill and esteem from the community, prior to this fiasco.

At the onset, they quickly sided with the community, recommending them to downvote the game so they could exert pressure on the publisher, Sony.

So, after a “hellish” weekend for Sony, they finally caved in, and announced they wouldn’t enforce the PSN account linking on PC for Helldivers 2 anymore.

The Helldivers had won.

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What can we learn from this fiasco

Let’s do a quick analysis using some of our Customer Experience Strategy tenets, to see what went wrong and how it could have been done differently.

30% of service contact disputes could have been avoided by better upfront communication

This was probably the key factor here.

Sony knew.

Arrowhead knew.

Steam (probably) knew.

The only ones that weren’t informed of the lingering requirement to link the PSN accounts to Steam were the customers.

At least not with the level of clarity required.

Arrowhead develops the game.

Sony, as the publisher, is responsible of managing the distribution channels.

Why did they let Steam sell it worldwide if they had a large country restriction list for PSN accounts that would have to be created and linked later?

This one has an easy fix: better communication to manage the expectations.

Over 70% of returned products didn’t have anything wrong. Once the manuals were enhanced, the number decreased dramatically

The game didn’t have anything wrong in it.

If the documentation, for both marketing and sales, had been accurate, nobody in a restricted zone would have been able to purchase the game.

And those not comfortable with the idea of creating an additional PSN account, wouldn’t have, either.

The game would still have had millions of users, without any angry outcry.

Four times more impact of negative than positive Word of Mouth

This one is pretty obvious.

Just look at the timeline of the positive vs negative review charts.

What took months to accomplish was demolished in a couple of days.

5% of users post reviews, but 30% of users read them

These negative posts will remain in the platform.

It is very unlikely every disgruntled gamer will make the effort of going in and changing their ferocious rant.

So, future gamers might still be discouraged from purchasing the game.

And a new game, Ghosts of Tsushima is coming also to the PC.

Its developer is already trying to appease the crowd, which is unsettled by the idea of having to create a PSN account and link it.

They have clearly stated that the single player experience can be used without PSN, but some optional features might require it.

The community already distrusts Sony. Even more than before.

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How could have Sony managed this in a less toxic way?

There are many situations where the platform needs to ask from the gamer actions that can be unpleasant or become a chore.

For example, the use of 2-Factor Authentication to avoid the accounts from being easily stolen.

How do smart companies handle this?

They make it optional, but provide strong perks to those who do it.

Those “perks” usually entail zero real cost for the platform and the game, like cosmetic items, quality of life items, etc.

All of them virtual. Zero cost.

But high value for the gamer.

Sony could have offered something very desirable, and with zero cost for them, in exchange of the PSN account linking.

And nothing of this would have happened.

Conclusion

“Only a fool learns from his own mistakes. The wise man learns from the mistakes of others.” - Otto von Bismarck

We have to be wise, and learn from Sony’s mistakes.

Most importantly, we must realize that our Customer Experience strategies and principles are there to avoid this kind of situations from ever happening.

At Sony’s, its Customer Experience principles failed.

They should have served as guardrails, preventing some executive from taking these misguided decisions.

Whenever you don’t have a clear rule or directive, it is the principles who must come forward to save the day.

This didn’t happen at Sony.

And now they have a diminished customer goodwill to show.

Do you already have clear Customer Experience principles in your organization?

Is everyone aware of them?

If not, you should start right now.

Thank you for reading my publication, and if you consider it helpful or inspiring, please share it with your friends and coworkers. I write weekly about Technology, Business and Customer Experience, which brings me lately to write a lot also about Artificial Intelligence, because it is permeating everything. Don’t hesitate in subscribing for free to this publication, so you can keep informed on this topic and all the related things I publish here.

As usual, any comments and suggestions you may have, please leave them in the comments area. Let’s start a nice discussion!

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

Buy me a coffee

Subscribe now

Share

References

Cost reductions. The Holy Grail for many managers and businesspeople. So many grave mistakes have been made in its name. Every new technology and business trend gets its share of people trying to jump into it in the wrong way, without considering first what they are really trying to achieve and if they are prepared as an organization to do so. Narrow Artificial Intelligence, the current trend, is not an exception.

A couple of weeks ago, an USD$100m entrepreneur became infamous by celebrating having replaced 90% of his Customer Support Representatives workforce with an AI chatbot1 on Twitter. He did it as a marketing stunt, trying to promote the AI chatbot his team had built to deal with his business’ Customer Support tasks, that he was trying to launch as a new product. But instead, he was met with a very negative backlash (even being analyzed in media like Business Insider2), by a large amount of people questioning many things: from the wisdom of his decision, to the way he executed it and its social impact on his former workers.

In a previous publication I have discussed Artificial Intelligence as a tool for Customer Experience Management3, not only from the Customer Support automation capabilities, but as a whole. Narrow Artificial Intelligence (the researchers give this name to the current state of Artificial Intelligence, capable of excelling in specific tasks, but not so good at general ones, as a Human Intelligence might be, or a future Artificial General Intelligence) can be used for many things. Building a Customer Experience strategy requires lots of information gathering, analysis, synthesis and visualization. AI tools can be used to save lots of time and effort on these tasks. The Customer Experience operation itself can be subject to AI tools too: live data collection and analysis, task automations, general classification and, of course, automating interaction with customers in some degree (“chatbots”).

But I also discuss in my publication that we must be careful on how we apply technology that puts distance between a human representative and a customer that might be angry, confused and in pain. It is not the first time the Customer Support industry makes the same mistake, and I doubt it will be the last. With the advent of Interactive Voice Response, or IVR (“Press 1 to know the state of your order, Press 2 to make a modification, Press 9 to speak with a representative…”) business leaders were sold with the idea of replacing the “expensive” humans with automated telephone menus that were capable of solving customers’ issues 24/7 at a fraction of the cost. Eliminating most of the support workforce became an objective, creating intricate multilevel menus, trying to force customers to find their way to a solution without ever contacting a human representative. If you have more than 20 years, you probably have had to deal with the frustration these kind of menus can produce.

Fast forward a few years later, and we had the same situation with the WhatsApp, Facebook, etc. chatbots. Those were basically IVR menu trees translated into a messaging platform, with some intelligence added. But the results are mostly the same: frustration and anger for the customer. The questions we should be asking ourselves as the ones in charge of the Customer Experience Management in our organizations are: Why does it fail once and again? And why do we keep trying?

