How CEOs can get strategic business planning right, according to this Houston expert

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Companies rely on strategic planning. The problem: Many are doing it wrong. Photo via Getty Images

When Jeff Immelt took the helm at General Electric in 2001, he shifted the company’s strategy radically. Under his leadership, GE grew more inwardly focused, relying more on financial engineering and acquisitions in a bid to add revenue and cut costs. The company’s stock plummeted. Yet Immelt stubbornly stuck with what many saw as a failing strategy.

Strategic planning is a core activity for senior leaders, regardless of business size. Over 88 percent of all large companies and 80 percent of small to medium-sized companies engage in strategy planning. For CEOs like Immelt, strategic planning is one of their most important duties, and they take great pains to communicate company strategy to stakeholders.

But there’s a problem: many are doing it wrong. In the research for our new book, FOCUS: How To Plan Strategy and Improve Execution To Achieve Growth, we found that many CEOs have simply been mistaken in their approach to strategic planning. Contrary to popular belief, our research shows many CEOs fail to make their strategic decisions based on a systemic, science-based, statistical process. Instead, they rely on gut feel, emotions and salient information from past experience.

In this piece, the first in a nine-part series, I’ll discuss why this is a major problem. In upcoming articles, I’ll show how CEOs can get strategic planning right.

CEOs usually rely on strategic planning to set goals for their senior executives, define major initiatives, allocate and track resources across initiatives, create budgets and hold mid-level and frontline employees accountable. Strategies become the means through which a CEO sets goals, measures success, executes plans and communicates progress to the board and outsiders.

To be sure, strategic planning is a complex process and many CEOs agree current practices need improvement. Immelt, for his part, was unsuccessful at turning GE around in part because senior and mid-level executives weren’t persuaded that his proposed strategy was coherent or would work. As one insider said, “We just became too internally focused and lost touch with our consumers.”

Another example is Wells Fargo. In 2016, regulators fined the bank $185 million for opening around 1.5 million bank accounts and applying for some 565,000 credit cards that weren’t authorized by customers. The bank’s strategy and employee incentives emphasized maximizing sales through cross-selling to existing customers rather than providing customers with real value.

Like GE and other companies that rely on a budget-based strategy to drive sales, Wells Fargo’s strategic plan prioritized how internal activities affected revenue rather than the effects of those activities on customer value. The problem was not that Wells Fargo’s strategy was poorly executed – it was that the company followed it.

But what is it, exactly, that makes a strategy fail? When strategic planning goes wrong, our research indicates that it’s typically for two main reasons. First, planning can fail when executives craft strategies based solely on their gut feelings, intuition, emotions and salient beliefs — beliefs that are top-of-mind. When these salient beliefs form the basis of the company’s strategic priorities, mission, or vision, they become a vehicle for executives’ desires and aspirations.

Strategies based on executives’ salient beliefs often fail because they discount what’s important to create customer value – and customers are the largest component of a company’s cash flow. A company that relies on executives’ salient beliefs, by default, discounts customer value and simply can’t create a healthy and sustainable cash flow.

This is what happened at Wells Fargo, which began using the salient personal beliefs of its leaders to justify its cross-selling strategy. That strategy drove employees to open accounts rather than help customers, ultimately eroding customer value, sinking the company’s stock and resulting in fines.

The second reason why strategic planning often flounders is executives’ belief that if they simply ask customers what they want, the customers will seamlessly communicate exactly what’s important to them. That’s rarely the case. Instead, what customers state as their desire often differs starkly from what actually creates value for them.

Take, for example, the relationship between a doctor and a patient. A patient walks into their doctor’s office with a health issue. Imagine what would happen if the doctor asked the patient what medicine and tests they desired and prescribed them. Or imagine the patient simply insisting on certain tests and medications without being asked. In both cases, customers have effectively stated their desires and wants, but the doctor is unable to discern what would truly help the customer. It’s up to the doctor to perform tests and use accumulated statistical benchmarks to detect how best to help the patient.

Simply put, you cannot create customer value by simply fulfilling your customers’ desires and wants.

Companies need to use the same process – using science, statistical expertise and data – coupled with effective listening, to set a customer-based strategy.

