July 17, 2019

We Are People: What it Means to Have a People-First Approach

By Shayna Stewart / July 17, 2019

A people-first approach is neither easy to create or quick to implement. But it is the secret sauce at the core of the biggest and best brands in the world today.

Customer experience is a strategy that all digital insiders know has to be a focus if they want to have a lasting impact in their industry. However, the execution of customer experience isn’t as easy as just coming up with a plan to leverage emerging technology and building digital products. It’s as much about igniting cultural change within a company as it is about planning for the evolution of the experience.

At YML, we’ve designed a dynamic and thorough people-first strategy built to cultivate cultural change.

That people-first approach is what is missing from the majority of CX initiatives — and it shows. 

  • Executives who have made a push for a CX strategy have not seen a tangible business improvement.  20% of companies scored 9-10 for seeing a Return on Investment, with 14% of companies scoring 0-2 (Confirmit, 2018). 
  • The public doesn’t believe they have reaped much benefit from CX initiatives. 
    • 54% of U.S. consumers say customer experience at most companies needs improvement (PWC, 2018).  
  • Culture and legacy technology systems have been major reasons for people not seeing the consumer benefit of CX initiatives.
    • 54% of organizations cite culture as the primary challenge to becoming more agile, followed by the inflexibility of legacy technologies (Confirmit, 2018).
  • The companies who are reaping the rewards of CX initiatives, whom are mainly located in Silicon Valley, are the ones who have unequivocally added benefit to people’s lives.  
    • The S&P Index is largely a Technology Index as of 2018, including companies such as Alphabet/Google, Facebook, Apple, Microsoft, Amazon (Seeking Alpha, 2018).

What differentiates the Silicon Valley behemoths and startups is the people-first approach. 

Source: Conformit, 2018

A people-first approach comes with a shift in mindset that is drastically different from the historical business executive mindset. You suddenly are talking about the broad spectrum of all people, internally and externally,  instead of just customers, and ultimately revenue. You are talking about emotions as opposed to products. Instead of technology solutions, you are building conversational tools. Lastly, whereas a business-centric mindset is one that optimizes based on minimizing risk and maximizing revenues, a people-first mindset is one that optimizes for transparency and intrinsic value.

In the short term, when first making this cultural shift, these optimization goals can constrain each other. In the long run, a people-first approach will maximize revenue, reduce risk, build loyalty with your team, and, quite frankly, keep your business relevant. 

But this is a very difficult story to tell when in a boardroom meeting. Often times a savvy executive can make the initial case for investing in CX, but isn’t able to clarify the full scope of that CX investment, which includes a gradual and tangible, cultural change to people-first. What ends up happening is that the first part of the project may go well and the customer may come first, but then the returns on revenue and reduced risk are not immediately recognized and therefore the mindset shifts back to business as usual.  

The trick is to trust the strategy. Trust consistency of message and approach. 

Here are some examples of companies optimizing for people-first.

  • Netflix created an easy to cancel monthly subscription experience along with reminders to cancel after the trial period so that customers never felt like they were overcharged or cheated in someway. However, this people-first change, optimizing towards transparency, had an estimated loss of $50M in subscription revenue. At the time, that was still a small percentage of overall revenue and in making the change towards transparency it built long term trust. As a result of improved brand perception, they continue to increase their monthly subscription base, hitting their highest level of subscribers in Q1 2019. 
  • In 2016 McDonald’s invested in elevating the interior environment of their stores to feel more premium, along with adding in self-ordering digital kiosks and table service. Investing in improved interiors is a table stakes strategy. Let’s face it — they needed to make this people-first investment just to stay relevant. It is table stakes because the outcome will get you to a net-neutral spot; it’s not going to increase customer base, it’s just going to make sure you don’t lose customers at a faster rate than if you did not implement that update. A clean, premium eating environment is the expectation. But the digital kiosk paired with the improved interior is what took the strategy to a level that would actually increase sales.
Image result for mcdonalds self service

  • The digital kiosk solved a customer pain point of waiting in lines in a way that was hard for competitors to copy right away.  Their strategy was to ensure their experience met standards and then improved the standards of the industry. This investment didn’t start to see a return until 2018 for stores within the test. McDonald’s has many other competitive pressures, such as new restaurants with the perception of better quality food and convenience offered through delivery overriding in-store speediness. But refreshed strategy may not be enough to overcome these new customer expectations. Changing expectations raises the importance of adopting a people-centric approach that will allow you to rethink the entirety of the business and how it can pivot from an existing model to a new one.

In both of these instances with Netflix and McDonalds, the immediate impact on the business metrics (revenue, profit) went down. In the long run, these CX strategies resulted in heightened retention over time. Brand perception and revenue drastically improved. They illustrated how creating a people-first culture will help mitigate the initial shock of investment and reduce risk over the long run because the investments made are directly informed by people’s emotions. 

Image result for netflix
According to Forrester, a one point gain in CX index results in a $5M-$185 million return on the business (depending on industry). Netflix has been ahead of the curve when it comes to CX and a people-first approach.

At YML we have created a step by step hierarchy to help you understand what actually goes into creating a people-first cultural mindset. Breaking it down into steps can help your teams understand where they are in maturity. The plan is also a tool to understand what steps were missed in the past. The key to this model is that it implies a high level of collaboration from stakeholders from historically siloed teams at every step.   

