The Future of Virtual Reality — Key Takeaways from the Oculus Connect Conference (OC6)

Posted on October 24, 2019 by Kopius

Last month, Oculus and Nestle Purina showcased our work at the Oculus Connect 6 Conference (aka: OC6) which we attended in San Jose CA. The conference looks at the future of virtual reality, and we want to share what we learned.

OC6 opened with a Keynote by Facebook Founder and CEO, Mark Zuckerberg. In this opening, Mark shared why he believes Virtual Reality is the universal computing platform everyone will use in the future. Here are some important takeaways we noted from our time at OC6.

Empathy Training Using VR

Through the research and understanding of what makes a great connected human experience, Oculus is finding ways to apply the three dimensions on closeness in human relationships (Frequency of interaction, Diversity of Interactions, and Impact of the Experience) to Virtual Reality (VR). This leading research will enhance and improve not just social VR experiences but also business interactions. The enterprise world requires trust as a critical component of any business relationship.

Nothing showed this better than the prototype Oculus Labs displayed where they were able to reconstruct and demonstrate facial expressions in real-time. Of course, it will be a while before the hardware that makes this possible is available in a consumer-friendly form factor and price point, but it’s coming.

Empathy training is a natural use case for VR, especially in the hospitality industry. Hilton used VR to train their employees and help them understand what the guests are feeling under certain conditions. In their experience, the employee actually became the guest and was able to gain a lasting empathetic state for their guests.

Facebook, along with other major companies, is making a huge bet on the future of AR/VR

There’s no doubt that the AR/VR space is continuing to grow. It was apparent at OC6 that major technology companies like Facebook are making a large investment in the development and expansion of that industry, for both consumer and enterprise users. The result will be the sustained growth in adoption, accelerated innovation, and the creation of new types of solutions, hardware, and paradigms to support AR and VR.

AR/VR is about the world around you

A point made repeatedly at OC6 was that the long-term vision of AR is, in fact, to make us more present and aware of the non-digital world around us. Imagine a day where an AR system is accessible through a standard pair of glasses, or even a pair of contacts. At that point, we will work less with devices, and more with subtle, non-intrusive AR systems that allow us to keep more of our attention on our real surroundings. By doing so, AR can actually bring people closer together.

We’re still in the good old days

No one at OC6 was shy about the fact that we’re still in the early days of AR and VR. That means for consumers, developers, and anyone else in this space we are in an exciting time to be working on this technology. Foundational solutions and patterns are still being developed, and the space for innovation will continue to allow for new game changes to enter the arena of AR/VR.

Quest is a game changer

With that said, the Oculus Quest really is a game changer in terms of the VR experience offered, as well as ease of development on that platform. The Oculus is the best in class hardware solutionfor enterprise customers looking to use VR in their daily operations. Oculus announced that the Quest will also soon have hand tracking and therefore, controllers will become optional or only required for certain applications.

Augmented reality won’t just be about headsets

Eventually, augmented reality will be about far more than just VR headsets. With Facebook’s acquisition of CTRL-Labs, it is clear that while the Oculus Quest is the cutting edge for AR/VR today, future iterations of AR will be about how we fully augment our digital experience with the physical. While the idea of truly connecting our brains to a digital system is still far in the future, we’re fast approaching the time when these types of technologies are major parts of the AR/VR roadmap.

The Issue Isn’t With Technology Consultants, It’s With the WRONG Technology Consultants

Posted on October 8, 2019 by Kopius

By Glen Lewis

When businesses embark on a digital transformation, they often need to bring in technology consultants. Choosing the right technology consultant is the first and potentially most important step in the journey.

In the March 13th Harvard Business Review article “Digital Transformation Is Not About Technology,” the authors outlined “Five Key Lessons” that helped them lead their organizations through successful digital transformations. Lesson #2 stated:

Smart businesswoman sharing her ideas with her team during a meeting. Group of creative design professionals having a discussion while working on a new project.

I would offer that using technology consultants is not a poor idea — using the WRONG consultants is. In fact, using a technology consultant has distinct benefits.