What’s important for customer value, in other words, is typically not be obvious to customers themselves. More often than not, they lack the expertise, data and statistical expertise to state what they need in a conversation. Yet a surprising number of senior executives rely on such conversations or “listening exercises” to unearth surface-level desires and wants and use them to develop a strategy. Such a strategy is doomed to fail.

Often, adversity provides the opportunity to pivot. During the COVID-19 pandemic, for instance, companies and leaders have been forced to rethink and retool their strategic habits, forced to learn about what’s most important to customers.

This transformation can be powerful. When CEOs continue to evolve – embracing humility and no longer relying on past experiences, emotions or gut feelings – they can organize around the most important, rather just than the most salient, customer needs. They can simplify their plan. As a bonus, a cleaner, simpler strategy will be more engaging to employees.

CEOs can get strategic planning right. For companies willing to dedicate the time and resources to strategic planning, the research we describe in Focus: How to Plan Strategy and Improve Execution to Achieve Growth offers a road map of exactly how to do it.

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This article originally ran on Rice Business Wisdom and is based on research from Vikas Mittal, J. Hugh Liedtke Professor of Marketing at the Jones Graduate School of Business and author of “Focus: How to Plan Strategy and Improve Execution to Achieve Growth.”

Why relying on intuition can backfire when it comes to crafting a successful business strategy. Photo via Getty Images

Houston expert: Know when to trust your intuition — and when to think outside the box

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When a fast-casual restaurant chain started to see stagnating sales, the company’s CEO came up with a solution: adding new, higher-quality menu items.

To validate his thinking, he informally interviewed a few dozen diners at different locations, asking them how they would feel about more and higher-quality menu items. After getting enthusiastic responses from several customers, he went ahead with his plan. Sales fell further.

So what went wrong? Systematic surveys showed that what created the most value for customers was a fast dining experience with a short wait time, a simple menu with a few items, ample parking and a bill under $12 per meal. Higher quality and an expanded menu did not correlate at all with customer value. Where the company’s CEO went wrong was by relying on salience instead of importance.

Salience refers to factors that are top-of-mind and easy to recall, which become prominent and are then incorrectly prioritized. A classic example is a 1979 study that surveyed people about their perceived risk of dying from causes like drowning, murder or cancer. The study’s authors found that people thought they were more likely to die from causes that were mentioned more often in their local news, such as murder, when in fact they were at much greater risk of dying from common but less prominent causes such as cancer.

The CEO made food quality and expanded offerings salient to himself by talking about them to a small group of customers. It was an easy way for him to feel good about his efforts. But, like many executives, he relied on salience.

Salience is easy and convenient, but it’s also the curse of decision-making. It simply reinforces executives’ prior beliefs rather than diagnosing the true cause-and-effect relationship. Imagine if a doctor saw a patient with stomach pain and recommended an appendectomy because a patient she’d seen the day before needed one. Or if the doctor asked the patient to recommend the treatment himself. Patient outcomes would likely falter and the doctor would go out of business (and perhaps lose her medical license).

Thankfully, doctors don’t operate this way. They rely on the statistically measurable relationship between critical inputs and outputs for decision-making. So should senior executives and CEOs.

Informal customer conversations draw primarily on gut feelings, hunches and top-of-mind ideas, and as such, aren’t reliable indicators of true customer value. Like all of us, customers often tailor their responses to the audience they’re addressing. So a company’s vice president of service might speak with a customer who says they love the service, while the same customer might tell an HR executive they love the employees and then go on to tell the VP of sales that they would like lower prices. These on-the-spot responses typically have no significant impact on or statistical correlation with customer value, which ultimately drives sales and profits.

To craft a successful strategy, executives need to use a systematic, statistical process that starts with choosing a clear outcome or output, such as customer value or employee retention. The next step is to measure inputs that drive that output, and then quantitatively correlate each input to the output. Only those inputs that drive the desired output should be included in the company’s strategy.

Take, for example, a nursing home that attempted to craft a strategy for decreasing employee turnover. Relying on casual conversations with a few dozen employees, executives assumed higher pay would increase retention. They were wrong.

When they statistically correlated multiple inputs — higher pay, health benefits, supervisor respect, promotion opportunities and paid vacation — with retention, they realized their intuitive leaps had been incorrect. Only health insurance and promotions were correlated with increased employee retention. Higher pay had no effect.