Levels to Creating a People-First Culture: 

  1. Feel What People Feel 
    • Extensive marketing research that looks beyond your customers, your competitors’ customers and the points of interaction with you and your competitors
    • Employees from each team pretending to be your own customer
    • Employees from each team pretending to be a service rep that interacts with the customer
  2. Empathize to Solve Problems
    • Build your strategy around the crucial moments of emotions in step 1
    • Identify what part of the strategy is table stakes vs. what will move customer expectations
    • Projects that only have table stakes will fail because that only postpones the inevitable of customers churning, it will not promote long term engagement
    • Ideas that will move customer expectations should be prioritized despite being harder to develop (See how to prioritize innovation with Innovation Index
  3. Igniting Cultural Change
    • All team members should be aware of the new people-first research and strategy 
    • The people-first strategy should be outlined in terms of how every person and team can help implement this new strategy and what is expected of them
    • New rules of engagement defined, highlighted by a culture of not being afraid to fail, must be adopted.  This about making a transition from fear of change to perceiving of smart risk-taking as admired 
  4. Talk the Way People Talk
    • Your backend systems and content need to reflect the nomenclature of the way people talk, as opposed to the way an industry insider speaks.
    • The backend systems must be able to support people’s desired navigation 
    • This sometimes can be a significant change to legacy data architecture. 
  5. Build The Experience
    • Design, develop and deploy
  6. Continuous Optimizing of The Experience
    • Must have the ability to move quickly and make quick decisions.
    • Much of this is about empowering mid-level employees with the ability to have more decision making power.  
  7. Creating New Customer Expectations 
    • Continuous pulse on changing expectations and creating new solutions to meet those new expectations
    • Creating new technology
    • Taking a new technology to solve an unsolved problem 

Each step is crucial, and completion of a step without completing the one before it will invalidate all steps. In addition, the investment in each level is additive and represents a cost that is continuously incurred. This means the investment does not go away once a team has leveled up. The result for each step will be unique to every brand and even the approach to all steps is not a one-size fits all. Even if you meet the requirements in each step there are still some cultural habits that will undermine this entire investment. 

Habits to Avoid in Order to Preserve a People-First Mindset

  • Don’t forget to create advocates across all teams. Be sure to allow lines of communication for input and collaboration from all teams. This is a high-collaboration sport. 
  • Don’t say the investment will end with a specific project. Your teams should be continuously optimizing the project and there is no end to the investment. Remember, the CX leaders are actively investing billions every year in creating new expectations (i.e., Steps #1-#7 never go away).  
  • Don’t a business case around just Step #5. Steps #1-#4 are crucial to making sure the investment incurred in Step #5 is not wasted. 
  • Leveraging emerging technology without contextualizing why and how people would use it creates costly mistakes. That can only come once you have hit step #7 and shouldn’t come sooner. 
  • Not investing in robust people-first research. This seems simple enough, but most companies think they have the right research based on satisfaction scores from customers. This is too narrowly focused for a people-first approach.  
  • Not properly communicating the people-first research and initiatives built from it to all teams in the entire company. Teams need to understand what this shift means for them and how they can support it. 
  • Not allowing for employees to feel comfortable about outcomes that weren’t positive. Not everyone will get it right the first time, but they should learn from why it didn’t work. That insight will get teams to the next big thing.  
  • Not expecting team structure shifts in order to become more agile. 
  • Not expecting major changes to database warehousing teams. Usually CX initiatives are considering just what it takes to build a website or app, but fail to consider that the systems that they may read from are not set up to comply with the new people-first strategy.  

A people-first mindset should permeate the underlying thinking of all teams. It should be an iterative process that produces long term business results.

It should unite and empower all employees to stand up for what’s right for the customer.

Employee thinking should be able to shift seamlessly between their executive persona and people persona. And most importantly, it should allow employees to feel like people feel because, at the end of the day, all of us are just people.

July 8, 2019

Speaking the Same Language: How UX and Data Strategy Can Work Together to Design for Voice-Based AI

By Shayna Stewart & Amit Garg | July 8, 2019

To say the stakes with voice interactions are high would be an understatement. This is the moment for voice technology.

Voice has the power to capture attention like never before because it hooks directly into the mechanism for how people think. It removes the friction of reading, clicking and translating like with other technologies. 

However, voice-based AI is highly constrained in terms of what it can and cannot do:

  1. Voice can only perform tasks that it was programmed to do, which can result in inaccuracies
  2. The user is not aware of all the potential tasks that can be completed
  3. Obviously, voice is not suitable for tasks that require sight 

These constraints make the design of voice one with little room for error.

At YML, we think about building products (including voice projects) in the form of an infinity loop, a repeated steps of moments that are continuously optimized as you learn more.

Below we outline how UX and Data Strategists can partner in each moment for a voice-based AI project to reduce risk of voice AI going terribly wrong.

1. DEFINE - Align tone and personality of the conversation

Having a clear, distinct vision is essential for voice.

The utility of the conversation is the most important part of the vision. At this point in time, the voice-based AI has not mastered the art of casual conversation where it can react to what the person has said and feed them what they are anticipating to hear through compliments and relatability.

AI is programmed to learn in smaller verbal tasks and take the learning of the smaller tasks and associate learnings to other tasks (though progress is being made there).

UX should define the utility, personality and context of the conversation. Why is this interaction important to have? At what point should the conversation happen, particularly if the conversation is prompted by another interaction? What is the intended outcome of the conversation? What qualities of our brand will this voice represent?

We must provide evidence for why voice is the right channel to design for in a given interaction, especially by understanding its context. For example, a bedroom voice interface that reduces volume to 25% at late night and is less wordy understands we don’t want loud robotic voices at midnight. Practically, a feature like that could be documented in a user flow.

Understanding the tone and the personality is not something a data person typically creates (or even understands in the real world), however, in voice this is a critical element because the personality is actually a data requirement. For example the answers to the following questions are data requirements:

  • How human does the voice need to sound?
  • How should the AI respond?

They should also start identifying any current or potential datasets that may be relevant to help train the AI in the subsequent product phases. They will need to work with a variety of teams to collect and get it into a format that can be easily used during training.

2. DESIGN - Examining vocal vs graphic UIs

Fundamentally, the way we think about the design of sound is different from sight. 

Of course there’s overlap, but it’s interesting to take a closer look. For example, a designer strives for visual consistency. Repetition and visual hierarchy help us stay organized when looking at an interface. 

But with speech, that kind of repetition gets rather annoying. Therefore, we should think of the journey as a carefully crafted conversation full of familiar variety.