After 12 years of working with change management consultants partnering with domestic and international clients, the good consultants that I have seen hold one truth sacred in their engagements — their clients have unique cultures, with unique issues, and require solutions tailored to their unique needs. While these consultants will reference analogous past experiences, it is to help encourage clients to share details (Harvey, 2018) by reassuring them that they are not the only ones suffering from similar issues (Adamson, 2011). That is about as far as the “one-size” mentality goes with good consultants. The good consultants, through consistent engagement of “insiders,” subsequently updates those initial analogies as they come to understand and appreciate their client’s culture, governance, change readiness, etc. This produces a new unique view of the client, their gaps, and potential solutions. Finally, if good consultants do recommend that their clients consider a best practice, it should service only as a starting point, because those practices should evolve into a solution meeting the client’s unique needs.

Technology consultants also bring important benefits often overlooked in change management such as enabling clients to continue to run/operate their businesses unencumbered by having to manage an enterprise change initiative. For example; a regional dialysis provider recently switched their electronic health record (EHRs) systems. While the CIO highlighted how he had participated in EHR transitions before, he, along with his PMO or IT, did not have the expertise to manage such a daunting endeavor. As a solution, they brought in a consultancy who partnered with the organization’s key “insiders” to build and manage the following: (1) the layers of schedules, (2) the change communication plans, (3) the EHR transition training plan for almost 1000 staff members who were scattered over 2000 square miles.

This allowed the dialysis provider’s staff to remain focused on operating their 15+ clinics. Their training department could continue to focus on serving current and new employees, meeting federal regulations and their PMO could continue implementing the HR and logistics projects already in motion. Finally, the public affairs department could continue focusing on community outreach communications.

The issue is not the use of technology consultants to facilitate digital transformation, it is about using the WRONG technology consultants to facilitate digital transformation. Good consultants establish partnerships with their clients, working with them to craft and implement solutions, while absorbing burdens that allow “insiders” to continue focusing on the daily run/operate of their businesses.

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Training Data Sets in Machine Learning Models

Posted on August 12, 2019 by Kopius

By Yuri Brigance

I have a particular interest in how to train data sets in machine learning models.

This year’s TC Robotics & AI conference had all the proof we need that consumer robotics, powered by the latest Machine Learning science, is quickly becoming a booming industry with lots of investor interest behind it. New Machine Learning (ML) architectures and training techniques are coming out almost every month. It was interesting to see how these algorithms are being used to create a new wave of consumer tech, as well as large numbers of service offerings springing up to make machine learning more user-friendly. How we train data sets in machine learning models is increasingly important.

superannotate.ai machine learning models

Training Data Sets

Training data sets in machine learning is one of the noticeable priorities in this new and growing ecosystem.

Machine Learning relies on A LOT of training data. Creating it is no easy feat. Much of it requires manual human effort to correctly label. A lot of companies have sprung up to help address this problem, and make data collection and labeling faster and easier, in some ways automating it completely.

Aside from labeling, collecting such training data can be just as difficult. Self-driving cars are a well-known example — we’ve all heard of, and maybe even seen autonomous vehicles being tested on public roads. However, it might come as a surprise that most of those driving miles aren’t used for training data collection.

As Sterling Anderson of Aurora and Raquel Urtasun of Uber explained, most self-driving technologies are actually trained in simulation. The autonomous fleets are out testing the trained models in the real world. On occasion the system will disengage and flag a new scenario. The disengagement condition is then permuted thousands of times and becomes part of the simulation, providing millions of virtual miles for training purposes. It’s cost efficient, scalable, and very effective.

Creating such simulations is not trivial. In order to provide the right fidelity, not only must the virtual world must look visually hyper-realistic, but all the sensor data (lidar, radar, and a hundred others) must also be perfectly synced to the virtual environment. Think flight simulator, but with much better graphics. In many cases, sensor failures can be simulated as well, and self-driving systems need to be able to cope with the sudden loss of input data.

Visual data is notoriously difficult to label. Simulation aside, imagine if you are tasked with outlining all the cars, humans, cats, dogs, lamp posts, trees, road markings, and signs in a single image. And there are tens of thousands of images to go through. This is where companies like SuperAnnotate and ScaleAI come in.

