4 Ways Small Businesses Can Internally Prepare For Holiday Shoppers

4 Ways Small Businesses Can Internally Prepare For Holiday Shoppers

It is nearly Halloween, which means savvy small businesses are already looking ahead to the holiday shopping season. Many business owners have long been strategizing and planning to ensure they are bringing their A-game for each major shopping day – Black Friday, Small Business Saturday and Cyber Monday. They’ve reviewed data from last year to determine what promotions worked versus those that did not. Business owners have also defined and set sales goals, optimized their websites for desktop and mobile devices, and created marketing campaigns targeted to their ideal shopper audience.

Much of what I have mentioned above is external holiday prep, which is a bit different from internal preparations. Businesses preparing for the holiday season internally must make sure they are covering these four key areas before their doors open and shoppers, and sales, begin flooding in.

1. Staff seasonal employees

Chances are you may hire seasonal employees to help out and you won’t be the only business to do this. The National Retail Federation has forecast that 2018 holiday retail sales in November and December could hit as much as $720.89 Billion. This is an increase between 4.3 and 4.8 percent over 2017.

Naturally, this many sales call for a seasonal hiring boom. However, working during the holidays is no easy feat. Customers can easily get upset if something from their wish list is out of stock, payment systems can go down, and there are long lines to stand in which can cause tempers to flare up.

As small business owners look into hiring for 2018, they’ll need to make sure the candidates they bring on are qualified for the job. Keep an eye out for resumes that have been tailored for the position. These resumes should showcase how the applicant has demonstrated initiative during the holiday season and helped solve problems in past, relevant positions.

Applicants interested in seasonal employment should also include skills that illustrate how they have quickly trained for their roles. They should be able to demonstrate how they kept their cool during stressful situations and went above and beyond to meet the needs of customers. Keep your eyes peeled for buzzwords about their personalities, too. You’ll want to hire individuals that have a good attitude that is patient, understanding and full of good cheer.

2. Prepare holiday schedules for employees

All hands are on deck during the holiday season, especially major shopping days like Black Friday, Small Business Saturday and Cyber Monday. Aside from making sure that your staff has been trained for their workload, prepare their schedules.

Planning to keep your storefront open later or earlier than usual business hours? Make sure you have members of your staff available to come in earlier or stay later as needed. Encourage employees, seasonal and full-time alike, to schedule in their time off early, should they decide to go home or on holiday vacations. If someone is unable to come in for a shift, make sure you have backup employees on deck that are ready to step in and assist as needed.

3. Add a little decoration pizazz to your storefront

Is your small business a brick and mortar storefront? If so, you’ll want to make it as inviting and enticing to shoppers as possible. That means it’s time to decorate and add festive touches to the space!

A few areas to consider in your decor scheme may include window and product displays, lighting and music. You may even add scents and smells, like peppermint and gingerbread, throughout your storefront. Get your entire team involved in the process, too. Make it a fun, team building activity to deck the halls with one and all!

4. Remember to go the extra mile with customer service

No matter how busy you may be, make sure you and your team know that this is your company’s time to shine and to provide excellent customer service.

Pay attention to the overall customer experience. Remember names of frequent shoppers, offer complimentary gift wrapping and shipping, and provide free sample products with purchases. Be ready and prepared to help out and embrace the holiday spirit to the fullest with a friendly, can-do attitude.

Social Media Strategists: How to Set Your Prices

As a freelancer, deciding how much to charge for projects is an important decision. Charging too little may result in a lot of work for not a lot of pay. If you charge too much, you could have a tough time finding and keeping customers.

A Business.com community member asked, "What should I charge to run a small business's social media?" This is an important question for a lot of social media strategists who struggle to set prices. We asked social media experts to find an answer.

1. Decide how much you want to make a year.

When you're deciding how much to charge as a freelancer or social media strategist, you need to take a step back and decide what your yearly salary should be. To determine this figure, you can conduct research on sites such as Glassdoor or LinkedIn. Once you decide how much you want to make yearly, work backward.

"I try to work backward from what I would want my hourly rate to be, charging a flat rate based on the projected number of hours I'd be spending on each social platform," said Nicole Fallon, a social media consultant.  


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When you're deciding how much you want to make, you need to consider industry averages and your experience level. If you're just starting out with no customer testimonies, you shouldn't charge as much as someone with five-plus years of experience.