In an app or website, the way people interact is relatively constant. GUI interaction occurs in a fairly regular rhythm of cognitive load. Mentally navigating the interface, reading text, and executing tasks requires a sustained level of attention through out. 

It’s a very different situation for voice. 

People make the first move, unprompted, and the system responds immediately. And, due to the transient quality of sound, people need to give their full attention to process the response. The luxury of closing an alert dialog without reading it on a GUI is not afforded by voice, nor is the action of reading and re-reading information. Instead, our full attention is required during voice interaction, and absolutely no attention when not interacting. 

Therefore, voice experiences should feel like a conversation - an interaction that we want to give more of our attention to when it matters - in order to have the highest likelihood of re-engagement.

UX should define a framework for the desired flow of the conversation.

Similar to designing for GUIs, the overall flow of the interaction needs to be designed, as well as defining the user intent the system should be recognizing.

UX should be asking questions like:

How can we remove friction in the process? Is this how someone would actually think about this interaction? Is the system doing everything possible to pick up on the nuances of speech and trying to move the conversation forward?

Even in the case that the engineering team leverages machine learning techniques to let the AI learn the conversation flow on its own, this framework will help the team identify if it is producing the intended results. 

For example, at YML we recently worked with a Fortune 500 insurance company to reimagine their self-service digital strategy, which involved a concept for using in-home voice assistants to handle basic transactions like paying a bill.

Along each step of the conversation, we outlined how the system should move the conversation forward by capturing user intent, setting the variables of intent, and the next action to be taken - all packaged into a helpful and professional voice that emanates confidence and security.

The data strategy team should partner with UX to understand the basic conversation framework and then work with the engineering team to understand their methodology for building the dialog model.

The data strategy team member will need to be able to translate the constraints of each methodology whether its’ rule-based or machine learning-based.

For example, Amazon Alexa skills experts recommend that the conversation has no hierarchy.

This makes sense when designing skills - which typically are a one use product. This is because it prevents questions to have to go through a menu-like conversation (think pretty much any credit card company call centers first line of defense, having to answer a multitude of yes’ or nos before just getting directed to someone).

Though the implications of having no hierarchy mean that:

  • The skill does not have to go through a rule-based system to answer the question (positive) 
  • However, the conversation can get repetitive leading to an outdated dataset in which the AI is making conversation from, reducing engagement overtime (negative) 

This synthesis of the UX framework and engineering approach is important in this step because it will provide input on how to evaluate the success, the learning methodology and optimization strategies post-launch.

3. DEVELOP - Bringing the vision to life and defining metrics

This step is owned by the engineering teams, but this step should entail having regular meetings with both UX and data strategy to ensure that the assumptions they are making are in line with the overall vision from UX and data strategy.

This is also where the AI starts to learn from the team.

Part of defining conversation flows requires defining trigger words in order to move forward in the task. These are documented in the user flow, and are launching points for a task.

During development, UX can conduct usability testing. The classic task-based metrics (effectiveness, efficiency, and satisfaction) are still relevant here, in addition to qual research (in-home ethnography, surveys, interviews, etc.) to learn how customers respond to the design in context.

Data strategy should be listening in to how the AI is progressing over time. If it is not producing the anticipated results, the data strategy expert will need to evaluate why. It may be because the dataset is biased, it may be due to the training dataset not being reflective of the task at hand.

Once the issue is identified, the data strategy expert can make recommendations as to how the dataset should be modified.

Also in this phase the data strategy expert should be outlining a measurement strategy for how the AI will be evaluated based on its current progress. Datasets that will evaluate the performance also need to be built into the development phase. This measurement strategy should include workflows and resources needed to update the AI as it encounters new phrases, as this can be a manual process post-mvp launch.

4. DEPLOY - Monitor, measure, and understand

Voice-based AI is a product that needs constant optimization to not only ensure that it continues to work as anticipated, but also to keep audiences engaged.

If the experience starts to lose it’s initial utility or becomes repetitive, the usage of the product will plummet. Teams should be monitoring and optimizing based on the workflows outlined in the data strategy to ensure the sustained quality of the product.

In addition to refining the design, UX can provide insight into why any failures may be happening.

Was there an insight missing from the define period that changed the perspective of the utility of the conversation? Or is there a technical failure happening? Is the developed conversation mismatched from what was designed? Maybe the personality feels off.

All of this needs to be caught as soon as possible and translated into any new requirements for refinement.

To get deeper insight, UX should review transcripts from all conversations had, as these will provide rich qualitative data to help understand how the product is performing.

The data teams should be analyzing the number of failed conversations, understanding why they failed and making recommendations on how to teach the AI based on these conversations.

This is when the measurement strategy workflow outlined in “Develop” is working.

The data strategy team member will likely need to ensure that the work flows are increasing in efficiency overtime through monitoring the AI KPIs. This is what will lead to continuous optimization of the product infinity loop.

In Conclusion

The qualities of voice-based AI defined in this process result in the underlying identity of a brand.

It’s a high impact touchpoint, that when it goes wrong, goes really wrong.

Though, it also has the potential to reach people in new ways. It is the personification of a brand and has the potential for businesses to create new relationships with their customers.

To protect your brand from a potentially high-risk situation, partner your UX and Data Strategy teams together.

Sources

June 25, 2019

Don’t Overthink It: Design is a Tool For Making Businesses Better

By Stephen Clements — June 25, 2019

Talking about good customer experience is easy. It’s just doing it that is hard.

You’d think it’d be easy for me to talk about it because I have spent the last 15 years talking about it to people who do it every day — clients and designers.

And I have often wondered why is it that all these hours get spent — often on conference calls or in long meetings — by people trying to do the same thing, but in constant disagreement about how it should be done.


A colleague of mine, his wife works as a United flight attendant. That's not her below 🙂

His wife says that on every trip, without fail, at least one customer complains. And they are so bitter, so vitriolic, and they get so angry that they say things like:

“United is the worst.”
“You have just lost yourselves a customer.”
“I am never going to fly United again.”