SuperAnnotate provides a tool that combines superpixel-based segmentation with humans in the loop to allow for rapid creation of semantic segmentation masks. Imagine a drone orthomosaic taken over a forest with a variety of tree species — tools like this allow a human to quickly create outlines around the trees belonging to a specific category simply by clicking on them.

SuperAnnotate’s approach is interesting, but it likely won’t be sufficient for all scenarios. It’s useful for situations where you have well defined contrasting edges around the objects you are attempting to segment out, but it would likely not work so well for less defined separation lines. A good example is when you may want to figure out where the upper lip ends and the upper gum begins in portraits of smiling people. This will likely require a custom labeling tool — something we at Kopius have created on a number of occasions.

ScaleAI takes a different approach, and relies on a combination of statistical tools, machine learning checks, and most importantly, humans. This is a very interesting concept — effectively a Mechanical Turk for data labeling.

So it is quickly becoming apparent that data collection and training are whole separate pillars of the ML-powered industry. One might imagine a future where the new “manual labor” is labeling or collecting data. This is a fascinating field to watch, as it provides us with a glimpse of the kinds of new jobs available for folks who are now under threat of unemployment via automation. With one caveat — these systems are distributed, so even if you get a gig as a human data labeler, you may be competing with folks from all over the world, which has immediate income implications.

On the other hand, setting up simulations and figuring out ways to collect “difficult” data may be an entire engineering vertical on its own. As a current video game, AR/VR, or a general 3D artist/developer, you might find your skills very applicable in the AI/ML world. A friend of mine recently found an app that allows you to calculate your Mahjong score by taking a photo of your tiles. How would you train a model to recognize these tiles from a photo, in various lighting conditions and from all angles? You could painstakingly take photos of the tiles and try to label them yourself, or you could hire a 3D artist to 3D model the tiles. Once you have realistic 3D models, you can spin up a number of EC2 instances running Blender (effectively a “render farm” in the cloud). Using Python, you can then programmatically script various scenes (angles, lights, etc.) and use Blender’s ray-tracing engine to crank out thousands of pre-labeled 3D renders of simulated tiles in all sorts of positions, angles, colors, etc.

But what if your task is to detect weather conditions (wind, rain, hail, thunder, snow) via a small IoT device with just a cheap microphone as a sensor. Where do you get all the training sounds to create your model? Scraping YouTube for sound can only get you so far — after all, those sounds are recorded with different microphones, background noises, and varying conditions. In this case, you may opt to create physical devices designed specifically for this kind of data collection. These may be expensive but might contain the required set of sensors to accurately record and label the sound you’re looking for, using the microphones you’ll use in production. Once the data is collected, you can train a model and run inference on a cheap edge device. Coming up with such data collection techniques can be an engineering field of its own, and execution requires manual labor to deploy these techniques in the field. It’s an interesting engineering problem, one that will undoubtedly give birth to a number of specialized service and consulting startups.

Here at Kopius we have the necessary talent to collect the data you need, either via crowdsourcing, simulating (we do AR/VR in-house and have talented 3D artists), using existing labeling tools, building custom labeling tools, or constructing physical devices to collect field data. We’re able to set up the necessary infrastructure to continuously re-train your model in the cloud and automatically deploy it to production, providing a closed-loop cycle of continuous improvement.

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Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
Rapidly prototype data & AI solutions
And, fast-forward success

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Announcing New Voice Solution Offerings

Posted on July 21, 2019 by Kopius

We couldn’t be more excited to attend VOICE Summit 2019 this week in Newark, NJ! We’re eager to deliver a session on Building Voice Solutions for the Enterprise and engage with voice experts on the latest perspectives, developments, and trends in this exciting space.

In two previous posts, we shared how consumer engagement with voice drives demand in the enterprise space, and how voice technology is set to evolve rapidly in the next few years to allow features such as speaker recognition and emotional awareness. As voice technology becomes more deeply embedded in homes and workplaces, the technology is set to become a natural part of our lives.

