Does It Make Sense to Open a Physical Retail Store in 2019?
Does It Make Sense to Open a Physical Retail Store in 2019?

One of the great myths of the late 2010s is that brick-and-mortar retail is as dead as paper napkins, canned tuna and golf. The craze of online shopping – exacerbated by "holidays" like Cyber Monday – has created the sense that people don't shop in physical retail spaces anymore. Seemingly, we're living in an entirely digital retail era, and e-commerce giant Amazon holds the rulebook in this complicated new landscape.

As with most myths, there are nuggets of truth that inspired the belief that physical retail is no longer a profitable way to run your business. It's true that there are more online shoppers than ever. So what does that mean for new entrepreneurs looking to launch their physical stores in real life? Let's take a deeper look.

The way things were

Five years ago, an easily navigable e-commerce platform with good inventory and a robust Instagram following was enough to secure relatively consistent business. Unique companies filling ultra-specific niches – like eyeglass designer Warby Parker, mattress company Casper or straight-to-your-door men's apparel brand Bonobos – were popping up everywhere, and creativity and innovation were the names of the game. As long as you had the tools to get your name out there, physical retail space was irrelevant – if not detrimental – to your fiscal success.

Fast forward to late 2018, and all those special niches are suddenly overcrowded with virtually identical companies. Everyone has an amazing brand, strong social media presence and a killer product. The reality of this new world is that there is very little to distinguish these companies from one another, and thus, one or two naturally emerge as leaders of the pack and the rest are left in the dust.

As a result, even those big-name digital native retailers that began with a bustling online business have turned back to physical retail spaces. Warby Parker alone now has over 64 locations in the U.S. Even Rent the Runway, a brand whose entire purpose is predicated on the convenience of shopping online, has seen great success by strategically opening brick-and-mortar showrooms. So why does shopping in real life matter if consumers agree that online shopping is generally more convenient?

Experiences vs. objects

Shoppers today are prioritizing experiences over objects, which makes sense in the great migration from e-commerce to physical retail. While digital commerce platforms can be customized and personalized to a certain extent, most successful companies follow a strict template of simplicity, making it difficult to hammer home your brand in a saturated market.

Brick-and-mortar retail spaces, on the other hand, are free to customize every detail of their space, from the obvious aesthetic choices like paint, furnishings and flooring to subtle, experience-defining details like music and scent. Ultimately, it's these details that allow companies to distinguish themselves in a crowded market.

In addition, convenience simply doesn't stack up to the tactility needed for shoppers in the late 2010s to secure confidence in their purchases. Physical retail spaces allow shoppers to physically see and touch the objects of their consideration. For a generation of shoppers that cares more about how it spends its money than any other before it, being able to critically consider a physical product as opposed to analyzing a product page online makes all the difference.

What to consider when opening your retail location in 2019

Not only is physical retail not dead, it's on the rise. If you're an online retailer or an entrepreneur looking to jump on the brick-and-mortar bandwagon in 2019, here are some things to keep in mind:

Opening a physical retail space doesn't mean you should abandon your online presence. One can aid the success of the other, and many companies are reaping the benefits of having both.

If you're starting from scratch, do your homework. You need to be certain that your business idea is relevant, both generally and to your community. Be specific and intentional about your location, your product offering, your branding, and your social media presence.

If you're not sure what kind of retail store you want to open, consider purchasing an existing business. An already-operating retail location has the benefits of an existing clientele and an established presence in the community. For many new business owners, this is a much less risky investment. There are excellent resources available to help you identify an existing retail business available for purchase.

While opening a retail business – or any business, for that matter – is never a risk-free venture, physical retail locations are thriving and will continue to thrive in 2019. If your apprehensions are built on the assumption that e-commerce is the new and singular shopping reality, you can rest assured that brick-and-mortar has a long future ahead of it.

McKinsey Insights & Publications
Focusing on these six themes will help institutions take into account potential for regulatory relief, realistic expectations for CCAR 2019, and the business benefits of thoughtful stress testing.
Paul Taylor/Getty Images

Management teams are responsible for making sense of complex questions. Maybe it’s estimating how much a market will grow next year, or finding the best strategy to beat a competitor. One popular approach for navigating these questions is turning to the “wisdom of crowds” – asking many people for their opinions and suggestions, and then combining them to form the best overall decision. Evidence suggests that the combination of multiple, independent judgments is often more accurate than even an expert’s individual judgment.

