March 13, 2020

How to Become a Steady Disruptor in the Workplace

For Women's History Month, YML is shining a light on the powerlessness far too many women feel, and how to be proactive about changing the status quo to drive impact.

by Marcela Lay | March 16, 2020

I was recently at a Together Digital meetup —a networking event where women help women succeed in digital - and in life — where a colleague shared something powerful and unfortunately disheartening with me:

“I feel about changing the status quo, the same way I feel about global warming – overwhelmed. I don’t even know where to start.” 

It was not the first time I heard this powerlessness sentiment when a gender equality topic has been brought up. So, it got me thinking about the state of women today, how our company perceives of and hires women in tech, and about where all of this fits in Women's History Month in 2020. 

What has created this analysis paralysis? 

On the one hand, we bring awareness to gender inequality issues through stats that, well, let’s face it, are overwhelming. 

I have used many of those data points during presentations to our staff and in public events alike, and I found it critical to create a sense of urgency. So, to be blunt — there's nothing we can do in the immediate that will change the fact that this issue is overwhelming, massive and institutionally bound to the culture we live in.

On the other hand, we read articles like the one published in December by Forbes, “The World’s Most Powerful Women: Newcomers To The 2019 List” that puts the spotlight on powerful women who are using their power to create change.  Or we participate in women marches that make us believe a massive movement is required to influence change.

Image result for women's march 2019

While the efforts from powerful women and women's movements have been necessary and the catalyst to influence change today, there is very little visibility into the small improvements that regular women are leading today. And if we don't have visibility into those more modest efforts done by regular women out there to drive impact, then that feeling of being overwhelmed and stuck will only persist.

Under our current climate, and when it feels like the world is finally listening, we can’t afford feeling powerlessness. If a window for change has finally opened, then it's our responsibility to take action. We owe it to ourselves, to our friends, to our daughters, and the generations behind us. We all have the opportunity to become agents of change today. If not today, then when?

So, what do we do?

We can start by bringing attention to our day-to-day and by embracing a steady disruptor mindset in our organizations.

Step 1: Identify the issue and label it.

In order to become a steady disruptor, first, we must become hyper-aware of the issues around us. And I know what you might be thinking, "We are already aware."

Yes, to a certain degree. 

But remember that for centuries, women have been trained to dismiss or justify the warning signs. A constant example I've encountered involves blaming ourselves for our co-workers’ inappropriate behaviors – maybe I smiled too much?

Becoming a Steady Disruptor requires an awakening.

Only then will we be able to recognize the warning signs at our organizations. As we start paying attention to that tension inside of us when something is not the way it is supposed to be, we will allow ourselves to admit the tangible problems of equality our team or organizations are facing. Once you identify a particular issue, go ahead and label it (harassment, bro-culture, mansplaining, etc.)  because unless you can accurately identify the problem, you won't be able to identify a potential solution. 

Image result for bro culture
Source: MindTools

For instance, our tech industry is dominated by men (80%), but at YML Atlanta, we ended 2019 with 57% women. How did we achieve this result? By identifying the right issue.

The issue I came across took place during hiring periods.  Our recruiting team would share a wide variety of candidates' resumes, but they were resumes of mostly men. So, I asked, where are the resumes of women? And they would answer, women didn't apply. 

The label I gave to this issue was: "Unbalanced pool of candidates." Then I moved to step #2.

Step 2: Assess opportunities for small and impactful change

Now, because of the countless years women have been socialized to dismiss the tension inside of us, as we try to identify opportunities for change, we will find many reasons that will stop us from doing anything about it. But now that you are not ignoring the tension anymore, fight those mental blocks.  

Here are some example of mental block:

  • Am I qualified to help? — This is the self-doubt mental block. 
  • I'm not sure if I have the right answer. — The uncertainty mental block.
  • What if I fail? — The indecision mental block.
Image result for small and incremental change

Get rid of those mental blocks, whatever those might be. That takes practice, and intentionality. But once you overcome them, you will allow yourself to move into a more creative space where anything could be possible.  It is at this stage that you will start asking yourself a different series of questions: 

  • How can I make this situation better?
  • How can I create an opportunity for change?
  • Who can I invite to help? What role could I play?
  • What type of small improvements could we implement?

The key here is to consider small improvements. Remember — we are not trying to boil the ocean.

Small and incremental improvements are the key to steady disruption. 

On our ‘unbalanced pool of candidates” issue example, I assessed three opportunities for small and impactful change:

  1. Educate our recruiting team on the fact that women only apply to jobs when they match 100% of requirements and that men often apply to jobs when they believe they match about 60% of those requirements. 
  2. Proactively reach out to female candidates to balance the candidate pool.  
  3. I needed to work on a women's empowerment initiative at YML to ensure our women developed a strong sense of confidence.

Step 3: Commitment to translate the opportunity into action

Once you have one small opportunity for change, all that is left is to commit to translating that opportunity into action. This is a critical step to ensure change. Unless we act on our ideas, the attention is not going to translate into meaningful results. 

Here is how I translated into action the three opportunities I assessed in step 2:

  1. I added to our ATL leadership scorecard a metric to ensure our team was requesting and only interviewing a balanced pool of candidates.
  2. I know our Atlanta talent better than my California recruiting team, so I needed to lean into a recruitment role.  I vetted local talent by reaching out on LinkedIn to potential female candidates and inviting them to connect over coffee. 
  3. I spearheaded our Women Initiative (WIN) at YML. Every month, we present content related to personal development, career development, and women empowerment across our US offices.

The first two small actions resulted in a more balanced pool of candidates and how we were able to reach 57% women by the end of 2019. While our efforts ensure more women participate in the interview cycles, we only hired the best candidate for the role regardless of gender.

Yes, small changes can have significant impact at your organization.

Women are poised today with the most power we've ever had to initiate change. The question is, what are you going to do with this power? 

Pay attention to the tension and translate that insight into action.  It is the ripple effects of our combined small actions that will redefine the status quo for this and the next generations. 