To answer these questions, let’s use the recent case of the entrepreneur mentioned as an example for our analysis.

First of all, he clearly states what were his drivers: the metrics he objectively measures and he thinks show he has made progress with the decision made.

• Time to first response

• Resolution time

• Customer support costs

All of those metrics are important. But do you see the pattern there? Those are Customer Support metrics, not Customer Experience ones! All of them are inward facing (looking at the organization), instead of outward facing (looking at the customer). Yes, collecting Customer Experience data is harder than collecting Customer Support data. But it is indispensable, or you might end up shooting yourself in the foot. To do a proper analysis, we must follow the entrepreneur’s reasoning, so let’s keep going.

OK, so he is clearly trying to optimize costs because of the “state of economy”. And he is making a comparison between being “profitable” and “trying to become a unicorn”. Let’s stop here for a moment. The assumption he is taking is that “becoming a unicorn” means spending money into solving properly your customers’ problems, because “unicorns” strive to delight their customers. So, going into “profitable mode” means cutting Customer Support costs, because you are not trying to be a unicorn. Customer Happiness is no longer relevant if you are going for survival in a harsh economy. So it becomes highly important to you, as a business, to close tickets fast, without even checking what is the impact of that newly found speed: was the customer satisfied? was he just sent away with an automated half-baked answer to try by himself, only to come back angrier a day later, or not to come back at all? That’s why you cannot judge the success of these strategies solely by Customer Support metrics. But let’s continue, as there is more.

He asks “Why would someone with tech/product expertise work as a support agent?”. This is a complex question. We are entering the personal preferences and psychology terrain here, and the answers can be as diverse as people are. This is like asking: “Why would someone enter a building in flames to rescue people?”. Having led many people in Customer Support functions I can say that some of them just liked solving other people’s problems with their knowledge. And anyone who has ever taught anything knows that you learn more by teaching than by learning in the first place. Others do it because they prefer to solve issues than to use the product for its intended purpose. “Why a racing car mechanic tunes racing cars instead of driving them?”. And we can continue listing personal motivations. Try to ask your Customer Support team this question, and get surprised.

But he also lists some “reasons” to consider human Customer Support representatives as a liability, when compared to AI ones. Let’s work over those one by one.

• Generic responses. “They provide vague or scripted responses most of the time”. Yeah, but that’s why Customer Support tiers and escalation exist. Imagine that you have a large user base (like a telecom or a university). You have TONS of people making the same small mistakes day after day. So you can expect to give the same answers constantly. That’s why you have a Tier 1 Customer Support, which handles those massive amounts of requests. Can you apply Artificial Intelligence here to automate? Sure. But you must have the proper metrics in place to know that you are not breaking things more than they currently are.

• Inefficient collaboration. “Poor communication with product/engineering teams”. And the AI chatbot is supposed to communicate better? He is stating a problem with his Customer Support escalation process. That is not going to be solved with a bot, but by fixing the process.

• Unnecessary tickets. “Creation of needless tickets for minor problems”. Minor problem according to who? The business? A worldwide statistic found that in average only 5% of customers having issues report them. 95% just live with the issue and complain with others about it (bad Word of Mouth). A “minor problem” as seen from the business, can be a dealbreaker for the customer. No ticket is unnecessary. You should, instead, be trying to encourage your customers to let you know what is wrong with your product or service, so you can fix it.

• Poor communication. “Difficulty in explaining technical concepts”. Again, that’s why Customer Support tiers exist. If you have a low cost/lowly paid Tier 1 Customer Support representative, trained to only answer things from the knowledge base, you cannot expect him to give a lecture on software engineering and the inner workings of your product. And probably the customer doesn’t want to hear that either. They just wanted to use your product for what they purchased it, and having it failed in delivering that, at least to have the issue fixed quickly and continue with their lives. Do you want an AI chatbot to spew paragraphs of elaborate technical explanations to a customer that is probably time-pressed and for sure didn’t expect your product to fail and require support?

• Delayed responses. “Longer response times due to the lack of tech understanding”. Again, this is why Tier 2 and Tier 3 Customer Support Representatives exist. Are you asking from your Tier 1 representatives to have deep knowledge of the product? Or perhaps those tickets should just be escalated? How do you decide if something escalates? Can your AI chatbot do it without first angering the customer by making him waste time?

• Escalation issues. “Failure to escalate serious issues promptly within time”. Same as before. Can an AI chatbot promptly determine that it will be unable to service the customer, so it can escalate the issue? Would you place a complicated voice menu in front of a medical emergency telephone number? Surprisingly, I know several that do that. Currently AI is capable of determining the “sentiment” of a user, either by his voice or the words it is using. But, would you risk it? For non life-threatening services it could be attempted but, again, you must measure the results from the customers’ side.

• Limited availability. “Lack of support during critical times or off-hours”. Most Customer Support services provided by businesses also have SLA tiers. If someone requires 24/7 support, he needs to pay for that SLA tier. If you are trying to give everyone 24/7 support, then it is a business decision and it comes with a cost. Some issues will be fixable by Tier 1 representatives (that could be potentially be replaced/supported by AI chatbots, online FAQs and knowledge bases, etc.) but others will still require Tier 2 and 3 experts. So the human availability problem won’t go away unless you plan for it. And for its costs.

• Wrong troubleshooting. “Struggles with diagnosing and resolving complex tech issues”. Again, are you asking the proper Tier of support? Is an AI chatbot going to be able to troubleshoot better than a human? Do you even understand how GPT-built LLMs work and how they produce their answers? You are placing too much faith on it, my friend.

From what the entrepreneur publishes I can perceive an interesting struggle in his reasoning:

• Why to have experts (Lionel Messi) as troubleshooters in front of my customers? Let’s get rid of them! They are costly!

but then:

• Why are the cheap guys I’m trying to place in front of my customers aren’t able to: give less generic responses, collaborate like project managers, communicate like CEOs, respond quickly by solving complex technical issues from memory, be available 24/7, etc.?

His business has probably accumulated over time tickets because of bureaucracy, bad escalation policies, poorly trained Tier 1 Customer Service Representatives, insufficient Tier 2 and 3 product experts. He is also only monitoring inward facing metrics, so he really doesn’t know what the customers’ sentiment about his Customer Support, and the whole Customer Experience around his business are. And without a baseline on this, he just replaced 90% of his Customer Support staff (probably all of the Tier 1s, and some of the Tier 2 and 3). The result?