Committing to this type of systematic review to drive strategy requires humility on the part of senior executives. The nursing home executives were able to look past their own assumptions and learn from this type of statistical analysis, recognizing the limits of salience-driven thinking and deferring to algorithms that could better predict the inputs of turnover than they could.

Doctors understand this as well. To treat their patients, they rely on data from groups like the Food and Drug Administration or the National Institutes of Health, which run clinical trials and rely on data, statistics and an infrastructure of knowledge.

Unfortunately, many senior executives lack humility when it comes to strategic planning. They equate decades of salience-laden thinking with a deep understanding of correlations between inputs and strategic outputs. They might think, “I’ve been in this industry long enough to know what works,” or “Since this worked then, it will work now as well.” But more often than not, relying on salience-laden intuition alone will not achieve the desired outcome.

“Focus: How to Plan Strategy and Improve Execution to Achieve Growth” lays out specific steps for senior and mid-level executives who want to follow systematic, statistical processes to drive their company’s strategy.

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This article originally ran on Rice Business Wisdom and is based on research from Vikas Mittal, J. Hugh Liedtke Professor of Marketing at the Jones Graduate School of Business and author of “Focus: How to Plan Strategy and Improve Execution to Achieve Growth.”

When business leaders make assumptions, they may overlook key factors driving desired outcomes. Photo via Getty Images

Houston research: Why business leaders should adopt a science-based approach to decision making

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Life is full of intuitive leaps. Whenever we make a judgment or choice based on past experience, limited examples or case studies, we make assumptions to fill in the gaps in our knowledge.

Consider someone who wants to lose weight. They might assume they only need to exercise for the pounds to disappear. They attribute 100 percent of weight loss to exercise, when in reality physical activity isn’t the only variable to consider. Instead, multiple factors could be at play, including diet, lack of sleep or even an underlying health condition.

When you make intuitive leaps, you may wrongly attribute success to a single factor, when in fact many different factors may be driving an outcome. In this case, making an intuitive leap rather than considering all the factors may not lead to the desired outcome: significant weight loss.

It’s the same in business, where intuitive leaps run rampant. All too often, executives make intuitive leaps that end up derailing their strategy planning and negatively impacting business operations.

Take, for example, executives at the nursing homes we studied while researching “Focus.” The nursing homes were experiencing high employee turnover they needed to correct. After speaking with a few dozen employees, executives thought that higher pay would cut back on turnover. They had made an intuitive leap, assuming that pay was the sole driver of turnover.

When executives stopped relying on intuitive leaps, they discovered many different factors causing turnover. They started to identify, analyze and prioritize these factors, which included promotion opportunities, respect from supervisors, flexible schedules and access to health insurance. Ultimately, they were successful at reducing turnover — and not by increasing pay. Had they relied on their intuitive leap, they would have spent money raising wages with no reduction in employee turnover.

Other businesses struggle with intuitive leaps, too. Often, the problem is that individual departments believe their lever is 100% responsible for solving a certain problem, such as lackluster sales. An HR executive might believe that to increase sales, the right solution is to get frontline employees more engaged. A sales executive, however, is adamant that the company has to hire more salespeople or adjust pricing. Someone in charge of product development might say product quality needs to be improved. The chief marketing officer may believe advertising will lift sales.

Intuitive leaps are unhelpful to strategic planning. In fact, they often lead to increased silos within a company. CEOs exacerbate this siloing tendency when they call for presentations from executives across departments on how they would contribute to strategy.

To stop making intuitive leaps, executives must accept that their department alone can’t fully inform or deliver a company’s strategy. They must realize and embrace the fact that multiple factors are almost always at play. This requires humility and the ability to look beyond their own department.

For executives, the first step is to identify all factors driving a company’s strategic goal — say, increasing sales. Factors impeding sales might include having too lean a sales team, a low-quality product, an inadequate marketing campaign or even lack of distribution.

Next, executives need to determine the relative weight of each factor in impacting sales. That’s where statistics come into play. Relying on statistical analysis rather than intuitive leaps tells executives how much weight each factor has in driving sales. To build a sound strategy, executives can rank the factors and focus their strategy on the top two or three. Almost always, the top two or three factors drive 70-80% of customer value.