About the Author

Marcela Lay is the co-head of YML's Atlanta office, and she shoulders leadership roles across account management, strategy, and people operations. As a strategic customer experience executive with over 18 years of experience, Marcela has worked with Fortune 500 clients like Lowe’s, Delta Air Lines, The Coca-Cola Company, State Farm, and Fresenius Medical Care. Marcela’s experience allows her to bring a holistic understanding of all aspects of digital, including marketing strategy, experience design, and platform development. Throughout her career as a trusted advisor to her clients, combined with a solid background in customer experience strategy and process optimization, Marcela has led cross-disciplinary teams to deliver a range of transformational customer experience solutions. 

March 3, 2020

Alumni Spotlight: After Spending Eight Years with YML, Ryan Spencer Moves to Product Design at Uber

by Ryan Spencer | Mar 3

As mentioned in my article last year, I joined YML in its first version (1.0).

At that time, creating mobile apps was our only priority. Since then the company has made many strides (maybe none bigger than Steve Jobs shouting out YML), gaining customer focus, becoming more strategic, and in turn creating exceptional multi-touchpoint experiences. 

When I look back at my career trajectory and personal growth at YML, the decision to leave could not be harder. Every year I look back astonished at all I was able to complete the previous year. In 2019, I worked on a wellness e-commerce website, a robotic surgery experience design (coming 2021), a financial tool for high net worth individuals, as well as a mobile design refresh with a major medical provider (you'll know it when you see it!).

All of this while somehow adding a newborn baby girl to my family. 

YML's co-founder and CTO, Sumit Mehra, meets Ryan's baby at YML's 10 year anniversary celebration.

Every year is a similar story, and as I move into my new product design role at Uber I’ll take with me the diverse challenges and remarkable design opportunities I experienced at YML. Here are a few:

Breadth – Experience design across industries

Working at an agency can be many things, but it certainly is never boring. It’s frenetic and wild a lot of the time, and embedded in that energy is the freedom to grow and master new skills.

At YML in particular I had the opportunity to jump in the ring and say, “Hey, I might be able to figure that out.” YML was my first position immediately post-college, and since then I’ve touched some 100 products in some way.

From finance with PayPal, to retail with The Home Depot, to healthcare with Research Kit, we’ve been able to dig deep into some tough customer and business problems and create some award-winning and industry-defining solutions together.

The people and the work

When interviewing candidates for a position at YML, many of them ask me “What do you love about working here?” and “What keeps you motivated every day?”, and I always had the same answer: the people and the work

From the design team to project management, engineering and executive teams, I can attest to the fact that every person here at YML has been hand-selected due to their talent, perspective, growth-mindset, and ultimately their drive to push us collectively forward. It’s truly a team that supports collaboration, putting the product at the center of their focus rather than internal politics or ego.

Depth – Focus and amplify experience 

I’ve decided to take a position at the largest mobility company that is consistently producing incredible products. My goal is to learn from and work with people who ship some of the most polished products on the market today.

I’m especially eager to hone in on the depths of product iteration cycles, become more intimate with data, and bring new products to life with the amazing talent on their team.

Having been part of a company that’s grown so quickly, it’s hard to say “my work here is done” as there are always new exciting opportunities just around the corner.

But as it’s time to move on, I wanted to pass on some of my personal values that guided me through work at YML and that I’ll bring to my next chapter of professional life.

  • Set the bar high for each other and for yourself. Believe in the power of rapid iteration to push yourself until that art-board feels just right.
  • Be curious. Know that there’s always more to learn about users, business, or technology. Constant curiosity can turn your passive questions into active ideas.
  • Be proactive. Seek out your next project, goal, or opportunity. Don’t wait for it to appear. 
  • Be available. For your team when they need your attention and need time to collaborate. But also to yourself when you need to recharge your creative batteries.
  • Be positive. Through difficult projects or client relationships, we all need to be the light, to take a step back see all deflated, down moments as an opportunity to re-organize and ideate. 
  • Be organized. No one wants to see group 219 in the layers panel. Make the work predictable for yourself and for developers and continue to work smart. 

Thanks to Ashish and Sumit for taking a chance a young guy with no previous experience who was just eager to learn.  

I am excited to see from the sidelines the heights YML reaches — it will always be a big part of who I am and I truly appreciate everyone who’s helped me through this journey. 

January 30, 2020

Predicting the Future of the Economy with Machine Learning

By Prasad Pai, Technical Lead at YML | Jan 30

Presently, there is a major concern on the probability of the likelihood of having a global economic crisis.

In spite of the supposedly cautious mood adopted by few countries, nobody is willing to give a clear message on whether the next recession is just a week away or a year away. And then there are few others, who are giving indications that there is absolutely no economic slowdown at all.

Above all, the opinions of these financial gurus are changing daily from positive to negative outlook and even vice-versa. Everybody’s opinion is justifiable as predicting the future is not an easy task and everyone has his/her wealth of experience behind him/her.

Hence, we wanted to establish a quantifiable assessment to gauge and decide on what the world’s well known financial investors are thinking about the future of the economy.

We wanted to solve this problem through Machine Learning with the least available information/resources in the quickest possible way.

Data collection

To collect data for our problem, we cannot have a one-on-one discussion on a recurring (potentially daily) basis with these investors but we can scrape their interviews, discussions, speeches, etc from YouTube and their messages from Twitter.

To start with, we short-listed few financial behemoths and scraped the transcripts of YouTube videos through YouTube-Transcript-Api and Twitter feeds through Tweepy. We split each YouTube transcript into a duration of a minimum of 5 seconds each and then order them serially to preserve the time-series nature of data.

This is the summary of the data collected in our experiment:

Data summary

Let us focus on all the subsequent discussion in this article with Warren Buffet’s point of view.

Data Validation

As our data has been collected from YouTube and Twitter, we have to benchmark the authenticity and genuinity of the text data with the thoughts being as close to the financial world. This is necessary because we are going to train our models to predict the future of the economy and our text data transcripts have to be related to finance and economics.

