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Can’t buy love: Corporate-start-up partnerships in the DACH region

Five principles for start-ups and corporates to unleash the full potential of their partnerships, overcome common challenges, and pursue opportunities for new partnerships.
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Women in the Workplace 2020

In a year marked by crisis and uncertainty, corporate America is at a crossroads. The choices companies make today will have consequences on gender equality for decades to come.
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The coronavirus effect on global economic sentiment

Executives are more hopeful about the economy—and their own companies’ performance—than they have been since the COVID-19 crisis began.
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The future of car buying: Perspectives from Saudi Arabia

Two longtime industry executives predict ‘hyper-personalization’ and more dealer partnerships.
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Economic Conditions Snapshot, September 2020: McKinsey Global Survey results

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Car buying in 2030

In this video, three McKinsey partners describe how the experience of purchasing an automobile might change in the coming decade—and how car dealerships should evolve.
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How European marketing-and-sales leaders handle COVID-19’s effects

To mitigate the financial impacts of the pandemic, executives focus on consumer needs and reorganizing their businesses for the next normal.
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Transforming the US consumer bank for the next normal

A “wait and see” approach to transformation will not work for banks. Instead, it is a moment for radical creativity. Banks that reimagine how they engage their customers and empower their employees will emerge as leaders.
Transforming the US consumer bank for the next normal

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A “wait and see” approach to transformation will not work for banks. Instead, it is a moment for radical creativity. Banks that reimagine how they engage their customers and empower their employees will emerge as leaders.
Transforming the US consumer bank for the next normal

Why Culture Is the Greatest Barrier to Data Success

In order to compete in the new digital economy, businesses must become increasingly data-driven. Few executives would dispute this objective. Recent events, including the global outbreak of COVID-19, have underscored the critical importance of having reliable data to inform organizational decision-making. Yet companies continue to struggle to operate in a data-driven manner. Why?

Even though we are now decades into the age of competing with data, a 2020 NewVantage Partners survey of C-suite executives representing more than 70 Fortune 1000 companies found that only 37.8% of companies have created a data-driven organization. In the same survey, 45.1% of executives reported that they compete on data and analytics, but a majority — 54.9% — stated that they do not.

Meanwhile, investments in data are increasing, with 98.8% of organizations reporting that they are investing in data initiatives. Nearly a fifth of these companies — 18.3% — have invested more than $500 million. Changes are also happening when it comes to enterprisewide governance. Large corporations have increasingly embraced the newly established role of chief data officer (CDO). While only 12% of companies had appointed a CDO as of 2012, this percentage increased to a high of 67.9% by 2019 before falling back to 57.3% in 2020. Among major companies today, there is nearly universal acceptance that data-driven management is strongly preferable to the alternatives.

In spite of the growing consensus and investment levels, only half of organizations — exactly 50% — reported that they are managing data as a business asset. The advent of big-data solutions and a next generation of data management capabilities — Hadoop, data lakes, DataOps, and modern data architectures — have been helpful but have not assured successful business adoption or outcomes. Technology does not appear to be a barrier or the problem. Only 9.1% of executives pointed to technology as the principal challenge to becoming data-driven.

Albert Einstein is said to have remarked, “The world cannot be changed without changing our thinking.” What is clear is that the greatest barrier to data success today is business culture, not lagging technology. In fact, cultural factors — that is, people and process issues — were cited by 90% of executives as the principal obstacle that they face. It is not enough for companies to embrace modern data architectures, agile methodologies, and integrated business-data teams, or to establish centers of excellence to accelerate data initiatives, when only about 1 in 4 executives reported that their organization has successfully forged a data culture.

Forging a data culture is a relentless pursuit, and magic bullets and bromides do not deliver results.

Cultural change and business transformation must be adopted at all levels of an organization for data-driven management to be truly embraced. Having professionally engaged with scores of large organizations over the course of the past two decades, each at a varying stage of maturity, I have found that certain actions distinguish successful data-driven companies from those that continue to struggle.

1. Secure executive commitment, not just lip service. Executive commitment is essential to building a culture where data is central. Pay attention to what companies do, not what they say. Most pay lip service to the criticality of data in their annual reports and company mission statements, but far fewer companies embody it in their DNA or in their day-to-day business practices. Some companies, like Capital One and American Express, have a history of embedding data in all aspects of their business culture. For most legacy companies, however, building a data culture remains a challenge. Companies that have overcome it instantiate data processes throughout their supply chains, from data production to data consumption. I have met with organizations that have proclaimed their commitment to creating a culture where data is a priority without making the necessary investments and following through on their proclamations in order to drive real change and data-driven business outcomes. Don’t be one of them.

2. Expect to work hard, and forget about magic bullets. Companies that have succeeded in their data-driven efforts understand that forging a data culture is a relentless pursuit, and magic bullets and bromides do not deliver results. To borrow from Thomas Edison, becoming data-driven is 1% inspiration and 99% perspiration. I have seen too many companies undertake big-bang, overly ambitious initiatives that fail over time. Start simple. Focus on key business questions. Tie data investments to business outcomes. Realizing quick wins enables organizations to build credibility and establish sustainable momentum. Companies such as Cigna, Nuveen, and Citizens Bank are embarking on long-term efforts to define use cases with strong business sponsorship. Companies that accept that there is no easy path to success fare best over time.

3. Establish realistic expectations, not unattainable goals. Only 28% of companies reported that the CDO role has been successfully established within their organization. Why is this? One reason is that many organizations have struggled to establish realistic and achievable objectives for the CDO. Data is an asset that flows across an organization, and managing data is therefore complex. CDOs must establish achievable expectations to ensure success. I was once asked by a business line president for a plan to make his organization data-driven within 90 days. Until organizations develop attainable goals, it will be impossible to achieve successful data outcomes.

4. Make steady progress, and overcome false starts. For many organizations, impatience to see results in the short term often leads to false starts, whether it is the investment in and then abandonment of technology initiatives or the launch of and lack of follow-through in data programs across the organization. The CDO’s short tenure for many companies reflects both the nascence of the role and a lack of clear expectations. Some of the largest banks are on the fourth or fifth iteration of their CDO role within the past decade. For many organizations, the tenure of the CDO is averaging less than three years. Companies must reach a point of stability and consistency in leadership and approach to maximize the return on their data investments.

5. Learn from the experience of others. Organizing around data is a new principle for most companies, many of which have continued to evolve from a product-centric view to a customer-centric view in recent decades. Many organizations tell me that they have their data and cultural challenges all figured out, but in practice, those that think they have it all worked out rarely do. They would benefit from understanding what other companies have done so that they can avoid the same pitfalls and replicate formulas for success. I have seen organizations seesaw in hiring external CDOs as change agents and revert back to longtime company insiders who know the culture of the company. Staying the course is important. Keep an open mind, learn from the experiences of others — their failures and successes — and look beyond your four walls for inspiration and success models.