Yeah, the business experienced an abrupt consumption of all the tickets that probably were stuck because of bad processes over time, but we know nothing about the resolution level of the issues, the customer sentiment after he was serviced, if the solution provided was the right one, if there are other issues not expressed, etc. For all we know, the chatbot might be giving hallucinated answers to the customers, who angry after wasting their time, don’t even come back to try again, adding themselves to the 95% of people who doesn't ever complain (and the 96% of people that will leave you as soon as they can because of bad Customer Experience4, according to Forbes).

Don’t get me wrong. I am an Artificial Intelligence enthusiast, and have dabbled with it for many years now. And that’s exactly the reason why I know how it has been evolving over time, and its current limitations. It is an excellent and powerful tool when used under human supervision. It can help to automate many processes, almost eliminate effort for certain type of tasks, and provide timely information that previously would have taken hours or days of human work. That’s the reason it is tempting to place it in front of information hungry customers, who are trying to solve an issue with a product. But every customer is different, and also every customer is in a different situation at the moment of requesting support. The absolute lack of empathy of an AI chatbot can be, on itself, a cause of anger for a customer that is in a dire circumstance because of our product. AI cannot do empathy (yet). Also, the personal experience that a human representative brings to the table on each interaction can have the magic required for a quick and satisfactory resolution. So, by replacing humans with AI for human interaction roles we are leaving behind the many human frailties (like the need to sleep), but also the many human strengths (like empathy, personal experience and even instincts).

If we are going to try to introduce Narrow Artificial Intelligence into our Customer Experience workflows, we need to first be sure we are measuring the right things. AI can make things happen faster. But faster and wrong will always be worse than slower and right. So, as with everything related with Customer Experience, we measure, then we try, then we measure again, then we adjust for better results. Embrace an Agile approach for Customer Experience Strategy implementations. Embrace Agile Objectives and Key Results definition and measuring. First apply AI in the areas that are completely under your control (like internal processes) so you know its benefits and deficiencies (internal customer support perhaps?). Then try it outside, and always actively measuring the impact (the final customer impact, not just some ticket closure metrics).

Thank you for reading this article, and if you consider it is being of help to you, please share it with your friends and coworkers. I write weekly about Technology, Business and Customer Experience, which brings me lately to write a lot also about Artificial Intelligence, because it is permeating everything. Don’t hesitate in subscribing for free to this publication, so you can keep informed on this topic and all the related things I publish here.

As usual, any comments and suggestions you may have, please leave them in the comments area. Let’s start a discussion on this topic.

Cheers.

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

Buy me a coffee

Subscribe now

Share

References

In Management we frequently see new concepts appear from time to time (and yes, many of those end up being empty buzzwords used to sell us stuff). New concepts, tools and methodologies that promise to fix the problems brought by the current ones. But I can assure you: OKR is not an empty buzzword, and the track history of corporations that have used them successfully, proves it.

Objectives and Key Results (also known as OKR) are not new. Even if they became notorious because of Google’s widespread implementation in the 1990’s, OKR are an evolution of Peter Drucker’s Management by Objectives (MBO) concept, that ruled the business landscape for more than 50 years.

Drucker, in The Practice of Management (1954) tells the story of three stonecutters, where the first of them thought of his work as a mean to earn his living, the second one as a way to show his professional expertise and excellence, and the third one said he was building a cathedral (as part of the whole effort). The third stonecutter was aligned with the main objective, and probably measured his progress in terms of the cathedral’s completion. But the second one was primarily motivated by his personal objectives, and that could lead him to take action in a direction that might be against the real goal.

So it became management’s responsibility to make sure everyone in the organization was working towards the business objectives, and was very clear of what those objectives were. Frameworks and methodologies arose to facilitate the implementation of the concept, and billions of dollars were spent in consulting and projects to that end. But soon it became a rigid, top-down process, where senior management fixed the objectives, and then it dripped down all the way to the front lines. Key Performance Indicators (KPI) were born as a way to measure and control the level of compliance of the objectives: monthly, quarterly, yearly, etc.

Then the world changed. Organizations needed to become faster, and recognized that much of the extremely valuable information that was collected at the front lines never reached senior management, so the objectives and strategies developed were lacking of a needed dose of reality.

This led to the rise of OKR. Andy Grove, CEO of Intel, from the 80’s to the 90’s, adapted the Management by Objectives frameworks to turn them more agile and responsive, incorporating by design the input from every layer of the organization, making it a bidirectional process, instead of a top-down one. As soon as the benefits of this new framework were known, many Silicon Valley companies begun adopting it, getting extraordinary results as well, even in the middle of the ever-changing chaos that the DotCom Bubble presented them. Key to this process was John Doerr, who learned the OKR framework directly from Andy Grove at Intel, and then became an early investor in Google, taking the framework with him as part of the board of directors. Then OKR popularity exploded.

In this article we will explore:

• How OKR works? How are they different from the traditional MBO frameworks?

• Why are OKR a great management approach for my Customer Experience initiatives and operations?

How is OKR different from MBO and why it works?

The OKR framework can be analyzed in two parts: the structural one (the Objectives and Key Results) and the process.

Objectives

We might have already learned the concept of Objective elsewhere. Probably we have heard about the SMART way of defining an objective: Specific, Measurable, Achievable, Relevant and Time-bound. For OKR, it is no different. When we define an Objective, it must allow us to clearly articulate what must be the outcomes we expect from it. Let’s dig a bit deeper on that. An objective is not an outcome. But it must be clear enough (Specific) to make it possible to define a set of Measurable outcomes we can expect once we Achieve it. As you might see, we have already used most of the SMART definition. Now just add the fact that any objective we set must be Relevant for the general effort, or it would be a waste of resources, and that it must happen in a constrained Time period to be useful.

Key Results

Now, how do we know we achieved the objective? We can think this from the Agile perspective of “Definition of Done”. When are we done? How do we know we were successful (or not)? Here is where we define the “Key Results”. Key Results are always tied to an Objective, and they require to be measurable and have some kind of number or tangible quality that can be evaluated. Typical Key Results take this form: “60% increase of active users by the end of the quarter”, or “Average output of 5 articles per week”. They conform a metric that can be objectively measured and compared, allowing us to know where we are with respect to the objective achievement.