But our research identifies a hidden cost to this approach. When someone has already formed an opinion, they’re far less likely to be receptive to the opinions of others – and this can lead to evaluating other people and their ideas more negatively. Fortunately, our work also suggests a few ways to minimize this cost.

The problem of independent judgments

The “wisdom of crowds” refers to the result of a very specific process, where independent judgments are statistically combined (i.e., using the mean or the median) to achieve a final judgment with the greatest accuracy. In practice, however, people rarely follow strict statistical guidelines when combining their own estimates with those of other people; and additional factors often lead people to assess some judgments more positively than others. For example, should the boss’s estimate count for more simply because of status? Shouldn’t an expert’s opinion count more than a novice’s?

In our research we find another factor that seems to impact how we evaluate other people’s opinions: when someone forms his or her own opinion. As team leaders, we started to notice that a common source of team friction came from members committing to their own ideas before the team as a whole agreed to a course of action. We wondered whether a simple matter of workflow ordering – forming a judgment before evaluating someone else’s judgments – was causing tension.

To test this question, we conducted an experiment where we randomly assigned the order in which individuals formed an estimate of their own versus evaluated the estimate of another. We asked 424 parents in the U.S. to estimate the total cost of raising a child from birth to age 18. They also evaluated another person’s estimate – which we framed as that of “another parent.” In fact, it was the consensus estimate created by financial experts.

Even though the estimate being evaluated was always exactly the same, we found that parents who had made their own estimates first evaluated the other person’s estimate more negatively. Parents who first made their own estimate were 22% less likely to think that the other estimate was at least “moderately likely to be correct” than were parents who evaluated the other estimate before making their own.

We wondered if this effect varied among different types of people. In this study and the others we conducted, we looked at whether men responded differently than women, whether older individuals responded differently than younger individuals, and whether experts responded differently than non-experts. None of these differences mattered. Regardless of their gender, age, or expertise, decision makers who first formed an opinion of their own were more likely to negatively evaluate another’s opinion.

In a second study, we asked 164 U.S. national security experts to assess a hostage-rescue strategy and evaluate what “another national security expert” proposed. Unlike the cost-estimation question of our first study, this question was not quantitative, nor did it have a clear right answer. Despite these differences, and despite the fact that the individuals in this case were experts, the effects of forming an opinion before evaluating someone else’s were the same. Those who first formed their own opinion offered systematically lower evaluations of a peer’s strategy, compared to those who evaluated the peer’s strategy before forming their own opinion.

We also asked participants how intelligent or ethical they perceived the other person to be, based on their recommendation. Even though the actual recommendations were exactly the same across our ordering conditions, those who first formed their own opinion made more negative inferences about the peer than those who formed their opinion later.

Why do people penalize the judgments of others after forming their own opinion? The key factor seemed to be how far someone’s estimate diverged from the other person’s. When we asked participants in these two studies to simply look at someone’s judgment and form an opinion about it, participants own estimates were pulled toward the estimate they were considering, a phenomenon often referred to as “anchoring.” By contrast, when participants made their own estimate independently, they were more likely to disagree with the estimate they had to evaluate later, viewing it as too different from their own, and thus less likely to be correct.

While disagreement is not necessarily a bad thing – combining diverse judgments and estimates underpins the wisdom of crowds –in order to be effectively leveraged it first has to be correctly interpreted. In most cases, disagreement should signal that either or both parties are likely to be wrong. Our data suggest the problem is that people interpret disagreement in a self-serving way, as signaling that their estimate is right and the other party is wrong.

We ran a final study to test this interpretation. We asked 401 U.S. adults to form a judgment before seeing the judgment of another participant selected at random from a prior study. Some participants saw peer judgments that were in close agreement with their own, and others saw estimates that differed dramatically. We then asked them to evaluate the quality of both judgments. We found that, as disagreement increased, people evaluated others’ judgments more harshly – while their evaluations of their own judgments did not budge. Our participants interpreted disagreement to mean that the other person was wrong, but not them.

Across our studies we found that forming opinions before evaluating those offered by others (compared to evaluating first and forming one’s own opinion later), carried social costs – participants thought less of the other person’s estimates and ideas, and, in some cases, thought the other person was less ethical and intelligent.

How to make better decisions

What should a manager do if she wants to get to better judgments and minimize the costs that arise from people getting enamored with their own opinions? The evidence is strong that to maximize accuracy, team members should form independent opinions before coming together to decide as a group.