While collecting the data we assumed that these financial investors are quite dedicated to their field and will mostly talk publicly every time related to finance and economics. But still, we have to validate our assumed heuristic.

We don’t wish to perform the recommended way of painstakingly filtering individual text statements in our dataset. Hence, we create a small sample of statements of what we believe talks about finance and economics and should represent the state of our dataset. Here is an example of one such sample set.

Custom_1: How is the economy doing in the United States of America?
Custom_2: The current state of affairs is not doing good.
Custom_3: Life will get difficult when inflation kicks in.
Custom_4: We are in a bull market.

a) Known language model embeddings

We generate the sentence embeddings of all the text transcripts in the dataset along with our artificially generated samples by making use of TensorFlow Hub’s Universal Sentence encoders embeddings.

You can experiment with other language model embeddings as well but we chose Universal Sentence encoder as it has been trained on a wide variety of topics. We plot these generated embeddings using TensorFlow’s embedding projector website. Upon performing T-SNE, we observe that most of the sentence embeddings quickly converge into one cluster along with our typically generated examples.

This is an indication that most of our text samples are related to the domain of finance and economics. Here is one of the example cluster what we observed in our experiments.

T-SNE convergence of dataset using Universal Sentence Encoders embeddings

b) Using custom-built language model embeddings

Another thing we have to validate and experiment is the coverage of our dataset. The dataset should extensively talk about as many concepts related to the finance and economics worlds. To check this aspect, we have to obtain a language model created out of general finance and economics.

We weren’t able to get any publicly available language model in this domain, so we ended up training our language model using free to use publicly available textbooks in finance and economics.

We generated the sentence embeddings for our dataset from the newly created language model specialized in finance and economics. We plotted the generated PCA components out of these sentence embeddings using embedding projector website and we were happy to observe that PCA components were wide-spread in all three dimensions.

This indicates that our dataset represents a wide range of subjects in our language model and is not restricted to one particular topic within our domain. Here is an example of PCA projections which we observed in our experiment.

PCA projections of the dataset using custom trained language model embeddings

We performed T-SNE on these sentence embeddings and we found that embeddings were converging into multiple dense clusters. Each cluster is an indication of a specific concept in our specialized domain of finance and economics and this proves the extensive coverage of various topics in our dataset.

On the whole, we are able to validate our heuristic that our financial gurus are speaking only of their area of interest. Here is an example of cluster projections using T-SNE.

T-SNE convergence of dataset using custom trained language model embeddings

Data Filtering

Though this particular dataset has been good enough for our experiment, we may always never encounter such good datasets. We may have a dataset that has text samples related to general discussion and not related to our desired subjects of finance and economics.

In such cases, we will have to filter out the samples whose sentence embeddings are located quite far from any of our artificially generated typical examples embeddings in a standard language model.

To achieve this, we make use of the NMSLIB library. We weed out all those text samples whose cosine similarity lies furthest from all of our custom-generated samples.

To attain a proper dataset in this crude but yet simple way, we may have to keep repeating this cycle of procedures described in data validation and data filtering section multiple times with several custom generated samples.

Sentiment analysis

Once we gather a good dataset of text samples, it is time to process them. Our problem statement is of arriving at a quantifiable measurement to forecast the economic outlook based on the public statements made by the financial investors.

Our dataset comprises only of finance and economic subjects and if we perform a simple sentiment analysis on these samples, we would be able to achieve a quantified metric to understand the underlying sentiments in the statements made by investors.

We make use of Google Cloud’s Sentiment Analysis from Natural Language APIs to perform sentiment analysis on each of the samples in our dataset. We get sentiment values ranging from -1.0 to 1.0 resembling bad to positive sentiment, thereby giving a sense of inner feelings of the person.

Training models

Now it is time to train the models. We have a univariate time series data comprising of sentiment values. Let’s train different types of models to solve our problem and compare them against each other. In each type of model, we split the initial 95 percent of data as training data and the trailing 5 percent as testing data.

a) LSTM model

We will start with a deep learning solution. We will make use of LSTMs in TensorFlow to train our model. After the training is over, we forecast the output one time-step at a time. The obtained result of predicted value vs ground-truth value is shown below. We are not plotting the confidence interval in our graphs as this is based on making predictions by using all the previous correct values after each time step as we proceed to predict the next timestep value.

Here are the graphs obtained in our experiments after training 10 and 25 epochs respectively.

LSTM test predictions at end of 10th and 25th epochs of training

b) ARIMA model

A deep learning solution doesn’t work well in scenarios where you have less amount of data and particularly when you are forecasting using a univariate dataset. We attempt to solve our problem using the statistical-based approach of ARIMA.

ARIMA will internally capture the trends inside the dataset but to do so, we have to transform the dataset to a stationary time series one. This method gives us a better result as we obtain a much smaller amount of test loss.

ARIMA test predictions

c) TensorFlow Probability model

TensorFlow has launched a new framework of TensorFlow Probability which can be used to leverage domain knowledge of various probabilistic models with deep learning. Like how we had employed simple models previously, we create an elementary model using TensorFlow Probability and fit our univariate dataset into it.

TensorFlow Probability can be trained to capture local, seasonal and many other trends inside the dataset which was either absent or little difficult to be explicitly instructed to do so in earlier models.

TensorFlow Probability test predictions

Comparison of different models

This is the average test loss we obtained in our experiments. Note however that these results are local to our dataset and need not necessarily conclude anything.

Loss summary

Understandably, we observe that the ARIMA model is giving the least test loss as our dataset was small and univariate in nature.

Forecasting economic outlook

Finally, we feed the entire dataset and we make use of our best model to predict the future economic outlook. This is the result we obtain in our experiment.

Forecasted Output: 0.100

We will however not emphasize this result as our experiment had several shortcomings which we are listing next and the quality of the result can be improved when we solve them.