But she smiles professionally, all the while thinking:Of course you will. You will go on Kayak and you will pick the cheapest option—you will consider your milage plan—and I will see you next time.”

In short, improving the customer experience sounds cool, but it will no doubt raise the cost of a United flight. And this might actually turn off more customers than it’s worth.

I was recently having dinner with my wife, and she asked me, “What are some of the questions you have been asked a lot by clients recently?” (A fun date night, right?)

Well, I thought, something I have been asked a lot is,Yeah, yeah, yeah. This customer experience thing sounds cool. But how will it move the needle?” I have been asked this question in many different ways. By many different clients. At many different organizations.

And, look, I get it. No one wants to invest in all this trendy customer experience goodness, but not see any actual results. Or worse still, see negative results, and they get fired. 

For example, I was at a meeting with the head of e-commerce from a major airline, and he said to me that their most vocal customers — the ones who complain the most bitterly — are in fact the ones that are most profitable.

Funny right?

Probably because they have to pay extra for all the things they didn’t plan for—extra bags, extra legroom seats, extra food, etc. Which got me thinking.

First-of-all, is all revenue good revenue? And is it worth putting short-term profits ahead of long-term customer lifetime value?

It has taken me many years as a designer to reach this state of business-minded enlightenment. And finally I have come to the conclusion: Clients don’t actually hire us to make their customers happy. They hire us to make their businesses better.

*And sometimes (sometimes) it’s the same thing.

Now, I realize this might strike you as a little odd. I mean, is this a creative person giving us a business lecture? Just please stick with me.

You see, in the beginning — when I was a young and idealistic designer — I wanted to make things that were beautiful. I wanted them to be just drop dead gorgeous. And I didn’t care about much else.

I call this my aesthetic design phase.

All I wanted to do was create porn. Not real porn. Visual, aesthetic, product porn — and I was lucky to get to do this for brands like Nike and Xbox.

Then, as my career matured, I began to develop more empathy for people. I wanted to create experiences that helped people, were loved, and talked about. Experiences that tackled really high-value life moments, like shopping for a car — like the work I did with Audi, or buying a house — like work I led with Trulia.

I call this my experience design phase.

And then, when I matured again, I developed a deeper understanding for the clients I served. I began to appreciate and better understand their decision making process, and how they are optimizing to make more money.

I wanted to create experiences that solved real business problems — which we proudly did here at YML by doubling The Home Depot’s mobile revenue in one year.

I call this my business design phase.

I think all great designers must go through this evolution. It is the holy trinity of design. 

First it was aesthetic problems.
Then, experience problems.
And lastly, now it’s business problems.

Designers that don’t graduate along this path… well usually, they fall short of their promise, and their careers are stunted. They aren’t opening their minds to this business imperative.

Some years ago, I was given some advice to buy my clients’ stocks on Robinhood — a beautiful, designer friendly app, if you don’t know it. I bought just a hundred bucks worth, here or there. I bought Nike. I bought Activision. I bought NVIDIA (that one went gangbusters). I’m definitely not a power-investor, by any measure, but it gave me valuable insight into my clients’ world — the business of business.

At YML we did work for First Data on their Clover point-of-sales system, and a year ago I bought some of their stock. It’s been quite a ride, but I have nearly doubled my money.

Now, I am no expert in macro-economics, but that trough on the right of the graph pretty much happened all across the stock market — because of the China tariffs, the fed raising interest rates, reports of economic slowdown, and maybe the Mueller investigation, too. 

Right at the bottom of that trough we had a meeting with the head of the Clover team. It didn’t go too well. She was obviously distracted and when she was engaged, she was asking very tough, business-minded questions. Maybe it was bad timing, but it was also a reminder of how significant business realities are, each and every day.

In the past, I have heard promising designers, sometimes Creative Directors even, saying things like:

"Money is evil."

"Business is for other people."

Clients...they just don't think like us." (But, of course, they all still want a raise, come review time.)

And it is true, most clients don’t think — or talk — like us. Or real people, for that matter. Imagine if they did.

I mean, I don’t know about you, but as a real person, I often find myself wishing there was more info.

Or, looking for a solution that is more scalable. 

And, who isn’t absolutely thrilled when they fall into the correct user segment?

But designers aren’t any better.
They are just different.
I mean, that must be an awesome Kleenex site!

What I have learned, over the years, is that clients and designers are often talking about the same thing. But they come at it from different angles.

It's like they are speaking different languages. It leads to a lot of disconnects, frustration, lengthy meetings, and rather cantankerous conference calls.

Let me tell you a true story. Many years ago, I was on an hours long conference call to discuss a website with a client. And we were talking round in circles when this happened.

"I want you to uplift the branding quotient," the client said.

"Do you mean make the logo bigger?" one of our designers asked.

"Yes" the client said.

And that is a true story.

It’s a perfect example of where we are talking about the same thing in different ways. And it’s this sort of disconnect that makes it a lot harder for anyone to actually buy or sell work. Therefore, I find my day job is partly to play the role of translator. 

As translator, I have spent years helping clients understand designers, and I help designers understand clients. And I have discovered it works like this. 

Designers love to create simple, human, 1:1 experiences. They obsess over all the small details, crafting quality experiences that connect with people on a personal, emotional level.

And clients, well, they love scalable, enterprise-ready solutions that are 1 to many. They, quite rightly obsess over how it will make them a gajillion dollars.

But, of course, these things aren’t at odds. They are 2 sides of the same coin, and they have mutual benefit. Each one makes the other better.

It was this realization that has led me to not only be a better consultant to my clients — because I learned to speak their language — but it also made me a better designer — because I learned to understand their business.

The business of business.

It might seem obvious, but design is a tool for making businesses better.

We are not artists. Plain and simple.

At Y Media Labs — where I am the Chief Creative Officer — we have this way of thinking built into our DNA. And every day, we strive to use our superpowers of strategy, design and technology to make a lasting impact.