"I think it's important to be realistic to consider your portfolio and expertise," said Corri Smith, owner of Black Wednesday. "We charge much more now that we have had a full book of business for four years and have happy clients, testimonials and 100 percent of business coming from referrals."

2. Determine what the project entails.

Before you throw out any numbers or prices, you need to chat with the business owner to learn about their goals, social media accounts and what they expect from you. Once you have all the information, you can start figuring out your pricing.

"Social media needs vary so much from business to business, as do their goals and expected ROI for social," Fallon said. "Before I take on a new social media client, I usually set up a discovery call to talk through their vision for their social presence, including how many accounts they have, how often they would like to post, and what types of content they want to see on each channel."

If clients don't have an idea for their accounts, Fallon provides a standard base package and will work from there. The base package includes a predetermined number of posts a month. Then businesses can decide if they want to go up or down from there.

3. Charge per project or social account.

As a freelancer, it's always smart to charge per project, not per hour, because you end up hurting yourself if you become faster and more effective.

"Generally, I charge per project – or, rather, per social account," said Fallon. "Hourly rates can get messy, especially because that's so much 'off the clock' work that needs to be done with social media (checking notifications, liking/sharing follower content, responding to DMs, etc.)."  

When you're talking with the business before starting the project, you both should be clear about your expectations. If, for some reason, you have to work more or with more urgency than expected, it's OK to add extra charges – as long as you're upfront about it.

"We add additional charges when hours go over or when there is a sense of urgency," said Smith.

4. Do your research.

It's best practice to constantly do research on your industry, no matter your job or title. However, it's even more important for freelancers who set their own prices. You should always know industry best practices and how much other social media specialists are charging.

"I talk to everyone I possibly can who does this as a business!" said Fallon. "I've also done tons of research on my own to see what bigger agencies charge. That being said, 'average' prices can vary a lot. Get as much information as you can from as many sources and individuals as possible."

Once you decide how much to charge, be sure you set realistic expectations upfront.

"It's impossible to promise you'll get them X number of followers or X percent increase in engagement," Fallon said, "and anyone who says that they can is definitely using shady tactics to do it."

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How has technology changed which deals venture capitalists (VCs) fund and how they fund them?

Venture capitalists essentially invest in startup ‘experiments’, and subsequently provide more funding to the experiments that work, so that they can run more experiments. This leads to many failures (roughly 55% of startups) and a few successes (6% return > 5x the total amount invested).  So an innovation that changes the cost of experiments changes the landscape for venture funding.

In recent research, we examined a particular innovation that had a broad impact on some types of startups but little to no effect on others.  The introduction of cloud computing services in the mid 2000s allowed Internet and web-based startups to avoid large initial capital expenditures and instead “rent” hardware space and other services in small increments, scaling up as demand grew.  Cloud computing made the early ‘experiments’ for these firms significantly cheaper.

We combined data from VentureSource, VentureEconomics, Correlation Ventures, CapitalIQ, LinkedIn, Crunchbase, firm websites, and LexisNexis to examine the funding of startups in the period around the introduction of Amazon Web Services (AWS) in both affected and unaffected sectors. (Thank you to Correlation Ventures for helping us with this study. Both Rhodes-Kropf and Ewens are advisors to and have a financial interest in Correlation Ventures.)

Startups founded in sectors that benefited from the introduction of AWS raised much less capital in their first round of VC financing after AWS. On average, initial funding fell 20% relative to unaffected sectors.  Interestingly, this fall in costs only changed the initial capital required for the startup – total capital raised by firms in affected sectors that survived three or more years was unchanged.  Thus, the effect of cloud services significantly impacted the initial costs of trying an idea, but not the costs of scaling a successful business.

This fall in the cost of starting businesses dramatically impacted the way in which VCs manage their portfolios.  Some VCs shifted toward an approach colloquially referred to as “spray and pray” — VCs ‘sprayed’ money in more directions, and ‘prayed’ rather than governed. In sectors impacted by the technological shock of cloud computing, VCs tended to respond by providing less funding and limited governance to an increased number of startups. These VCs were also more likely to abandon their investments after the first round of funding. In fact, the number of initial investments made per year per VC in affected sectors nearly doubled from the pre-cloud to the post-cloud period relative to unaffected sectors, without a commensurate increase in follow-on investments. In addition, VCs making initial investments in affected sectors were less likely to take a board seat following the technological shock.

