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.

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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

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: cult.fit, eat.fit and mind.fit. 
  • 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 ✌🏼.

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

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.

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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.

Passive

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

Quick

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

Informative

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

Observational

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.

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.

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.

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

Read more

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

Read more

By Melati Belot & Shayna Stewart, October 7th

The CX Multiplier is a tool to help brand and marketing professionals make the case for CX and drive exponential returns on their marketing spend.

See how it works 👇

Take a moment and think about a few of your favorite brands 🤔

Okay, now, why are those the ones that popped into your mind?

It’s likely they made the cut based on a first-hand experience with the brand or perhaps even an emotional reaction you had to that experience.

Yes, brands can cause an emotional reaction outside of tear-jerking or laughter-provoking Super Bowl spots. Those emotions can range from relief and a feeling of being understood to frustration and even abandonment.

(hello, broken [brand] promises)

Beyond getting over that first emotional brand hurdle, what makes a good brand experience?

Do what you say you will do. Be the brand you say you are. Or, in other words: cut the B.S.

Today’s thriving brands live up to their external persona by backing it up via the service and technology experience. It’s an unmistakable attention to detail, such as personalized or customized attention, making things easier on customers, acting with empathy, and responding to a situation on a customer’s terms.

Case in point, Nordstrom may serve as one of the best examples of a customer-centric brand that has empowered it’s customer experiences with technology to create value-adding services (sometimes even creating a “want” that customers didn’t even know they had).

Buying via Text

Nordstrom shoppers can receive tailored purchase recommendations via text or Messenger. Their TextStyle allows a sales representative to text you a product photo and/or description of a product. All you have to do is respond with “buy” and a unique code and it’s all yours.

Source: What Does This Nordstrom Shopping Experiment Have to Do With Facebook?

Reserve & Try

Select and reserve your desired items online and within two hours you’ll be notified via text that your potential purchases are ready to be tried on at the nearest Nordstrom. No purchase remorse, here.

Curbside Pickup

Buy online and bypass annoying shipping delays. Simply text the store when you’re close by and have your new purchase delivered right to you.

Talk about convenience and knowing your audience.

A fast and reliable experience thanks to a PWA

Nordstrom rebuilt their mobile site as a React Progressive Web App for their 20 million+ monthly visitors to deliver a faster, more reliable and more engaging experience.

By the way, Nordstrom is not the only company that uses a PWA. Take Walmart, for example:

Source: Why Progressive Web Apps Are The Future of Mobile Web (2019 Research)

These services now define the Nordstrom difference and even made their way into one of the most critical customer communications, holiday TV campaigns.

All of these seemingly minor touch points are additive and create the overall customer impression of a brand. Therefore, especially in today’s digital-centric world, customer experience (CX) has become synonymous with a brand.

As a marketing or brand professional, it may be hard for you to internally make the case for better CX because at face value it may seem outside the swim lanes of your typical responsibilities.

Marketing and advertising are historically the best ways to broadcast your brand to the masses, and remain vital today.

However, YML’s array of experience with brands ranging from retail to fintech, auto and even healthcare proves how powerful insights that could improve pain points and key moments are often lost in translation or disjointed from the reality of the experience, resulting in decreased efficiency and impact of dollars spent.

Therefore, CX becomes both everyone’s responsibility and more importantly — opportunity:

That’s why we are introducing the CX Multiplier to help you demonstrate how CX elevates your brand and makes marketing more efficient.

What is it?

The CX Multiplier is a simple way to think about return on CX initiatives that relates back into marketing and business KPIs. It measures the impact of the improvement of product retention on marketing and business KPIs.

There’s two phases where the multiplier happens:

  1. Improvement of Business Metrics
  2. Increased Competitive Marketing

1/ Improvement of Business Metrics

As digital product strategies improve retention, they in turn improve two important business metrics:

Customer Lifetime Value

Needless to say the more times someone comes back, the more possibilities there are for monetary touch points.

Source: Why Lifetime Value is the Most Important Metric in eCommerce

In addition, better digital products elicit more trust from people and therefore open the opportunity for the brand to widen the net of products the person buys from the brand.

Both of which increase customer lifetime value.

Payback Periods

If the product’s retention is improved, payback periods are lessened. Which means you make the money you have invested in acquiring new users back faster.

A quick example of this:

You spend $100 acquiring 10 new users. Of those new users only 2 buy your $10 product in the first visit ($10*2=$20 in sales total). Those same two visitors are retained and come back every month to buy again. In this case, the payback period is 5 months ($20*5 months=$100). Let’s say your product doubles retention and you have 4 people who buy your product to amount to $40 in sales and they also come back every month. You will receive your payback in 3 months (plus some!). The money acquired at a faster rate means access to more money sooner to reinvest.

2/ Increased Marketing Competitive Edge

As these two business metrics improve, these marketing metrics will improve:

Customer Acquisition Cost

Because returns are higher from the uplift in business metrics, the team now has the ability to increase the cost for acquiring new users, in other words, increase marketing spend.