Drawbacks in our experiment

  1. First and foremost is the data. We need data to be as recent as possible. As we had a limited amount of data, we had to scrape quite old videos and tweets from YouTube and Twitter respectively.
  2. Data has to be obtained periodically. We had completely ignored this aspect in our experiment and if it is not possible to obtain regularly spaced data, we have to interpolate the missing values.
  3. We evaluated sentiments of our dataset using a generally trained sentiment analysis tool. It would have been better had we created our own sentiment analysis tool which was specifically trained in finance and economics statements.
  4. We factored only sentiments of the statements made by the investor as the training attribute to our model. Though the sentiment is a major factor, yet there may be other minor factors worth exploring like assessing in what mood was the statement made, was it an interview or discussion, etc.
  5. We didn’t concentrate much on hyperparameter tuning as the motivation was to just prove our concept and we employed only simple models.

Future work

Apart from the above-listed problems, there are few other good things worth looking into in our experiment.

  1. The public statements made by investors keep coming every day and the dataset keeps evolving continuously. Online learning methods have to be integrated into our work and the best way to do this is to fit our entire pipeline into TensorFlow Extended flow.
  2. All three models used in our experiment may individually be good in certain cases and it is in the best interest to apply boosting techniques to improve the results.
  3. Club the individual investor’s economic outlook forecast to form a single score.

If you would like to take a look into code used in this experiment, you can look into my GitHub repository.

Y Media Labs is closely working with Google in improving the experience of TensorFlow to all its users across the world and is a part of one of our case studies of our work.


About the author

Prasad is a Machine Learning Engineer at Y Media Labs. He is currently responsible for developing prototypes showcasing machine learning capabilities to prospective clients and the development of full-fledged projects which involves experimentation with neural network architectures.

January 29, 2020

Getting to Know Debasish Bhadra, Program Manager at YML

January 29, 2020

Who are you, and what do you do?

Hello! I am Debasish, an ardent technophile and one among the passionate ‘Dreamers and Doers’ of YML 💪

As a program manager, I lead and drive program objectives, determine deliverables, make milestones, and pursue the critical path to help achieve strategic and business goals of YML and it’s clientele. 

Fresenius Medical Care, ADAA, Orig3n, FS and EMC are a few of my most recent completed programs at YML. 

Where are you from?

I was born and grew up in the suburb city of Joy, Kolkata, India. 

Coming from a city with a soul which carries 330+ years of rich culture, as a kid I was curious to explore creative, abstract art, sports, technology, and education. The pursuit of knowledge and the warmth of being together with family helped me to build my 'never back down' attitude towards life. 

I moved to Bangalore, India, to study information science and engineering. With ample freedom to flourish and nurture my passion that converge — skills, value, quality, and yes, the cult of the great minds around — I’ve discovered what it takes to deliver a lasting impact on everything I do. 

Tell us a little about your background.

I have had a strong passion for technology since my childhood. It fascinates and pushes me to find a simple tech solution for a complex problem. 

When I received my first computer, I was dazzled to experience the symphony of MS-DOS, Windows 95, CPU system and a printer orchestrating together. I was happy to be hooked to the computer all day long.

Fast forward 15 years, I carry the identical enthusiasm every moment with strong tech management in mobile platform, AI/ML, data science, enterprise and human aspects of software engineering. 

I joined the Vimeo Livestream team as a fresh grad and over the years contributed significantly to deliver world class quality products and projects across multiple Fortune 500 clients later on including Facebook, Dell, HP, and Disney before joining YML.

Why did you choose to come to Y Media Labs?

I believe in the vision and mission of YML's founders and leadership team.

We have some of the brilliant people in the industry working here. 'Work hard, play hard' is our culture. YML means business and value with a lasting impact.  

Apart from work, I love the engaging events of YML. YML hack day is organized for 'YML'ers who want to showcase their unique ideas and coding skills. We regularly participate in corporate sports events such as marathons, duathlon, and corporate relay races. 

Above all, I love the energy, the vibe, and passion of YML. Thats why I choose to come to this world-class mobile development studio. 

What about this industry are you most passionate about?

Technology, software, people, innovation and value converges to motivate me. Faster access to internet enables ubiquitous access to new innovation and new opportunities at our finger tips. Clearly, the world is evolving at a rapid speed.

My conscious involvement with the rapid transformation in the tech industry helps me with effective visualization and successful execution. It feels great to be a part of this industry! And, It's a feeling that will never go away. 

What are some other companies you admire?

Microsoft, Google, Apple, Amazon, and Samsung need no introduction and I admire them for the contributions they have made so far to the technology industry.  Lesser known start-ups and good business models I admire are:

  • CureFit - A digital and offline platform offers a healthy lifestyle and holistic cure across fitness, food, and mental well-being through its three products:, and 
  • Byju’s - Ed-tech startup that offers highly-adaptive, engaging and effective learning programs for students from classes 4-12 and competitive exams.
  • Practo - Digital healthcare platform that enables users to find medical services & solutions. 

What are your favorite spots to eat?

My favorite spot is my residence because I am in control of taste, portion and quality of the food. We like to experiment with fresh quality ingredients and we absolutely love what we cook! Here is a list of nearby favorite places in Bangalore, India, we enjoy dining out:

My favorite spot to eat outside of India is Epicure - Le Bristol Paris and Hibou Deli in Chamonix, France because of their heavenly dishes and flavors of the world they offer. 

How do you spend your spare time?

My family is surrounded with strength of love and with every union in my spare time, our bond grows stronger.

I enjoy reading, boxing, football, yoga, and activities for physical & mental fitness. I also love adventure sports and building prototypes. Expressing myself through fine art painting is another treasured passion of mine. I feel accomplished and get the sense of fulfillment when I create a wonderful masterpiece because art works as a springboard for exploration of my inner self and peace ✌🏼.

January 28, 2020

The Product Design Process: How to Capture the Right Product Moments

By Joe Johnston and Amit Garg

Jan 28, 2020

Why product moments are important

Finding the ‘just right’ experience

We all know the story of Goldilocks and the three bears: the little girl wants to find the perfect porridge. Not too hot, not too cold, but juuust right.