These effects arose both because some firms changed the way they invest and because of entry by new VC firms.  Even the most active firms investing both before and after the shift tended to invest smaller amounts in a larger number of deals in sectors affected by cloud computing, relative to unaffected sectors.

This falling cost of experimentation allowed a set of entrepreneurs who would not have been financed in the past to receive early-stage financing — leading to greater democratization of entry into high-tech entrepreneurship.  In affected sectors, VCs increased their investments in startups run by younger, less experienced founding teams.  These firms were subsequently more likely to fail. But among those who earned a second round of funding – a indication of initial success – they had nearly 20% higher increases in value across rounds than equivalent startups in untreated sectors, and ultimate exits generated greater returns. In other words, these companies were more likely to fail, on average, but the ones that succeeded did so more dramatically.

An interesting implication of our results is that VCs seem to have provided less guidance during the earliest part of a startup’s lifecycle — when that guidance is arguably most needed. Moreover, younger and less experienced founders likely need the most mentorship and governance. Yet the VCs’ desire to make a larger number of smaller investment, implies that earlier startups with younger founders only get financed with limited mentorship and governance. This finding helps to explain the rise of new financial intermediaries such as accelerators (which have emerged in the last decade) that provide scalable, lower cost forms of mentorship to inexperienced founding teams. These are a natural response to the gap in value add created by the evolution of VCs’ investment behavior in early rounds to a more passive “spray and pray” investment approach.

Our work shows how a technology shock actually alters the funding landscape, shifting which future ideas get funded and how.  A new innovation may open up a whole new range of opportunities, but also necessitate new ways of funding them.  In the case of the innovation of cloud services, some venture investors moved toward a “spray and pray” investment style placing investments into more long-shot bets.  Thus, our findings suggest that an initial technological innovation increases the pace of future innovation by allowing even greater experimentation.

This continues today. Innovation is decreasing the cost of experiments in areas as diverse as biotech, hardware, and agriculture, resulting in the need for new sources of small amounts of capital such as angel investors. Simultaneously, the falling cost of experimentation explains the anecdotal view that some VCs are waiting longer to invest and expecting startups to have accomplished more. In a market with many more small experiments, some investors will choose to wait to see the outcome before investing more. Innovation that affects the costs of experiments creates a financing market with many smaller investors in less well-funded startups and larger investors who are naturally investing after startup’s achieve commercial traction.

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The debate about superstar firms and superstar effects has been intensifying, partly in response to the rapid growth of global US tech companies. However, scratch the surface and the superstar phenomenon may not be quite what it seems. Wider dynamics may be at play.

In our recent research at the McKinsey Global Institute, we examined the superstar phenomenon across firms, as well as sectors and cities. We define superstar to mean a firm, sector, or city that has a substantially greater share of income than peers and is pulling away from those peers over time.  Yes, we found that a superstar dynamic is occurring for firms, cities and, to a lesser extent, sectors. In this article, we will focus mostly on firms, but with some brief commentary on sectors and cities at the end.

We analyzed nearly 6,000 of the world’s largest public and private firms with annual revenues above $1 billion. These firms make up two thirds of global corporate pretax earnings (EBTDA) and revenues. To analyze the superstar dynamics of firms, our metric was economic profit, a measure of a firm’s profit above and beyond opportunity cost. (To do this, we take the firm’s returns, deduct the cost of capital, and multiply by the firm’s total invested capital.) We focus on economic profit rather than revenue size, market share, or productivity growth because these other metrics risk including firms that are simply large and may not create economic value.

The top 10% of the firms we analyzed — the superstars by our metric — create 80% of all the economic value in our sample, meaning they account for 80% of the economic profits created by firms above a billion dollars of revenue. The top 1% accounts for 36% of all the economic value created by public and private corporations worldwide in this size range. The bottom 10% destroy roughly as much economic value as the superstar firms create. The distribution of economic value is also getting more skewed over time, and at both ends. Superstar firms create 1.6 times more economic profit on average today compared to 20 years ago. But this is also mirrored by firms in the bottom 10%, which account for 1.5 times more economic loss today than 20 years ago.

