6 things this Houston entrepreneur wishes he’d known before starting his company

Guest column

Learn from the mistakes of a successful Houston entrepreneur — from teamwork tips to reasons why you should network with other startups. Emilija Manevska/Getty Images

Recently, I was asked what it took to build a startup in Houston. It has taken me three attempts to create a successful startup, and there were a few things that I wish I'd known right out of the gate.

Whether your goal is to exit through a sale, an IPO, or turn your team of pirates into something that looks like a company, your business model will determine how you earn revenue and profits, and you want it to be repeatable and scalable to survive. With that in mind, here are the things I've learned along the way and what I wish I had known before I started my career as an entrepreneur.

Location does matter 

Houston is great for food, sports, and massive rainfall, but it's difficult to find a large pool of talented full-stack software engineers who speak cloud. I recruited some of the best, but it was incredibly difficult to find them compared to markets like Austin, Denver, and San Francisco.

I've seen successful companies build two separate offices, one for a headquarters, and another for development, but for us, we didn't need to build a massive team, so we remained close to customers in Houston and hired a remote team in California. If you need to build a large engineering team, consider a different city or go remote.

Startups have well-defined phases 

Your startup is not a snowflake. There have been thousands upon thousands of entrepreneurs that have succeeded and failed, and a few people have studied them to understand their histories and roadmaps. I wish I learned from them before I began, instead of spending every waking hour building a product, and competing with development time for research.

Looking back, we followed the same trail taken by many other B2B startups, like: Product-market fit, sales optimization, customer success, marketing focus, and eventually scale. It's important to know which phase you are in, who you need to hire in each phase, and most importantly, how your role changes in each one.

Partner roles need to be well understood 

One of largest factors on your probability of success is your team. When choosing your partners, I would suggest using an odd number of people to break stalemates, and to always have a CEO. One person needs to be in charge of execution, I can tell you first hand that committees do not scale well when you need a high velocity of decision making.

When choosing your team, make note of Cal Newport's research on career capital, which is the rare and valuable skills that one can leverage help your startup succeed. If your friend knows how to code or understands databases, ask yourself if he/she is the best in their class, because these are skills that you can hire for or contract out. The traits that accelerated our success were a unique blend of domain expertise, petroleum-specific software knowledge, deep business development expertise, and strong sense of diligence and commitment, which is what became our culture.

Finally, you and your partners need to know what needs to be done, and how you can individually contribute. Your contributions will change in each phase, and each of you need to understand how your roles will change, and be prepared to adapt quickly. If one of your partners writes the first line of code, doesn't mean they'll be the CTO when you have 150 people, the person that makes the first sale may not be the CRO when you have a 30 person salesforce. For those with a large ego, it's one of the hardest things to accept, but must be acknowledged in order for a team to succeed.

Your idea is probably wrong, but that is okay 

We used agile and lean philosophies to build our organization, and our approach was centered around what Steve Blank calls "customer discovery," the understanding of how to find a product-market fit. These methods subscribe to the hypothesis that successful startups are defined by their team's execution, and not the idea alone. We ditched our first idea after two weeks and pivoted to a new one, and we learned from our customers very quickly and created over 115 prototypes in 10 months before making the first sale. Each group of customers saw a different prototype, and each beta-tester used a different design, a different stack, a different user experience. We had to learn quickly. Agile and lean processes helped us iterate quickly and discover what our customers needed, but a highly skilled team was needed to figure out how to use the processes correctly.

Connect with others who have made it

Success is a multi-variate formula that compounds every good and bad decision unequally. If you don't know the answer to a key decision, your team can help, if they don't know, then find another team that has navigated your trail to provide advice.

In Houston, there are not many teams who have been through this, we leaned on help from the Austin network. I'm a big believer in helping the community of entrepreneurs, and I am more than happy to throw down the rope to help others in their ascent.