If Goldilocks was a business and the porridge an experience, you end up with the question: how can businesses find the just right experience for their customers? 

According to a recent Accenture study, 77 percent of customers feel a brand earns their loyalty if it takes immediate action when they are unhappy.

Conversely, the same study found that after a bad experience, 38 percent of customers gave a portion of their wallet to a different business, and another 39 percent stopped doing business with the brand. 

Those numbers are astonishing, and it got us to the realization that:

  1. Trust is earned when brands are there for customers in the moment, and
  2. It takes only a moment for brands to lose their customers’ business. 

Moments are emotional, and emotions dictate behavior. By designing for moments — for the emotions we want customers to feel when using the product — we can be sure to achieve that elusive ‘just-right’ experience.

We refer to these as Product Moments, and they form an overarching theme to our design philosophy here at YML.

How do you capture the right product moments?

Designing the right thing, the right way

Identifying the most important moments in your customer experience is just as crucial as designing for them.

In an ideal world, we’d kick off a project, come up with great ideas, design them, develop them, launch them, and get it all right the first time. Luckily, and you’ll see why in a moment (ha ha), our world doesn’t work that way. 

Rather, in our world the best and most efficient way to design the right product is by doing the right research. We’re not talking about a drawn out 6-month-hire-a-consulting-firm-with-100-page-reports type of research. There’s a time and place for that.

For capturing moments, we stand up and leave the office. We spend time with people in their space as they interact with the product (or your competitor’s). We ask the right questions that help us get to the right information. 

We’re lucky that our jobs require us to interact and empathize with people, face-to-face, so that we may design a part of their life to be just a bit more delightful. The crux of capturing the right moments lies there: do the research that helps you understand the behaviors, attitudes, desires, and frustrations of your customers.

Gathering that wealth of information helps create a future forward story with customer needs at the heart of the to-be product, and allows you to design for moments that drive better experiences and better business outcomes.

Experimentation focuses on understanding user expectations, behaviors, needs, and motivations through methodical, investigative approaches. Insights are then used to ensure that all product design decisions do benefit the user.

Let’s address the elephant in the room: design research is never really done. 

We capture questions from all of our stakeholders, send out surveys, host interviews, and conduct contextual research. But questions lead to answers and even more questions, and even more research.

For the insatiable UX researchers, marketing campaigners, designers, and strategists here, we know that feeling - and the frustration of research not making it into a project plan.

At YML, our approach to the ‘just right’ research with our partners is fourfold:

  1. Know what you don’t know. Gather all your assumptions and knowledge gaps, then draft questions that will fill them. 
  2. Draft a set of learning objectives and share it around to make sure there’s understanding on what the research will gather. 
  3. Create a prototype of something (yes, as part of research) that can be put in front of people, and see how it performs. We don’t call it ‘validation’ because we don’t assume that we’re right.
  4. Follow a directional, 2-week (agile) experimentation sprint that can run in tandem with design or development sprints depending on the phase of the project. 

For identifying moments, this almost always involves conducting field or remote research like interviews or mobile video diary studies (we like dscout). By seeing it ourselves, we recognize workarounds, physical artifacts, and motivations that are subconscious to our participants.

From the field: Look for the“wooden ruler”

While conducting observational research for a financial digital product, we conducted desk ride-alongs asking employees how they went about their day and how things got done. As you can imagine, the employees dove into several different applications going from one to another copying and pasting info across systems, showing all the normal challenges with complex financial software. 

While walking through all the same flows and challenges with the final participant, we noticed an old wooden ruler on her desk. You know, the ones we had in elementary school. We made a note to ourselves to ask her about it at the end of the ride-along. 

As we ended our conversation we asked the question: “Why the ruler? Her colleagues chuckled and she blushed and said, “It’s for me to keep track of my check list”. She said, “We have to follow a very rigorous list of items to make sure everything is done in the right order”. She had a hand written checklist on her desk and she used the ruler to keep track of each item she was on, and would move it down the list as she completed tasks.

This observation would later become the key to streamlining the digital product. Something we captured in a few days, not months, would point us in a direction that would have huge impact on the product. This manual checklist was integrated into the flow of the digital product and allowed the company to remove errors and increase productivity of investments going through the system.

Without this observational research the team would have never seen or uncovered the hidden pain points that can completely change a digital product’s success.

How to keep a customer focused mindset when designing for product moments

In their book on customer centricity, Peter Fader and Sarah Thomas lend us their definition of product centricity and how it vastly differs from customer centricity in practice:

  • Product Centricity is the practice of selling as many products as possible to as many customers as possible, no matter their level of anonymity.
  • Customer Centricity aligns the development and delivery of a company’s products and services with the current and future needs of its highest-value customers while also recognizing - and celebrating - customer heterogeneity. This practice maximizes these customers’ long-term value to the brand. 

Let’s unpack these for a minute. A product centric business strategy is not inherently bad. For some, it works.

A perfect example is the classic side of the street souvenir shop you see in tourist hotspots around the world. Owners of these shops don’t really care who you are, as long you buy their product, and as much of it as possible. For them, all customers are made equal and generally have an equal chance of a transaction.

We don’t really expect shops like these to transform with a customer centric business strategy because it wouldn’t be a worthwhile investment. Their value isn’t different enough from their neighbors to warrant a strategic overhaul (although admittedly, it would be a fun thought experiment). 

On the flip side, key to the definition of customer centricity is the recognition, acceptance, and celebration of customer diversity in the broadest sense. It’s the belief that in fact not all customers are made equal, and therefore don’t always deserve (or need) an equal share of your company’s valuable resources.

A customer centric brand seeks to understand the qualities and characteristics of its highest-valued customers, and strategically aligns business operations to meet their needs.

The culmination of these efforts leads to boosted CX metrics across the board, but most importantly to an increased customer lifetime value, or the value a customer brings to your brand over their lifetime. Achieving true customer centricity of course doesn’t happen overnight.