Contrary to popular perception, these superstar firms are not just Silicon Valley tech giants. They come from all regions and sectors and include global banks and manufacturing companies, long-standing Western consumer brands, and fast-growing U.S. and Chinese tech firms. In fact, both the sectoral and the geographic diversity of superstar firms is greater today than 20 years ago. The superstars tend to be more involved in global flows of trade and finance, more digitally mature, and they dominate the lists of the most valued companies, the most valued brands, the most desirable places to work, and the most innovative companies.

But uneasy should lie the head that wears the crown: Nearly half of superstar firms are displaced from the superstar top decile in every business cycle. Among the top 1% today, two-thirds of firms are new entrants that were not in the top 1% a decade ago. The high degree of churn among superstar firms cuts both ways: when superstar firms fall, 40% of them fall to the bottom decile with large economic losses; at the same time many firms have also risen from the bottom decile, in some cases all the way to the top. The rate of churn at the top has remained the same over the last 20 years.

A few key characteristics distinguish superstar firms from the rest, that perhaps others could learn from. They spend 2-3 times more on intangible capital such as R&D, have higher shares of foreign revenue, and rely more on acquisitions and inorganic growth than median firms. The greater economic profit and loss at both ends of the distribution is driven by greater scale and invested capital, not by increasing returns to capital. Some bottom-decile firms share many of these characteristics, such as size and even investments, suggesting that size alone is not sufficient; what sets superstar firms apart is their ability to select and execute on their bold investments well.

Superstar dynamics go beyond firms and can be observed among cities too, and to a lesser extent among sectors. We find that a handful of sectors account for 70% of value added and surplus across the G-20 group of major economies. These “superstar” sectors include financial services such as banking, insurance, and asset management, professional services, internet and software, real estate, and pharmaceuticals and medical products. The disproportionate gains to these sectors is in contrast to the previous 15-20 years when gains in surplus and value added were more widely distributed across sectors of activity. Today’s superstar sectors tend to have higher R&D intensity, higher skill intensity and lower capital and labor intensity than other sectors. The higher returns in superstar sectors accrue more to corporate surplus more than labor and flow to intangible capital such as software, patents, and brands.

For cities, we analyze nearly 3,000 of the world’s largest cities by population that together account for 67% of global GDP. Using our metric of GDP and personal income per capita, we identify 50 top superstar cities. They include cities such as Boston, Frankfurt, London, Manila, Mexico City, Mumbai, New York, Sao Paulo, Sydney, Tianjin, and Wuhan. These 50 cities account for 8% of global population, 21% of world GDP, 37% of urban high-income households, and 45% of headquarters of firms with more than $1 billion in annual revenue. The average GDP per capita in these cities is 45% higher than that of peers in the same region and income group, and this gap has grown over the past decade. The churn rate of superstar cities is half that of superstar firms. Often when superstar cities fall, they tend to be advanced economy cities, replaced by a developing economy city.

The link between superstar firms, sectors, and cities is complex. Some superstar firms benefit from being in “superstar” sectors of activity, particularly those in which value-added gains go to gross operating surplus (an economic measure that represents the income earned by capital). Yet many superstar firms endure even as their sector sees declining shares of value added and surplus. “Superstar” sectors’ gains tend to be more geographically concentrated, and mostly in large cities, many of which are superstar cities. For instance, gains to internet, media, and software activities are captured by just 10% of U.S. counties, which account for 90% of GDP in that sector. These cities see faster income growth than population growth, resulting in demand for high-skill, high-wage workers and limited supply—an escalating war for talent. Superstar firms and sectors also create strong wealth effects for investors, asset managers, and home owners, and these wealth effects are also concentrated among superstar cities.

While more research needs to be done to understand the full implications of superstars in the global economy, we believe enough evidence exists to give corporate decision makers some food for thought. Superstar status remains contestable, it’s easy to fall from the top, and possible to rise — even from bottom all the way to the top. Size matters, but it is not enough; value creation matters more than size for its own sake. Productivity can help; but it is not enough to achieve superstardom. Being in the right sector and geography can help; but this too can be overcome. Acquisitions, bold investment in intangible assets, and attracting talent can ultimately make the difference.

This post has been updated to clarify that the statistics regarding top firms’ share of profits are as a percentage of profits by firms with a billion dollars or more in revenue.

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The growing availability of real-world data has generated tremendous excitement in health care. By some estimates, health data volumes are increasing by 48% annually, and the last decade has seen a boom in the collection and aggregation of this information. Among these data, electronic health records (EHRs) offer one of the biggest opportunities to produce novel insights and disrupt the current understanding of patient care.

