Accelerating Growth and Business Performance in Middle Market Companies

About This Report

Analytics is not new, but it is only now coming into its own as a business capability and strategic weapon.

In a January 2000 Harvard Business Review article, Thomas Davenport, the President’s Distinguished Professor in Management and Information Technology at Babson College, described how a handful of companies including Amazon, Harrah’s, Capital One, and the Boston Red Sox, were at the time “dominating their fields by deploying industrial-strength analytics across a wide variety of activities.”1 These pioneers were among the first to learn and exploit the value of using data to make better and more strategic business decisions and propel themselves to the head of their industries.

Since then, the analytics industry in the United States has grown to employ roughly three million people, according to estimates by PricewaterhouseCoopers and others. International Data Corporation (IDC) puts the total market for analytics-related hardware, software, and services at $189.1 billion. Many companies have learned to use analytics as a strategic weapon, wielding the insights gained to innovate offerings, improve processes, and “outsmart” their competitors. As business analytics increasingly becomes a “must have” for organizations that wish to remain competitive and make more informed decisions across all areas of the business, these numbers will continue to escalate.


This report represents one of the first outcomes of the Center’s ongoing and comprehensive exploration of digital transformation in the middle market. It illustrates how analytics is an essential capability within the Digital Transformation Framework presented in The Case for Digital Transformation. As this report demonstrates, analytics has implications for every element of the framework and can help inform decisions at the enterprise level as well as within each of the functional areas of the business.


To better understand the analytics landscape in the middle market, the National Center for the Middle Market worked with its partners and Ralph Greco, Senior Lecturer and the Director of the Nationwide Center for Advanced Customer Insights, The Ohio State University Fisher College of Business, to query 1,000 middle market CEOs, CFOs, and other C-suite executives about the importance of analytics to their business and the analytics capabilities and tools they deploy. We asked about the kinds of business questions they answer with analytics solutions and explored the challenges associated with adopting and using the technologies. These questions were part of the Center’s quarterly Middle Market Indicator survey.

This report shares the results and explores the characteristics of strategic analytics users as well as the impact of analytics on growth and business performance.


Analytics unlocks stronger middle market performance


    There is a strong positive correlation between company growth and the use of analytics solutions. There is also, however, a large gap between current and potential use of analytics in the middle market.

    Analytics investments in specific areas (such as workforce optimization or marketing effectiveness) produce demonstrable performance improvements in those areas.

    Middle market companies that use both outward-facing tools (demand planning and forecasting, marketing effectiveness, etc.) as well as inward-facing tools (such as workforce optimization, quality improvement) enjoy faster growth rates than firms that use only one type or the other.

    Formidable resource, cultural, and talent challenges stand in the way of full-force adoption of analytics, but these difficulties become significantly less pronounced as companies gain experience. Poor quality data is also a formidable obstacle, leading to both direct and opportunity costs for businesses. But good data has the potential to transform business performance.


Analytics is an essential capability within a middle market company’s digital transformation journey. For this report, we have adopted the definition from INFORMS, the world’s largest professional association dedicated to best practices and advances in operations research, management science, and analytics. The organization describes analytics as “the scientific process of transforming data into insights for the purpose of making better decisions.”

The INFORMS definition of analytics includes three noteworthy elements:

  • Science: Analytics follows the scientific method of question-hypothesis- experiment-observation-analysis-conclusion.

  • Insights: Analytics transforms data into insights—“aha!” moments that reveal something not known before.

  • Actions: Those insights lead to decisions that are better than they would have been without analytics.

Analytics capabilities are themselves agnostic. Their value emerges when they are used to advance a defined strategic purpose and, as INFORMS says, “empower an organization’s vision.” Given the investment in technology and talent analytics requires, middle market executives must be especially mindful of how they allocate their limited resources to make sure they are investing in tools that will help them make smarter decisions about key issues for their businesses. Analytics makes sense when it makes an impact.