Transitioning from product to customer centric requires an organizational culture shift with a forward momentum increasing in maturity. We see our clients in various stages along the customer mindset maturity scale.

Our calling is to arm our clients with the tools, strategy, and execution to get to full customer-mindset maturity, and designing for moments is a key step along this path. We’re proud to have done that for leaders in entertainment like UMG, health and wellness like dosist, and insurance like State Farm.

Orchestrating moments across the organization

Connecting the dots

At YML, we take pride in design not living in isolation. A great solution can only be great when it fits within the holistic brand experience. When designing for moments, we take a service design approach to align internal services like roles, processes, and workflows including all physical and digital touchpoints.

It’s important to start with defining who the consumer of the service is - and we intentionally say consumer because Product Moments can apply externally (customers) or internally (employees) - and knowing the moments that matter to them. Then we define how the different parts of your organization can work together to support those moments. 

We treat products like theater: there’s a front stage with actors performing for an audience. Behind the curtain are backstage coordinators that support the actors in putting on the show. Those backstage do just as much to shape the final performance as those in front.

Your customers, employees, technology, products, processes and operations, your business model... all these relationships formulate who you are as an organization. The backstage employees, technologies, and processes help to power the touchpoints, that are then delivered by frontend technology and frontline employees into moments that the customer experiences.

All the pieces play their part in making the experience come to life.
The benefit of using a service design approach is that it guides decision making for the whole organization. Teams are able to see why their work matters and what value it brings to both the customer and the business. It brings to light the careful orchestration of all touchpoints, and the moments they’re designed to support. 

Creating a Product Moment Map

The details of a Product Moment Map

Moment Maps are like short fiction stories about how you want your customer to experience your brand. The only difference is that these stories are meant to become real.

How do customers become aware of you? What happens in their life where they will need you? How will they use a product or service you offer? Start by writing the story of each moment: what led to it, what’s happening during, and what happens after the moment has passed. 

Next, define the type of touchpoints that you’re providing in those moments. Is it an employee? A checkout screen? Maybe a kiosk? They usually fall into one of five categories:

  1. People, including employees and other customers, encountered while using or delivering the product.
  2. Place, such as the physical space or the virtual environment through which the product is delivered
  3. Props, such as the objects (Digital & Physical) used while experiencing the product 
  4. Partners, including other businesses or entities that help to produce or enhance the product
  5. Processes, such as the workflows and rituals that are used to produce the product (this relates the people, place, props, and partners)

Because you know your customers and your business so well, next describe the work that needs to be done, both in front and back of stage, to deliver on those touchpoints.

An employee that reaches out during an accident, a recommendation engine to suggest a product on a site, or a 3D map of a store for customers to browse -  these resources and processes either exist in your business now, or don’t and need to be built. You can even prioritize the moments based on the potential impact they might have on your business. Now you have a roadmap.

Finally, define the outcomes that you expect to see when fiction becomes fact. You can call them “metrics”, but they could include more than traditional KPIs. Use this space to talk about both business outcomes and the impact on your customer.

By following this process, you start to see customer centricity take shape: you’re crafting a business plan based on moments that define a customer’s experience with you, and everyone in your organization can see how their work supports your customer. 

How it’s different from a journey map

We know there’s other cartographers out there, making journey maps, experience maps, and the like.

While useful as a research deliverable or in compliment to a persona, we’ve found that limiting the map to thoughts, feelings, and emotions doesn’t result in the action needed to deliver on the moment. That's why many end up as stale posters on the wall or even rolled up in the corner collecting dust. Instead, we see a journey map as the precursor to a Product Moment Map. 

The value of Product Moment Mapping

  • Alignment - Share alignment across the product teams, leadership and the organization
  • Execution - Visibility in prioritization and the ability to make decisions  faster as teams.
  • Scale - Brings visibility on how a product moment can scale across the product teams and organizations
  • Organization - Easily see how the product teams needs to be aligned to execute the work and what product moments need to be road mapped into features. 
  • Cost reduction - Increased visibility and speed to marketing due to product team alignments 
  • Reduced Churn - Due to the team alignment less time spent on what should be done and how it should be done. 
  • Faster to market - Faster decisions and alignment creating with product teams lead to faster prototypes and faster to market with quicker feedback cycles to narrow the focus on product moments.

How to start Product Moment Mapping? 

The best way to design for product moments is to immerse yourself in the process, and YML is here to help.

Reach out to us and let’s make something that matters for your customers and your business.

Additionally, take a look at tangible examples of how designing for product moments is already driving impact for category leading brands like State Farm, dosist, and Universal Music Group

January 9, 2020

Healthcare Anytime Anywhere: 7 Principles That Point to the Future of Healthcare and Technology

By Ashish Toshniwal, CEO and Co-founder of YML

January 9, 2020

The digital transformation that has reworked countless industries finally seems to be getting real traction in the medical world. The intersection of genetics, biotechnology, data and science is starting to be realized throughout healthcare. 

With political pressures building and new demands from consumers, startups and large-scale healthcare providers are in a race to drive impact.

What is the best way forward that balances emerging technologies while exceeding customer expectations? How do we improve healthcare while lowering costs for all Americans? What does anyone who is focused on this issue need to consider to ensure success?

There’s no single, right answer.  This is a big, messy, complex system. But it’s also a massive opportunity (which I recently discussed with leaders from Abbott, Sutter Health WIRED and more).

I believe the future of healthcare will be defined by technology so seamless that it will disappear into the background; as reliable and essential as running water.  Healthcare will become anytime everywhere.

To understand the future of seamless, anytime everywhere healthcare, we need to have a firm grasp on the context of this moment in healthcare technology.

Here’s the seven insights about the current system and the principles that will inform the future of healthcare and technology. 

Principle 1: Be Human

Patients want to connect on an emotional level. That means creating an experience that leverages all we know of our patients.

The key insight to being more human is to provide individualization versus just personalization. It’s critical that the healthcare community balance functional goals with emotional need states. 

