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 15, 2019

YML Partners With C3.ai on Customer Experience Design Work

YML is ecstatic to be partnering with Tom Siebel and the C3.ai team to produce category defining customer experience design work. C3 is the leader in how to bring digital transformation across massive organizations, and we believe this work will be transformative for their brand.

Read more about the partnership here.

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 23, 2019

YML X — A Memo to Our Team and Partners

Nobody starts a company at the bottom of a recession. Well, nobody smart that is.

Read more

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?

March 27, 2019

Why I’m diving into the Valley to make things that work, matter, and drive results.

I’m proud to be joining the amazing story that is YML. There are numerous reasons why I'm excited about my new role in the Valley. Here's 3 of them.

Read more

March 7, 2019

International Women’s Day 2019: Empower Her

YML's 2019 Women's Day T-shirt designed by Sadhvi Konchada

YML's 2019 Women's Day T-shirt designed by Sadhvi Konchada

 

Last year at this time, YML created some fun Women’s Day T-shirts for the team.  Being really passionate about the topic, I decided to take the initiative to design this year's tees. Being born and raised in India and now working in such a multi-cultural workplace in the United States, diversity is a topic I hold close to my heart. I knew that was the theme I wanted to explore. As I was in the process of designing it, I shared a draft with our Chief Creative Officer, Stephen Clements, who posed a question which really made me think. He said, “Diversity is a topic so big, so vague, and so well explored by other people. Ideas begin with insights. Otherwise they aren’t really ideas. Insights challenge people. They surprise them. They make them think about an old problem in a new way. What’s your insight?” I paused. I thought. I dug deeper, until I found my insight that empowered me to write this poem —

“There are a world of problems that women have to deal with. Differences between them shouldn’t be one of it.”

 


 

 

Empower Her 

It’s 2019.
The war with patriarchy has lasted centuries,
Across continents.
And the world still fails us.

She and I might have our differences.
The color of my skin,
The language of her words,
The music that makes our bodies sway,
Our childhood memories so unlike.
And yet,
Our struggle so alike.
The world has failed us both.

Every time,
Someone speaks over her,
Every time,
She says “Sorry” before speaking her mind,
Every time,
She’s paid less than she deserves,
The world has failed her.
And me.

Every time,
Her heart beats fast as she walks alone at night,
Every time,
She changes her path to avoid passing a group of men,
The world has failed her.
And me.

Every time,
She’s told what to wear,
What to eat,
What to do,
How to be.
The world has failed her.
And me.

This is just the tip of the iceberg,
Of how the world has failed us.

You.
Yes, you.
Reading this.
Every time,
You see our struggle and do nothing,
You have failed us both, too.

This is not your fight.
This is not my fight.
This is our fight.
To make the world better.

So listen,
I promise,
To stand with her.
To stand up for her.
To fight for her.
To empower her.
And can I ask,
If I’m in need,
You do the same for me?

 


 

Written by:

Sadhvi Konchada is a UI/UX designer at Y Media Labs. She enjoys telling stories, most times with color, and sometimes with words.

LinkedIn  -  Instagram

October 18, 2018

The Secret Sauce: Prioritizing Innovation in Roadmaps

by Shayna Stewart

In the digital industry we all aim towards the aspirational, yet colloquial goal of innovation. Yet more and more, true innovation is harder to come by. We believe this is due to the way roadmaps are prioritized. The frameworks that product leaders use for prioritization tend to overemphasize the manipulation of features that are already in place. The metrics, Level of Effort (LOE), Scale, and Customer Value, used in the framework do not favor injecting any new concepts.

It favors optimizing existing concepts.

Recently when working with a client to prioritize ideas within their roadmap, we created what is called the Innovation Index. In this case, our client and team brainstormed on all of the possible ideas for a second phase of a recent app that was built.

The problem we ran into ranking all of the ideas was a lack of data to support one feature over another as the first iteration of the app was yet to be launched. Therefore no data had been captured and we were too early in the process of vetting more ideas to gather consumer feedback. From our need to evaluate ideas objectively in lieu of usable data, the Innovation Index was born.

To create this index, we first did some research toward an objective definition of innovation. We landed on two important pillars:

From there, we took the list of ideas generated by our client and our team and researched if any of these ideas already existed. If the idea didn’t exist, we gave the idea a score of 5. If it did exist we ranked the idea with a score of 1-4, heuristically representing market saturation. Therefore, 5 was a completely distinct idea and 1 was an idea that has a high market saturation.