Consider the New England Patriots, a middle-market-sized organization that uses analytics in novel and effective ways to help build and maintain a championship-caliber football team. The Pats have developed a proprietary database that the team uses for scouting purposes and to assess the effectiveness of its individual scouts. The organization also employs the NFL’s only Player Personnel Engineer; when the team found the right candidate to staff the role, it was someone who was also entertaining an offer from Google. While the Pats’ use of analytics in its business office has been heralded in numerous publications, during an interview early in the 2019 season, Coach Bill Belichick said he relies on analytics “less than zero” when making on-field decisions like whether or not to “go for it” on fourth down. This comment might have been somewhat disingenuous—hiding the football, so to speak; Belichick is, after all, the recipient of a lifetime analytics achievement award from MIT. Still, when he contends that “football is ultimately determined on the field,” he is highlighting a crucial point: Analytics is an input to decisions, not the decision itself. Teams still need strategy and leadership to win.

In the game of business, too, analytics appears to affect results, particularly when executives use its insights strategically. Overall, the more a middle market company invests in analytics, the faster it grows. And when businesses use analytics for clear purposes—optimizing the workforce, demand planning, or optimizing revenue from a new product launch, for example— those areas of the business improve. Analytics also positively affects operational competencies, resulting in improved machine uptime, quality control, and supply chain management, as well as risk and compliance. And the benefits of analytics appear to compound; companies that use analytics for both outward-facing purposes (to gauge marketing effectiveness, for example) and inward-facing purposes (i.e. workforce optimization or quality improvement) grow faster than peers that use analytics for just one objective or the other. Heavy analytics users also predict much more aggressive growth for the year ahead than companies that use analytics sparingly or not at all.

However, there is an enormous gap between the current use and value of analytics and its potential contribution. Most middle market companies use at least one type of analytics tool. However, no one capability or tool type that we explored in our study is currently in use by a majority of all middle market firms. Embracing analytics full force, it appears, may take more effort than many companies have been willing or able to give. Indeed, many executives express desire to use more tools and expand their analytics capabilities, but issues such as data quality, resources, culture, and skills stand in the way.

Still some companies have found a way: Every one of the tools and capabilities in our study is currently in use by 50% or more of the subset of middle market organizations that consider themselves to be digitally strategic firms. And the performance of these companies consistently outpaces their peers.

By exploring solutions to adoption challenges, pinpointing the business goals where analytics can deliver the greatest value, and investing strategically, middle market organizations can unlock performance gains, increase profitability, and accelerate growth.

Overview: Middle Market Companies Use Analytics to Inform a Wide Range of Business Questions

Analytics can be used to study anything for which data exists. As this framework from MIT Sloan Management Review illustrates, the science can be leveraged for operational or strategic purposes and can be used to monitor and control processes and systems, or to provide input and context for decisions. Most companies first employ analytics in operational areas, to monitor and control equipment and processes. As they gain experience, executives discover opportunities to use analytics to gain insights that shape strategic decisions. The more uncertainty exists around a decision, the more valuable analytic input becomes. Indeed, many experts suggest that analytics should always be action-driven, providing insight for the purpose of making better decisions.

The Center looked at the middle market’s use of analytics across a number of inward-facing, outward-facing, and foundational analytics tools and capabilities and explored how analytics can help business leaders answer questions within each area with a greater degree of accuracy.

Most firms use analytics now and plan on using more in the near future


Most middle market leaders believe analytics is important to business and will become increasingly so over the next five years. The vast majority (85%) of middle market companies currently use analytics to some degree; and most have adopted multiple tools. Analytics is most popular for outward-facing activities, such as demand planning, forecasting, and marketing effectiveness. However, no one capability or tool is currently in use by a majority of middle market companies.


Many companies plan to begin using analytics technologies within the next 18 months. But for now, it appears that most mid-sized companies are in the beginning phases of analytics use or are focusing on one area or another rather than deploying tools across many activities. Even foundational tools like business intelligence reporting and data visualization are only in use by a little more than a third of middle market organizations, and roughly another 30% have no plans to pick up these foundational tools in the near term, which may put them at special risk of falling behind in their digital journeys.

Industry and company size shape analytics adoption and priorities

Middle market companies benefit from analytics in different ways and, accordingly, make different choices about what capabilities to use. Some of these decisions depend on the type, size, and digital attitudes of a firm:

  • The digital intensity of the organization appears to be the single greatest factor in analytics use. Companies that say their digital vision is clear and comprehensive are by far the heaviest users of analytics. Among this subset of firms, every one of the analytics solutions we explored is in use by a majority of the firms.