Principle 2: Be Available

There’s a misperception that care is difficult to access. Providers need to make it easy to navigate various care options. They can do that by sourcing options by availability as well as patient need.

They key insight here is the same one that must permeate the majority of digital, customer-centric experiences — empathy.

That means everything from being sensitive to financial constraints, or considering the intricacies of the experience by streamlining the path to appointments and visits.

Principle 3: Be Seamless

A fragmented ecosystem leads to a disjointed experience. One’s health is perhaps the most personal experience people have. There’s a legitimate need to design the ‘universal remote control’ for care and coverage.

Connect the systems (plan, delivery, fulfillment) around moments in the journey to remove friction. Once that foundation is built, we can create stickiness through engagement mechanisms that reinforce a central source of truth.

Principle 4: Be Clear, Informative and Encouraging

Clinical information is often hard to find, confusing, and doesn’t typically motivate adherence. Tone of voice matters, which is why it’s critical to communicate in our patients’/members’ voice, not compliance speak.

Part of that is also about identifying opportunities to explain the ‘why’ behind medications and therapies.  

Principle 5: Be Transparent

Costs, billing and benefits are obscure at best, black box at worst.

Healthcare numbers tend to be daunting, but again, this is an opportunity. We can find creative ways to estimate average costs, prioritize plan benefits as a ‘progressive reveal’, and ultimately centralize and modernize billing and payments.

Principle 6: Be Omni-Useful

Patient experience is tied to ecosystem adoption. It’s by no means easy, but it’s urgent that we define the value proposition for each ecosystem actor.

Once we do, we can ensure patient solutions reduce, rather than increase, patient, provider, and fulfiller complexity, ultimately creating stickiness and giving them answers to the question why they would never leave.

Principle 7: Be Anticipatory

Patients work hard to advocate for themselves. It’s on the healthcare community — providers, nurses, doctors etc… — to ensure the experience is low on cognitive load and high on emotional satisfaction.  

Be one or two steps ahead of patient needs.


Google has been at the forefront of this type of thinking.

They’ve laid the foundation with their product strategy of ambient computing, which ensures the services and features of its technology are accessible everywhere at any time. As Rick Osterloh, Vice President of Devices and Services, shared in the Made by Google 19 Keynote, “Pixel phones, wearables, laptops, and Nest devices for the home. Each one is thoughtfully and responsibly designed to help you day to day without intruding on your life.”

This presents a new opportunity to transform the most critical part of the healthcare system — the patient and doctor experience. The future of this relationship will be impacted by the four pillars of the anytime anywhere future.


Wearable activated - Jacquard’s Jacket allows users to interact with Google’s Assistant with a gesture or touch to their sleeve.


Instant Aid: Mobile AI platform that engages in natural conversations with patients through the use of a virtual healthcare assistant. 


AI Powered Reoccurring Checkins: Sensely's Mobile AI platform that engages in natural conversations with patients through the use of a virtual healthcare. 


Trackable wearable health: Integrating wearable tech, capturing daily snapshots of patients activity and health

How do I know this?

Over a decade of experience in Silicon Valley building digital products and experiences for Fortune 500 brands, an array of innovation technology work with companies across the healthcare spectrum, and all the while listening and learning from our clients and partners about the needs of their customers.

October 29, 2019

How Data Might Blow Up Your Project Plan, and Why That’s Actually a Good Thing

By James MacAvoy, October 29, 2019

Data. It’s a word that strikes fear and excitement in the hearts of all project managers, scrum masters, and project teams alike. 

We know we want it, but we’re not 100% sure what to do once we get it. 

“Now what.”

We request, remind, chase down, test for, and eventually receive this precious data - only to have these familiar questions raised:

  • Where do we fit this into our project life-cycle?
  • How do I make this data actionable?
  • Who ate my clearly labeled chicken salad sandwich in the office refrigerator?  (I know it was you, Jeff)

Although answering these questions is an important step, at the core we have to dig into why we have to ask these questions in the first place. 

1 / Fear of Data

The primary issue we have to deal with when it pertains to data is fear. 

At its root the inherent nature of data can force us to rethink our direction, disprove our hypothesis, or cause us to realize that we’re trying to solve the wrong problem. 

Any of these results can force a major shift in your project direction. For project managers in particular, who typically hate seeing their project plans flushed down the toilet, at first glance data can feel like a problem.

Data does present a problem also familiar to project management regarding the implications of data in the project and how do we mitigate potential issues.  The reality is, those questions are much easier to answer than potentially developing a product that is completely useless to the user.

As Shayna Stewart asks in her articleDoes the consumer find value in my product?”, data — no matter how scary it might be — allows us to answer that question before our product potentially falls flat with that consumer.  

2 / Project Management Life-Cycle

The standard project management life-cycle typically consists of:

Initiation, Planning, Execution, Performance Monitoring, and Closure. 

In a typical digital project, if we incorporate data at all then it is usually within the planning phase. Then, often to a lesser extent, the performance monitoring phase and even worse, usually with a brand new team with no historical knowledge.

To effectively deliver a consumer-centric product that adds value to our users we need to incorporate the use of data throughout the project life cycle.

This means that we need to continuously be reviewing our direction against any learned insights as well as continue testing to validate our hypothesis and the decisions we are making through the project.

Additionally, the considerations we make while running a project will need to be reconsidered. 

As Project Managers, it is ingrained in us to deliver a project that meets all scope requirements, on-time, and at/under budget. 

We’ve all seen the project management triangle of constraints - and likely seen the illustrations of how when one of those constraints is affected the overall quality of that project is in jeopardy. 

3 / Value Delivered

What is typically not considered in the triangle of constraints is an incomplete picture of project quality: in addition to these constraints we should be considering value.

We have all delivered a project over budget, or later than planned. All of those situations are never fun, but the far worse situation is delivering a product that the consumer finds no value in. If we do that, then it really doesn’t matter if it's over budget or late because it’s already a failure.  