Source: Calculate CAC for Sustainable Growth

If you are making more money from acquiring a new user, you now have more money to spend on acquiring the next. With the value of the customer increased, you can bet marketing spend can be increased.

This allows marketing teams to spend more in competitive environments and even branch out into emerging platforms.

A More Efficient Reach

As your user base increases due to more retention, the reach of your retargeting and email campaigns will be improved. This is the marketers main goal — expand reach efficiently.

In this light, you can think of your digital product as driving a whole new marketing audience, instead of trying to seek out new audiences from 3rd party vendors.

Secondly, as you have more customers, you have more people that are inviting or talking about the product helping to convert new users into your product for free!

The key to making this a success is that the digital product and services must back up the marketing, otherwise the new user base won’t have nice things to say. When your acquiring users for free or a through a small incentive such as a discount, that means higher business metrics and more opportunity to increase CAC once again.

CX Product & Service Improvements > More People Retained > Earnings for Business > Marketing Increases > More Acquisition

All of these improved metrics are underpinned by an improvement of your customer-centric product strategy. This often times is missed when your teams are siloed or you have disconnected data sets.

How can you implement a CX Multiplier Strategy?

They key to unlocking the CX Multiplier is embracing holistic adoption of a people-centric culture.

The organization needs to align on a unified data strategy that can measure the CX impact and then each department and partner should be briefed either on improving or communicating the brand promise through CX.

About the authors

Melati Belot, Director of Client Engagement

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.

Shayna Stewart, Product Manager

Shayna is passionate about consumer-centric product strategy and design and an advocate for consumer-centric data strategies to match.

By Adam Talcott, September 30th

Imagine yourself as a software architect or tech lead, and a project manager brings you in to a new software project.

She describes the client and the problem they want to solve, and it definitely seems to be an interesting project. It’s for an exciting brand in a very interesting space, and it would likely leverage some exciting technology.

You’re looking forward to being part of the team which will bring it to life.

Cool!” you tell the PM. “Let’s get started. When’s the kick-off meeting? It will be great to meet with strategy, design and the client to start talking about what we want to do here.

Well,” she replies. “That’s already happened. We kicked things off six weeks ago. We’ve already identified what the product needs to do. We have some designs we’ve been testing with users, and we’re just about ready to hand the finished designs over to your engineering team so we can deliver an MVP in two months.

You’re incredulous, but you’ve unfortunately seen this before. You sigh. “Okay. Tell me more about what this does and then show me the designs.

The PM fills you in on more details, and the solution sounds good to you. But they’re talking about leveraging some immature technologies that you haven’t found to be quite yet ready for primetime.

The designs look good, but there are some interactions which aren’t the easiest to pull off on the targeted platform, and you’ll have to collaborate with the designers.

Also, there is that one screen which seems to need a lot of data. These issues will have to be addressed, and that’s going to mean more design time, more back and forth with the client and therefore an unhappy client (“Why didn’t you plan for these things earlier?”). You see missed deadlines and unfulfilled promises ahead.

Couldn’t we have avoided this?

Fortunately, the answer is yes, but more often than not we don’t do what’s necessary and technology projects end up in this situation.

Creating a product is a team effort, and every discipline has a role to play, some of which are overlapping to some extent, but you need to have every discipline represented in the room throughout the process to develop a robust solution on time and on budget.

Every discipline needs a seat at the table.

Photo by Thomas Drouault on Unsplash

The example above is written from the perspective of a software architect or a tech lead, but a similar story could have been written from the perspective of a creative director or a lead designer. Imagine a project in which designers don’t have an opportunity to review the finished product and provide feedback to make sure it works as well as it should:

What do I hear the designers out there saying? That also happens more often than you would like? How did I know you would say that?

So every discipline needs a seat at the table, even before or after that discipline’s primary phase is underway.

Consider the following diagram which shows how a team’s involvement varies over time depending upon the process’ current phase (note that project managers are not included here as they are, by definition, already included throughout these phases of the project lifecycle):

In a sense, one can compare the approach outlined here with the practices associated with DevOps.

Just as DevOps is focusing on better collaboration between disciplines (software development and operations), the inclusion of multidisciplinary teams across the entire product-development timeline is intended to improve collaboration across the disciplines of strategy, design and engineering and improve project outcomes.

Source: DevOps is a culture, not a role! by Irma Kornilova

Most teams have at least one team member on the project throughout each phase, with the number of team members varying over time, and obviously peaking when their phase is the primary one.

The number of strategists is highest during the strategy phase, the number of designers is highest during the design phase and the number of engineers is highest during the development phase, of course, but there are representatives from each discipline present and involved throughout.

Designers are still involved after the “design” phase is done, just as engineers are involved before “development” officially kicks off.

What is such a truly multidisciplinary team able to achieve?