Decades of research have shown how these types of statistical models are better than humans at capturing and quantifying how multiple inputs connect to and inform an output. Used correctly, they can also get rid of intuitive leaps.

In one study, doctoral program admissions committee members used inputs like test scores and grade point averages to select students. Years later, when predicting students’ success, researchers compared experts’ assessments to that of a statistical model.

The model better predicted success. It assessed the data in an unbiased way, while committee members selected candidates based on intuitive leaps, bringing their idiosyncrasies and biases to bear. It’s these types of models that make for effective corporate strategy.

Microsoft is a prime example of a company with leaders that consider multiple variables with an eye for prioritizing ones that drive customer value. Prior to 2014, when CEO Satya Nadella took the helm, CEO Steve Ballmer’s acquisition strategy was seen as more reactive than proactive. Nadella’s approach to acquisition was more “forward-thinking,” and he added to the company’s focus on the cloud and subscription services. He focused on providing tangible benefits to Microsoft’s customers.

Berkshire Hathaway CEO Warren Buffet does this, too. When Buffet bought California-based candy maker See’s Candies, he rightly understood that the quality of the company’s chocolates mattered. But it’s not the only factor at play.

Unlike some executives who would make an intuitive leap that the chocolate drove 100% success, Buffet has the humility to understand the company’s success depends on much more than how its chocolates taste. Buffet knows a huge driver of customer value is people’s experience inside See’s stores.

“In the weeks before Christmas and on Valentine’s Day, there are long lines. So at 5 o’clock in the afternoon, some woman is selling the last person the last box of candy, and that person’s been waiting in line for 20 or 30 customers. If the salesperson smiles at that last customer, our moat is widened,” he said in remarks to MBA students, referring to the company’s competitive advantage. “And if she snarls at him, our moat is narrowed… That’s the key. The total part of the product delivery is having everything associated with it say See’s Candy and something pleasant happening.”

Buffet prioritized experience along with the quality of the chocolates, and he continues to do so. Since he bought See’s, the company has grown from $30 million in annual revenue to several hundred million. Humility enabled him to get rid of his intuitive leap and that drove success.

In “Focus,” we delve into exactly how executives can shift to a science-based approach to strategy to grow their business.

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This article originally ran on Rice Business Wisdom and is based on research from Vikas Mittal, J. Hugh Liedtke Professor of Marketing at the Jones Graduate School of Business. Other researchers included: Jenny van Doorn and Peter C. Verhoef of the University of Groningen, as well as Katherine N. Lemon of the Carroll School of Management at Boston College, Stephan Nass of the University of Münster, Doreén Pick of Hochschule Merseburg, and Peter Pirner of Petlando, i-CEM and www.CX-Talks.com.

Corporations need to leverage customer engagement behavior in an integrative and holistic way. Photo via Getty Images

Houston research: What corporations need to know about customer engagement behavior

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Every customer-focused CEO wants to know the formula for increasing customer engagement because it can boost sales, expand margins and help their company build a better product or service.

Vikas Mittal, Rice Business J. Hugh Liedtke Professor of Management and Marketing, studied the existing data and research about customer engagement. His findings suggest practical ways to support customers, encourage their long-term engagement and perhaps increase sales and margins in the process.

Researchers define customer engagement behavior (CEB) as behaviors that “go beyond transactions, and may be specifically defined as a customer’s behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers.” These behaviors can include word-of-mouth communication, product recommendations, helping other customers, blogging, writing reviews and even engaging in legal action.

Apple is one of the most effective brands when it comes to engaging its customers. Every time customers buy an Apple product — an iPhone, smartwatch, tablet or computer — they engage with an ecosystem of services in multiple ways.

Co-creating a product or service is another CEB strategy. Suggestions, shared inventiveness, co-design and production are all ways that customers can help create a product and service that aligns with their needs and the company’s goals.

CEB can be analyzed through five key characteristics, Mittal’s team wrote. The first is an individual’s ability to significantly affect or react to something. The second and third are form and modality — the different ways in which CEB can show itself. The fourth is its impact, both on the brand and customers. The fifth is purpose: to whom is a company’s engagement directed, how much engagement is planned, and how much are the customer’s goals aligned with the firm’s goals?