But analyzing the EHR data requires tools that can process vast amounts of data in short order. Enter artificial intelligence and, more specifically, machine learning, which is already disrupting fields such as drug discovery and medical imaging but only just beginning to scratch the surface of the possible in health care.

Let’s look at the case of a pharmaceutical company we worked with. It applied machine learning to EHR and other data to study the characteristics or triggers that presage the need for patients with a type of non-Hodgkin’s lymphoma to transition to a later line of therapy. The company wanted to better understand the clinical progression of the disease and what treatment best suits patients at each stage of it. The company’s story highlights three guiding principles other pharma companies can use to successfully deploy advanced analytics in their own organizations.

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Generating meaningful hypotheses (and organizational buy-in) requires engaging the right stakeholders. While the impulse might be to rush straight to the data and begin analysis, a critical preliminary step is to lay out the key business questions that must be answered and generate hypotheses. Building a comprehensive list of addressable hypotheses will allow the analytics team to determine which types of data will be necessary to test and prove (or disprove) the hypotheses.

It’s important to pull in the perspectives of key stakeholders on functional teams across the business to ensure hypotheses incorporate the right expertise and provide the highest value to the company. This also helps build buy-in and trust in analytics.

In this case, the pharma company brought in teams from its brand, medical, and business intelligence groups to generate hypotheses on the likely predictors that patients would have to move from one therapy to another and the triggers of those transitions. For example, in trying to hypothesize what drives fast or slow disease progression, the medical group contributed its clinical understanding of the disease, the brand team offered its detailed understanding of the company’s treatment offerings and how physicians use them, and the business intelligence team presented the analytical methods and datasets it had already used to shape the current understanding of treatment and disease courses.

The best data set might be a combination of data sets. It’s critical to identify a data set that is extensive and rich enough to properly train a machine learning algorithm. This is especially true in oncology, where a large number of variables — including age, gender, diagnosis history, medication and treatment history, laboratory values, and hospital encounters — collected on many patients over a sufficiently long historical stretch are needed for an effective analysis.

The pharma company’s analytics group realized that its internal data didn’t capture the variables likely to predict patient transitions in sufficient depth. The group therefore pursued a strategy in which it used internal and external data, combining an oncology-specific, integrated, structured EHR data set with some analysis replicated and validated on claims data.

All the data were stitched together and fed into an automated-feature-discovery (AFD) machine learning engine that allowed the company to test millions of hypotheses within hours. The engine explored every possible variation of the patient data to see if any variables had a statistically significant correlation with the transition to a later line of therapy. The insights gleaned from subject-matter experts helped ensure that the AFD results were clinically relevant. For example, when results indicated that an elevated liver function marker correlated with disease progression, medical officers confirmed that, although it wasn’t a factor they’d previously considered, it was clinically possible.

Feedback loops (many times over) are the key to great results. An iterative test-and-learn process is critical to developing an accurate model. The pharma company’s analytics group tested more than 200 lab values, major chronic comorbidities, and elements of medical history. Machine learning helped identify and isolate the critical variable combinations that predict transitions. Models were validated and refined to avoid noise and reduce the number of variables.

After weeks of iteratively learning and validating, a model was successfully developed to predict progression from initial diagnosis to later lines of therapy. Specifically, machine learning was used to extract features and triggers from the patient’s treatment, lab, and medication history, and the validated features were used to score and rank patients by expected likelihood of transition.

The models uncovered many critical insights, including:

Abnormalities in select lab results, such as the elevated liver function marker, increased the likelihood of a patient transitioning to the next line of therapy by in as much as 140% in some cases. Patients on maintenance therapy were 20% less likely to transition to the next line of therapy.


With the right data, organizational processes, and clinical knowledge applied, machine learning and artificial intelligence can make a significant difference in pharma and health care today despite some limitations that still exist. It can, for example, be difficult to understanding why some complex models come to their conclusions and labeling the massive datasets required for the hungriest models can be haltingly laborious.

However, limitations like these are currently being addressed, with techniques like LIME (local-interpretable-model-agnostic explanations) helping to show model reasoning, and efforts are underway to use machine learning itself to label datasets. As limitations lift, the opportunities for pharma and health care will greatly expand. Those companies that have already begun leveraging machine learning will have the established base of infrastructure and processes needed to take advantage of these opportunities.