Money is your oxygen

Lastly, learning to hold your breath isn't a long-term strategy for deep sea dives. You'll need to know how many months of oxygen you have in your bank account at all times. There is no magic number of months for runway, but I can tell you from experience that three months is too little for oil and gas tech startups, especially when OilCo's take three to six months to sign and pay your invoices.

I can't emphasize how difficult starting a company can be. By reflecting on the points I mentioned here, I believe that I would have avoided some pitfalls, and maybe even made it a little farther in the journey.

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James Ruiz is the founder of Houston-based Q Engineering, a data driven solutions company for E&P professionals.

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Rice University MBA programs rank among top 5 in prestigious annual report

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Rice University’s Jones Graduate School of Business MBA programs have been ranked among the top five in the country again in The Princeton Review’s 2025 Best Business Schools rankings.

The university's MBA program in finance earned a No. 3 ranking, climbing up two spots from its 2024 ranking. Finance MBA programs at the University of Virginia's Darden Graduate School of Business and New York University's Leonard N. Stern School of Business were the only ones to outrank Rice, claiming No. 2 and No. 1 spots, respectively.

Rice's online MBA program was ranked No. 5, compared to No. 4 last year. Indiana University's Bloomington Kelley School of Business' online program claimed the top spot.

“These rankings reflect the commitment of our faculty and staff, the drive and talent of our students and the strong support of our alumni and partners,” Peter Rodriguez, dean of Rice Business, said in a news release. “They are exceptional honors but also reminders — not just of our top-tier programs and world-class faculty and students but of our broader impact on the future of business education.”

Rice also ranked at No. 6 for “greatest resources for minority students."

The Princeton Review’s 2025 business school rankings are based on data from surveys of administrators at 244 business schools as well as surveys of 22,800 students enrolled in the schools’ MBA programs during the previous three academic years.

"The schools that made our lists for 2025 share four characteristics that inform our criteria for designating them as 'best': excellent academics, robust experiential learning components, outstanding career services, and positive feedback about them from enrolled students we surveyed," Rob Franek, The Princeton Review's editor-in-chief, said in a press release. "No b-school is best overall or best for all students, but to all students considering earning an MBA, we highly recommend these b-schools and salute them for their impressive programs."

Rice's finance program has ranked in the top 10 for eight consecutive years, and its online MBA has ranked in the top five for four years.

Rice and the University of Houston also claimed top marks on the Princeton Review's entrepreneurship rankings. Rice ranks as No. 1 on the Top 50 Entrepreneurship: Grad list, and the University of Houston ranked No. 1 on Top 50 Entrepreneurship: Ugrad. Read more here.

Houston named ‘star’ metro for artificial intelligence in new report

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A new report declares Houston one of the country’s 28 “star” hubs for artificial intelligence.

The Houston metro area appears at No. 16 in the Brookings Institution’s ranking of metros that are AI “stars.” The metro areas earned star status based on data from three AI buckets: talent, innovation and adoption. Only two places, the San Francisco Bay Area and Silicon Valley, made Brookings’ “superstar” list.

According to Brookings, the Houston area had 11,369 job postings in 2024 that sought candidates with AI skills, 210 AI startups (based on Crunchbase data from 2014 to 2024), and 113 venture capital deals for AI startups (based on PitchBook data from 2023 to 2024).

A number of developments are boosting Houston’s AI profile, such as:

Brookings also named Texas’s three other major metros as AI stars:

  • No. 11 Austin
  • No. 13 Dallas-Fort Worth
  • No. 40 San Antonio

Brookings said star metros like Houston “are bridging the gap” between the two superstar regions and the rest of the country. In 2025, the 28 star metros made up 46 percent of the country’s metro-area employment but 54 percent of AI job postings. Across the 28 metros, the number of AI job postings soared 139 percent between 2018 and 2025, according to Brookings.

Around the country, dozens of metros fell into three other categories on Brookings’ AI list: “emerging centers” (14 metros), “focused movers” (29 metros) and “nascent adopters” (79 metros).

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

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