A lasting impact on the people that use our experiences. And on our clients’ businesses, too.

1 to 1. And 1 to many. 

Formerly co-founder of Junior: the Rapid Invention Company, a product design accelerator for big brands, and before that, Executive Creative Director for AKQA, San Francisco, Stephen has over 15 years industry experience working at the top of the game. An accomplished product design and innovation leader, he created breakthrough work for brands such as Activision, Anheuser-Busch, Audi, eBay, Jordan, Levi’s, NVIDIA, Verizon, Visa, Xbox, and YouTube to name a few.

June 3, 2019

Y Media Labs Partners with Google To Define Future of Machine Learning with TensorFlow and Tensorflow Lite

June 3, 2019 — By Will Leivenberg

After months of working together informally, Y Media Labs is proud to announce it's partnership with Google focusing on their TensorflowLite technology. YML is helping the worldwide developer community learn and adopt TFLite technology — the core open source library to help developers of all levels understand and train machine learning models.

Google worked specifically with YML's Innovation Lab team. The T-Lite team challenged YML to translate a complex topic like machine learning into something approachable and easy to navigate. Since then YML has become a leader in the application of Tensorflow for developers worldwide.

"The YML team produced thoughtful and thorough documentation concentrating on how best to on-board developers from various backgrounds, ranging from machine learning to mobile and web," says Sumit Mehra, YML Founder and President. "The work ultimately laid the foundation for how developers across the spectrum can create world changing applications, not to mention a collaboration yielding four applications that make the framework as seamless as possible," Mehra added.

The partnership is ongoing.

May 29, 2019

YML Teams Up With Damjanski to Build First of Its Kind, MoMA-inspired AI

May 29, 2019 — By Will Leivenberg

Listening to the dialogue between Stephen Clements, YML's CCO, and Damjanski is like overhearing two mischievous kids hatching their next scheme. It's whimsical, suspenseful, and kind of terrifying.

Only difference is it's happening over Zoom, and each of these guys are sporting a bit of gray hair.

The two have a storied past of creative endeavors and recently found their way back together about one year ago around an unexpected project. Damjanski had become fascinated with AI, to the point that he'd began considering if there was a way to actually collaborate with a program.

"Was there a way to integrate it into my thinking process?" said Damjanski about his original idea. "Could I create original work that demonstrated this concept, was ultimately the idea."

Stephen saw Damjanski's vision, and knew just the team to bring it to fruition — YML's Innovation Lab, the scrappy team of six based in YML's Bangalore office. What ensued was a plethora of emails, Zoom calls, sketches, math equations and countless cups of coffee over more than six months. Led by Innovation Labs Director Darshan Sonde and Kinar Ravishankar, YML helped not just conceptualize, but ultimately build the AI for Damjanski's project.

The final work would come to life as "Damjanski: Natural Selection," a collaboration between Damjanski, his longtime collaborator, Vasco, and YML that debuted May 1st at ONCANAL in New York City.

From left to right: Damjanski, Darshan Sonde, Kinar Ravishankar, Vasco.

As the exhibition website reads, Natural Selection "investigates ideas of collaboration with an AI and its integration into the artist’s practice."  Damjanski specifically wanted the AI to build around all the archived exhibition statements of the MoMA, in New York.

"The challenge was to create more lifelike speech for AI," said Darshan Sonde. "We had created lots of models, but in the end the model released by OpenAI worked the best. This wasn't the work we were used to doing, and it was especially different because this is for an art show, but that also gave us more liberty in what the text could generate."

The exhibition comprises a headset where people can interact with the AI to create new exhibition statements that will be delivered by a printer. Each statement is a new source of information that will inform the artist’s thinking process. Damjanski compares this process to the biological evolution of genes, which is driven by reproduction and survival in order to procreate or grow.

"The unique challenge was training the AI on data," Sonde said. "Data has to be large, so we had to write custom scripts to scrape the data from the MoMA website and cleanup and tweak it to generate good results."

"The YML team was an outstanding partner and I'm proud of the work," said Damjanski, who's work at the exhibition is live through the end of May, and open 7 days a week (11am–7pm) located at 322A Canal Street, New York City.

---

Damjanski is an artist living in a browser. He is a co-founder and member of the incubation collective, Do Something Good, and also the co-founder of the MoMAR gallery within New York’s Museum of Modern Art. More info: http://damjanski.com

For more information contact 

@d.a.m.j.a.n.s.k.i                  #oncanal

May 23, 2019

YML Partners With FinTech App, Earnin, on Customer Experience Development Work

YML announced Wednesday its partnership with Earnin, the Fintech payday advance app.

YML will work closely with Earnin to create a category defining digital experience in the coming months.

Earnin helps workers track and cash out wages in real time. The YML work will focus on the development of their mobile app, which is built to aid workers in getting paid as soon they leave work with no loans, fees or hidden costs.

YML is committed to advancing the cause of the gig economy, and working with Earnin exemplifies that effort.

May 22, 2019

YML Presents on the Economics of Design at Adweek Elevate: Creativity

We were out of place no doubt. A Silicon Valley-based, technology-driven, design and innovation agency surrounded by some of the most elite marketers and advertisers in the industry, if not the world, in midtown New York City at The Times Center.

But then we started talking. The room quieted, and the audience was suddenly captivated.

Check out our presentation from CCO Stephen Clements and product strategist Shayna Stewart. Together they illuminated something that related to all parties in the room, whether representing an agency or brand — design is a tool for making businesses better. We're not creating art for art's sake. We're creating to make businesses better.

That idea is rooted in our DNA at YML, and it's how we make lasting impact.

And the impact was strong! We even got featured in Adweek.

Until next year.

Reach out to marketing@ymedialabs.com with any questions.

May 22, 2019

Cult of the Machines

By Hsio Ling Hee

We are all scared that machines will take our jobs.

I was at an exhibition at the De Young Museum last year - the Cult of the Machine.  