6. Maintain a long-term view, and expect a journey. It should be clear by now that achieving data success is a journey, not a sprint. Companies desire to accelerate their efforts to become data-driven, but consistency, patience, and steadfastness pay off in the long run. Companies that set a clear course, with reasonable expectations and phased results over a period of time, get to the destination faster. Develop a plan. Create a data strategy for your company if you do not already have one. If you do have a data strategy, make sure that it is updated annually to reflect changes in the business and the ongoing and rapid evolution of emerging data management capabilities. Define your future state, and build an execution road map that will take you from your current state to the target outcome. It is hard to reach any destination without a good road map. Companies need to maintain a long-term view and stick to it while making periodic adjustments. Patience, persistence, and commitment are the ingredients for ensuring a successful long-term outcome.

Change Means Finding New Ways of Doing Things

Organizations must evolve and change the ways in which they structure current business processes if they expect to become more data-driven. In short, companies must be prepared to think differently. In 1997, Apple launched its legendary “Think Different” advertising campaign, noting that it’s often “the misfits, the rebels, the troublemakers, the round pegs in the square holes” who “see things differently … change things … change the world.” Those companies that recognize that competing with data and analytics requires them to do business a little differently and embrace change will likely be well positioned to realize the benefits of a data-driven culture.

Why Culture Is the Greatest Barrier to Data Success

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Moving from cash preservation to cash excellence for the next normal

Companies can build on their initial response to the pandemic to elevate their cash-management capabilities.
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The future of life insurance: Reimagining the industry for the decade ahead

Over the past decade, life insurers have struggled with growth and profitability. By focusing on three priorities, insurers can reinvent themselves and reestablish their vital role in customers’ lives.
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Ethics or Compliance in a Crisis?

Companies are judged by how they act in difficult times, such as the current coronavirus crisis. Making a charitable contribution is a positive action that some companies are in a position to take during a crisis, but such gestures can draw negative scrutiny for the givers. In a high-pressure environment when the chance of losing perspective is high, it’s worth remembering that while times of urgency and crisis call for unusual measures, breaches of compliance and ethical standards can expose an organization to serious risks.

As COVID-19 first swept the globe, Michael, a senior sales representative for a small but growing biotech company in health care, was alarmed to hear about a personal protective equipment (PPE) shortage at the hospital that was his key account. Concerned to hear that staff members were forced to reuse masks and wear flimsy plastic aprons rather than proper PPE, he wrote an email to his company’s medical affairs department to ask for a meeting to discuss ways to help.

However, he soon had second thoughts when he realized the donation process involved the approval of five senior managers from different functional areas. Sensing the absolute urgency, and without waiting for a reply, he quickly packed up 20 boxes of masks — part of a supply given to the sales team by his own company — and sent them to the hospital’s head of operations for immediate use. Given the crisis conditions and with lives quite literally at stake, he figured there was no time for lengthy discussions or bureaucracy. Feeling that his company’s good deed and the agile response deserved some attention and recognition, he contacted the communications manager to publish the donation story on LinkedIn.

Michael’s actions were in many respects a very human, even commendable, response to the ongoing COVID-19 catastrophe. Nonetheless, his response raises alarm bells at many levels, including from a legal, reputational, and ethical perspective. Like many industries dealing with public money, the health care sector has developed sophisticated control processes and procedures regarding charitable giving by suppliers to ensure that employees and companies act ethically and lawfully.

Many in this industry would argue that Michael’s actions were inconsistent with standard compliance requirements. Nonetheless, in his mind, the situation was so dire that he was justified in taking a shortcut and disregarding well-established procedures to do something positive.

Was he right from a compliance perspective? Was he right from an ethical perspective? Was this a case of choosing ethics versus choosing compliance?

There are sound reasons for restrictions on charitable behavior — which can encompass donations of money, equipment such as PPE and ventilators, or time volunteered by staff members. These constraints ensure that any actions taken are in the interest of philanthropy and are not used, or even perceived to be used, to advance the commercial interests of the donor. Indeed, Michael’s initial idea of writing to the medical affairs department — a function focused primarily on scientific criteria and patient needs, not on commercial interests — to discuss ways of providing help was more consistent with standard industry compliance requirements than his later actions.

While acknowledging the positive intent of some companies’ pandemic responses, and indeed the pressing need for such responses in many cases, senior leaders have raised concerns about cavalier approaches to philanthropy and about the resulting risk of future regulatory or legal action. It’s therefore less a question of whether companies should contribute than about how.

Organizations may wish to rethink their charitable approaches amid the current crisis. How far should they adjust the stringent processes that are in place to ensure that they routinely meet high standards of ethics and integrity? These questions are of critical importance to health care companies in particular, but they have broader application too. Organizations in many fields must consider the extent to which compliance rules and procedures should be modified — albeit temporarily — during a crisis response.

Based on our research, including discussions of crisis response with business and compliance leaders, we have developed some key recommendations for all companies wrestling with how to provide help to those affected by a crisis while keeping the business going and sustaining ethical standards.

1. “Why are we doing this?” This is the fundamental question a company needs to ask before it undertakes any charitable action. What underpins the urge to offer help? Is the offer intended to be altruistic — or are there sales, networking, or communications opportunities that could stem from the offer? The altruistic vision must prevail over commercial and image interests.

Doubts about why Michael was so keen to send the PPE to his key account hospital might be heightened by his apparent eagerness to also communicate his company’s donation via LinkedIn. One is left to wonder about his true motivations and whether altruism did indeed prevail over commercial and image interests.

2. Identify “crisis mode” processes. It is up to senior management to consult with cross-functional teams in order to gain an understanding of a crisis’s exceptional circumstances and determine a response plan. If this means, for example, relaxing or shortening the approval process, relevant senior leadership must be more involved in the decision-making. Systematically reviewing existing processes and inserting crisis mechanisms will alleviate the perception that a policy is inadequate or too difficult to follow — or the risk it may be ignored altogether — in times of crisis. Companies should also consider creating an exception document that outlines changes to compliance during the crisis and communicate it effectively to the entire staff.

Michael did not have the benefit of crisis mechanisms in place to address his urgent situation. If there had been an exception document succinctly describing a simplified process for donations during a crisis — such as seeking approval from two instead of five senior managers — he may have acted on the basis of this process instead of his own initiative.

However, when temporarily relaxing a stringent procedure, it is crucial to ensure that a company’s ethical standards are still rigorously applied. A company’s code of conduct is an appropriate starting point for understanding the relevant ethical standards, but senior leaders must also have a much deeper knowledge reflective of their good judgment and experience as well as a rich appreciation of the company culture.