In light of this forward-looking view, Kopius is excited to announce a new set of enterprise voice solution offerings, across both the Microsoft Azure and Amazon AWS platforms.

The newly released Azure Chatbot Services: 8 Week Implementation solution enables customers to get started quickly with their enterprise voice skill. Recognizing that standard access to company-related data and systems can be difficult, Valence is enabling conversational interfaces such as chatbots to provide a much more natural method of communicating with systems to retrieve data and perform actions.

To illustrate the impact of this technology in the enterprise, we have released a new case study on voice-enabled inventory management. Valence worked with SteppIR Communication Systems, a next-generation communications company based in the Pacific Northwest, to deploy a voice-enabled chat bot that provides easy access to key data sets. Built on the Amazon Alexa for Business Platform, our bot integrates with the inventory management system Order Time. SteppIR’s key challenge is to keep their product moving through the pipeline as quickly as possible without bottlenecks. With voice-enabled access built into their inventory management system, anyone can access information on part number levels, order status, and more.

“At Valence we focus on digital transformation technologies and how they work together to deliver real business results for customers,” said Jim Darrin, President at Valence Group. “We believe natural language interfaces — and specifically the ability to access enterprise data with voice commands — is one of the next frontiers in enabling easy access to all sorts of data in the enterprise. Both Microsoft and Amazon are making incredible advancements in fundamental platform capabilities, and we are thrilled to be a partner to both companies in helping translate these cloud services into business solutions for enterprise customers around the world.”

Given the success of this project deployment and the future trend of voice technology, we expect voice will unlock key efficiencies in the enterprise space and create new competitive advantages for companies.

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Voice Summit: Where Voice is Going

Posted on July 15, 2019 by Brian Lamilla

By Ben Parkison

I recently attended the Voice Summit, and have thoughts about where Voice is going.

My daughter and Alexa were born the same year, making them both four years old. My daughter regularly interacts with Alexa, playing music, asking questions, and most importantly making Alexa make animal sounds. From watching this interaction every morning, I can tell you that my four-year-old is a more capable communicator than Alexa. And that’s no knock on Amazon: This is the case for all voice platforms today.

While we’re building real value for enterprise clients with voice today, at Valence we’re most excited about what we see coming in the next few years:

Better Technology — These systems are improving every day in their ability to understand inputs and their ability to naturally deliver a response. As this evolution continues, we’ll see more adoption of voice interfaces and more capable solutions.

Access to More Data — Enterprise companies are continuing to adopt modern data management strategies and cloud first designs. The rising tide lifts all ships, and this progression will allow enterprise applications, including voice solutions, to do more.

Systems That Listen — The idea of invoking a voice interface will be an artifact of our time. As voice recognition and contextual understanding gets better and better, voices assistants will be able to listen, know who is speaking, understand when action is needed, and respond immediately.

New Hardware — The number and variety of ways we can access digital assistants will only increase. AR headsets, wearables, and even more amazing tech like this will make voice assistants ubiquitous.

Conversational UIs — There’s more to how we use language today than just the words coming out of our mouths. As AI becomes more integrated with voice interfaces, we will truly move to conversational user interfaces that can visually identify the speaker, detect mood, understand if you are confused, impatient, or interested in the conversation, and more. All of this will allow conversational UIs to become more emotionally aware. Do I want my conference room Alexa device to read my mood? Maybe not. But image an emergency room, or a high stakes negotiation, or a combat scenario, and any other emotionally charged human environment. Then does it make sense for my interface to be more emotionally aware? Maybe.

As these advances continue in the next few months and years, conversational interfaces will be the tip of the spear in how we define AI and how it reshapes our relationship with digital systems.

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Voice Technology: Designing for the Enterprise

Posted on July 1, 2019 by Brian Lamilla

By Ben Parkison

Valence has been in the trenches of design, development, and deployment of systems that use voice as an interface at enterprise companies for years. We’ve implemented voice-based solutions in companies ranging from small startups to fortune 50 enterprises. Today I’ll share some of what we’ve learned, what we’ve seen, and why we see voice as a key opportunity for enterprise companies.