A little iPad in the corner asked me, “what do you do for a living?”I typed, “partnerships”. It did not compute. I retyped, “sales”.

Bip, bip. “There is a 14% chance you will be replaced by a machine.”
Software engineers, as it turns out, are more prone - 42%.

It made me feel less bad about myself (suck it, computer scientists!) - but also realized how this little iPad made me vulnerable, less valuable, replaceable.

Source: CULT OF THE MACHINE, Young Museum

We are ultimately scared that AI will replace us, that it will replace our humanity.

By understanding what AI can and cannot do - I no longer feel threatened by AI. AI is a friend, not foe. AI is there, so that we can enhance our humanity. Do things that make us more human.

Caring for your family. Exploring other parts of the world and understand how other cultures live. Leaving a legacy behind for the next generation. 

Imagine ensuring the safety of your family with a lock that unlocks only for people you know. Imagine sharing stories with a new local friend, who you just met on your backpacking trip through South East Asia, with your phone as a translator. Imagine if disasters can be detected earlier if we watch out for warning signs, no one has to lose their home ever again.

This is magic - made possible by AI.

At YML, our Innovation team saw the light too. Actively experimenting and publishing findings since 2016, we have built models that make expressing your ideas and thoughts easier by predicting the next word as you write.

We have even proven that patience does pay off

So when the Google TensorFlow Lite team reached out and wanted to partner with YML to make machine learning more accessible - there was only one answer.

With the Google TFlite team, we built examples and documentation, so other developers may benefit from our experimentation and as a result, reduced the time they may need to deploy a solution to solve a human problem.

Source: AI in motion: designing a simple system to see, understand, and react in the real world

Our own experimentation in machine learning paid off.

Machines give us room to expand our human minds. It gives the mind much needed oxygen to birth a creative solution to a human problem.

Because if a machine with no consciousness (topic for another day) can do your job, wouldn’t you want to work on something more impactful and fundamentally worthy of your humanity?

Improving and evolving (albeit with help from our AI friends) - what is more human than that?

May 9, 2019

Three Ways the Hertz / Accenture Drama Underscores the Need for a New Working Model With Technology Companies

By Stephanie Wiseman

A few weeks ago, all of us saw our LinkedIn feeds and inboxes fill up with the major lawsuit Hertz filed against Accenture.


And honestly, I — and presumably the collective folks on the “agency” side — cringed a bit because we’ve been there.


Initial promises were made, people change, new information comes to light, and a contract is the last thing anyone is thinking about when there is a list of 50+ priority QA Bugs with an imminent launch and marketing campaign staring you down.


The scale of this situation, however, is significant. And it underscores several points that all of us — client and service provider — need to realize as we work with one another.


You can’t figure everything out in a contract.

A statement of work for any technology project is full of optimism from the sales team (um…me), past traumas from delivery, and a bunch of assumptions everyone has come up with about time, tasks, and people.


Yes, you can look back and say historically you know how long integrations, features, strategy deliverables and design may take to produce. But every client and every engagement is unique, making a contract impossible to be perfect. (My favorite example is the list of items that are “out of scope” — which really is confusing since isn’t everything not listed in scope just out of scope?)


At the end of the day, we’re all just hiring experts that we trust — based on referrals and past work — to help us get to an end goal. So let’s start focusing on that goal, and the major things we need to do on the way there, rather then several paragraphs of who is creating what wireframes. 
(And by the way, that goal should be related to your customer…but that is a different rant.) 


Technology companies are not consulting companies.

Some of my favorite (and smartest) people in the world have come from big consulting firms, they are technologists through and through. But we’re seeing the industry shift significantly with acquisitions of product design firms and technology integrators. And with that we’ve all just assumed that an “end to end” solution for clients has been created overnight.


But, there is a big difference between the agility / product mentality that comes from organizations that started off as designers and engineers, and the ones that started off as consultants. 


And while I’ve probably just barred myself from ever working with/at a consulting firm, hear me out: they both can and should exist. But in my experience the model of a long, analyst-driven strategy is at the opposite end of the spectrum then the prototype-test-iterate methodology that us development-folk are used to. 


We call all agree that the perfect mix is somewhere in between. A middle ground where you’re not jumping into the waters blind, but also not spending several quarters doing research. But in the meantime, we all must remember that these are different people, mindsets, and approaches that many times are opposing forces.


We need to put our money where our mouth is.

This, to me, is where the real change needs to happen. Everyone has to start being honest that technology and service models are endlessly changing. No one company can do everything. And a level deeper, brands must shy away from the knee jerk reaction to “whitespace” in an industry report, but rather emphasize creating things that are meaningful to customers because that is just as — if not more — important then a marketing campaign.


Providers need to put the bets on themselves. At YML, we call it putting our money where our mouth is’. Specifically, we work towards a joint goal — an actual specific and measurable KPI — and if we don’t hit it, we don’t get that final invoice. 


One could call it risky, but if we are saying we’re going to do something, shouldn’t we actually mean it? You wouldn’t give your contractor for your house the entire check at mid-demo, would you? Why should the core to your business be any different?


Like anything, the struggle that Hertz and Accenture have gone through show that there is yet another evolution coming to our industry and this one being a focus on the balance of strategy, innovation and technology. 
In the meantime, our point of view remains unchanged: joint goals are core to a successful partnership and premium work.

April 9, 2019

6 Ways Artificial Intelligence Can Deliver Superior Customer Experience in 2019

WHITE PAPER FREE DOWNLOAD: How are the most prolific companies using AI to deliver a better customer experience?

Last year, PWC named AI as one of the eight essential technologies in business and 38% of businesses employed AI in its systems.

That percentage is expected to grow to 62% this year.

IDC estimates that the AI market will grow to be more than $47 billion by 2020, and Gartner predicts “more than 40% of all data analytics projects will relate to an aspect of customer experience.”

Let's see how customer experience and Artificial Intelligence are blending together to deliver superior customer needs and increase customer satisfaction in the years to come.