3. Be careful who you donate to. The COVID-19 pandemic has seen companies take a more proactive approach to monetary and product donations, and in loans of products and staff hours. However, some lines should not be crossed. Companies must ensure that donations cannot be construed as inducements or rewards for sales, so it’s essential to keep sales and marketing concerns separate from the decision-making process. Decision makers must also confirm that any organization assisted has been identified on the basis of need — not as an organization their company may wish to please or engage with.

Every donation should be assessed and approved by individuals who are able to ensure that altruistic vision prevails over commercial and image interests. Importantly, donations should be made to entities, not individuals. In most cases, this will guarantee that more people will benefit from the company’s help and reduce the risk of the donation being interpreted as a gift. In Michael’s case, it would be hard for his company to justify his donation, originating as it did from a sales representative and, worse, not vetted by any senior manager.

4. Review and adjust your sales targets and reassure your staff. The perspective of the sales staff and ethical decision-making processes can be affected by the mounting uncertainties and financial pressures of a global pandemic. For example, staff members may be tempted to push donations toward recipients that are commercially attractive rather than those truly in need. We’ve already seen that Michael’s actions raise concerns about the intent behind the donation because regulators might well question whether it was a way of winning business as opposed to an altruistic gesture. But another question also arises: What should managers do to ensure that employees stop and consider the ramifications of altruistic gestures and avoid overtones of self-interest?

There are corruption risks if your sales force feels that the company is focused on maintaining aggressive pre-crisis commercial objectives. Companies can address this by ensuring that their objectives, while ambitious, are adjusted to the reality of the crisis. It is also important that senior managers reassure their sales forces that doing business with the highest integrity is part of the corporate culture. The message should be, “This is the time to be remembered as an ethical company” rather than “We are under too much pressure to make compliance a priority.” The right tone at the top has never been more important.

Michael might have been under pressure to meet sales targets, which could have influenced his judgment. Nonetheless, messages from senior leadership about maintaining an ethical reputation during the crisis may have added an important perspective. With a clear message from leadership, Michael may have questioned whether his idea of helping his key account was ethical or if it exposed the company to risk.

5. Document everything. Having transparent documentation of any assistance granted is critical. Correspondence on the topic must be clear and unequivocal. While it may not be possible to complete all of the paperwork up front, it should not be forgotten after the fact. Documentation should clearly show how the proffered help is directly linked to the circumstance, and the responsible executive should ensure that any donation is traced and tracked. This is especially true for donation contracts. Although you may not be able to sign your contract in advance of the donation, as per normal practice, it is crucial that this is done within a reasonable period.

Michael could have continued with his initial plan to involve medical affairs and trigger the expedited donation approval process. The company might well have felt the need to respond very rapidly, but there would still be a requirement for some documentation at the time and more detailed documentation subsequently. This would have helped the company demonstrate the good faith of the donation.

To conclude, a crisis such as the COVID-19 pandemic is not a time to lose your company’s moral compass — quite the opposite. A crisis will eventually end, but unethical behavior could have serious long-term effects on your company’s sustainability and credibility. It could also result in significant costs, including potential financial penalties, legal expenses, and the opportunity cost of lost management time, not to mention a possible negative impact on the share price. It is vital to get the actions and messaging right.

Although Michael’s intentions were good, his actions could easily be interpreted differently, with harmful consequences for his company and the public interest. His actions might have also led to significant consequences for his own career, such as an internal investigation, disciplinary measures, or even the possibility of dismissal.

In times of crisis, both ethics and compliance are required — albeit with compliance procedures potentially adapted to the circumstances of the crisis — and they demand not just a swift response but a careful one.

Ethics or Compliance in a Crisis?

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Getting Serious About Data and Data Science

Data science, including analytics, big data, and artificial intelligence, is no longer a novel concept. Nor is the important foundation of high-quality data. Both have contributed to impressive business successes — particularly among digital natives — yet overall progress among established companies has been painfully slow. Not only is the failure rate high, but companies have also proved unable to leverage successes in one part of the business to reap benefits in other areas. Too often, progress depends on a single leader, and it slows dramatically or reverses when that individual departs the company. In addition, companies are not seizing the strategic potential in their data. We’d estimate that less than 5% of companies use their data and data science to gain an effective competitive edge.

Over the years, we have worked with dozens of companies on their data journeys, advising them on the approaches, techniques, and organizational changes needed to succeed with data, including quality, data science, and AI. From our perspective, these are the two biggest mistakes organizations make:

1. They underinvest in the organization (people, structure, or culture), process, and the strategic transformations needed to get on offense — in other words, to take full advantage of their data and the data analytics technologies at their disposal.
2. They address data quality improperly, which leads them to waste critical resources (time and money) dealing with mundane issues. Bad data, in turn, breeds mistrust in the data, further slowing efforts to create advantage.

Although the details at each company differ, seeing data too narrowly — as the province of IT or the data science organization, not of the entire business — is a recurring theme. This causes companies to overlook the transformative potential in data and therefore underinvest in the organizational, process, and strategic changes cited above. Similarly, they blame technology for their quality woes and failures to capitalize on data, when the real problem is poor management.

We’ve all observed how companies behave when they are truly serious about something — how the goal changes from incremental progress to rapid transformation; how they muster both breadth and depth of resources; how they align and train people; how they communicate new values and new ways of working; and how senior leaders drive the effort. Indeed, it almost seems as if companies go overboard when they are truly serious about something. Amazon’s Project D initiative to develop the Echo/Alexa smart speaker is a great illustration of that seriousness, with hundreds of employees, several startup acquisitions, heavy CEO involvement, and no expense spared. DBS Bank’s journey to being named World’s Best Digital Bank by Euromoney is another good example. The company’s CEO, Piyush Gupta, said the following upon receiving that award in 2018:

At DBS, we believe that banks tomorrow will look fundamentally different from banks today. That’s why we have spent the past three years deeply immersed in the digital agenda. This has been an all-encompassing journey, whether it is changing the culture and mindsets of our people, re-architecting our technology infrastructure, or leveraging big data, biometrics, and AI to make banking simple and seamless for customers.

The contrast with most companies’ data programs is stark — one can only conclude that many are not yet serious about data and data science. For those only beginning to explore data, this may be understandable. But, if you’ve been at it for three years or more, it is time to either get serious in addressing mistakes or invest your resources elsewhere — and expect to lose out to competitors.

Stop Wasting Effort on Data Quality

The obvious approach to addressing these mistakes is to identify wasted resources and reallocate them to more productive uses of data. This is no small task. While there may be budget items and people assigned to support analytics, AI, architecture, monetization, and so on, there are no budgets and people assigned to waste time and money on bad data. Rather, this is hidden away in day-in, day-out work — the salesperson who corrects errors in data received from marketing, the data scientist who spends 80% of his or her time wrangling data, the finance team that spends three-quarters of its time reconciling reports, the decision maker who doesn’t believe the numbers and instructs his or her staff to validate them, and so forth. Indeed, almost all work is plagued by bad data.