As in any relationship, a customer’s attitude about a particular brand may change over time. To encourage CEB that evolves with the company, corporations should take a holistic approach, Mittal’s team advised.

To do this, they proposed a 3-step process: identify, evaluate and act.

Step one: Identify where CEB is occurring. This may be online or in real life. Step two: Evaluate your company’s CEB and your short- and long-term objectives for it. You may find, for example, that your company’s CEB affects the customer more than your company or visa-versa. Step three: Act or react, by creating internal AND external resources to effectively manage CEB.

One example: creating and managing an appealing feedback or review portal. When a firm offers processes and platforms that allow customers to engage, it encourages CEB that lasts beyond the actual purchase. Customers who feel their interaction with the company is successful, Mittal noted, will engage more frequently and intensely. If the interaction doesn’t seem to be effective, the customer may try another engagement approach — or they may just drift away.

That’s why businesses need to remove any barriers to CEB by making it simple, easy and pleasant to interact with the brand.

Marketers who learn how to engage customers using a holistic and integrative approach can harness CEB for their company’s advantage. In addition to investing in tools that facilitate customer-generated reviews, blogs, word of mouth and referrals, companies can invest in ways to make the consumption process more meaningful.

Managers focused on making the best use of CEB, Mittal’s team concluded, should follow these steps.

  1. Listen to customer feedback and complaints. Use this information to improve your goods and services.
  2. Invest in processes and platforms where your customers can express themselves.
  3. Don’t make the mistake of letting customer feedback vanish into a virtual vacuum. Create a system to ensure these manifestations of engagement reach the right department — then make sure they’re resolved or acted on quickly.
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This article originally ran on Rice Business Wisdom and is based on research from Vikas Mittal, J. Hugh Liedtke Professor of Marketing at the Jones Graduate School of Business. Other researchers included: Jenny van Doorn and Peter C. Verhoef of the University of Groningen, as well as Katherine N. Lemon of the Carroll School of Management at Boston College, Stephan Nass of the University of Münster, Doreén Pick of Hochschule Merseburg, and Peter Pirner of Petlando, i-CEM and www.CX-Talks.com.

A Rice Business Professor shows how tailored, personalized health care marketing works better to convince at-risk patients to get screening for liver cancer. Photo via Getty Images

Research: Rice professor reports on the impact of personalized health care marketing

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Amazon is famous for targeted marketing that approaches customers based on their unique needs. Like other successful businesses, such as Netflix, the company taps into machine learning, which uses customer data to understand their behavior.

Hospitals and medical centers rely on marketing too, investing heavily in direct-to-patient outreach to urge at-risk people to get regular screenings. Johns Hopkins Hospital's cancer center, for example, uses emails, letters, seminars and community events to encourage patients to get screened for potential cancer. The high cost of cancer treatment makes this effort worth it: research shows regular screenings help with early detection, leading to more cost-effective treatments and better prognoses.

But hospitals can – and must – improve their outcomes much more, by melding this essential outreach with individually tailored communications based on machine-learning insights.

In an award-winning paper, Rice Business Professor Vikas Mittal and colleagues developed new algorithms indicating that targeted, personalized outreach can increase screenings among at-risk patients. "Outreach marketing" – including sending informational letters and talking to patients about potential barriers to screening – was indeed a powerful motivator for patients to get screened, ultimately lowering health care costs for patient and hospital. But patients with different characteristics, Mittal's team found, responded differently to marketing interventions. When it came to marketing campaigns for cancer screening prevention, one-size-fits-all outreach efforts were neither effective nor economical. Personalized marketing works better for preventing cancer.

To conduct their research, the researchers randomly divided 1,800 patients at UT Southwestern Medical System at risk for hepatocellular carcinoma – the most common type of primary liver cancer – into three groups – usual care, outreach alone, and patient navigation, which includes help such as follow-up calls, motivational messages and assistance spotting specific barriers. They followed each group to see if patients scheduled an MRI or CT scan within six months, from 6-12 months and from 12-18 months.

The first group was asked to receive a screening during their doctors' visits and wasn't contacted after that. The second group received a one-page letter in the mail, then staff called patients who didn't schedule a screening. The third group receiving patient navigation got the same treatment as the second group supplemented with phone calls designed to identify potential barriers, which they used to give customized motivational messages encourage coming in for a screening.