Table of contents

  1. Are we delivering empathetic customer experiences?
  2. The shifting battleground: CX
  3. AI and the zettabytes of data
  4. Six types of AI engines helping brands create empathetic CX
  5. The next steps for brands

Are we delivering empathetic customer experiences?

Ever lost or misplaced your phone? Or how about the time when your computer or hard drive crashed and there were key files and data you hadn’t transferred yet to the cloud?

Remember how distraught and helpless you felt?

Now imagine if the customer service person you come in contact with was an imperious know-it-all? Imagine how that experience would add to the mental and emotional strain you already felt. Imagine how you would perceive that brand moving forward? In moments like these, it’s true that you want someone to fix your problem, but more importantly, you want someone to listen and acknowledge your distress.

Apple understands this all too well. The company includes an empathy guide in its training manual for frontline workers providing customers with device support in Apple retail locations.

Source: How To Be a Genius: This Is Apple's Secret Employee Training Manual

The training manual teaches Genius Bar employees how to assess what the customer needs based on their body language, and even suggests phrases to use depending on the customer’s specific needs at the time, like “I can appreciate how you feel…” which is a suggested phrase listed in the manual.

Today, nailing the consumer experience means going above and beyond, taking the time to understand customers, and applying insights to every aspect of the business, from new product development to call center training to designing a comprehensive user experience. The only way that businesses can do this effectively is by having actual empathy for the customer experience.

Here, we define empathy as a brand’s ability to experience their own product or service from the point of view of their customer.

There is no current shortage of brands claiming to deliver empathetic customer experiences.

But have we as an industry actually delivered?

According to Bain & Co., 80 percent of companies say they deliver 'superior' customer service — however, only eight percent of people think these same companies deliver 'superior' customer service.

The shifting battleground: CX

These harsh realities have made customer experience the new battleground at the top of business agendas today.

But while budgets and platitudes may continue to focus on CX inside boardrooms, the speed of technological change and the ephemeral wants and needs of consumers make this a quickly moving target.

And it’s easy to miss.

Consider how we got here: the modern manufacturing age — roughly between 1900 and 1960 — was marked by few pools of capital able to fund and maintain factories. As a result, the marketplace saw little legitimate competition, and an oligarchy of industrialists owned much of the global supply chain.

Source: Forrester

Starting in 1960 there were transformative changes to the industry, highlighted by globalization, deregulation and free trade deals that made it possible to manufacture goods more cheaply in other parts of the world. Even though customer experience were still important, it was price and distribution that were the real decision-makers for customers.

Then, in the 1990s, information and technology became readily available and accessible to the average consumer. This Information Age brought with it a shift in power dynamics, from sellers to buyers as customers now had information to easily compare brands.

The customer experience became a part of a business’s product and service — part of research & development, content marketing, public relations, social media, mobile presence and usability, and website design.

Today’s connected life — there are 4.3 billion mobile phone users worldwidewill see mobile experiences as the key touchpoint for businesses. Modern consumers want a seamless and integrated digital customer experience that ties into various devices and screens.

Source: Accenture

That means everything in the customer experience, from targeting to messaging needs an empathetic approach. Accenture found out that 44 percent of customers are “frustrated when companies fail to deliver relevant, personalized shopping experiences”.

Consumers today are increasingly less forgiving after a bad experience:

According to a study by WOW Local Marketing, 52 percent are less likely to engage with a company after a bad mobile experience — that includes everything from poor design, missing content, and even slow loading times. In Right Now Technologies’ 2011 Customer Experience Impact report, nine out of every 10 customers said they would walk away after a poor customer experience to conduct business with a competitor instead.

But it’s not all dire news. Studies have shown that customers will pay more for a better experience. After all, how many of us have paid for a more expensive airline ticket because we prefer the experience we have with a particular airline? Study after study show that good customer experience will boost even your stock value:

CX is an opportunity.

Source: The Customer Experience ROI Study

Businesses that truly understand the demand for empathetic design in the customer experience know that they can’t do it with human help alone. There’s just no way for humans to deliver against these expectations. Forward-thinking companies are turning to machine learning and massive data to make better decisions on their customers’ behalf.

According to IDC’s 2016 Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, the market for cognitive/AI solutions is expected to experience an annual growth rate of 55.1 percent between 2016 and 2020.

Using the powerful combination of AI and data means businesses have a better chance of giving customers exactly what they want — and even preemptively anticipate their needs before they’re even aware.

It’s this kind of empathy from brands that deepens trust and keeps customers coming back.

AI and the zettabytes of data

Think about how much data is produced everyday, from photos to videos to songs to text messages. The total amount of data in the world was 4.4 zettabytes in 2013 and that number is expected to rise to an astronomical 44 zettabytes by 2020.

To put this in perspective, one zettabyte is equivalent to 44 trillion gigabytes and to break that down even further, one gigabyte is enough data for the books needed to fill a

30-foot shelf. That’s a lot of collected data.

So how can companies make sense of all the information, especially considering most of it is unstructured?

They can’t. Not with human employees in real-time anyway.

In short, this means that brands that stand out during the customer journey have to turn to artificial intelligence to personalize experiences by identifying areas that are relevant to individual customers.

If empathy is all about understanding and being aware of and sensitive to the experience of another, then empathetic customer experiences should be focused on recognition and responsiveness.

Six types of AI engines helping brands create empathetic CX

Empathic thinking requires thinking the right systems in place so that you can proactively respond and resolve issues that come up at the speed of human expectation. While only a few brands employ empathic design well, the ones that do should serve as examples for the rest of the business world.

Below, we’ve listed six examples of AI engines that help brands transform the customer experience through empathetic designs.

WHITE PAPER FREE DOWNLOAD: How the most prolific companies in the world are using AI to deliver a better customer experience

1 / Recommendation engines

Recommendation engines are probably the most common form of machine learning and currently used largely in online retail and media industries and rely on algorithms based on the customer’s past behaviors and patterns. Like receiving a gift from a friend who really knows you. Brands implementing recommendation engines connect the massive data they collect to personalize every aspect of the customer experience.