Nevertheless, there is an important difference between Management by Objectives and OKR when it comes to measuring results: while MBO tends to demand strict achievement of the objective to be considered a success, OKR sets the Key Results as “Stretch Goals”. This means that when we set the desired result level in our Key Results (the numeric or measurable part of it), we set the bar high, beyond what we think it would be normally achievable. It is common to expect around 70% of the desired result, under normal circumstances. So, why to set the bar high? Because most of the time we don’t know our own potential. If we set the bar at a reasonable level, we will easily achieve the result, and probably just keep it there, even if we had the capacity for taking it further. It’s just human nature. But if we challenge ourselves, we probably will be surprised by reaching results we didn’t imagine were possible. For example, if we set a defined goal as “60% increase of active users by the end of the quarter”, we will probably get there. And nothing more. But if we know the realistic number is 60%, and we set a stretch goal as “80% increase of active users by the end of the quarter”, it is probable that the first time we attempt it, we will reach 65%. It won’t be 80%, but it will be 5% more that what we though we could achieve. That 5% wouldn’t have happened without the stretch goal.

Process

Now we know the nature of the Objectives, and the set of Key Results they must have associated. It is time to learn about the process.

We have already mentioned that a strong difference between the MBO frameworks and OKR is the rigidity that was introduced into MBO by the implementation processes that were developed over the years. A word of caution: exactly the same is happening nowadays with OKR. The original concept by Peter Drucker never had in mind for the process to be top-down and to ignore the feedback from the lower ranks (who, in the end, are the ones that deal directly with the customers and know all the truths that need to be known).

So, once you decide to implement OKR in your organization, make sure you don’t make the same mistake. Keep the process open, keep it honest, and keep everyone involved. That’s the best way to make them own the objectives, the expected results, and make them happen.

Let’s imagine a full iteration to make it easier to grasp. We are now in December, closing the current year. We have a bunch of reports from the yearly business performance, and we want to apply OKR for the first time during the next year. As senior leaders, we know what we want to achieve next year. We might want to launch new products, or increase the marketshare of the ones we already have, or a combination of it, for example. We know our current numbers: our production capacity, our marketing budget, etc. So, we set a number of stretch objectives. Between three and five. No more (where three is better than five). And we define their Key Results, following the guidelines we already mentioned: measurable, time-bound, achievable but challenging, etc.

Leading and Lagging Indicators

Let’s stop for a moment to discuss an additional tip for developing Key Results. It’s almost sure you have heard about “indicators”. But it’s not as certain that you have heard the difference between Leading Indicators and Lagging Indicators. Without going into much detail, because it is a topic that will have its own article, lagging indicators are measured at the end, when you want to know if you achieved the objective or not, while leading indicators measure the steps you take during the process, so you can steer your performance and make sure you meet the goal. Having only lagging indicators is a sure way to find out you failed, and not having anything else to do about it. Associated leading indicators can help you save your objective while there is still time.

The nature of leading vs lagging indicators is the reason why I recommend to have them at least in pairs when we define our Key Results. We want to have a lagging indicator, that measures if we accomplished the stretch goal. But we also need associated leading indicators that help us move through the quarter adjusting what we are doing until the projected result is satisfactory.

Back to the Process

Ok, back to the Process. Now that we have the Organizational OKRs, we need to show them to the whole company. Because yes, OKRs must be public, and we will see soon why.

Remember, this is important: this is not a top-down process. You are not the evil overlord mandating on your minions the Objectives and Key Results you have masterfully crafted. You are presenting them to everyone so each department can take them as a starting point to try to develop their own set of OKRs (Department or Business Unit OKRs). They might question the feasibility of some of the Organizational Key Results, or even the Objectives. You must be prepared to answer why you considered those as attainable. Or they might bring valid objections that might force you to adjust them. That’s the whole idea. They know operational things that you don’t, and that can have an impact on whether an OKR makes sense or not.

Once the discussion over the Organizational OKRs is over, it is time for the Business Units or Departments to develop their own. This is also a public process. The reason is that rarely an objective is something a department can achieve on its own, or won’t affect another department. So, if I need help from someone, I need to let them know so they include our request in their planning, as this affects their total capacity. And the same in the other direction: we must account for the work we are helping achieve others, as part of our plans.

Departamental OKRs must be fully aligned to the Business OKRs, and must explicitly state how each Departamental Objective supports the achievement of one or more of the Business Key Results (and its associated Objective). And also must be aligned between Departments, making sure everyone knows what we need from them and we know what everyone else needs from us. This is an iterative process where everyone in the Department must participate, because once the Departmental OKRs are settled, each individual must elaborate their Personal OKRs, where the process is the same: fully align them to the Departamental OKRs, and with each other individual, to make sure everything is accounted for.

Once the OKRs are considered finished, the teams meet again to present them to everyone (not only upper management), so everyone must agree on them. This has been a collaborative effort, so there shouldn’t be any surprises, but this is the perfect moment to catch them if they exist. If any fixes are needed, they are noted, and the OKR planning process is over for the quarter.

Then, work starts and everyone keeps public their progress on their respective Key Results. And around mid-quarter the organization meets again so each department can show their current progress, and assess if they will be able to meet or exceed the proposed Key Results, and what this means to the achievement of the objective. It is very important to keep this meeting honest and constructive, because hiding information might impact the results not only of one department, but others that depend on it.

Finally, before the quarter ends, the organization meets again to start the development of the draft Q2 OKRs and to reflect on the Q1 OKRs, so any mistakes made can be avoided during the Q2 process.

At the beginning of the Q2, the final grading of the Q1 OKRs is made (this is also a public event, with everyone attending) the results of the retrospective are mentioned so everyone can learn from them, and the Q2 OKR planning continues in the same fashion as the Q1 one.

Why are OKRs a great management approach for my Customer Experience initiatives and operations?

So far, we have reviewed how OKRs work, and their main differences when compared to Management by Objectives. But something that might not be evident is the flexibility that OKRs introduce by planning quarterly, in an iterative fashion over the year, while incorporating lessons learned from the previous iteration and the feedback from the whole organization. Quoting Dwight Eisenhower: “In preparing for battle I have always found that plans are useless but planning is indispensable”, it should be obvious that under a volatile and uncertain environment like today’s, it’s almost sure we will have to adapt our plans (and a lot). OKRs brings us a clear process and the needed artifacts to do that.