  • Industry, too, has a profound impact on analytics use and specifically influences which business questions are answered and the types of tools deployed to glean those insights.

  • Larger middle market firms are much heavier users of analytics than their smaller peers.

  • Younger companies—those born in the digital era—are no more likely to be analytics users than older ones.


Digitally strategic firms combine vision with the right toolkit to drive growth

While size and industry correlate to analytics use, another factor supersedes these: the digital intensity of the organization. Across revenue and industry segments, middle companies that have a clear, comprehensive digital vision to guide strategic decisions grow, on average, 75% faster than less digitally sophisticated peers.

Companies with this vision and those that are pursuing digital transformation as a strategic priority place much greater importance on business analytics than others. They use more tool types and they are heavier adopters of each type of tool they use. Among those companies saying their digital vision is clear and comprehensive, every one of the analytics solutions we explored—inward-facing, outward-facing, and foundational—is in use by a majority of the firms. These companies use technologies not only to drive efficiencies or automate manual processes, but also to better connect with and serve customers and set themselves apart from the competition.


While analytics drive growth and value, there are challenges to adoption

As we’ve seen, the use of analytics tools by middle market firms, as well as the specific types of capabilities used, vary considerably based on a firm’s digital intensity, industry, and size. While most middle market companies are at least dabbling in analytics and many have plans to expand use, the majority of mid-sized companies can be categorized as moderate analytics users; they have ground to cover to fully exploit the capability and achieve a state of analytics maturity. The exception to this is the subset of digitally-strategic firms, which have found ways to integrate the power of analytics throughout their businesses. The next section of this report will focus on insights related to the impact of analytics as well as the challenges middle market companies must overcome in order to increase analytics use and take advantage of the benefits that come with it.


Analytics and growth go hand-in-hand


Middle market companies growing revenue at 10%+ per year are more likely to place high priority on business analytics than slower-growing peers. The correlation runs both ways: Businesses that say analytics is extremely important boast a revenue growth rate that is more than double the rate of companies that pay little attention to their data. And they add employment nearly four times faster.

Current use of analytics will fuel tomorrow’s growth

Fast growers use more analytics tools. They are especially more likely to use business intelligence reporting, a foundational tool, and somewhat more likely to have invested in many of the inward- and outward-facing analytics technologies.

Pronounced differences show up when looking at growth projections. Middle market companies with the most aggressive revenue growth expectations are much more likely than others to be current users of outward-facing analytics capabilities. This suggests that middle market leaders have a high level of confidence in the insights they are gaining from their tools and the power of those insights to generate sales.

Those with the highest growth projections also use inward-facing analytics much more than their peers. The implication is that leaders of these companies have a more sophisticated understanding not just of their market, but also of the resources they need to mobilize to satisfy demand.


Specific types of analytics tools improve core functional capabilities

Use of analytics appears to strengthen capabilities within the functions where it is used. For example, workforce optimization analytics makes companies stronger in many dimensions of talent management; marketing analytics improves both marketing and sales effectiveness.


Workforce optimization helps companies get the most from their employees by having the right labor at the right place and time to meet the needs of customers and the business. Workforce optimization can include tools for helping managers develop staffing plans and determining the best times to schedule certain types of work, such as restocking or receiving inventory. It can help plan seasonal work. It can help with assessing employee performance while also driving higher employee satisfaction through tools that monitor employee engagement and help promote work-life balance. And it can help companies better respond to and prepare for the ways their customers want to do business. For example, in the retail industry, shifts to BOPIS (buy online, pickup in store) as well as BOSFS (buy online, ship from store) have required store staff to become pick/pack/ship experts. Retailers and other service businesses can also use analytics to determine the best mix of live, person-to-person interaction verses self-service activity and kiosks, such as offering customers the option of ordering different sizes off iPads in the changing room.

Like many tools, workforce optimization can be used for good or ill. Starbucks was heavily criticized in 2014 for its (software-driven) scheduling practices; but it has since made improvements, including adopting software that helps ensure employees get adequate breaks in between shifts.