The researchers used patient data from medical records, including patients' age, gender, ethnicity, income, commute time, health status, how often they received healthcare services, whether or not they had insurance and how populated their neighborhoods were.

Following traditional methods, Mittal's team found that the patients who got a letter and call were 10-20% more likely to complete a screening, while those who got the customized motivational messages were 13-24% more likely to schedule their screening. But this is where traditional medical research stops, without asking a crucial question: Within each group, such as those of the 600 patients receiving patient navigation, could screening rates differ based on patients' individual characteristics?

In past research, everyone receiving the same stimulus is presumed to respond the same way. There was no statistical technique to separately estimate the responsiveness of patients with different characteristics. Mittal's team solved this problem by using a machine learning technique called causal forests.

By using "causal forests" to quantify how each of the three marketing approaches could be applied to different patients, Mittal's team found, improved returns on the traditional approach by a remarkable 74-96% – or by $1.6 million to $2 million.

Using traditional methods, physicians would have concluded that every patient should get patient navigation because it was a more intensive marketing approach. The causal forest method showed otherwise: there are small groups of patients with unique characteristics who respond best to specific types of overtures. Minority women in good health who had insurance, visited the doctor often and lived close to clinics in more populated neighborhoods responded especially well to all three types of outreach interventions. Younger patients with long commutes who live in neighborhoods with more public insurance coverage embraced the second type of intervention, outreach alone. And older patients in higher-income neighborhoods favored the patient-navigation approach.

The stakes for common marketing practices like "AB testing" could not be higher. In AB testing, marketers run randomized experiments such as showing ads to some people and not to others. If those seeing an ad, on average, buy more, the conclusion is to blanket the market with ads. But AB testing ignores the fundamental idea that customers exposed to an ad might buy differently in response to an ad based on their individual characteristics. In fact, research shows, many customers seeing a non-tailored ad will buy less than those not seeing an ad.

Personalized marketing can uncover these differences and substantially increase the return on marketing investments in many settings such as retail and ecommerce, services marketing, business-to-business marketing and brand management. Healthcare companies should consider dedicating more resources to machine learning, which can power data-driven patient-centric outreach programs. Because individual health is a civic good, policy makers and organizations need to support these personalized outreach programs.

As for patients themselves, giving detailed personal data to a doctor or receiving highly personalized, unsolicited phone calls legitimately can seem like an invasion of privacy. But Mittal's research shows, it measurably has the potential to save your life.

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This article originally ran on Rice Business Wisdom and is based on research from Vikas Mittal, the J. Hugh Liedtke Professor of Marketing at the Jones Graduate School of Business.

Customer satisfaction directly influences a company's sales, margins, and earnings, and companies that track and measure customer satisfaction have a leg up on competition. Getty Images

Tracking customer satisfaction is essential to business success, Rice University research finds

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Back when people flew nonchalantly for business, an unabashed fan of Great Reputation Airline took a flight where almost everything went wrong. First there was a weather delay. Then there was a mechanical issue. The crew was surly, the pretzels stale. Finally, after landing, when she finally made it to baggage claim, her suitcase was MIA.

But instead of complaining on social media, Great Reputation's passenger wrote off the problems to a rare bad day for the airline – which showered her with drink coupons and later delivered her luggage to her hotel.

GRA's response exemplifies customer satisfaction principles outlined in a paper by Rice Business professor Vikas Mittal and former Rice Business doctoral student Carly Frennea. Summarizing the major research about customer satisfaction, the coauthors codified their findings into a checklist for managers.

While most people understand the general concept of customer satisfaction, in business it's a specific term summarizing a consumer's post-use evaluation of the extent to which a product or service met their expectations. Satisfied customers are more likely to buy again, buy more, recommend a business to others and cost less to serve in the future. A satisfied customer doesn't just cut customer-acquisition costs. She can also help a business attract the right customers through online recommendations.

But the most compelling reason to chase customer satisfaction, say Mittal and Frennea, comes from the University of Michigan's American Customer Satisfaction Index, which tracks customer satisfaction ratings of public companies. Decades of studies based on this data show that customer satisfaction and financial performance go hand-in-hand. While the strength of this association can vary, the link is indisputable. "Nowhere else in marketing has the impact of a customer-based metric on a firm's financial performance been so clearly and consistently established," Mittal and Frennea write.