Perhaps the most influential recommendation engine in the world today is YouTube’s Recommended Videos on their infamous right rail. Of the more than ONE BILLION hours of Youtube we consume, fully 70 percent of it comes from these algorithmic modules. That’s a lot of cat videos, man.

Source: YouTube's AI is the puppet master over most of what you watch

In the future, we could see recommendation engines more prevalent in industries with vast amount of data like healthcare, where AI can help personalize care by taking into account patient history, lifestyle information, medical records, and more. Algorithmically recommended treatment courses, or specific prescriptions are not out of the question.

2 / Predictive Searches

Predictive Searches allow web visitors to get results faster by automatically populating results while the user is still typing. Typically, there is a drop-down list that pops up during the search, which guides users to potential results. When thinking of predictive searches, most of us think of Google’ Autocomplete invented in 2004. Then, in 2010, Google’s search technology expanded to include Instant Search, which uses machine-learning to predict what it thinks you will type, simultaneously streaming results for those predictions in real-time.

Many brands have since adopted Google’s original technology, but there are some drawbacks. In 2017, Google announced that it would drop instant search as its default setting given most searches are now done on mobile, and loading results for predictive searches on a limited screen ends up being a poor user experience.

A Google spokesperson said at the time:

“We launched Google Instant back in 2010 with the goal to provide users with the information they need as quickly as possible, even as they typed their searches on desktop devices. Since then, many more of our searches happen on mobile, with very different input and interaction and screen constraints. With this in mind, we have decided to remove Google Instant, so we can focus on ways to make Search even faster and more fluid on all devices.”

Even after developing an empathetic design — Instant Search — Google continues to find ways to be even more empathetic by thinking about how the product can be fast and fluid on all devices, for all users!

3 / Virtual Assistants

Virtual Assistants, like Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri, and  Google Assistant, range from chatbots to more advanced systems that are changing what customer engagement looks like.

  • Google Duplex is a new capability of Google Assistant that can make calls on your behalf and book your next hair salon appointment or table at your favorite restaurant for you. And the person on the other side of the phone won’t even notice she’s talking with a bot.

Source: Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone

Virtual assistants are definitely a growing niche, so why have only 10 percent of enterprises in the U.S. employed a virtual agent? Mainly because there are still a lot of challenges that can interfere with the digital customer experience. For instance, while virtual assistants are improving greatly with natural language, most of them are still far behind humans when it comes to understanding slang, typos, misspellings, or complex grammar.

4 / Natural language processing

Natural language processing is AI that can process massive amounts of natural language data and can therefore, understand human speech the way it is spoken. When thinking of NLP, an Amazon Echo often comes to mind with its voice recognition, but NLP’s technology holds promise in a lot of industries, including healthcare where it can help with faster diagnosis by finding patterns in a physician’s unstructured notes. Think of how many lives can be saved with AI mining our health records!

Think about Skype Translator, that can understand several languages at the same time, in real-time, which can encourage conversations between people who speak different languages.

5 / Sentiment Analysis

Sentiment Analysis evaluates voice inflections to determine the emotions, attitudes, and opinion in normal human conversation to determine what’s actually being said.

Vibe is a product created by Tokyo-based software company AIR that can scan conversations on workplace communication tool Slack to determine team morale. The product analyzes keywords and emojis used during conversations and places the team’s mood into five emotions

At YML, we used sentiment analysis to understand which company is loved the most: Uber or Lyft?

Source: Uber vs Lyft – Who is loved more? A deep dive analysis using Google’s Sentiment Analysis API

6 / Computer vision

Computer vision uses machine learning models to teach computers to see things the way humans see them.

A company doing a great job is Uru, that uses computer-vision to find spaces in videos where native advertisements can appear to create a non-obtrusive, uninterrupted, more organic experience for users.

For instance, the company’s algorithm identifies spaces, like a blank wall or the board of a snowboarder, where ads or brands graphics can appear. The startup has caught the attention of the industry’s top accelerators and investors.

The next steps for brands

The rise of the internet has provided us with various ways to communicate and interact. Even brands now communicate with their consumers through multiple platforms and channels, so it makes sense that customers expect you to “get” them.

After all, customers know brands are tracking, personalizing, and optimizing every step along the customer journey, so why shouldn’t they be more empathetic to CX? With so much competition out there, the only way a brand can have a competitive advantage is by maintaining an obsession with customer experience.

Once a brand has determined that they want to create empathetic experiences, they need to then do two things: (1) focus on results, and (2) focus on small wins.

When focusing on results, brands should identify the tasks they want to tackle first, then determine the optimal technology to help them accomplish those tasks.

This can get a little tricky with various AI technologies competing in the market, from machine learning, chatbots, virtual assistants, robotics, natural language processing (NLP), and much more: in order to achieve business goals, businesses need to think about the big picture, then work backward.

For instance, if your customers want 24/7 support, then it might be worth it to invest in a chatbot experience so customers aren’t waiting to get through to a human employee. Or if customer satisfaction seems to be an issue, consider a technology that can take the data you already have and connect them with what customers may need, based on their individual preferences. Customer satisfaction will surely improve if businesses present relevant recommendations rather than spewing out random offers.

Finally, brands need to focus on small wins if they want a chance at any of the big wins.

How do you know which small win you’d like to tackle first? Identify the low-hanging fruit. What complaints are you getting the most from customers? Tackle that area first.

As soon as you’ve identified the problem, it might be tempting to use AI to solve dozens of issues right away. This is not the best way to start improving customer retention quickly. Instead, if you want to make the most impact, you’ll want to tackle what’s easy to measure and achievable first.

In other words, think big, start small. What small wins can you achieve in the next few weeks or months?

AND NOW LEARN MORE: How are the most prolific companies using AI to deliver a better customer experience?

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