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An old saying sums up the data skills of most HR professionals: “The shoemaker’s children go barefoot.”

In today’s tightening labor market, HR leaders must work relentlessly to develop and recruit people who advance digital transformation across their organizations. Yet most have struggled to advance their own digital competencies. This neglect has hindered their ability to leverage data into talent strategies that can help transform their businesses.

We base this claim about HR’s digital skills gap on the results of our latest global leadership survey. Co-produced by our three organizations, the survey gauged nearly 28,000 business leaders across industries about the state and trajectory of leadership. Among the findings: On average, HR leaders lag far behind other professionals in their ability to operate in a highly digital environment and use data to guide business decisions.

It comes as no surprise that this skills gap has spurred a credibility gap between HR professionals and their colleagues. Only 11% of business leaders trust HR to use data to anticipate and help them fill their talent needs. When we last fielded the same survey three years prior, 20% of business leaders felt that way — still a low number, but nearly twice what it is today.

Finding ways to improve HR’s digital acumen and data skills can challenge even the most well-resourced companies. HR leaders can start by upskilling their teams in areas that impact two critical business outcomes: building bench strength and tying HR metrics to financial success. To achieve both, companies can support their HR leaders in taking these steps:

Forge internal partnerships. At most companies, other departments use data and technology in ways that HR could apply to their own work. For example, HR can work with marketing for guidance on search engine optimization (SEO), a skill that can help HR improve its recruitment efforts. They can also consider partnering with colleagues proficient in finance technology for guidance about blockchain, a technology capable of transforming how HR stores and verifies private employee data. Such internal collaborations may not only help HR attain new skills, but also help to foster a data-driven culture across the organization.

Map talent analytics to business outcomes. HR should learn how to tie its data about people to performance and business outcomes. This process must begin with gathering data about the skills, capabilities, and behaviors of the existing leaders and workforce, often done through assessments. For example, a hospital seeking to improve patient safety might look to HR to discover that the highest rates of patient safety are tied to nurse units where supervisors showed specific behaviors, such as demonstrating empathy. By collecting data on employee skills and experience and tying it to business outcomes, HR can highlight key areas of risk and opportunity for the company.

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Develop data visualization skills. Simply collecting data and analyses won’t help HR leaders advance their efforts unless they know how to leverage that data to influence others. One study found that when presenters supplemented their stories with visuals, audience members had around a 40% greater likelihood of taking the desired course of action versus those who received non-visual presentations. As such, HR should learn how to create graphical presentations of data. HR needs to get more proficient with sophisticated software programs such as Power BI, Tableau, or R Studio, all of which give visual context to data.

Implement leadership planning models. Beyond using data to highlight current talent trends and gaps, HR should use it to fuel predictions about future talent needs, especially for leadership positions. HR professionals should employ leadership planning models to map a business’s long-term strategic plan to the leaders it will need to implement that plan. Leadership planning models enable HR to create data-driven projections for the quantity of leaders needed, the skills they will require, and where they will be located. On an ongoing basis, these models can compare the leadership talent it has against what it needs. As such, HR can course-correct when necessary by revising or shifting its priorities among hiring, development, and performance-management systems.

Taking these four initial steps can yield big dividends. Our research shows that companies excelling in using data and analytics to drive their talent strategy are more than six times more likely to have a strong leadership bench. Moreover, those with the strongest digital leadership capabilities outperform their peers by 50% in a financial composite of earnings and revenue growth.

And when HR executives use their digital savviness to advance their companies, they often move up themselves as a result. We found that HR professionals who leverage advanced analytics are over six times more likely to have opportunities to climb the corporate ladder.

Today, unemployment stands at the lowest level in nearly five decades. As the economy continues growing and Baby Boomers retire in droves, the labor market will further tighten and increase the pressure on HR. These demographic and economic dynamics will push HR to be better, faster, and smarter about how it finds and develops the talent their organizations will need to execute their business strategy. Investing in developing HR’s data and technology skills should be a top priority if companies want to win the war for talent.

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Innovative. Agile. Collaborative. Bold.

Over the past year, we’ve been struck by how many times we’ve heard C-suite leaders use these words, or very similar ones, to describe the strengths they believe are critical to transforming their businesses, and to competing effectively in a disruptive era.