Customer Experience initiatives require a lot of trial and error. It is a deep Cultural Change, and what works for one organization not necessarily will work for another. There are no magic recipes. So it needs to be addressed in an experimental way, permanently adjusting the organization to achieve Continuous Improvement. OKRs are the perfect management framework to accomplish this. The fact that OKRs are built both top-down and from the bottom-up means that the knowledge the front line personnel have of the Customer will be part of the strategy, planning and metrics developed by the organization, avoiding the typical problem of disconnection from reality that afflicts senior management.

Where do I begin?

By reading this article you have taken the first step in the right direction. You can start doing some research on OKRs on your own, so you become familiar with the framework, and perhaps start implementing it in your organization. I really recommend the book Objectives and Key Results: Driving Focus, Alignment and Engagement with OKRs from Paul R. Niven and Ben Lamorte (Amazon link here1). Check my previous article on Customer Experience Strategy2, if you need some ideas on what you will need to do to implement your own Customer Experience program. And, of course, you can join my newsletter and Facebook Group to keep in touch and receive additional information. If you have any comments or suggestions, don’t hesitate to let me know in the comments section.

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

Buy me a coffee

Subscribe now

Share

References

I have been dabbling with Artificial Intelligence stuff since I was at the university (I am a Software Engineer after all). It is mainly related with what we call “algorithms” (the way we make computers do things) and “data structures” (how we make computers store its information so it can be retrieved later). Having participated in the prestigious ACM Computer Science Programming Contests1 (and winning several awards at it) made me an Artificial Intelligence junkie. I even went a bit further and was awarded an Artificial Intelligence certification.

So all of this mayhem created by the now popular “ChatGPT” and its siblings (like Dall-E2, capable of creating images from text describing them, etc.) took me by surprise (and I am sure it also did the same for most of the enthusiast community). Why? Because the GPT algorithms all of these products are based on are incapable of producing “true” Artificial Intelligence, or as we call it, General Artificial Intelligence (more on this later). This is a known scientific fact that is not in discussion.

If that’s the case, why many industry experts have raised their voices stating “the dangers of uncontrolled Artificial Intelligence development3”? There are diverse reasons, perhaps as many as people involved. First of all, the fear of social and economic disruption AI might suddenly bring if it receives widespread adoption without first preparing the job market and the people4. And perhaps the most important, the danger of developing a General Artificial Intelligence that might, for real, surpass us intellectually in every possible sense, and not being ourselves, as a society, ready for it.

But, if the GPT-based products aren’t (and won’t ever be) a General Artificial Intelligence, why the ruckus? Several prominent Artificial Intelligence scientists have also raised their voices pointing this out. Perhaps the most important has been Noam Chomsky’s New York Times’ article “The False Promise of ChatGPT5”. He clearly points out why GPT-based products aren’t a real threat, from the General AI point of view, and a moderate threat to the current workforce, if we consider it as “Narrow AI” assistant. And how those products lack the ability to produce counterfactual conjectures or causal explanations, limiting themselves to spew statistically generated responses based on the terabytes of data they have been fed upon. Simply, they do not “think”.

For us, Customer Experience specialists, all of this is of utmost importance. It is tempting to join the hype train and start thinking we will be able to replace our Customer Service Representatives with ChatGPT to cut costs, or even ask it to draft a proper Customer Experience Strategy for our business. But nothing can be further from the truth. We have a lot to learn from the near past trends and mistakes (from maddening telephone voice menus, commonly called IVRs, to the most recent service chatbots, that can make a distressed customer throw his smartphone away in anger). Stay with me, as we explore how this newly available tool can be used to enhance our Customer Experience capabilities, while avoiding the pitfalls of automation (old and new).

On this post we will cover:

• What is ChatGPT and how it works

• What are we trying to solve with AI for Customer Experience?

• What can we do with the current AI capabilities?

• Where are we headed?

What is ChatGPT and how it works?

We have already used the term “General Artificial Intelligence”. Let’s explain it a bit. Formally, the Artificial Intelligence experts divide the discipline in two: General Artificial Intelligence and Narrow Artificial Intelligence. General AI is the kind of Artificial Intelligence we are used to see in science fiction movies. General AI should be capable of what we define as thinking: reflexive reasoning, moral evaluation, abstraction, conjectures, self consciousness, etc. We will be able to establish an intelligent discussion with it, as if it were a human intelligence. Narrow AI is the kind of Artificial Intelligence we are currently used to: Tesla’s Full Self Drive Computer Vision, Natural Language Processing tools like Alexa and Siri, Google’s Image Recognition and Translation, etc. Narrow AI is focused on solving a very specific problem (recognize images, translate texts, etc.) and it achieves this by using algorithmic techniques that usually have nothing in common with the way the human intelligence approaches the same problem.

ChatGPT belongs to the second group: it is a Narrow AI. GPT stands for Generative Pre-trained Transformer (the name of the machine learning technique it uses). A GPT is an artificial neural network, that is fed with terabytes of data (OpenAI’s GPT-3 was trained with more than 40 terabytes of text data pulled from open sources like Wikipedia and others). Trying to simplify as much as possible the explanation of its inner workings, GPT ingests the text it is pre-trained with, and builds a network of probabilities between the terms. Those connections in the network are called “parameters”, and like with neurons, more connections means the network is more powerful (that’s why GPT-3 has 175 billions of parameters, and GPT-4 has been speculated to have more than 100 trillion parameters). Then, a GPT, when prompted by the user, builds an answer that is the probabilistically most accurate, using the probabilities it has stored.

Think of how your smartphone’s predictive text completion works: you start writing, then after a few characters, the phone offers you the three more probable words. Then you either select one, or finish writing a new one. Then the predictor suggests the three more probable words that might come after the last one. How does it do that? It has been fed with millions of texts, so it knows which words happen more often after the one you just wrote. Then it suggests the next one. And the next one, etc. Now imagine you can also do that with entire sentences, not only words. What is the next more probable sentence. Then with entire concepts/paragraphs. And then you find a way of doing it all in parallel (thanks to some algorithmic voodoo), not having to do it in sequence, like the phone text predictor. Voilá, you have ChatGPT.