The secret to wasting less time and money involves changing one’s approach from the current “buyer/user beware” mentality, where everyone is left on their own to deal with bad data, to creating data correctly — at the source. This works because finding and eliminating a single root cause can prevent thousands of future errors and eliminate the need to correct them downstream. This saves time and money — lots of it! The cost of poor data is on the order of 20% of revenue, and much of that expense can be eliminated permanently. That’s more than enough to fund the needed investments.

A good rule of thumb is that you should estimate that for every $1 you spend developing an algorithm, you must spend $100 to deploy and support it.

Get On Offense

Now consider the budgets for AI (as an example of “offense-minded” data efforts). It appears to us that, in many cases, the data science work to develop a new algorithm is funded well enough. Algorithm development is getting cheaper anyway, given that automated machine learning programs are doing more of the work. But useful algorithms die on the vine because the work to build processes, train people, address fear of change, and adapt the culture is substantially underfunded. Based on our experience, a good rule of thumb is that you should estimate that for every $1 you spend developing an algorithm, you must spend $100 to deploy and support it. A few of these dollars will go to building algorithms into work processes, and many more to training, building a culture that embraces data, and change management. Most companies aren’t spending this money yet, and it explains their lack of production AI deployments.

Make Bold Moves

What tangible steps should business leaders take to demonstrate that they are serious about data? First, they should more tightly couple their business and data strategies with an eye toward driving revenue growth. From the data perspective, opportunity abounds in fully exploiting proprietary data, driving analytics into every nook and cranny of the company, and augmenting virtually every decision using AI. You cannot — and should not — do them all, so you must select those most closely aligned with your business strategies. One sign that you’re on the right track is that there will be fewer data efforts. But those you do have will be far larger, more comprehensive, and more closely managed.

Second, get everyone fully engaged. After all, everyone is technically involved in your data efforts already. They interpret data correctly, or they do not; they create data correctly, or not; they use data to improve their work, or not; and they contribute to larger data initiatives, or not. Today, there are far too many “nots.” Similarly, managers push back against the nots, or they do not, and more senior leaders get in front of them, or not. So you must reach out to people, educate them, and enroll them in the effort, even as you grow increasingly intolerant of the inefficiencies stemming from bad data. This is going to take some time. One sign that you’re on the right track is that morale will improve. In our experience, once people get the hang of it, most of them find data work quite enjoyable. Importantly, in the data space, talent wins.

Third, draw a clear distinction between the management of data and the management of technology. Just as a movie is a different sort of asset than streaming technology, data and tech are different sorts of assets. Each demands its own specialized management. Yet today, too many companies subordinate data to tech. The result is that topics such as data architecture do not get the attention they deserve, leading to such absurdities as a bank having 130,000 databases, not including spreadsheets. Meanwhile, technology programs spend too much time dealing with the consequences of having systems that don’t talk to one another and spending too little time introducing new technologies to employees. One sign that you’re on the right track is that technology departments will become more effective and, in time, strategic.

Finally, now is a good time to start thinking about the longer-term roles data will play in your company. It is easy enough to recite the mantra “Data is the new oil.” And according to The Economist, data is now worth up to $2 trillion in the U.S. alone. But, of course, not all data is created equally. Some data — such as proprietary data, data needed to run the company, and data associated with other key assets — is so important that it should be treated as an asset in its own right. At the very least, you should make sure that end-to-end accountabilities for this data are clear.

We fully recognize how challenging these recommendations will prove to be. Yet they signal great opportunity, especially for the first companies in their sectors to embrace them. The needed approaches, methods, and technologies are widely available and have proved themselves over and over among digital natives and at the department level for established companies. It is clear enough that the future depends on data, so sooner or later, you have no real choice. As in all things, audentes Fortuna iuvat — fortune favors the brave.

Getting Serious About Data and Data Science

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The Best of This Week

Rooting Out Bias in AI Tools — and the Industry Itself

Bias has plagued the AI field for years, particularly around the way tools work — or don’t work — when analyzing people of color. Given the growing role that AI plays in organizations’ business processes, including the development of products and in the products themselves, a lack of diversity in AI and the invisibility of people of color is poised to grow into a cascade of crises.

Tech Platforms, or Manipulation Machines?

U.S. legislation passed in 1996 exempted internet platforms from the responsibilities of regulating the content on their sites and providing equal service to all customers. This lack of regulation has allowed Amazon, Facebook, Google, and other tech giants to become “the most powerful middlemen in history” at the expense of exploited consumers.

Before Investing in New Technology, Check Your Bias

In the same way that leaders may harbor an implicit bias about groups of people, they may also harbor implicit biases about new technology. Unconscious ideas about the dazzle of new tools can cause leaders to make poor investment decisions, whether it’s ignoring expert advice, taking unnecessary risks, or investing in products and services that are unproven and even unsafe.

Yes, You Are Working More These Days

The first large-scale analysis of digital communication early in the pandemic crisis has confirmed what you may have suspected. Since the sudden, widespread practice of working from home began, people have been working more hours and Zooming through more meetings — and managers are facing significant challenges.

How Retailers Can Address Evolving Customer Needs During COVID-19

Which retail customers will return to in-person shopping as the economy reopens, and why? The authors offer a new analysis and an actionable framework to help brand owners and retailers understand and address the consumer needs and preferences of five retail customer segments, whether the goal is to recapture former customers or retain new ones gained during the pandemic.

What Else We’re Reading This Week

• Why businesses should pursue a “cooperative advantage,” and how to achieve it
• Ernő Rubik, inventor of the eponymous cube, wrote a book to try to understand its “real nature”
• Transforming the future of learning and development

Quote of the Week:

“When crises first strike, people tend to react based on reflex, reverting to what they have been trained to do. With a framework for front- and back-stage leadership, managers will have a better chance of dealing with the current COVID-19 fallout and the next crisis to come.”

— Sameh Abadir, professor of leadership and negotiation at IMD, in “The Two Roles Leaders Must Play in a Crisis”

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Evaluating New Technology? You’re More Biased Than You May Realize

In the same way that leaders may harbor an implicit bias about characteristics of groups of people, they may also harbor implicit biases about new technology — including new technology they might be considering investing in to improve productivity or competitiveness.

You may think that you make decisions about technology tools with an open mind and a clear process for evaluating options. But our review of hundreds of published studies on new technology adoption reveals that personal beliefs about new technology — that it’s wonderous, complex, and alien — prompt specific, unconscious biases about how and why it’s better than older options.

Implicit bias toward the dazzle of new tools can cause leaders to take unnecessary risks and ignore the advice of human experts in decision-making. Further, implicit bias toward new technology may lead to sizable investments in products and services that are unproven or even unsafe.