But our findings suggest that groups of decision-makers should also pre–commit to a strategy for combining their opinions. The specific strategy will depend on the type of question a team faces. However, committing to an aggregation strategy ahead of time can protect teams from the negative social consequences of evaluating each other’s judgments in light of their own previously-formed opinions.

Teams facing quantifiable questions should aim for strategies that, as much as possible, remove human judgment from the aggregation process. A team estimating how much a market will grow faces a quantifiable question; they should pre-determine an algorithm (such as a simple average or median) for combining the opinions of different team members.

Teams facing non-quantifiable questions will have to rely on human aggregation in some form. For these questions, teams should prevent the person responsible for the final judgment from forming an opinion of her own before seeing the opinions of others. This is not always easy. By the time managers evaluate their subordinates’ ideas, they often have already formed their own opinion.

This highlights an important point: committing to an aggregation strategy is as much a structural matter as an in-the-moment decision. Unbiased aggregation requires structuring work flows so that those responsible for combining opinions do not first form their own, or at least work to not let that opinion undermine the decision-making process.

At the individual level, team members should reframe how they think about disagreement. Our studies suggest that many people interpret disagreement to mean that someone else is incorrect. With a concerted effort toward intellectual humility, however, this does not have to be the case. For teams, disagreement should be thought of as valuable information. Thinking of it as signaling value, rather than as a reason to derogate, may be the single-best way to defray the costs of turning to the crowd to answer complex questions.

Menahem Kahana/Getty Images

I’m working with a CEO who’s in the midst of rethinking her company’s strategy so it can better meet customer demands and thrive financially. These are major changes that will affect every aspect of how the firm operates — from the services it offers to the structure of her organization.

When I sat down with the CEO and her executive team to think through their communication plan, I asked not about the change itself, but about how her employees might feel about what’s ahead. We started with her team because, in my work as a communication consultant, I’ve observed the same thing time and time again: how information is communicated to employees during a change matters more than what information is communicated. A lack of audience empathy when conveying news about an organizational transformation can cause it to fail.

Studies on organizational change show that leaders across the board agree: if you want to lead a successful transformation, communicating empathetically is critical. But the truth is that most leaders don’t actually know how to do it. In fact, at Duarte, the communication consultancy where I’m Chief Strategy Officer, we conducted a survey of over 200 leading company executives and found that 69% of respondents said that they were planning to launch or are currently conducting a change effort. Unfortunately, 50% of these same execs said they hadn’t fully considered their team’s sentiment about the change. Worse, about half said they were just approaching the change “going on gut.”

If you are a company leader hoping to undertake a successful organizational change, you need to make sure your team is onboard and motivated to help make it happen. The following strategies can you help you better understand your employees’ perspectives.

Profile Your Audience at Every Stage
Change consultants typically advise leaders to create personas of various audiences when they kick-off a change initiative. But, considering that people’s wants and needs will evolve throughout the process, you should reevaluate these personas during every phase of the journey.

With the CEO I mentioned earlier, we first created audience personas that mapped to key employee segments in the company by level and function. Then we interviewed individual employees in each segment to get a sample perspective on typical mindsets. During the interviews, we asked questions designed to uncover beliefs, feelings, questions, and concerns about the company’s current strategy. We also asked if there were specific changes they hoped management would (or would not) make. 

Using the insights from these interviews, we were able to identify how each employee segment felt about the change effort, and planned communications based on whether they were excited, frightened, or frustrated. Employees who were excited about the change, for example, received communication that encouraged them to motivate their reluctant peers.

As your organizational transformation unfolds and you enter new phases of the change, make sure you repeat the interviewing and empathetic listening process. That way, you can gauge how people are feeling over time, and tailor your communication to match their mood.

Tell People What to Expect
While you may need to keep some facts private during a transition, the general rule is that the more informed your people are, the more they’ll be able to deal with discomfort. So, learn about your team’s specific fears, then acknowledge them openly. 

While working with the CEO who was making strategic shifts in her company, we talked about how she could acknowledge some of the fears revealed in a company-wide survey. One employee had expressed concern that the changes would cause talented employees to leave, which would lead to a greater burden on remaining employees.

In the next company-wide meeting, the CEO acknowledged there was worry about brain drain, then shared statistics about how the recent company turnover was designed to reduce the number of low performers and alleviate resulting drag on other employees. She also explained how the HR department was redoubling its efforts to speed up the recruiting process and add more rigor to interviews to ensure new hires were more likely to be high performers.