Across all industries, companies that invest in workforce optimization analytics grow both revenues and employment faster than their peers and have much stronger predictions for future growth. They are also significantly more likely to rate themselves as very good or excellent on talent management metrics, including attracting top talent. However, even heavy analytics users report facing both long- and short-term talent management challenges. While analytics can and does help, companies still need strong leadership and vision to address strategic issues related to talent, including upskilling, outsourcing, and developing forward-looking versus reactive talent plans.


Customer-facing analytics is a complex set of outward-facing tools that includes demand planning and forecasting, sales and marketing effectiveness, and churn prediction and prevention. These tools allow companies to understand customer behavior with unprecedented detail, identify the most profitable customers and those most likely to jump ship, and make smarter marketing plans and pricing decisions tailored to different customer segments.

Netflix provides a textbook example. A digitally-born company, Netflix used analytics from the get-go to disrupt and transform the entertainment industry, almost singlehandedly putting Blockbuster out of business before moving into streaming content, and finally, original content generation. Today it uses what it learns about customers through analytics to gain and retain subscribers by suggesting microtargeted genres—they have created 76,897 different ways to describe types of movies specifically for this purpose!—and pretest series ideas to ensure their success.

Middle market companies, too, perform better when they leverage customer-facing analytics. The online stylist, Stitch Fix— which recently graduated from the middle market—has made it a critical component of its business model. Founder and CEO Katrina Lake wrote in a 2018 Harvard Business Review article, "The part of me that loves data knew it could be used to create a better experience with apparel." She built her business around data with a human touch. Customers supply Stitch Fix with millions of data points each year by completing an initial style profile and subsequent reviews of each order. This information, along with data and measurements on each garment, are fed into a set of algorithms. The results are interpreted, reviewed, and approved by a stylist, who is algorithmically matched with each client. The combination of the right data and the right expertise allows the company to successfully deliver personalized boxes of clothing and accessories to each of its two million-plus clients, driving the company’s revenues up each year.

Like Stitch Fix, middle market companies that use customer-facing analytics grow faster than those that don't. And they self-rate themselves as more adept at customer acquisition and business performance competencies.


Another outward-facing tool, revenue optimization analytics focuses on maximizing revenue over the long term by looking at the relationships between pricing, inventory, demand, and distribution. It seeks insight into demand patterns and how pricing and other factors can help to influence and shape that demand. For example, retailers today use analytics to selectively distribute targeted ecommerce incentives. They use the data to understand what offers will entice which groups of consumers to sign up for their emails or rewards program and what offers are most likely to result in trial and repeat purchases. Over time, as they learn what a customer’s email address is worth and analyze the behavior of individual customers, they are able to refine these offers even more.

Such knowledge becomes especially important when launching a new product and making decisions about where, how, to whom, and for how much to sell it. Among middle market companies, over a quarter (28%) currently use new product revenue optimization analytics tools. That percentage rises to 40% among the largest middle market companies.

The insights these companies gain drive innovation and growth: Users are significantly more likely than peers who do not use revenue optimization analytics to have plans for introducing a new product service in the next year. They are more than twice as likely to have plans to increase their investment in R&D. They consider their businesses to be much more advanced than their peers in both innovation and customer acquisition.


Analytics increases in value when it is used organization-wide

Middle market companies have many options when investing their analytics dollars. As we’ve seen, many companies are beginners, testing the waters by choosing to start with one type of tool or another rather than deploying analytics full force across business functions and activities.

Where they start often comes down to the types of business questions a company wants to inform as well as the company’s overall objectives. Businesses looking to maximize revenue growth may naturally gravitate toward outward-facing tools. And the data suggest that’s a smart move: Companies that use only outward-facing analytics grow revenues considerably faster than business that use only inward-facing tools. On the other hand, companies looking to expand their workforce or improve labor productivity may fair better by choosing inward-facing tools. Employment growth is higher among companies that only use inward-facing analytics compared to those that use only the outward-facing set of tools.

Combining the tool types, however, may increase the return on investment in each of the individual tools. Businesses that invest in both outward- and inward-facing tools post the highest revenue and employment growth rates. This suggests that positive synergies exist between different types of analytics tools and that the value and impact of analytics compounds and accelerates in a virtuous circle. As we will show in the next section, the more analytics tools a company uses, the more it reduces the challenges to analytics adoption, and the more it improves overall data quality, ultimately making the outcomes more valid and more useful.