Consumer issues are enterprise issues. As voice interfaces become more and more common in the consumer space (more than 120 million smart speakers in the United States!) the same people that use voice at home to shop, control smart homes, and communicate, will begin to expect it as employees and as customers. The result, aside from consumer usage driving demand in an enterprise environment, is that the standards that people expect in terms of UX, flexibility, and intuitiveness in their lives as consumers is exactly what they will expect at work.

Start small, learn, and build. Voice interfaces have one very unique characteristic for development teams: you get to see what works, but also what doesn’t work. Unlike other interfaces, with voice you get this view into what your users tried to do and failed (think when Alexa says “Sorry, I didn’t understand that”), and your list of utterances that didn’t result in an understood and actionable response is both fascinating and incredibly useful. Often times that can be key data for defining the roadmap for your voice interface.

Voice can be a ubiquitous experience. For enterprise apps, spatial placement and utilization of voice interfaces is an important part of the design. Considerations should be made before any code is written regarding not only how a person would interact with the app in any scenario, but also how they might interact differently a multitude of settings. Take some common possibilities for example — if they are at their desk vs. the factory floor, if they’re sitting at a computer or have their hands full with equipment, or if they are in a private office or in a conference room with their peers.

Build voice to play nice with other interfaces — If I’m in Sales I might interact with our CRM in the morning sales meeting, throughout the day via a browser as I’m doing my work, on an app when I’m at a client site, and through integrations with other apps like Outlook or Slack. Adding voice to this ecosystem should be an intentional and well thought out process, leveraging voice where it is powerful, not forcing it where it is not, and allowing these different interfaces to interact with each other to create new opportunities.

Your target user will change. From hiring and attrition to reorgs, what you build today is probably going to be used by a lot of different people with, potentially, a lot of different job titles you didn’t expect. Because of this serious consideration needs to be made to make your app as approachable, intuitive, and as helpful as possible. Use nudges, session and user contexts, follow up prompts, and other best practices to design for a user that may be well versed in the company subject matter, but can have a wide and changing variety in technical comfort and fluency.

Well-designed voice-based solutions for the enterprise will create an intuitive way to access information, streamline workflows, and create a new way to do business across industries. At Valence, we’re excited to build voice-based solutions for our clients to build real value today, while also laying the groundwork for voice to be a key part of how we think about and interact with AI-driven interfaces going forward.

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Identify unmet customer, employee, or business needs
Align on priorities
Plan & define data strategy, quality, and governance for AI and ML
Rapidly prototype data & AI solutions
And, fast-forward success

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Project Success is About the Tools, Not a Methodology

Posted on May 28, 2019 by Brian Lamilla

by Glen Lewis

What is the best methodology for successful change management? Organizations need the right partner to achieve competitive advantage.

Scientists are searching for a theory of everything — a framework linking together all physical aspects of the universe. There is a similar quest among Project and Change Management practitioners, the search for a framework linking together all the methodologies and supporting tools out there: Lean Six Sigma’s (LSS) DMAIC, Prosci’s ADKAR, International Institute of Business Analysis’s (IIBA) and Project Management Institute (PMI) methodologies, etc. Unfortunately, proliferation of these methodologies and their supporting tools often leads to confused practitioners who choose to follow a single methodology, ignoring other tools that may enhance their approach.

The key to a “theory of everything” linking these methodologies together lies in how you define: (1) what a project is and (2) what these methodologies are. My experience has shown that projects are more often efforts to correct a problem. Whether building an overpass to relieve congestion or implementing new processes to improve compliance, projects tend to revolve around problem solving.

Training and experience have also shown that the numerous methodologies — ADKAR, DMAIC, PMBOK, etc.- are essentially a set of tools linked together in a prescribed fashion.

Using these general views, we can see how the various tools underlying the methodologies can be mixed, matched, and linked together across a single project.

Most of my clients view their projects as 2-part: (1) defining the problem and solution, and (2) implementing that solution. I usually follow IIBA’s business analysis methodology during part 1; however, this is also an ideal time to leverage LSS’s tools associated with DMAIC’s Define, Measure, and Analyze phases — such as value stream mapping or design of experiment. If LSS tools don’t fit, one can then fall back to such tools as the IIBA’s functional decomposition, to provide that complete picture of the problem.