Photo by Randy Fath on Unsplash

Engineers

When engineering has a seat at the table from even the earliest, business-development-focused stages, the entire process can be grounded and inspired by what technology can do.

Other team members may have an understanding of the technologies in question, but members of the engineering team will bring a different level of understanding, particularly if they have previously built something with the same or related technology.

Designers

When designers are actively engaged during the development phase of a project, they can help to ensure that the designs delivered by the engineering team are what were intended and that any necessary tradeoffs are approached in the best possible way.

It’s obviously also engineering’s responsibility to deliver the required design and user experience, just as it’s the responsibility of the design and strategy teams to understand technology sufficiently well to have a broad sense of capabilities, but it is the design team’s responsibility to ensure that the final product delivers the experience they intended.

Everyone is invested in making this product the best it can be.

In this way, all disciplines are committed to collaborating with each other to maximize their contributions and help the product have the greatest impact. Everyone is invested in making this product the best it can be.

Furthermore, each discipline becomes more skilled with the other disciplines, upleveling the capabilities of the entire team. They’re by no means experts, but other disciplines are less of a mystery.

For example, hearing an engineer ask about error states may prompt a designer to think about error conditions earlier on in the design process and develop a more modular approach to design which makes it easier to incorporate loading, error and empty responses. In addition, hearing a designer question the spacing between elements or the fluidity of an animation will prompt a developer to spend more time on making sure these nuances are as accurate and as solidly built as they should be.

It also helps us communicate better with each other as we have more practice speaking with individuals who approach problems from a different perspective or have a different skill set.

As the old adage goes, before you judge someone else, be sure to walk a mile in their shoes.

What better way to have empathy for what others are facing than to be confronted with the problems they have to solve and the language they use to talk about and solve those problems?

But what do other voices have to say on this topic?

In an effort to practice what I preach, I asked representatives from several other disciplines at YML to contribute to this article and share their thoughts on the benefits of multidisciplinary teams.

Marcela Lay, Head of YML’s Atlanta Office and VP, Client Strategy:

“When we include all disciplines to collaborate from day one, we ensure coverage on different vantage points on the challenges we are trying to solve for our clients.

It also provides visibility into various positive and negative ways in which decisions impact each discipline, enabling the right collaboration when defining the best solution.”

Ryan Spencer, Creative Director in YML’s Redwood City:

“Often times developers are seen as the ‘magicians’ who are responsible for turning design tasks or solutions into code. In my experience I’ve found that this perception to be misleading and not an accurate representation of their actual skills.

Developers can be the most creative people in the room, because solving problems in creative ways is what they’re driven to do — it is their passion. And problems always have constraints, whether it’s time, budget, or resources.

Developers are first and foremost problem solvers who are the best at breaking down and solving problems under a set of constraints.

They also provide a different perspective on solving the problem better or faster.

An example might be ‘What if this API takes a few seconds to display information? Can we instead load the info in a different way?’ For this reason, it’s incredibly important to create a robust design and developer QA process where the two disciplines work together to push and perfect the final product.

The goal is to make sure the product doesn’t just look perfect, but also feels fluid given real-world data and constraints.”

Stephanie Wiseman, VP of Business Development at YML :

“We constantly remind ourselves that good ideas can come from anywhere. Interns, junior designers or our culture team.

Having every discipline — especially technology and engineering — involved from day one ensures that we’re pulling from our collective experience and creating truly innovative and customer-centric solutions.”

Patricia Alonzo, Senior Resourcing Manager at YML:

“Having a representative from each discipline as projects kickoff is integral to catching potential issues early in the process. Especially as it pertains to resourcing.

While something might have sounded feasible during the project estimation phase, it’s during kickoff exercises when the team may realize that the staffing plan isn’t quite right.

Getting ahead of this allows for enough runway to add the right resources to the project.”

The benefits of a multidisciplinary team are clear, but this doesn’t mean we can take a shortcut and keep our teams maximally staffed throughout their lifecycle. That’s a waste of resources and typically just not possible given the amount of work we ask our team members to complete.

Furthermore, given that communication is one of the more complicated things we do in our daily work, we want the team to be small and nimble in order to reduce the complexity of communication.

As a result, the representation from each discipline will inherently vary over time.

In conclusion

So now you know how we like to approach the projects on which we partner with clients here at YML.

It’s not always easy to get this approach right, and there will be some growing pains as you start to adopt this approach, but when it works, the results are worth the effort.

I like to think of such a truly multidisciplinary team as a choir accompanied by an orchestra: there’s nothing as amazing as having all those instruments and voices playing and singing together, supporting each other and making the whole sound better than the sum of its individual parts.

About the Author

Adam Talcott has more than 20 years of experience developing digital technologies ranging from microprocessors to mobile apps.

He is focused on bringing great customer experiences to life and partners with clients to see projects from inception to deployment through strategy, design, and development.

He has worked closely with such clients as Universal Music Group, PayPal, State Farm, and Dell EMC.

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