As you can see now, ChatGPT is not magic… but being able to produce such complex and articulate answers only from tons of data and probabilities for sure deserve to be considered magic. But to use it responsibly, we must understand the basics of how it work, so we can be aware of its limitations. The first limitation you should consider is the training data. A GPT is only able to answer inside the boundaries of the information it has been fed, when its neural network was built. GPT-3, for example, only had information up to the last quarter of 2021. It ignores anything that happened after that. It also has only the information domains that was provided with. For example, there might be some medical, legal, astronomical, etc. concepts that it is not aware of, because there wasn’t any data about it on its training. So, what happens if you ask it about something it doesn’t know? Well, it tries to answer you with what it has, but it can end up making up stuff. Wildly. This is called an “hallucination”.

You solve this issue by performing additional training on the GPT model, with current data, and information specific to your domain. For example, Customer Experience and whatever your business is related to. Your customer support documentation and everything that you consider the model might need to generate a proper answer. Then you test it. Exhaustively. You simulate being a customer in need of support, and ask questions to the GPT. You check the responses. And tune the model, feeding it with more info, reinforcing the good answers or penalizing the bad ones. Eventually it will get there and be useful for your business. But it will require work. The stock ChatGPT doesn’t come out of the box ready for your business.

So, if we have a tool that produces text that seems written by a human, but a human that has been asleep since 2021, and it has the drive to answer, even if it doesn’t know anything about a topic, could we really put it in charge of servicing our customers? Angry customers? Customers that are in sore need of a solution… and fast? To answer that, we should first outline the typical tasks we humans currently do in the Customer Experience domain, and see where a properly trained Narrow AI could be of help.

What are we trying to solve with AI for Customer Experience?

If we are to divide the kind of work we need to do with Customer Experience in our businesses, it would probably be at the Strategic level and the Operations level.

• At the Strategic level we collect information about the environment (both internal and external), analyze it (like SWOT and PESTEL methodologies), create objectives, craft metrics and indicators, define plans, Customer Journeys, etc.

• At the Operations level, we track progress on the projects, control the day to day work, collect customer information through surveys, interviews, etc., receive customers’ calls, emails and visits, that we need to answer with proper solutions, or in some cases, escalating the issue to a supplier and tracking the resolution on behalf of our customer.

As you can see, at the Strategic level we require a lot of creativity and analysis. But also a great deal of data collection from diverse sources and calculations. While at the Operations level we have more tasks that can be automated, but also a lot more of direct interaction with customers, that will require careful responses, effective and timely solutions, and sometimes the ability to think out of the box or to escalate the issue to a third party. Let’s take into account that the customer is probably not very happy of having to contact our support channels, so if he feels mistreated or delayed, it could worsen the relationship. Narrow AI can help us on both scenarios, but we need to carefully choose which tasks are appropriate to apply AI to, and which ones require human intervention. We’ll go deeper on that later on this post.

The importance of NOT applying the wrong technology to the wrong problem cannot be understated. We have many examples of this in the past:

• With the advent of the IVR (Interactive Voice Response) in the 70’s came the widespread idea that most of the work done by customer representatives on the phone could be automated. Tasks like getting your bank accounts’ balance, even making simple payments and transactions could now be done without the intervention of a human representative. This, of course, meant a considerable reduction in customer service costs, and 24/7 availability (a win-win for both the businesses and customers). But then, IVR became complicated. Layers and layers of menus to get to the desired answer, while many times the answer wasn’t the expected one, or even suffering repeated hangups from the IVR platform without solving the problem first. No direct way to contact a (costly for the business) human representative, while he was buried beneath many levels of interactive menus. IVRs became a symbol of bad Customer Experience.

• Natural Language Processing (NLP) systems became stronger with both Machine Learning techniques and the increase in computing power. Tools like Amazon Lex became available to provide voice recognition IVRs. You no longer had to press numbers in your phone to state what you wanted to do, you could now speak your intentions and the platform would understand you, and ask you in a natural human language way the rest of the info it needed to collect, and fulfill your request. The same kind of NLP powers all kind of chatbots (Social Media, WhatsApp, websites, etc). But all of them are just IVRs now able to collect info and answer using natural language (without pressing buttons). They need a possible response tree pre-programmed behind to be able to work (layers and layers of menus). Same issue as before. How many times you have yelled angry to the bot: “I WANT TO TALK WITH A HUMAN REPRESENTATIVE!”?

• And now, the all new and shiny GPT-powered chats (and soon voice systems). We have already discussed the difference between Narrow AI and General AI, and the current limitations. Even if the response might seem articulate and apparently correct, the risk of it being an “hallucination” exists, specially if the model hasn’t been properly trained for the knowledge domain where it is going to operate. This can lead not only to angry customers, but to malpractice lawsuits and liabilities that the business will have to absorb, because the automated tool cannot. Will we make the same mistake as we did on the recent past?

What can we do with the current AI capabilities?

So, are GPT and the rest of the models doomed? Not at all. AI Agents are tools. We, humans, are responsible for the success on the use of such tools. We need to carefully pick the situations where we can apply AI safely, and it will enhance our capabilities beyond what we can do now. But by understanding how they work, and their limitations, we can choose the right time to integrate them into our workflows, for the best results. We have not yet reached the point where General AI is available, and we can depend on it as we would on another human being, side by side. Narrow AI tools need careful management and oversight.

Some examples of proper usage for Customer Experience, following our previous analysis, would be:

• For Strategic level Customer Experience, an AI tool can help us to collect and summarize information we require to analyze, saving us lots of time rummaging through the Internet and local documents. Products like Microsoft Copilot already have shown what can be achieved with this.

• It is also useful for strategy to use an AI tool to build analysis charts, like SWOT and PESTEL, based on the information available for the GPT model. The result probably won’t be perfect, but it is a great starting point, and it can even unearth trends that weren’t easily detectable by a human analyst (data mining style).

• It can be used to produce content, presentations, even publishable articles, all of them based on the information fed to the model. That’s an enormous time saver, that allows to dedicate more time to the real strategic work.

• At the Operational level, it can help craft action plans, based on the strategic documents already fed into the model, and update them based on changes that might happen over time. This greatly reduces the overhead of project management, and can even allow a project manager to oversee more projects with ease.

• It can also help Customer Support Representatives, both on the phone, chat and email, to find solutions to problems faster. It is the representative’s job to make sure the solution proposed is fit for the situation, but the average amount of time saved will be enormous.

• For some self-support tasks, the use of a GPT powered chat, properly trained with the information regarding the problem domain, can perform as a first level of service. If the customer feels that the information provided is not enough, they must be able to quickly transition to a human representative, to avoid the bad Customer Experience we already mentioned on the IVR and chatbot discussion. It could even be part of the intended workflow: save time from the human representatives by collecting all the necessary info and intended service with a GPT bot, before turning them over for the real execution.