During part 1, one can also break apart another popular methodology — Procsi’s ADKAR — layering their “build awareness and desire” tools upon those mentioned above, building a comprehensive enterprise understanding of the problem.

For the second part — implementing a solution — I prefer a hybrid process of agile principles supported by traditional project management planning tools — WBS, resource calendars, etc. Again, weaving in Procsi’s coaching and training tools as applicable, one can provide a holistic enterprise wide solution.

Finally, after delivering the solution, Prosci’s and LSS DMAIC’s “Control” offers tools ensuring change will be sustained. Another example is where it’s the tools and not the methodology leading to success.

In the end, these project management methodologies are a series of tools contained within prescribed frameworks that — if teased apart and recombined — provide unprecedented views of problems, solutions, and next steps. Ultimately organizations are not interested in a methodology. They want a practitioner who can understand and appreciate their unique problems, then drawing from a plethora of tools deliver the solution that will allow them to maintain their competitive advantage.

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Identify unmet customer, employee, or business needs
Align on priorities
Rapidly prototype solutions
And, fast-forward success

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Where to Start Your Digital Transformation

Posted on March 4, 2019 by Brian Lamilla

There are many ways to start a digital transformation for an organization, and there is not a one-size-fits-all approach. In this post, we explore where to start your digital transformation. The best first step is to talk to an expert. Contact us any time to chat.

In January, Kopius launched a “Month of Retail” initiative to help our audience better understand the unique challenges and opportunities retailers — both online and brick and mortar — face in implementing technological change to increase efficiencies and develop lifelong fans. With that well under way, I’m getting back to my series on Digital Transformation, Explained.

To recap, the first post in this series — “What is Digital Transformation?”) — provided a definition of digital transformation. The second — “Who Should Lead Your Digital Transformation?” — shared insights on who should lead digital transformation. Now let’s assume that, as the CEO or business leader, you are prepared to transform your business. Where do you start?

Here are a few example starting points from my work with recent clients:

Cloud and Mobile Fundamentals — Companies undertaking digital transformation start from different places. The current state of enterprise data may dictate the starting point. Although many digital transformation initiatives are conceived around an emerging technology like artificial intelligence, there may be significant foundational work required to organize data before moving beyond an emerging technology proof-of-concept into full deployment.

Consulting — Some digital transformation initiatives are offshoots of business transformation. A business may be changing strategy, leadership, or culture and they need support from our expert consultants. Most of these projects ultimately include a technology component (if not, they should!). When business transformation becomes an engineering project for better data-driven decision making or customer engagement, a tight link between business expertise and engineering expertise creates success for the client.

Engineering — Some companies need software engineering resources immediately, faster than they can hire and train. This is true of both start-ups and large technology enterprises. We’ll provide a project manager and engineers and they’ll work with the client on an agile development project. Sometimes there is a defined outcome, but other times they just need resources for a proof-of-concept and want the ability to scale back down as quickly as they scaled-up.

As an aside, one of the great surprises for me in leaving a Fortune 500 industrial enterprise was how many businesses outsource software engineering. Specifically, big-name technology companies that are magnets for engineering and project management talent leverage outsourced resources to quickly scale up and down. Conversely, I have observed industrial non-tech companies behave as though they have cornered the market on engineering knowledge. My preliminary conclusion is that businesses view IT spend in one of two ways — either a fixed internal resource pool with variable backlog, or, as a variable investment with an expected ROI (where cost, revenue and the timing of both matters!). As discussed in my prior article, the latter approach better represents the future IT investments where decisions are made in the same room, at the same time, by the same leaders who are making the operational investment decisions.

The final example starting point is very basic but should not be overlooked. Most business leaders in their 40s or older didn’t carry a computer with them in college. These leaders’ understanding of technology can put them in a defensive position when confronted with a technology investment decision. Giving them foundational knowledge to build upon can lead to better business outcomes.