A reasonable argument might be that the value is already factored into quality, which in a sense is true. But all too often the project lead’s focus on quality is based on requirements or at the very least a project brief. Without the necessary data those requirements could be wrong. 

In this scenario how we calculate quality is just one part of what we need to factor. When we consider the overarching value to the customer, our definition of quality could actively change, as it should.

But There Is Hope…

Much of what we have discussed above revolves around being comfortable with fear and uncertainty.

We have to know and understand that the more information that data provides, the more that it could change our best laid plans. 

Additionally, the more we incorporate data into the traditional project management methodology and process the more likely we are to see those fears come to fruition.  

However, as project leads there are ways that we can avoid the potential pitfalls described above.  If we incorporate data into every phase of the project management life-cycle, and plan for the potential disruption that this new information may cause, we are far less likely to be surprised when this disruption happens.  

“What do you mean we need to revisit the problem statement?”

We know there will always be changes to a project, but as long as we do not ignore all the information we could have, no matter how scary, we can get in front of that risk and minimize what causes this fear in the first place. 

Training ourselves to understand that change is good, disruption is good, and ultimately adding value to our consumer’s lives is best.

October 16, 2019

Briefing with Customer Experience: How Marketers Can Optimize the Way They Brief Agencies

By Melati Belot, Director of Client Engagement, YML

Customer Experience is today’s strongest proof point or expression of a brand.

That’s exactly why we believe there needs to be a fundamental change in the way agencies are briefed on the brand itself. By integrating the core components of CX into your briefings you are able to provide a consistent experience that underscores the brand promise in each touchpoint and should ultimately increase customer satisfaction.

To be clear, we’re not recommending adding more content to your current briefs.

A powerful brief is concise and compelling.

Leveraging CX as the crux of the content isn’t about adding layers. Rather it’s about applying a lens that will distill and clarify customer-centric messaging.

It all starts with VMP — a Vision, Mission and Purpose that is core to the brand’s DNA. One of my favorite explanations of the differences between the three is an article by Dan Carlton, Founder of the Paragraph Project.

  • Vision — category-centric i.e. how the company performs / acts relative to others.
  • Mission — company-centric i.e. what the company does and how it does it.
  • Purpose — customer-centric i.e. who they serve & why they do what they do.

And, there are several tools and resources to help ground each:

  • Vision — Industry / Market Analysis, Trends, Competitor Audit and Analysis
  • Mission — Company Objectives and KPIs, Service Blueprint and/or Product Strategy
  • Purpose — Customer Segmentation and Research, Personas, Customer Journey Maps

To best tell the story of what a brand can mean to a customer we need to flip the typical order of the VMP (Vision → Mission → Purpose) on its head and instead lead with Purpose (“why”) to then inform the Vision (“how”) and then the Mission (“what”). When reframed in this format the ethos is not wholly dissimilar from Simon Sinek’s Golden Circle TED Talk.

The Purpose is customer-centric and clearly articulates the reason “why” the brand exists and what the brand promises to the customer (i.e. CX). It is fueled by a clear understanding of who the customer is, what they value, and where your company is or is not delivering along their journey.

Delta is perhaps one of the best-in-class examples of a brand that preaches customer-centricity at its core.

In a Wall Street Journal article, Delta CMO, Tim Mapes, said:

“A brand isn’t what a communications program says it is…A brand is what customers experience. For a service business like ours, that experience is defined by our 80,000 employees and their efforts to connect with our customers on a human level.”

Mapes elaborates:

“In an increasingly polarized world, there’s an opportunity for an entity like Delta — a service business that has international reach in over 60 countries — to pursue a more noble purpose. Yes, we’re a transportation provider, and yes, we fly people from point A to point B. But our deeper brand promise — our noble purpose — is to connect people all over the world to each other…We listen to what customers are telling us, we respond with products and services based on that input, and then we listen again and ask, ‘Did we get it right?’ It’s an ongoing process, and our tagline, “Keep Climbing,” reflects that. It’s the Japanese philosophy of Kaizen — continuous improvement at every level and in every corner of the organization.”

Delta has delivered just that.

By focusing on customer experience in the digital space, including replacing ID checks at touch points with fingerprint scanning, automating check-in, tracking bags in real time, and redesigning gate and boarding experiences, Delta has made the travel experience as seamless as possible.

The “never stop improving” mentality can be seen outside of the digital space in a variety of messaging manners across print ads, OOH, and even in their latest TV spot, encouraging all fliers to recognize our shared humanity (i.e. elevating your perspective above the petty differences that can cause us to feel far apart). In fact, Delta was recognized for the second year in a row as North America’s best airline by Business Traveller.

By starting with the ‘Purpose’ and using that as the foundation for all communications, you’re providing confidence, consistency, and clarity. You have the opportunity to address pain points and message the key values you know you can deliver upon (and subsequently measure), thereby providing a satisfying experience.

And, while the output will (and absolutely should) differ based on each agency’s area of expertise, the heart & soul of the brand — i.e. it’s promise to the customer — should pull through.

In fact, a recent Business Insider article headline puts a finer point on the impact that CX and a clear purpose can have on a brand: “Delta’s focus on passenger experience and loyalty has its profits and its stock soaring.” That’s the power of putting The CX Multiplier to work.

👉Want more CX ammo? Read 6 KPIs That Will Convince the C-Suite to Obsess over Customer Satisfaction.

About the author

Serving as Director of Client Engagement, Melati’s focus is on driving thought-leadership, strategic planning and creative excellence for our partners. With experience spanning brand and product development, digital, broadcast, social media and influencer marketing, Melati believes that the key to unlocking customer connection, loyalty, and advocacy is day-to-day interactions and customer-centric experiences.

October 14, 2019

Getting to Know: Marcela Lay, VP of Client Engagement

Discover Marcela, our VP of Client Engagement and Head of our Atlanta office.

Read more

October 8, 2019

Don’t touch the coffee! The value of paying attention to people’s micro-experience when dealing with change.

Change is a trying time for everyone. Leadership can facilitate it by understanding the micro and macro employee experience.

Read more


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