Beliefs and Biases About New Technology (and the Risks They Present)

We define new technology bias as automatically activated (that is, unconscious) perceptions of emerging technology. These implicit biases draw from general beliefs about technology, and they go on to influence our perceptions of everything from smartphone apps to flight instruments used to pilot an aircraft. Considering the high technological ferment companies are experiencing today, it is crucial for leaders to be aware not only of the existence of new technology bias but also of its consequences when it comes to adopting or discarding new tools. Here, we detail three general beliefs that people have about new technology, the bias that each leads to, and the risks that each bias presents.

Belief: New technology is mysterious and a “wonder.”

Bias it leads to: New technology is better than current options.

Risk: Leaders may favor a new technology even if it is unproven.

Any of us can easily conjure up thoughts of technological advances that seem miraculous — such as using nanotechnology to cure cancer — and advances that have changed our everyday lives, such as microwaves that make cooking faster, map apps on our phones that are updated by satellites in real time, and laser technology used to correct sight defects. Moreover, we tend to remember successful technological innovations and forget unsuccessful ones (can you name the earliest voice-recognition software?).

This sense of awe regarding new technology leads people to unconsciously perceive it as superior in performance compared with old technologies. This is particularly true of early adopters, who on average are more optimistically biased toward new technology. They tend to blame any failures of the technology on user error rather than on the product or service itself.

The risk for leaders is that they may unconsciously tilt toward new technology over existing systems merely because it is new, even when its newness may mask other problems or when existing technology that is tried and tested may work better.

There is danger, too, in buying into new technology that relies on an ecosystem — a collection of complementary services, standards, and regulations required for the technology to work — that is not yet mature. Think about how difficult it was to sell 100% electric vehicles before charging stations became common. One reason leaders may disregard the importance of a nascent ecosystem is that they are blinded by their implicit bias in favor of the new technology to begin with.

That’s part of what happened in the e-reader industry with Sony’s early entry, the Reader. While the product itself was solid, it needed — and didn’t have — a collection of e-books that could be read on it. This shortcoming was overlooked by executives who were wowed by the newness of the technology. When Amazon later introduced the Kindle, it solved this problem by introducing a huge collection of e-books that could be downloaded instantly and seamlessly. Amazon bulldozed together an ecosystem that would give its product a winning advantage.

Belief: New technology is complex and difficult to understand.

Bias it leads to: Leaders should follow the experts when they recommend new technology.

Risk: Leaders forgo due diligence and disregard the concerns of nonexperts.

Developments based on the latest scientific discoveries in chemistry, biology, physics, and computing — such as nanotechnology, financial technology, blockchain, and artificial intelligence — have certainly reinforced the belief that new technology is not comprehensible to the layperson. Strongly connected to this belief is a view of technology as the domain of quantitative scientists and engineers.

This sense of a new technology’s complexity leads people to view it as more legitimate and credible if experts, such as university scientists in math and science areas, recommend it. This is even true when other, less expert users disagree. (Our research shows this tendency is all the more prevalent when the expert is male — making this an area where interpersonal biases overlap with technology biases.)

The bias that new technology is credible when endorsed by experts can lead people to make decisions about technology and investments without confirming the claims around it. “Overtrusting bias” is represented dramatically by the early, unbridled support of Theranos, the now-defunct health technology company. Theranos claimed its technology could conduct many blood tests, very quickly, from a small amount of blood. The company’s affiliation with Stanford University (founder Elizabeth Holmes attended Stanford University and a prominent professor of engineering backed her idea), early partnership with the Cleveland Clinic, and star-studded board helped Theranos raise $700 million from investors — despite the company’s unwillingness to share detailed information about the blood-test technology and without providing financial statements audited by an independent public accounting firm. As Forbes wrote, investors “assumed that with all the luminaries associated with Theranos, someone must have done due diligence on its product.”

A sillier but still illustrative example is Juicero, a $400 vegetable juicer that epitomized Silicon Valley’s worst instincts. Venture capitalists threw $120 million at cold-press juice entrepreneur Doug Evans, whose technology sounded potentially impressive — a squeezer said to exert enough force to lift two Teslas, with a Wi-Fi-connected scanner to confirm the sell-by date of the veggie packs it pressed. But all Juicero did was squeeze juice from preformed $5 packages of organic vegetable matter, and soon videos emerged of people squeezing the packs manually. In the end, Juicero was tech for tech’s sake, and the low-tech alternative — putting veggies in an old-fashioned mechanical juicer (which appeared obvious to everyday consumers) — won out.

Belief: Some types of new technology are inherently alien — that is, they are not humanlike.

Bias it leads to: New technology that has humanlike features is more trustworthy.

Risk: Leaders will overtrust technology with humanlike features, despite performance shortfalls.

Most technology does not exhibit the social cues that help humans to trust one another, such as facial expressions or body language. For example, politeness and friendly chit-chat during interactions lead people to see others as sociable and warm — and ultimately trustworthy.

The sense that new technology is alien means that people tend to unconsciously overtrust new technology that does act more like a human. For instance, people are more likely to trust technology that is imbued with a human voice or social patterns, like turn-taking. This is why most interactive navigation systems are programmed with a friendly and warm human voice, rather than more robotic speech.

The risk from this bias is that leaders may be taken in by human features of technology, even though the actual performance of the technology is deficient. For example, research has shown that etiquette may overrule performance reliability. In one study, people’s trust in advice given by new information technology for diagnosing aircraft engine problems that was polite but reported to have only 60% reliability was equal to that of technology that was reported to have 80% reliability but engaged users in a rude manner.

How to Avoid New Technology Biases

Beliefs and biases can be tamped down. There are three actions leaders can take to avoid being misled by new technology bias in their decision-making:

1. Focus on a new technology’s functions, actual performance, and practical relevance. While downplaying a technology’s newness, leaders should focus on the problem they need to solve and how possible solutions compare. What does their company actually need? What are their customers really ready for?

Leaders also want to be cautious not to get caught in the bandwagon effect, another type of bias. For instance, there is an incredible amount of hype about artificial intelligence today. Businesses across the world are looking to this technology with the hopes of gaining a competitive advantage over their competitors, reducing operating costs, and improving customer experience. However, not all companies are ready to leverage AI. They need to carefully consider what specific business problems they need AI for, whether they have well-established data collection systems in place, and whether they have skilled specialists to implement and manage the algorithms.

2. Include nonexperts and everyday users on decision-making teams about new technology. We know from past research that technology geeks and scientists are more risk-seeking than nonexperts when it comes to new technology and overconfident about their ability to assess it. Many of the problems that arise with new technology become apparent only when nonexperts attempt to use it. Having them involved in decision-making makes it more likely that issues will surface that experts wouldn’t notice or would dismiss.