Training — Clients simply want to explore emerging technologies — i.e. what is machine learning or blockchain? We’ll provide an executive workshop to establish a baseline of knowledge to begin exploring digital transformation in the c-suite. This can be a great kick-off to a company-wide digital transformation initiative giving senior leaders the knowledge and confidence to uncover new opportunities with their teams.

With these starting points, required budget varies widely and often depends on the initial IT infrastructure. Whether the budget is acceptable in the business depends on the businesses approach to IT spending. To clients currently satisfied with fixed internal budgets and variable backlog, a $50,000 proof-of-concept can face heavy internal resistance. Internal IT is often most resistant as they would rather have the dollars in their budget to decrease current backlog, hire more people, or provide more training. Other companies see value in quick-scaling and delivery of engineering projects, without the long-term commitment to a growing internal “fixed” IT expense. In the latter case, a million-dollar spend to re-architect data into a cloud-based solution is appealing relative to building the internal IT team capacity for a one-time initiative.

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Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.

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Driving Your Retail Digital Transformation

Posted on January 28, 2019 by Brian Lamilla

Technology holds massive promise for the retail industry, and retail brands are each in their own stage of the digital journey. In this post, we look at how to drive a digital retail transformation.

As I look back at all the connections we’ve made, partnerships we’ve formalized, and solutions we’ve launched during Kopius’ month of retail, it’s apparent that this industry – which is no stranger to change – faces a large crossroad. There are major threats as well as exciting opportunities as the retail landscape gets more competitive, more personal, and more connected.

Throughout the month, we’ve covered a lot. I started by asserting that experiential retail is not new, but rather an ever-evolving paradigm. I then announced our Retail Innovation Accelerator, which will help incubate transformative solutions for our customers across voice, data, and blockchain. And finally, I recapped the 5 most important trends and insights we gleaned at NRF’s Big Show.

For my final blog post in this series, I’d like to provide some color on how you can help drive digital transformation in your retail business. Whether you’re a c-level executive, technology director, or store manager, there’s no doubt that you’ve been exposed to changes brought by a digitizing world. Just the fact that your customer carries a smartphone in their pocket should be seen as a major disrupter. In this post, I hope to illuminate how you can drive your organization to embrace these disruptions rather than fight them, and lead the right investments that yield future growth.

Embracing Change for Operational Efficiency

What does the term “digital transformation” make you think of? It more than likely conjures thoughts of big, flagship-style customer experiences: wow moments that are high-touch, highly-personalized, and focused on making a brand memory. While these kinds of experiences are important to engaging fans, they won’t drive lasting momentum unless coupled with internal and operational digital transformation.

Retail strategy can be mapped on a pyramid, similar to Maslow’s hierarchy of needs. While there are exciting opportunities around customer experiences at the top of the pyramid, retailers will have more success along their digital transformation if they consider basic and operational needs first to build a strong internal foundation. The best way to secure budget for transformative customer experiences is to save money through operational efficiency. Can you utilize AI to predict stocking needs? Can you better analyze sales data to inform your buyers’ decisions? How about use sensors to understand traffic patterns to more efficiently staff your store? By establishing transformation from the ground up, you’ll unlock more business opportunities and free up budget.

Justifying Your Digital Transformation Investment

Embracing any kind of transformative business practice takes an up-front investment. However, all too often this fact leads retailers to think that digital transformation is only for the risk-takers. I’ve heard it before: “we want to build a proof of concept and deploy a pilot experience, but don’t know how to justify it to our CEO”. The truth is, if you’re trying to paint the picture of a 1:1 connection between a transformative PoC/pilot and immediate profits, you’ve probably already lost the battle. Investing in the future takes a jostling of resources in the near-term, and I could write an entire thesis on the intricacies of reorganizing and reallocating your investments to drive change. However, justifying your investment in digital transformation can be a whole lot easier.

Simply put: learning about your customer is the most important investment you can make. A good digital transformation strategy always unlocks more data points – whether from IoT sensors, mobile traffic analysis, or AI services – about how your customers interact with your brand. The key to driving digital transformation investments with internal stakeholders is to communicate the ROI of enhancing your understanding of customers’ needs and desires. Furthermore, digital implementations can often act as a mirror: by learning more about your customer, you in turn learn more about your company.