Where are we headed?

Narrow AI based tools and solutions have been already with us for some time (Tesla’s self driving cars, Google Translate, Amazon Alexa, etc). GPT based tools, like ChatGPT and all of the derived products will be added to the mix, this time increasing the amount of work they can do for us. But, as we have discussed here, Narrow AI is not General AI. It cannot and won’t ever be able to “think”. It cannot know if something is factually right or wrong, cannot make moral evaluations, etc. Some people will misuse the new and shiny AI tools, and try to put the blame on them (remember IVRs). But some people, better informed, will apply AI the right way to the right problems. And thrive where others fail.

It is true the job market will be affected by AI: that’s inevitable. We are on the brink of the 4th Industrial Revolution. But you must learn to use AI to be more effective and efficient on the work you do. Then you’ll have an edge thanks to AI instead of being replaced by it. You must ask yourself: if you can really be 100% replaced by a Narrow AI tool, perhaps you have grown complacent with your skills, and need to rethink how to put your human skills, not available to any AI (not for now… until General AI comes), into use. Make of this an opportunity, not a threat.

Keep informed. Keep on top of the situation. Customer Experience is going to be transformed by AI, along with every other industry. So make an habit of learning about how to apply not only AI, but every technology, tool, strategy and methodology available to enhance your Customer Experience delivery. By reading this article you have given a first step on that direction. I encourage you to subscribe. It will give you access to a free webinar about how to make Customer Experience make more money for you. And you’ll also make sure you don’t miss any articles from me on this topic (I publish twice a week).

Let me know on the comments if this was useful to you, what would you like to be explained in more detail, or anything that comes to mind on this regard.

Cheers.

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

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References

If you have been paying attention, you have probably noticed plenty of reports about Customer Experience initiatives not producing the expected results. According to Gartner, 95% of companies collect customer feedback, but only 10% of those act on that information in a meaningful way1. And those studies are probably focused on large companies with large budgets devoted to Customer Experience. From what I’ve seen on Small and Medium Businesses, the numbers are worse. And even those who act, are probably just recording “happy faces vs sad faces” in a satisfaction survey, Net Promoter Score surveys, etc. It’s not surprising that the results are underwhelming, usually not justifying the money spent.

The problem is that Customer Experience (and its Management) isn’t what most people thinks. This leads to wrong expectations, cancelled projects and organizational dissatisfaction. Once its true nature is understood, and which objectives can be achieved through it, it becomes easier for the organization to reap its benefits.

Also, contrary to common belief, Customer Experience initiatives should not be exclusive of companies with big budgets or dedicated departments. Organizations of any size can design its own Customer Experience implementation project, according to its own capabilities, and then keep refining its operation over time. All it takes is having the knowledge, the commitment, and some technology support.

So, in this article we will explore:

• What should be the tangible results to expect for any organization implementing Customer Experience Management as part of its culture.

• Why to expect those results: understand what means to have a Customer Experience culture.

• An overview of the kind of techniques that, once implemented, help achieve the expected objectives.

• A general recommendation on how to get started.

Customer Experience should provide tangible results

Customer Experience Management is a relatively new business discipline. This means everyone is still learning how to make the most of it, and what works and what not. A recent similar example is the initial misuse of Social Media Influencers in Marketing: large sums were spent paying influencers, without a clear way to measure ROI (even today, almost 33% of businesses using influencers don’t measure it2). Eventually, the industry learned how to do it (as we can see in this MIT Sloan Management Review article3, and many others).

In the same way, most Customer Experience initiatives are restricted to collecting customer data, satisfaction levels, intentions of purchase and recommendation, etc. But as a Gartner study shows, just 10% of the businesses that collect data actually do something with it, much less get some tangible business result and apply metrics to it.

So, if Customer Experience Management is not only happy/sad face surveys and NPS reports, what should you expect from it?

Happy faces and willingness to recommend are just one part of the equation. It’s what we call Customer Satisfaction. It’s the emotional, subjective part of a product/service well delivered. It’s a perception (and there are decades of studies on perception and customer satisfaction, for those curious enough). The other important part, often neglected, is called Customer Success. It not only means the customer being happy with the product, but being successful in whatever he was trying to accomplish by buying it. It goes one step beyond.

Customer Success is often embodied in everything a business do so the customer has a smooth experience with the product: Customer Support, Customer Training, Guarantees, etc. So, Customer Success means for you, as a business, that you embrace the idea that your mission with your customer doesn’t end when he walks away with your product. You go all the way to ensure he is successful with it. And that he becomes knowledgeable about it, so he can make good use of all the bells and whistles your product have and the competition doesn’t. So, next time he’ll buy you again not because of price, but because of features that are useful to him.

This, of course, produces happiness and willingness to recommend (Customer Satisfaction) and that is measurable. It creates goodwill and good Word of Mouth for your products and your business. Now you have evangelists. But it also increases your bottom line by reducing you costs. And that is probably a much more tangible result than the wishful word of mouth. How so? Each customer has something called a “Customer Lifetime Value”. It is how much money he gives you (revenue) less what costs you to acquire him as a customer, and all the costs associated at providing him with products and services. This means that fewer support cases, fewer returned products, fewer complaints, etc. from a specific customer increase the lifetime profit he produces for the business. He becomes less costly to maintain.

As you can see, it is of absolute interest for the business to make sure he has a smooth experience, because an angry customer is a costly one (and I’m not even talking about bad rep). But, it is also of absolute interest for the business to achieve that smooth experience at the lowest cost possible. And here the same principle as in healthcare applies: prevention is far cheaper than remediation. So, if you apply proper Customer Experience strategies, aimed at preventing the Customer issues, and solving quickly and cheaply the ones that occur, this will be reflected in a measurable increase in the average Customer Lifetime Value, and the business’ bottom line.

To recap, you can improve your business results with proper Customer Satisfaction strategies, but those will probably pale in comparison to what you can achieve through proper Customer Success strategies. And those Customer Success strategies are more effective on the prevention side than the remediation side. And not only because solving an unforeseen emergency will always be more costly than preventing something, but also because Good Customer Service is not equal to Good Customer Experience. Let that sink in for a minute. When you receive Customer Support because something went bad, even if the response is stellar and solves the issue at the first try, you already had a bad experience. And in some cases, even some non remediable damage (your cable service just going down the day you wanted to watch some sports event with your friends, for example). No remediation will fix the embarrassment of that day.