To help make that satisfaction/revenue link a felicitous one, the researchers recommend the five kinds of data managers should collect.

  • Overall customer satisfaction: A summary evaluation of an overall experience.
  • Behavioral intentions: "Loyalty metrics" that measure the likelihood of buying again, recommending to others and intent to complain.
  • Attribute-level perceptions: Evaluating specific product or service features. For a doctor, this may include time spent waiting in the office, quality of care and explanation of diagnosis. For an oilfield services company, this may include product quality, safety, ongoing service and support, billing and pricing.
  • Contextual information: Comparisons to earlier experiences with a firm and against those with competitors.
  • Customer background variables: Includes gender, age and use of competitors' products and services.

Once these data are collected, the researchers say, managers should use statistical analysis that includes all relevant variables (a method known as multiple regression). This allows companies to figure out which variables have the largest association with overall satisfaction, and which have none. For example, a multiple regression might show that the bad effect of dashing customer expectations is stronger than the good effect of exceeding those expectations. The analysis may also reveal that this effect is stronger for ongoing service and support, say, than for pricing and billing. Conclusion: The company should fix problems with ongoing service and support before tinkering with its pricing and billing strategy.

Companies should also share such customer satisfaction insights with employees and incentivize them to make customer satisfaction a top priority, the researchers write.

To achieve this, executives need to see customer satisfaction as a strategic tool, not just a "good-to-have" afterthought. For this:

  • Treat customer satisfaction as a strategic investment and integrate it into the strategic planning process.
  • Don't skimp on the science. Use the most advanced multiple regression models, and now machine-learning technologies, to distinguish the important from the unimportant, and prioritize the important.
  • Using statistical science, link customer-loyalty patterns to actual behaviors such as repurchasing and repeat sales.
  • Remember that your front-line employees are vital and motivate them by linking their performance to the right customer satisfaction metrics.
  • Don't just maximize customer satisfaction. Balance decreasing and increasing returns on satisfaction initiatives. For this, don't rely on "voice-of-customer" based on casual interviews and discussions. Use rigorously designed customer studies that can be statistically linked to financial results.
  • Share! Summarize satisfaction findings in understandable terms and train employees to act on them. Smart companies use this approach to derive their customer-value proposition and focus the company's strategy.

The formula, after all, is a simple one. If customers are a primary source of your company's cash flow, the first variable in your strategy needs to be making them happy.

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This story originally ran on Rice Business Wisdom. It's based on research from Vikas Mittal, the J. Hugh Liedtke Professor of Marketing at Jones Graduate School of Business at Rice University, and Carly Frennea, now an executive at Nike, who received her M.B.A. and Ph.D. at Jones Graduate School of Business.

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2 Houston startups selected by US military for geothermal projects

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Two clean energy companies in Houston have been recruited for geothermal projects at U.S. military installations.

Fervo Energy is exploring the potential for a geothermal energy system at Naval Air Station Fallon in Nevada.

Meanwhile, Sage Geosystems is working on an exploratory geothermal project for the Army’s Fort Bliss post in Texas. The Bliss project is the third U.S. Department of Defense geothermal initiative in the Lone Star State.

“Energy resilience for the U.S. military is essential in an increasingly digital and electric world, and we are pleased to help the U.S. Army and [the Defense Innovation Unit] to support energy resilience at Fort Bliss,” Cindy Taff, CEO of Sage, says in a news release.

A spokeswoman for Fervo declined to comment.

Andy Sabin, director of the Navy’s Geothermal Program Office, says in a military news release that previous geothermal exploration efforts indicate the Fallon facility “is ideally suited for enhanced geothermal systems to be deployed onsite.”

As for the Fort Bliss project, Michael Jones, a project director in the Army Office of Energy Initiatives, says it’ll combine geothermal technology with innovations from the oil and gas sector.

“This initiative adds to the momentum of Texas as a leader in the ‘geothermal anywhere’ revolution, leveraging the robust oil and gas industry profile in the state,” says Ken Wisian, associate director of the Environmental Division at the U.S. Bureau of Economic Geology.