Contact Kopius Today to JumpStart Your Retail Success

At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Add our brainpower to your operation by contacting our team to JumpStart your business.

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The Future of Retail — Trends From NRF

Posted on January 21, 2019 by Brian Lamilla

After a great week at NRF talking tech with industry leaders about the future of retail, experiencing cutting-edge demos, and learning about retail’s biggest challenges and opportunities, we’re back in the office with a heightened appreciation for the exciting possibilities ahead.

In my last post, I announced our Retail Innovation Accelerator, which is focused on harnessing emergent technologies to develop customer solutions in an agile way. This retail solutions incubator — which is powered by internal innovation projects and strategic partnerships across voice & chat, telemetry & insights, and modern supply chain — provides a framework for our customers to more easily identify the technologies best suited to digitally transform their businesses.

This week, I’d like to switch gears a bit and discuss some of the trends we saw at NRF. While by no means an exhaustive list, I hope this helps paint the picture of how retailers should think about digital transformation efforts over the next year and beyond.

1. The whole is greater than the sum of its parts. One thing that stood out compared to past NRF Big Show’s is how technology companies are thinking about the power of multi-platform solutions. To unpack that, it’s becoming more and more evident that there isn’t one technology that can truly transform your retail and brand experience. Rather, a suite of technologies must be integrated into a cohesive, omni-channel strategy to really move the needle. If you are considering RFID sensors to make your dressing room “smart”, pair this with digital signage powered by a recommendation engine to help complete your customers’ outfit. Or how about taking that a step further with an AR-powered selfie app that shows customers what those boots would look like with their new dress? Individual technologies may generate some buzz, but the right suite of technologies can truly transform a retail business.

2. The year of the edge: connected everything. The best brands react to their customers in real-time, and the only way to do that is by listening smartly. The collection — and more importantly analysis — of data will continue to be one of the primary differentiators between successful and unsuccessful retailers. But how do you capture the kind of meaningful data that yields actionable insights? At Valence, we’ve been working in the IoT space for many years. However it was still eye-opening to see such a strong focus on the “connected everything” store. Edge devices and sensors are seeing exponential advancements in on-board compute power, connectivity, and battery life, while AI-powered cloud services continue to evolve. With technologies like computer vision, it’s amazing how much you can learn about yourself and your customers.

3. Retail robotics is maturing, but not there yet. We all know robotics in the warehouse is already here, but when will robots be roaming the sales floor? Can a robot re-stock shelves? How about provide wayfinding to a lost customer? We saw some compelling “front-of-house” demos from hardware and software companies alike, but at this time robotics still resonate best when tackling “back-of-house” challenges. However — as with all promising technologies — it’s only a matter of time until the cost-benefit ratio leads retailers to use robotics for more purposes.

4. Your platform is your product. While we used to only think of your platform — whether web site or store front — as the place to showcase your product, today’s competitive landscape means that your platform might just be your most important product. There are so many ways to buy, it’s important that yours is the easiest, fastest, and has a little extra flavor than the competitor. Improving your platform can be as simple as re-writing your web site copy to better match your brand’s voice, or can be as complex as restructuring your inventory management system and releasing a “buy online, pick up in-store” app. Whatever the approach may be, it’s important to understand how vital your platforms are in differentiating your brand.

5. Amplify your voice with…voice! It seems obvious, but in order to stay relevant brands must operate at the pace of consumers. And with the exponential evolution cycle in consumer technology, this is getting harder and harder to do. Voice may one day usurp touch as the primary user interface, and it’s important to consider the user flow for customers interacting with your brand on services like Amazon Alexa. We didn’t see as many voice-based demos at NRF as we expected…all the more reason to start investing in this space before your competitors do.

Next week, I’ll be providing a recap of everything we’ve learned and announced throughout our month of retail. I’m excited to share the opportunities on the horizon for all retailers who are ready to adopt emergent technologies.

JumpStart Your Future Retail Success

At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Add our brainpower to your operation by contacting our team to JumpStart your business.