A Good Customer Experience would be not having the outage in the first place.

Customer Experience Management must be embedded in the culture

The immediate question then should be: How do we get there? What should we do?

The answer is more complicated than that. It’s not just about applying some techniques, implementing some software, or a few coaching sessions. It’s a deep cultural change for the organization. The proper questions are: How should we be? How should we behave? There must be an understanding of all the interactions between the customer and the business. Those are called “Moments of Truth”, and mapped using the now famous Customer Journey. Each of those moments of truth need to be designed to be flawless, have a responsible in the organization, and be constantly measured so they can be improved over time. When the customer makes use of the product (or service) it is also considered a moment of truth. And everything must be set to ensure his success.

Changing the organizational culture, even in a small business, is never easy. Over the years “silo mindset” tends to settle in. That means people gets used to think they are responsible only for the small part of the work they are involved in, and if something else happens along the road and the service or product fails, they just say “it was not my fault”. The organization must shift its mindset into tangible business outcomes, as a team, where everyone is responsible of the final outcome. No partial outcomes. All or nothing. The customer succeeds or not. Without that mindset, no technique, no software will produce the expected results, because even the most polished of products will leave a bad impression if the delivery guy was rude, the invoicing was wrong, or the support team was clueless.

Tools of the trade

Now, how to make it happen? With the proper culture in place, tools and techniques are required to achieve the expected results. And here is where having a well crafted Customer Journey helps a lot. For each moment of truth on the Customer Journey you must ask yourself: What must we do to ensure the customer succeeds? What must we do to ensure a stellar experience? For example, the first interaction your customer has with you through the phone.

Do the person who picks up the call has all the information at hand to be useful? Is he or she trained to be courteous even if the customer is angry for some reason? Or when a customer opens the product package for the first time, and has no idea of how to operate the product. If the product is fragile enough to require some kind of special connection to the electric grid, did we put enough warnings in the box and in the Quick Start sheet? Did we assume that some people won’t read any of that, so did we put a seal in the electric cord plug that has to be removed before use, and has attached a big note with a warning?

As you can see, following our previous discussion, we are trying to avoid the customer to make costly mistakes (for him and for us). The cost we incur in those preventive measures is usually negligible in comparison to support, servicing and replacement costs.

A few examples of those techniques are:

• Short Youtube videos (5 minutes long at max) explaining each important feature in the product, in just enough detail to be able to use it, but pointing for any in depth information to a larger tutorial or user manual.

• An online Learning Platform with a “product certification course”, with short and precise information about the product features. If you pass the quizzes (optional) you can get some kind of reward, like a discount or some additional benefit related to the product.

• Information just at the moment of failure. This one can be very important: by knowing your customer behavior, you can statistically identify the moments where they most commonly have issues with the product, and have already prepared information to help them overcome the issue, which should be placed right at hand at the precise moment (for example, prominently in the packaging of a replacement part that customers tend to install improperly and damage their products).

As you can see, all of these measures can greatly help to avoid customers’ issues, are relatively cheap compared with remediation costs (replacements, support, etc.), are generic (works for everyone, so are cheaper to implement), etc. Customer Success strategies and Customer Satisfaction information gathering working together to produce an excellent Customer Experience, preventing the customer to require Customer Support and Services (costly for the business and a hassle for the customer).

Where to begin

OK, so you have decided to go the Customer Experience way and implement it in your organization. The focus of this publication are Small and Medium Businesses, so I am assuming you not necessarily have a large budget available, nor a lot of extra personnel to dedicate and specialize on this task. Probably you don’t have a large Technology Department either. Where to begin then? Begin with YOURSELF. As I already mentioned, Customer Experience is a strategic initiative, bound to deeply change the culture of the business. You, as part of the Senior Management team (or owner) need to understand well enough the concepts, strategies and techniques so you can properly manage the process, either by hiring external consultants to help you with specific tasks, or training your people to do it. You, as part of the organization’s leadership, cannot and must not extract yourself from the process. The whole organization must be able to see you as committed to the change as you are asking them to be. That’s the only way to make it work flawlessly.

Reading this publication is a good start. I’d also recommend you to subscribe, because I’ll be sharing here more content and helpful recommendations on tools and techniques to apply. And, of course, read, read and read. I’ll also recommend books, articles and authors through the newsletter that are a must for anyone interested in Customer Experience (and that should be all of us). I hope this content have been of help to you. Any comments and suggestions, please let me know at the bottom of the post.

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

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References

Yes. Providing an excellent Customer Experience means happier customers. And intuition tells us that it should, in turn, provide us greater revenues. But, is that even true? Do every Customer Experience effort result into additional profit for our business? How can we measure it? How can we find out which ones provide a positive cashflow instead of only being an unmetered cost?

Here, in The Angry Customer, I will share with you state-of-the-art research from the leading experts, useful trends, professional experience, and thoughts about why better Customer Experience means more money in your pocket, IF YOU DO IT RIGHT.

Stay tuned and subscribe so you won’t miss any of my publications. I have crafted a FREE WEBINAR for my subscribers, packed with useful information that can get you started in your Customer Experience journey, and I’m about to launch a Beta of my Customer Experience Management course for those who might want to go down the rabbit hole (make sure to check your spam folder or Promotions tab if you don’t see my welcome email with the webinar link right away).

Subscribe now

Let me know what topics you would like me to discuss here, and I’ll make sure to include them in my issues. Let’s grow this publication together!

Cheers.

When you are ready, here is how I can help you:

By subscribing to my publication and reading this article you have already taken the first steps into redesigning your business to succeed during the Age of AI, .

But if you want to go further, here are several options:

• If you want to start right away, you can join me in my 3-day Future-Proof Entrepreneur Challenge, where you will learn how to analyze your Profit Model (how your business makes money today), look around for the Exponential Technologies you will need to make it work in the Age of AI, and finally create a plan to make it happen.

• You can check my FREE guide “7 Profit Models to Succeed in the Age of AI”.

• And also my FREE guide “7 New Technologies that can wreck your Business like Netflix wrecked Blockbuster”.

The Entrepreneuring AI is free today. But if you enjoyed this post, you can let me know that my content is valuable by fueling my coffee addiction, so I can keep producing it for free 😸☕️

Buy me a coffee

Subscribe now

Share