Having the CEO talk about the departures in an open company forum might seem like a dicey proposition when HR usually prefers to keep exit details private. But feedback from employees afterward showed that the CEO was able to build credibility and trust by addressing the fear of talent loss head-on.

Involve Individuals at All Levels
A transformation won’t succeed without broad involvement. A large European retail bank modeled this well during an organizational overhaul. Following a “dialogue-based planning” model, the CEO created a top-level story for the bank, then asked his executive directors to add a “chapter,” sharing details relevant to their departments. Each director then asked their own team to add to the chapter, incorporating ideas about how a change would impact them and their unique responsibilities. This continued down five levels, all the way to branch managers, and helped every impacted individual understand their part.

An exercise like this can help everyone feel like an active participant with something valuable to add. At that same bank, the director of retail operations wrote about how customers wanted the banking process to be faster. When members of the branch staff read this, they added that document imagers broke down frequently, which was a major headache and caused regular slowdowns. In the end, these frontline employees ended up bringing about a practical, useful change at the organization — one that improved things for all parties.

Business practices evolve rapidly, but there’s one technique business leaders should always rely on to effectively motivate and lead: empathic communication. Develop and show empathy for everyone involved in your corporate transition, and you’ll lead a team that feels valued, included, and driven to help your initiative succeed.

MIT Sloan Management Review

The word “sensor” has become inseparable from the internet of things (IoT), where sensors detect environmental conditions and communicate these signals bidirectionally as data, whether it’s an industrial machine reporting its operating condition or your home thermostat being turned on remotely. This data is a key driver of the IoT’s global economic impact, which McKinsey estimates could reach up to $11.1 trillion per year by 2025.

Before the IoT, the two key functions of a sensor — to detect environmental change and to communicate that data — were largely carried out by humans. Today, as augmented reality (AR) technology gains adoption, humans will soon be equipped with sensors through various AR devices, such as phones and headsets. This augmentation provides uncharted opportunities for organizations to use these data insights to drive operational effectiveness and differentiate their products and services for consumers.

The AR market today is similar to where the IoT market was in 2010, generating considerable buzz and proving early value from new capabilities for users. AR’s capacity to visualize, instruct, and interact can transform the way we work with data. Based on the lessons learned in the early days of the IoT, enterprises should be asking the question: What’s the best way to plan for AR device data and see its value, so we can build better products and processes from user insights?

Smart, Connected Reality Means More User Data

As we do that planning, there is much to learn from our recent, connected past. The 2007-2008 iPhone and Android market releases provided significant data about how customers engaged with their brand, and it gave engineers new insights into user requirements. This market disruption flipped the value proposition — applications could sense and measure customer experience in conjunction with delivering it, and it opened the door to subscription- and use-based services. With similar sensing capabilities emerging through the IoT for physical products, companies quickly built in connectivity, giving rise to smart, connected products (SCPs) that make up the internet of things. The data arms race and the emergence of the data economy has been disrupting technology laggards ever since.

Considering these proven market dynamics, the potential for AR-as-a-sensor being the next-generation modality for gathering rich data is profound. Products are already equipped with APIs and connectivity, and AR devices are loaded with machine sensors, from multiple cameras to GPS, Bluetooth, infrared, and accelerometers.

AR also unlocks a set of sensors that are often forgotten amid the frenzy of machine automation — the human capacity for creativity, intuition, and experience. Humans have incredible abilities to recognize and react to novel situations, assessing them more quickly and accurately than current technology systems can.

Consider what humans using AR devices could add to such interactions. There are valuable new data and behavior insights to gather from both unconnected products and SCPs. For an unconnected product, a person using AR-as-a-sensor tech might ask: How is this product used, and what are the user preferences? For an SCP, they might ask: How does usage affect performance, and how can this product adapt to usage?

Human interaction offers context to answer how both unconnected and connected products are really being used, how they perform, and how they can be adapted for their purpose by bringing human creativity, intuition, and flexibility to AR data gathering.

Assessing the Business Opportunities From New AR Data

The new data created by AR establishes a feedback loop to answer questions about how a product is being used or where opportunities for customization exist. The value of this type of customer data has increasingly become core to business strategy in the new digital economy.

One way to understand the value of your data is to assess it using the DIKW Pyramid (see “The DIKW Model”), a hierarchy used in information management for understanding the transformation of raw data signals into value-rich knowledge and insights.