Secondly, we ranked the ideas on if the proposal was a more efficient way of solving the problem. If the idea was ranked as a 5 in the market saturation scale (meaning a completely distinct idea), it automatically got a score of 5 for being more efficient. Otherwise, we evaluated the ideas if they were more efficient approach to solving a problem that has already been solved by someone else.  

That’s it, the Innovation Index is made up of two scales: Market Saturation & Efficiency. These concepts mapped back to the pillars of innovation, which helped focus our next steps on differentiation as opposed to directly competing with existing products. In the end this exercise prioritized our product planning to helping an underserved audience of about 18.6M people.

After using the Innovation Index in a roadmap with no data, I started to think about how it would apply  to a roadmap with data already in place. I discovered that without including the Innovation Index, the metrics used to prioritize a roadmap (LOE, Scale, and Consumer Value) were actually working against innovative ideas. Here we dive into just how this plays out:

 

Level of Effort

LOE asks how easy is it to bring an idea to market. The reasons for an ‘easy’ LOE estimate is slightly different for each team, but both result in prioritization of ideas that already exist.

If the idea is deemed easy by the technology team, that means they either have already done this before or that there is significant documentation already published. It also is indicative that all data elements are readily accessible. Typically, data elements that are readily accessible are already in use -- i.e. the idea is an optimization of an existing feature.

If the easy LOE estimate comes from the design team, it means that a proposed feature will  likely have a small impact to the ecosystem. Designers spend less time when they do not have to think through the way that users move through the experience. When they do not have to think through the experience, that means they are manipulating or adding something to an existing page. This type of one-dimensional change is typically not symptomatic of building a new, innovative idea. Therefore an LOE of Easy and even Medium are deleterious to innovation.

Scale

Scale measures the potential number of users reached by the idea. If it’s high, then it gets prioritized over a niche solution. However, when you think about some of the most innovative brands today like Amazon, PayPal, Etsy, and Tesla, they all started by servicing niche markets. Often when innovative technologies and ideas are first created, the full breadth of implicated use cases are still unknown. In the case of PayPal, they worked from the insight that it was very challenging for auction houses (a small but extremely active part of Ebay’s user base) to collect mobile payments. PayPal was born from this insight. Ten years later, it’s rare that you find a retailer that does not support purchases through PayPal.

Augmented reality is another recent technology that hasn’t benefited from publicly scaled use cases yet, but we see companies like Google making significant investments. There’s value in testing early and learning fast, if you encounter an idea that may be  small scale but is potentially innovative. I would recommend prioritizing it and position it to leadership as a learning opportunity for the team.

Customer Value

This metric is our  most vague as it has the potential to be defined differently across multiple Indexes or use cases. We can’t completely rule out that this metric in some cases can be aligned to innovation, but in most cases I see that it is not.

Typically it is discovered through user validation or market research. CV often goes against innovation for two reasons:

The first - you may not be talking to the right group of people. In a recent study, we identified a target persona which clearly did not want certain innovative ideas because they weren’t geared towards that particular persona. This is also a factor when you have extremely small sample sizes. The users you are talking to just may not see the value of the idea. Second, I see studies that outwardly ask users what they want, what could be improved upon. This is important to do in order to find major usability issues, but it’s not the metric that will get you to focusing on innovation. It’s best to use a metric that prioritizes building a prototype of innovative idea that solves a problem that the users didn’t know they had. Then, subsequently present the prototype to potential users (pending you feel confident in your sample) to get feedback on usability. This methodology is better than asking users what they want as a means to prioritizing what you build.

Steve Jobs epitomized this very thought and opined - 

September 7, 2018

People don’t talk this way: The need for consumer-oriented KPI’s

by Shayna Stewart

Earlier this year someone asked me what my biggest lesson of 2017 was. My response surprised them:

“The Consumer Journey is not Consumer-Centric,” I said.   

Around this time, I started to present the concept to VPs of Consumer Experience or VPs of Consumer Data. The response was always one of shock.

“Wait, how can that be true?” they wanted to know.

The answer is one born of collective inertia. Customer experience and customer journeys have been hot-button topics for years now, and most forward-thinking companies have embraced the need for this fundamental reorientation of the ways they go to market.  What hasn’t changed, however, is our methodology and framework for measuring such change. We are still working with the old scorecard in a new game.

 

Where we stand today

If you think about the average consumer journey framework at face value, it’s obvious it is not a consumer-centric one. When was the last time you said, “I’m aware of this brand!” or “I’m engaging with a brand!” as you surf their website. Now all of sudden you tell your friends you’re “Really loyal to this brand!”