The Department of Defense kicked off its geothermal initiative in September 2023. Specifically, the Army, Navy, and Defense Innovation Unit launched four exploratory geothermal projects at three U.S. military installations.

One of the three installations is the Air Force’s Joint Base San Antonio. Canada-based geothermal company Eavor is leading the San Antonio project.

Another geothermal company, Atlanta-based Teverra, was tapped for an exploratory geothermal project at the Army’s Fort Wainwright in Alaska. Teverra maintains an office in Houston.

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This article originally ran on EnergyCapital.

Houston investor calls for all angels to move the ecosystem forward

houston innovators podcast episode 233

One of the biggest components of a well-functioning startup ecosystem is inarguably access to capital, and Mitra Miller is dedicated to enhancing education around investment and growing Houston's investor base.

As vice president and board member of the Houston Angel Network, the oldest angel network in Texas and one of the most active angel networks in the country, Miller strives to provide guidance to new and emerging angel investors as well as founders seeking to raise money from them.

"Most founders have no idea or understanding of how investors think — we are not an ATM," Miller says on the Houston Innovators Podcast. "We are really partners you are getting married to for the next 5, 8, 10 years — sometimes longer. We need to bring your allies in every sense of the word."



HAN, a nonprofit, provides a unique opportunity for its members, as Miller explains. The group consists of around 100 individuals from all business backgrounds, so evaluating companies together brings more than just a wealth of capital — but also a wealth of knowledge.

"What HAN provides is access to investments," Miller says, adding that members have the benefit in evaluating companies together with people from different backgrounds. "Inside a group like HAN, we have extraordinary expertise."

In addition to educating both founders and investors in the process, Miller has dedicated herself to providing opportunities for students in low-income parts of Houston to learn about the startup world. Eight years ago, she founded Eagle Investors, an organization that provides a year-long program that culminates in a group of students participating as judges for the Rice Business Plan Competition.

"They spend a year learning about innovation, entrepreneurship, and investing by meeting local investors, entrepreneurs, and service providers. They go around Houston to get to visit and ... experience all of the ecosystem," Miller says.

At RBPC, the cohort of high school students evaluate the pitches and select a company to provide a $5,000 prize.

Miller herself has been a judge at RBPC for 20 years now, and she's had a front row seat for watching Houston develop as an ecosystem. She's seen the city make real progress, but has one major call to action.

"The piece that I am most aware of that I would like to see grow is activating more accredited investors who are interested in this asset class of early-stage companies and plugging in," Miller says. "They can also invest their time and knowledge to help companies in our region grow and scale."

Report: Houston secures spot on list of top 50 startup cities

by the numbers

A new ranking signals great promise for the growth of Houston’s startup network.

Houston ranks among the world’s top 50 startup cities on a new list from PitchBook, a provider of data and research about capital markets. In fact, Houston comes in at No. 50 in the ranking. But if you dig deeper into the data, Houston comes out on top in one key category.

The city earns a growth score of 63.8 out of 100 — the highest growth score of any U.S. city and the seventh highest growth score in the world. In the growth bucket, Houston sits between between Paris (64.4) and Washington, D.C. (61.7).

The PitchBook growth score reflects short-term, midterm, and long-term growth momentum for activity surrounding venture capital deals, exits, and fundraising for the past six years.

PitchBook’s highest growth score (86.5) goes to Hefei, a Chinese manufacturing hub for electric vehicles, solar panels, liquid crystal displays, home appliances, and Lenovo computers.

The overall ranking is based on a scoring system that relies on proprietary PitchBook data about private companies. The system’s growth and development scores are based on data related to deals, exits, fundraising and other factors.

Houston earns a development score of 34.1 out of 100, which puts it in 50th place globally in that regard. This score measures the size and maturity of a city’s startup network.

Topping the overall list is San Francisco, followed by New York City and Beijing. Elsewhere in Texas, Austin appears at No. 16 and Dallas at No. 36.

The ranking “helps founders, operators, and investors assess locations when deciding where to expand or invest,” says PitchBook.

“Network effects matter in venture capital: Investors get more than half of their deals through referrals, according to research led by Harvard professor Paul Gompers,” PitchBook goes on to say. “So it stands to reason that dealmakers should seek these networks out when deciding where to do business.”

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