So, while many companies appear to want to start slow, it is worth considering the impact of a broader approach to analytics adoption: Use the right outward- and inward-facing tools (i.e. make an investment in analytics across your organization as opposed to one specific area, or, better, include analytics at the Enterprise level of your Digital Transformation framework) and you can position your business for improvement across multiple dimensions, unlocking the full potential impact of analytics as a strategic digital capability.


Challenges with adopting analytics fall into four categories: data, resources, culture, and skills

Growth and business performance data make a clear case for the adoption of analytics to improve both outward-facing and inward-facing decisions. However, no more than 50% of middle market companies currently leverages any one analytics tool; there is not a majority even for foundational tools like data visualization and business intelligence reporting. It’s not that executives don’t want access to data for analysis and action. So what holds them back? Middle market executives point to four groups of challenges standing in the way of analytics adoption: data, resources, culture, and skills.


Assessing and aggregating multiple sources of data is a challenge for companies of all sizes, but data quality issues appear to compound as a company grows. This is likely because larger middle market businesses have more customers, more employees, and, as a result, more data to manage. Quality issues can be as mundane as variances in how customers are listed—IBM, vs. I.B.M. vs. International Business Machines, for example—which can keep a company from seeing a comprehensive view of its business with a particular organization. Other problems, such as incompleteness, or, worse, inaccuracy, also detract from the value of analytics.


From our digitization trends study, we know that middle market companies are most likely to give themselves average scores in terms of digital readiness. Interestingly, large and fast-growing companies are more likely than their smaller and slower-growing peers to say limited IT resources hold them back—perhaps because they also are the companies with the biggest analytics appetite. As companies do more analytics, the costs also grow faster as strategic complexity, computational requirements, and the need for more talent escalates. Only 12% of all middle market companies point to budget as a deterrent to analytics adoption. But this percentage jumps to 20%, or 1 out of 5 companies, with revenues between $100M and $1B and 16% for companies growing revenues at an annual rate of 10% or more.


In general, companies that don’t see the business need for analytics, have a siloed as opposed to an organization-wide approach to technology, or look for the immediate ROI instead of appreciating the strategic, long-term benefit afforded by the insights, will have a harder time justifying an investment in analytics. In companies that just are not digitally intensive, the culture obstacle exacerbates all of the other challenges—data, resources, and skills—and makes each harder to surmount. Curiously, culture-related issues appear to be bigger challenges for faster-growing firms, particularly the ability to measure ROI. The explanation for this, too, might be a question of priorities all jostling for management and employee attention: Open a new facility or invest in analytics? Analytics can help companies make smarter growth decisions, but when you are head-down running the business, it can be difficult to look up to examine ways to change it.


Study after study of digital transformation shows that lack of digital skills is a challenge for middle market firms. Forty percent of middle market companies claim that talent gaps slow their adoption of analytics. The problem is most acute for the largest middle market firms, which, again, have the biggest appetites for analytics. Companies that are growing at a modest rate feel the greatest pinch when it comes to the skill level of their current workforce.

Analytics use can help solve analytics-related challenges

The more essential executives say analytics is, the more challenges they say they face in adopting and utilizing analytics solutions. Perhaps this is because they take a more strategic approach to analytics and, like teachers who demand more of a student with great potential, they ask analytics to do more complex things, and they place more value on the outcomes.

However, as companies integrate analytics into their businesses, using a greater overall number of tools as they do so, virtually all the challenges become less pronounced. It appears that the very act of using analytics can help with issues such as improving data quality, measuring ROI, and improving a company’s ability to aggregate its data. The more the data is used, the cleaner it becomes, and the more useful the outcomes, creating both a learning curve and a virtuous circle.

The major exception to this trend is the resources problem. The more analytics solutions a company adopts, the more resources it needs.

The High Cost of Bad Data

Data quality is an issue for middle market firms, regardless of which end of the revenue spectrum they fall on or how quickly they are growing. Middle market firms are not alone in this challenge. According to findings from Dun & Bradstreet’s B2B Marketing Data Report, only 51% of respondents are confident of the current quality of sales and marketing data, down from 75% the previous year. Findings from IBM data scientists reveal that one out of three business leaders don’t trust the data they use to make decisions and that more than a quarter are unsure of the “veracity” or accuracy of their data.