The DIKW Model

The DIKW model shows the hierarchy of raw data and value-rich wisdom from its analysis.

The DIKW Model

If this looks familiar, it’s because this is exactly the type of data flow that creates value for manufacturers and users of SCPs today. By feeding these insights back into their engineering systems, companies can optimize their product portfolio, design, and features like never before.

AR-driven data collection can be combined with IoT data streaming from SCPs, to drive additional context and generate more complete insights. For unconnected physical products or digital-only services, humans, interacting with these via AR, can act as sensors to deliver new insights about product or service use, quality, and ultimately about how to optimize user experience and value.

Early Use Cases for AR Insights

There are a wide range of early example use cases for these kinds of data sources. Companies like Honeywell, Cannondale, Amazon, and DHL have created new opportunities for product strategy, value chain, and quality control activities by utilizing AR data from users and providing personalization based on this data. (See “Early AR Use Cases.”)

Early AR Use Cases

Companies are already using insights from AR data to create new products and services.

Early AR Use Cases

These early examples clue us in to how AR-as-a-sensor will make its way into the mainstream, creating new opportunities for manufacturers, in both product strategy and value chain activities.

Expert Knowledge Transfer. While many tout the benefits of delivering AR experiences to users, Honeywell is using AR to capture expertise from seasoned workers and improve knowledge transfer to new employees. By digitizing knowledge about a product that is revealed only through experience, Honeywell can understand products and their use in ways previously unavailable, without using embedded sensors.

Voice of the Product. For Cannondale, its newest high-end bikes are being shipped with accompanying AR apps that showcase bike features and guide users through common maintenance procedures. This is fundamentally changing the definition of the product from physical bikes alone to combined physical and digital experiences. Thanks to these digital AR experiences, Cannondale has the opportunity to gather and analyze anonymized data to deliver the “voice” of the product. By understanding what features and procedures are most used, Cannondale has a potential window into their products that can drive improved user experiences and competitive advantage.

Personalized Services. AR is billed as being transformative to e-commerce and retail, because it allows customers to visualize and try before you buy, unlike other available media. Amazon Echo Look is a new device allowing customers to capture and see virtual clothing on themselves before purchasing the real thing. In January 2018, Amazon patented “magic mirror” technology, which combined with the Echo Look, will pave the way for the next-generation dressing room. The data captured today through the Echo Look is being analyzed to create user preference profiles and curate suggested purchases based on tastes. It isn’t hard to imagine how, combined with the ability to augment those clothing suggestions back onto the customer, this new source of AR data will lead to a new level of personalized services and experiences.

Quality Control. DHL has long been at the forefront of AR technology and is on the advanced end of current AR programs. By reducing friction across logistics processes, AR delivers great value for DHL’s emplyees as they go about daily tasks. But this data does not end with the user. Using integrated computer vision to perform the task of bar code scanning, DHL now has a way to capture and log quality assurance data, allowing the company to understand where human behavior may affect order quality and process efficiency.

All of these companies’ early implementations give a glimpse of what is to come in AR experience delivery and how that data can create additional value for businesses.

Connecting the Strategic Dots

What about the impact to a broader data strategy? Taking a step back to this level, the implications are potentially significant. The value of many data initiatives hinges on the ability to connect the dots. While IoT, digital engagement, voice of the customer, and other initiatives continue to create significant opportunities to optimize products and processes, many enterprises are running these projects in siloes because of technological or organizational constraints.

As AR emerges as a new source of context-rich data, companies that connect the dots between multiple sources from smart, connected products to CRM data, digital engagement, and other sources of insight will create the greatest opportunities.

Enterprises that want to capitalize on these opportunities should create cross-functional leads or tiger teams dedicated to the desired outcome — improving the customer experience — rather than by the traditional functional or technology-oriented alignments.

In this new data-driven world, the whole is greater than the sum of the parts, and AR just might be the missing piece you need to complete your vision.

Kamyar Shah
14 Common Pitfalls New Entrepreneurs Face And How To Avoid Them

Fake Expertise 
One of the biggest pitfalls I have observed in entrepreneurship is the ability to get actionable and real advice. To get there, one has to be able to discern real and practical advice from fake and useless "experts" or "ninjas." This is easier said than done. The only true way to avoid that is to get referrals from people that have successfully utilized such experts. - Kamyar Shah, World Consulting Group 
Originally published at

No comments