Consider the “antenna-gate” controversy that occurred with the Apple iPhone 4. Some consumers, when using the phone in their left hands, got a loss of signal strength and dropped calls. Their hands were apparently bridging the iPhone’s antenna. Initially, then-CEO Steve Jobs blamed users, telling them, “Just avoid holding it that way.” More testing, particularly by left-handed users, might have unearthed the problem earlier.

3. Separate the subjective “look” and “feel” of the technology from its objective performance. Because new technology that looks and acts human is likely to be overtrusted by potential adopters, decision makers must evaluate its performance in an objective manner, such as by the number of errors, time to complete tasks, time to learn the new technology, and privacy breaches. Decision makers should recognize which reactions are subjective, such as emotional reactions to the technology and even user satisfaction.

Consider personal digital assistants like Amazon’s Alexa or Apple’s Siri. A very futuristic idea only a few years ago, voice and chat assistants have found their way inside organizations: They observe data in real time and have the capability to pull information from sources such as smart devices and cloud services and put that information into context using AI, thus contributing to lower customer service costs.

But these tools have an often underestimated dark side. Much of the data they collect and use includes personal, potentially identifiable, and possibly sensitive information. Digital assistants can be hacked remotely, resulting in serious breaches of users’ privacy. Moreover, digital assistants have their failings. Humans who see several versions of a client’s name (including misspellings and nicknames) on different pieces of data will still know that this is the same person. In contrast, the AI algorithm that runs digital assistants won’t; it will classify spelling variations as different people. When adopting digital assistants in their businesses, therefore, managers need to be aware of the potential perils of overtrusting humanlike technology.

Let the Best Technology Win

Making decisions about the use of emerging technologies is now a routine part of most managers’ lives, and the pace at which such decisions need to be made only seems to be accelerating. Both of these trends make it more important than ever that managers understand both how implicit biases may influence their choices around new technology and how to minimize these influences to avoid damage to company performance and reputation.

Evaluating New Technology? You’re More Biased Than You May Realize

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The Two Roles Leaders Must Play in a Crisis

The coronavirus pandemic has already proved to be a litmus test of leadership as organizations around the world fight for their survival under unprecedented circumstances. In such dire straits, managing is in many ways dramatic — that is, it shares the qualities of onstage drama. As sociologist Erving Goffman put it, crisis managers need to present different faces at different times.

We often expect leaders to perform in predefined ways: Chief executives should be courageous, for example, and financial controllers conservative. Disaster managers, though, must be sufficiently flexible to don myriad masks, depending on the situation. We must, for instance, be humble with those who expect humility, and tough with those who expect toughness. Or, in other words, we must conform to expectations or risk backlash.

Great crisis captains need to play two main parts: the front-stage and back-stage roles of leadership. In the front-stage spotlight, leaders inspire and assure their teams, sending a message of hope and sharing their vision with the organization. They also show empathy and public commitment. These leaders are simultaneously kind and humble, showing the caring side of their personality.

All of these qualities must be combined with the back-stage role, in which leaders take a blunt and realistic approach to the serious threats at hand. Behind the scenes, leaders gather information and expertise, share facts, and dive deeply into processes — whether financial, technological, or human — to adapt and follow through on their plans. Such leaders are smart and confident, displaying the daring side of their personality.

It’s essential that leaders present their true selves without being fake. Those who play the role in a false or insecure way risk shredding their credibility because observers will sense the dishonesty and feel insulted. The common characteristics of great crisis leaders are a robust stress management capability, abundant resilience, networking prowess, vast social capital, a strong commitment to inclusivity, and a cool head when others overreact. History is filled with examples of versatile leaders, from Nelson Mandela to Singapore’s founding father, Lee Kuan Yew, who got this balance right.

Facing their own existential threats, how can today’s bosses play both the front- and back-stage roles of leadership? How can they strike the right balance between caring and daring?

Acknowledge the crisis in a serious way. Confronting the tough truth of your situation is essential to overcoming it. Optimism will spur you to action, but realism guards against naivete and crushing disappointment. Give your audience the raw truth so they can prepare for the worst while hoping for the best.

This balance reflects the Stockdale Paradox, a seemingly contradictory concept crystallized by Jim Collins’s 2001 book Good to Great. The concept is based on James Stockdale, a former U.S. Navy vice admiral who was a prisoner of war for seven years in Vietnam. The idea is that you must balance realism with optimism. Great crisis leaders are aware of what they cannot control, and by accepting this, they gain agility. The worst thing leaders can do in a crisis is be overly optimistic and overconfident, because when things go south, they will be taken by surprise.

Crisis leaders also have to accept that luck or circumstance may play a role in their success and admit this rather than claiming complete credit for every victory. Humility is, after all, vital to rallying an organization behind a strategy. For example, after American Army Gen. Dwight D. Eisenhower led Allied troops to victory in the D-Day landings in France during World War II, he publicly acknowledged that the weather played a crucial role in his success. Without a full moon to light up obstacles, a low tide to spotlight underwater defenses, and light winds for plain sailing, victory would have been unlikely, if not impossible.

Embrace the crisis as a team. Front- and back-stage leaders are members of and draw on many networks, acting as both a hub and a bridge for knowledge to flow. They collect expertise and have enormous social capital that allows them to pick up the phone and call in favors whenever necessary. The caveat is that they need to have built this social capital before a crisis.

They can also converse effectively with a wide array of people, regardless of their rank or socioeconomic status. They understand all of their stakeholders. An effective hotel manager, for instance, would draw on insight from both the laundry room and the boardroom to truly understand their organization and build a sense of community, which is a very special skill. Great crisis leaders are inclusive, embodying and conveying the values of the organization to inspire loyalty and generate the best ideas.

Take, for example, Naohiro Masuda, superintendent at the Fukushima Daini nuclear power plant, the sister site of Japan’s Daiichi plant that was rocked by reactor explosions and core meltdowns in the wake of a massive earthquake and tsunami in 2011. Masuda managed to spare Daini a similar fate, in part by galvanizing his team. He did this through a process called sensemaking, whereby the team reviewed and communicated information so everyone could collectively adapt to the unfolding situation. Essentially, sensemaking is when people adapt their behavior in response to understanding and experience. In teams, people can share information and collectively adapt.

Creating this sense of trust, community, and hope was partly how Masuda was able to persuade groups of workers to essentially risk their lives in surveying the damage to reactor units. Only later did he have time to come up with a comprehensive strategy and a list of the resources needed to execute it. The Daini plant went through the crisis without an explosion or a meltdown. Masuda is an unsung hero.

Explain why and how you made decisions. The back-stage role of crisis leadership involves execution of a strategy, the hard work behind the scenes. But in order for their decisions to be endorsed, leaders also must convey their reasoning to the entire organization. Transparency is key because emotions run high in a crisis.