These misgivings are well-placed. According to 2017 Deloitte research, data provided by U.S.-based big data brokers is only 50% accurate at best. Across categories, including economic, vehicle, demographic, interest, purchase, and home, 71% of consumers judged that the data collected about them was somewherebetween dead wrong and only half right (0-50% correct).

The impact of bad data is significant. Gartner’s Data Quality Market Survey put the average annual financial cost of poor-quality data at $15 million in 2017. Inaccurate data can cause companies to miss opportunities or, worse, to offend or alienate their customers. In healthcare, inaccurate data can lead to errors in as many as one in four explanation-of-benefits claims, degrading the patient experience and leading to confusion and delayed payments.

Good data, on the other hand, can transform a business and give it insights to more accurately forecast demand, optimize revenue and workforce, target and build customer relationships, manage risk, identify fraud, and comply with regulatory requirements. Validating the quality of data, then, should be a key part of any company’s analytics journey.


Finding ways to embrace analytics is vital to continued growth

Analytics adoption does not always come easy to middle market businesses. Formidable challenges associated with a company’s data, available talent, culture, and resources stand in the way of widespread use of analytics in both outward-facing and inward-facing areas of the business, where the insights could surely lead to better decision making and, ultimately, improved performance and faster growth.

However, companies that take a slow or scattershot approach to analytics do so to their own detriment. Our data show that middle market companies that use analytics outperform those that don’t, and the more capabilities they use, the faster they grow and the better they cope with the challenges of using analytics.

Those middle market companies that have found ways to overcome the hurdles and put strategic analytics capabilities and tools into place have seen the impact in their top line revenue, their employment growth, and their key business functions. And they are anticipating even stronger growth in the year ahead, likely fueled in part by the knowledge they will gain from their data analysis efforts.

Next Steps in Your Analytics Journey

Middle market businesses looking to increase their investment in analytics, or to derive more values from the analytics investments they have already made, can start by taking the following steps:


    Several tools exist that will provide your company with its current analytics “maturity” rating, typically on a scale from 1 to 5. One such model is the Analytics Maturity Model from INFORMS. This assessment can be completed at no cost. The questions focus on three areas: Data, Skills, and Corporate Structure, which align with the challenges most middle market companies face in adopting analytics. Once you have completed the AMM, you can benchmark yourself against others in your industry to see what gaps exist. The results of this exercise can go a long way in gaining C-suite support for the use of analytics in your business and further investment in the area.


    Measure the skills of the employees currently working in analytics as well as the skills of the employees who will be utilizing the output of the analytics. This will take time and effort; however, it is key to ensuring success with analytics. Again, there are multiple tools available to assist in this step. Once you have an understanding of your in-house skills, you can better determine where you may need to upskill current workers or look for new hires. And you can start to flesh out a plan to build and maintain the digital skills your firm needs.


    Companies need to be vigilant about understanding data sources. This starts with determining what business questions you want to ask, and then seeing if data are available to help you answer them. Leaders then need to get a sense of how accurate a data set is before relying upon it to inform critical decisions. For most middle market firms, financial and systems data tends to be strong, especially data that needs to be reported per any regulatory requirements. Customer and HR data, however, can be weaker.

    Leaders can use their business acumen and expertise to help gauge if a specific data set is valid or not. If the insights you’re receiving seem “off,” consider evaluating your processes for how you collect and store data, looking for weak spots that could compromise its integrity. External data experts can help with your assessments as well as with putting processes in place to help improve overall data quality. Keep in mind, however, that just because the data don’t gel with what you were expecting doesn’t mean they’re wrong. Analytics could simply be doing its job—opening your eyes to issues that need to be examined.


    Since analytics use in and of itself appears to mitigate many of the challenges associated with adoption, simply getting started may be the best place to start. You’ll want to strategically select an analytics solution or two that can inform issues that are critical to your business and industry, and then ensure that you have the right skills and data available in order to effectively use these tools. Once you begin the journey, the benefits will compound from there in several ways. First, the more you use your data, the cleaner it gets. And second, you’ll begin to generate important insights that inform new business decisions, including where to best invest future analytics or digital dollars, ultimately driving analytics maturity and making your business smarter and stronger along the way.