What’s equally striking is how difficult organizations are finding it to embed these qualities and behaviors in their people. That’s because the primary obstacle is invisible: the internal resistance that all human beings experience, often unconsciously, when they’re asked to make a significant change. Cognitively, it shows up as mindset — fixed beliefs and assumptions about what will make us successful and what won’t. Emotionally, it usually takes the form of fear.

The complexity of the challenges that organizations face is running far out ahead of the complexity of the thinking required to address them. Consider the story of the consultant brought in by the CEO to help solve a specific problem: the company is too centralized in its decision making. The consultant has a solution: decentralize. Empower more people to make decisions. And so it is done, with great effort and at great expense. Two years pass, the company is still struggling, and a new CEO brings in a new consultant. We have a problem, the CEO explains. We’re too decentralized. You can guess the solution.

The primary challenge most large companies now face is disruption, the response to which requires a new strategy, new processes, and a new set of behaviors. But if employees have long been valued and rewarded for behaviors such as practicality, consistency, self-reliance, and prudence, why wouldn’t they find it uncomfortable to suddenly embrace behaviors such as innovation, agility, collaboration, and boldness?

When we feel uncomfortable or stressed, we tend to double down on what has worked for us before. Overusing any quality will eventually turn into a liability. Too much prudence congeals into timidity. Overemphasizing practicality stifles imagination. Consistency turns into predictability.

Most of us tend to view opposites in a binary way. If one is good, the other must be bad. Through this lens, the only alternative to prudence is recklessness. If you’re not being practical, you’re being unrealistic. Both invite failure. Also, if you value a quality such as prudence, it’s easy to confuse its positive opposite — boldness — with its negative opposite — recklessness, which is precisely what prudence is designed to protect against.

What we don’t see is that it makes more sense to balance practicality and innovation, boldness and prudence, collaborativeness and self-reliance, agility and consistency — without choosing sides between them.

But it’s difficult to balance these qualities. We see this play out over and over in two contrasting styles of leadership. The challenging leader constantly pushes his people to stretch and grow, but under stress, he can be overwhelming, and even brutal. The caring leader makes people feel safe and valued, but may resist pushing them beyond their current comfort zones, and doesn’t always hold them accountable. The challenging leader tends to confuse caring with coddling, while the caring leader may feel challenging people is tantamount to cruelty.

This same phenomenon operates not just individually, but also organizationally. We worked with a venture capital firm that took pride in differentiating itself from competitors by building its culture around collegiality, care, and consensus. Sure enough, all voices were heard around decisions, and employees treated one another with consideration and respect. The problem was that these qualities were so overused that they prompted paralysis in decision making and an aversion to providing honest feedback, leaving employees feeling uneasy about where they stood.

It isn’t possible to truly transform a business without simultaneously transforming its people. This requires understanding and exploring the complex factors, both cognitive and emotional, that drive their behavior. Attention to people’s inner lives is rare for most companies, but we’ve found several moves that help make it possible.

Embrace intermittent discomfort. Our shared human instinct is to avoid pain at any cost, but growth requires pushing past our current comfort zone. To strengthen a bicep, it’s necessary to lift weight repetitively nearly to muscle failure. That’s what signals the brain to build more muscle fiber. The same is true of challenging ourselves to become more resilient emotionally, and less rigid and habitual cognitively. Focus first on building the muscle of self-observation, individually, and collectively across the organization. Self-observation is the capacity to step back from our thoughts and emotions under duress. We refer to this as the “Golden Rule of Triggers”: Whatever you feel compelled to do, don’t. Instead observe your internal experience with curiosity and detachment, as you might the action in a movie, or the behavior of strangers.  Rather than reacting, take a deep breath, and then ask yourself “How would I behave here at my best?” Design small, time-limited tests of the assumptions you hold about the negative consequences you imagine if you build a specific new behavior. Does setting aside specific times to think creatively and reflectively truly prevent you from getting urgent work accomplished, or might it lead to new ideas, more efficiency, and better prioritization? Does going out of your way to be appreciative require that you give up your high standards? Conversely, does providing tough feedback in real time have to feel unkind, or can it be delivered honestly as encouragement to grow?

Einstein was right that “we can’t solve our problems from the same level of thinking that created them.” Human development is about progressively seeing more. Learning to embrace our own complexity is what makes it possible to manage more complexity.

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