Source: Mckinsey

It just doesn’t happen. People don’t talk this way. Therefore any framework based on these expectations for consumer behavior cannot be a people-centric.

Now, this isn’t to say you shouldn’t be working from a classic consumer journey. It is actually a useful business framework that defines the checkpoints it would like to send consumers along (again, not consumer-centric). This can be necessary for contextualizing business and marketing strategies in how well they are driving an outcome and if there are any major leaks in the desired path to conversion.

A consumer-centric framework comes in to contextualize questions like, “Does the consumer find value in my product?”

Most brands believe they are answering this question by using a consumer journey KPI like conversion or, in other words, revenue. This is logical. It makes sense if revenue goes up, there must be some value the consumer is finding in the product. But we have to go deeper to find human contextualization.

I like to reference Google’s Micro-Moments as a best in-class example of a consumer-centric framework. Ideally, you would make yours more specific to your brand, but no matter what, it’s a great place to start.

The basic premise comes from statements such as “I want to learn” or “I want to go.” This actually frames up a person’s state of mind. If you can categorize content in this format, you can now understand the reason a consumer has visited your experience.

Source: Think with Google

If you can understand the reason someone has visited your experience, you can then personalize based their mindset. Voila! That is how you become a consumer-centric brand.

 

How we do it

Here at YML, one of our healthcare client’s (a major operator of healthcare facilities) strategic research discovered that there are four major pillars where their brand helps improve the emotional work environment for registered nurses.

These consumer-centric pillars not only guided the roadmap for developing a consumer-centric product, but also led the way to a fuller methodology for measurement. To build it, we mapped each of the features to the consumer-centric pillar that it was designed for and assigned usage and conversion metrics. Now, we can monitor what pillar is having the largest impact on the product as well as business outcomes. Optimization strategies are also guided by consumer-centric activity as opposed to a business outcome.

 

Follow the money

Let’s break it down a bit more on what the most common business KPI, revenue, is really answering.

Revenue is the cash amount of goods or services sold. This means that a lot of variables like pricing discounts, supply chain, interest rates, or the economy in general all come into play to impact performance of the KPI. Sure, consumer interest may cause an uptick in revenue. Or it could be performance of the website or AdWords. No consumer would buy something that isn’t valuable to them or from a non-working website.

But no matter how much economic data we input, there are just too many other factors that impact revenue to accurately describe how a consumer feels about your product.

Another danger brands run into is the optimization of their entire digital product around a single consumer journey point like conversion (revenue). When brands do this they drastically reduce the scope of their reach with potential buyers.

When optimizing a digital product around the final funnel stage of revenue conversation, you’re really only optimizing for finding consumers who were already in the market to buy your product!

Marketing algorithms, for example, are predicting the users who are likely to perform these kinds of actions in the first place (i.e., make a purchase) and then showing them an ad. In the context of A/B testing, you are optimizing around making it easier for users who were already looking to buy something by reducing barriers in that conversion funnel.  

The same holds true for optimizing around the other generalized consumer journey points. If you optimize towards awareness, you’re just optimizing to get more people to your website, or worse, optimizing around people seeing your banner ad. If you optimize towards research, you are actually optimizing to get someone to a specific page on your site or filling out a form, but you aren’t evaluating if the content was valuable to those users in the first place.

It’s for these two reasons -- consumer journey KPIs don’t accurately describe consumer desire and businesses optimize to their own desires instead of a customer -- that all brands must adopt a new consumer-centric KPI framework.  

 

So what is it?

Now there are a too many methodologies to create custom consumer-centric frameworks to name here. We can define, however, just what such a framework is and how it should be used to describe how a consumer feels.

As stated earlier, a consumer-centric framework uses words that consumers actually use. For example, if you’re a shoe brand a consumer might say, “I need a black heel in my size to wear to a wedding this weekend.”

In this case, the retailer likely already has their inventory categorized by color and type of shoe. This allows users to find possible shoes that are “black heels” on a site or through search. But it doesn’t actually answer the question.  

Doing a quick search on DSW, the query “Black Heel” resulted in 4,291 items.  They have a “Need It Today” selection that will filter results based on local store inventory. They even have a whole filtering section by occasion. When I select those two filters, 11 options pop up.

via GIPHY

 

What a wonderful way to optimize the experience around a consumer-centric question! That experience just built loyalty without the consumer having to say, “I’m loyal.” They delivered their experience in the way I was thinking about the product. #happycustomer

Of course you can’t account for all of the statements a consumer may ask as it relates to your brand when designing a product. This is exactly why you need a framework.

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