For instance, Winston Churchill, Britain’s beloved wartime leader, was an excellent orator who rallied a divided nation facing the Nazis. He communicated with striking humility, sharing rich details of battles, and he vividly discussed expected offensives while offering stark warnings that “the whole fury and might of the enemy must very soon be turned on us” when Hitler amassed forces close by in France. Churchill’s realism served as his justification for choosing to continue on with war when Britain faced an uncertain outcome and he was under pressure to negotiate a surrender.

Clearly, Churchill did not mince his words or downplay the risks. But this realism was tempered with optimism and defiance, which established a close bond between him and his countrymen by winning their respect and trust. His approval ratings were high throughout World War II. To this day, Churchill is widely admired for being one of the greatest crisis leaders in history.

Measure and adapt your strategies. Fantastic leaders debrief and analyze what has happened and, as a crisis unfolds, gauge their success and adapt their strategies. Leaders should stay true to their nonnegotiable values — such as accountability, credibility, integrity, humility, and kindness — which can be thought of as a compass guiding their mission. Strategy, on the other hand, is like a GPS that needs to be adjusted constantly in order to reach the final destination.

We derive our values from our surroundings, upbringing, and culture, and they rarely change throughout our lives. It’s important that our values are not compromised in a crisis because they act as a guiding star that will help us keep a cool head amid chaos. In such uncertain times, leaders who panic and become defensive can be too rigid when it comes to strategy. This is a mistake; crises unfold so quickly that strategies must be tweaked on a daily — even hourly — basis. There is a striking parallel between great crisis leaders and parents, who must stay firm on values and principles while still giving their children space to develop — a very delicate balance.

The way to achieve agility is to outsource some of the strategy development process by bringing in perspectives from your teams. This reflects the sensemaking process that can be and has been highly effective in crises.

Ultimately, the success of front-stage and back-stage leadership can be measured by how well a leader can mobilize resources across an organization, the commitment of stakeholders to the leader’s strategy and vision, and the leader’s sensitivity to threats and opportunities after the emergency abates. Front- and back-stage leaders should come out of a crisis in better shape than before, with abundant credibility and admiration.

When crises first strike, people tend to react based on reflex, reverting to what they have been trained to do. With a framework for front- and back-stage leadership, managers will have a better chance of dealing with the current COVID-19 fallout and the next crisis to come. The keys will be to acknowledge the seriousness of the situation, confront the crisis as a community, communicate why and how you make choices, and benchmark your success while adapting your strategies.

The Two Roles Leaders Must Play in a Crisis

As Stores Reopen, Which Customers Are Most Likely to Return?

The COVID-19 pandemic and associated lockdowns throughout the U.S. have dramatically affected shopping behavior and shifted priorities for retail customers. While the pandemic has resulted in revenue declines for the majority of businesses, some brand owners and retailers have seen significant growth by adapting their offerings and communication to capitalize on new customer needs and behaviors.

We investigated the scale and variation of the lockdown’s impact on shopping behaviors across a number of product categories, fielding three surveys (in late March, late April, and late May 2020) among a representative sample of more than 5,000 U.S. households. The key findings of our March survey formed the basis of our article “Growth Opportunities for Brands During the COVID-19 Crisis.”

As it became clear that the reopening of the economy would be phased, we extended the focus of our April and May research to identify a new spectrum of customer segments and the factors influencing their likelihood to return to in-person shopping. This article draws on these findings to identify how retail stores and brand owners that rely significantly on physical retail can reengage with former customers or tailor their offerings and communication to appeal to these new customer segments.

What We Found

The Research

• The authors conducted three surveys in late March, April, and May 2020 that represented a total sample of more than 5,000 U.S. households.

• Respondents were asked about more than 60 situational, attitudinal, and behavioral factors related to in-person shopping as pandemic-related lockdowns were eased.

• In order to understand the factors that might motivate customers to return to in-person shopping, respondents were asked about their quarantine experience and associated emotions and attitudes, including the degree to which they had missed nine aspects of the in-person shopping experience.

• Using a machine learning technique (statistical k-means clustering), the authors identified five major COVID-19-related shopper segments: functional, tactile, experiential, diversion, and reluctant.

Our research (see “The Research”) shows that, as the lockdown eased, retail shoppers could be grouped into five segments, each of relatively equal size, based on their motivations for in-person shopping — for example, whether shopping is an activity that they enjoy in its own right, as part of a sociable lifestyle, or simply as a means to an end.

In order to understand the factors that might motivate each customer segment to return to in-person shopping, we also asked respondents about their quarantine experience and associated emotions and attitudes. This revealed wide variability in these situational factors that greatly exceeded the differences observed on traditional demographic factors.

Studying the situational, attitudinal, and behavioral factors at play (more than 60 in total) has allowed us to develop detailed and actionable customer segment profiles, together with recommendations for how retailers and brand owners can appeal to each segment in a relevant and emotionally compelling way.

Segmenting Shoppers Based on Their Motivation

A key component of our approach was to ask respondents to evaluate the degree to which they had missed nine aspects of the in-person shopping experience. These included functional aspects such as “familiarity with the store layout,” experiential aspects such as “being able to touch and feel things,” and social factors such as “shopping with family and friends as a social activity.” We also included the option “There is nothing I miss about in-person shopping.”

Using a machine learning technique (statistical k-means clustering), we identified five major COVID-19-related shopper segments: functional, tactile, experiential, diversion, and reluctant. (See “Relative Importance of Nine Shopping Factors to COVID-19 Customer Segments.”)

Relative Importance of Nine Shopping Factors to COVID-19 Customer Segments

Scores represent the log values of the raw weights given by each segment to each of the nine shopping factors. The percentages represent each customer segment’s share of the overall population.

Functional shoppers are willing to return in-person to retail environments where they are already familiar with the layout and range of products offered. However, they remain concerned about health risks and won’t be going out without their hand sanitizer. This segment also values the experience of coming across new items and interacting with other shoppers, as long as the store has good safety protocols in place. They are keen to try something new, especially if it comes with a discounted price tag.

Tactile shoppers have been bored at home and are eager to hit the stores once they perceive there’s a relatively low health risk during the pandemic. They feel lucky to have kept their jobs but have overdosed on Zoom calls and Netflix and now are excited to get away from the internet browser and back to in-person browsing. When they are finally able to visit their favorite retailers, they will be thrilled to actually touch items (assuming it is allowed) that they have previewed online and will be open to trying new brands if products feel as good in real life as they looked on screen. Tactile shoppers have high standards, though, and will not return as customers if the experience is less than great.

Experiential shoppers aren’t at the store just to check off items on a shopping list. For this consumer segment, a shopping trip is an event in itself, offering the promise of novelty and the joy of finding something they weren’t actively looking for, with the added opportunity to bring a friend along for the experience. They are especially responsive to brand recommendations from people they trust and always keep an eye out for items they can recommend to others.

Diversion shoppers are eager for the excuse to get out of the house and were among the most excited about stores reopening — a little retail therapy is what they need to end the monotony of lockdown. A price discount might persuade a diversion shopper to try a new brand, and they’ll buy again if they like their purchase but won’t be chatting much to their friends about it. Diversion shoppers can take or leave the social aspect of shopping.

Reluctant shoppers were never ones to be excited about in-person shopping, even when there wasn’t a pandemic. Whether they go to a store or shop online, they just want to get what they need, avoid distractions, and get out again. This shopper appreciates the greater range of options now that stores are opening up again, but for them, shopping was and always will be a chore — convenience and simplicity are the main drivers that keep them coming back. To the extent that they have adapted to online shopping, reluctant shoppers are among the least likely to return to stores.

We profiled each segment across a large number of situational, attitudinal, behavioral, and demographic variables. (See “COVID-19 Customer Segment Profiles.”)

COVID-19 Customer Segment Profiles

This table indicates the most significant differences for customer segments across different attitudinal, behavioral, and demographic questions. The blue and yellow shading indicates a score significantly above or below the average on that metric.

Perceived health risk, quarantine mood, financial situation, and level of stress were each assessed on a seven-point scale. The percentages shown are the proportion of respondents in each segment who rated their experience as higher/lower on this dimension. (The remaining respondents said things were “the same as elsewhere,” “neutral,” “no change,” and “moderate,” respectively.)

“Supervising children’s schooling” represents the percentage of respondents in each segment who reported having children aged 14 and under at home who required assistance or supervision with remote schooling.

The “influenced to try new brands” score is an index based on the percentage of respondents in each segment who reported having been influenced to try a new brand by either a promotion, discount, or recommendation, relative to the percentage of trial among the overall population.

Finally, we measured sociability on a six-point scale ranging from “sociable” to “reserved,” with index scores above one representing greater sociability.

How Retailers and Brand Owners Should Target Each Segment

In our previous article, we put forward a framework for action based on a modified version of the four P’s of marketing (product, price, promotion, and place) that we call SAVE (solutions, access, value, and education).

Applying the SAVE Framework to COVID-19 Customer Segments

This framework highlights what each segment is looking for in terms of solutions, access, value, and education (SAVE). The percentages represent each customer segment’s share of the overall population.

Functional shoppers: These customers desire routine and familiarity despite the disruption. Office supply chain Staples recognized that this segment represented a majority of its customers and that many did not like to order online. The retailer now provides personal shoppers who take orders and source the items in the stores while customers remain in their cars. Extending this model, convenience store chain Wawa will have its first drive-through-only location by year’s end.

Safeway has taken a different approach to meet this segment’s need for familiarity by expanding the search options on its website. It is common for grocery store customers to search online by brand or product, but Safeway’s innovation was to simulate the in-store experience online by enabling shopping by aisle.

Tactile shoppers: This segment seeks opportunities to engage with products. The concept of a personal shopper is not new for many people familiar with high-end fashion brands, but the introduction of the service by thrift store Uptown Cheapskate is a welcome development for the tactile shopper in the age of COVID-19. Based on the responses to an online questionnaire, an Uptown Cheapskate employee selects garments matching the customer’s specifications. The retailer keeps customers coming back by featuring new outfits each day to entice them to connect with personal shoppers in their local markets.

Tactile shoppers love the browsing experience. Ted Baker (a U.K.-based luxury brand) launched a digital pop-up shop during the lockdown that featured limited-edition pieces and donated 100% of profits back to the community. This kind of event-based, product-centric customer experience speaks to the motivations of the tactile shopper.

Sephora recently joined traditional retailers H&M, Abercrombie & Fitch, and Target in using Instagram’s digital store platform to allow shoppers to buy products directly from its Instagram feed, expanding opportunities for new kinds of browsing, engagement, and sales generation.

Experiential shoppers: For this segment, it is as much about the people as the products — the “who” and the “how” of shopping as much as the “what.” Brick-and-mortar store owners might respond to the continuing health concerns of these customers by creating exclusive shopping hours for them (similar in concept to the “senior hours” offered by many warehouse clubs and grocery stores) or even dedicated venues. An example of the latter is the concept of the retail closet — small retail spaces within commercial or multifamily properties, where brands can bring their collections in front of customers, who can come in alone or with a few guests. This type of personalized and exclusive shopping environment is safe and entertaining, especially for the experiential shopper.

In the online environment, fashion retailer Madewell caters to this segment by satisfying their curiosity with unique grid-style curated collections and “inspiration boards” complete with videos. In an effective replication of valued aspects of the in-person shopping experience, customers can watch each product in use as it would be on a “typical day” while also receiving recommendations based on their selections. Madewell, along with other brands such as home goods retailer West Elm, have been ahead of the curve by using this strategy even before the pandemic. They are leaning into this strategy, appealing to the experiential shopper by allowing discovery and exploration while mitigating health concerns.

Diversion shoppers: These shoppers are looking to escape the monotony of their daily routines during the pandemic. The startup COVID-19 Essentials provides you with an unchallengeable reason for getting out of the house: stocking up on COVID-19 safety essentials. The pop-up shop features an array of face masks, UV-light sterilizers, and other pandemic accessories.

Atelier Restaurant in Canada was renowned for a dining experience that was as much about abstract art as food, featuring fantastical dishes reminiscent of snow globes, flower arrangements, and fashion accessories. When pandemic restrictions forced the restaurant’s closure in March, Atelier quickly pivoted to a pickup version of its multicourse tasting menu. Chefs compressed 12 courses into five and sold limited tickets at $100 per person, allowing only 10 vehicles per service, on weekends only. In what was often their first outing in months, customers brought their own picnic tables to dine tailgate-style in nearby parking lots or drove around the block before returning to pick up the next course.

Reluctant shoppers: These customers are a key focus for businesses that pivoted to digital offerings during the pandemic in order to continue to serve their core customers by migrating them online. Buffalo Exchange, a thrift and clothing consignment store, has transformed its consignment process to be completed totally online and by mail. A consignment customer can request a bag through the mail and fill it with 20 to 40 items of clothing. Once the bag is received by Buffalo Exchange, the commission payment for saleable items is digitally transferred to the customer, and any items not suitable for resale are mailed back. This example provides a practical road map for physical retailers to reimagine their shopping experience online, or for brand owners considering a move to a direct-to-consumer model.

The coronavirus and associated stay-at-home restrictions have given rise to new and evolving customer needs. Now that nonessential stores are starting to reopen, the segmentation presented in this article provides an actionable framework to help brand owners and retailers understand and address this new spectrum of consumer needs and preferences. Specifically, we highlight how businesses can tailor their offerings and communication, whether the objective is to reengage former customers or to retain new customers acquired in the age of COVID-19 — major challenges at a time when marketing budgets are being curtailed.

As Stores Reopen, Which Customers Are Most Likely to Return?