Houston expert breaks down how B2B companies should leverage data for growth

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How you can use your data to improve your marketing efforts. Photo via Getty Images

When focusing on revenue growth in business to business companies, analyzing data to develop and optimize strategies is one of the biggest factors in sales and marketing success. However, the process of evaluating B2B data differs significantly from that of B2C, or business to consumer. B2C analysis is often straightforward, focusing on consumer behavior and e-commerce transactions.

Unlike B2C, where customers can make a quick purchase decision with a simple click, the B2B customer journey involves multiple touchpoints and extensive research. B2B buyers will most likely discover a company through an ad or a referral, then navigate through websites, interact with salespeople, and explore different resources before finally making a purchasing decision, often with a committee giving input.

Because a B2B customer journey through the sales pipeline is more indirect, these businesses need to take a more nuanced approach to acquiring and making sense of data.

The expectations of B2B vs. B2C

It can be tempting to use the same methods of analysis between B2C and B2B data. However, B2B decision-making requires more consideration. Decisions involving enterprise software or other significant business products or services investments are very different from a typical consumer purchase.

B2C marketing emphasizes metrics like conversion rates, click-through rates, and immediate sales. In contrast, B2B marketing success also includes metrics like lead quality, customer lifetime value, and ROI. Understanding the differences helps prevent unrealistic expectations and misinterpretations of data.

Data differences with B2B

While B2C data analysis often revolves around website analytics and foot traffic in brick and mortar stores, B2B data analysis involves multiple sources. Referrals play a vital role in B2B, as buyers often seek recommendations from industry peers or companies similar to theirs.

Data segmentation in B2B focuses more on job title and job function rather than demographic data. Targeting different audiences within the same company based on their roles — and highlighting specific aspects of products or services that resonate with those different decision-makers — can significantly impact a purchase decision.

The B2B sales cycle is longer because purchases typically involve the input of a salesperson to help buyers with education and comparison. This allows for teams to implement account-based marketing and provides for more engagement which increases the chances of moving prospects down the sales funnel.

Enhancing data capture in B2B analysis

Many middle-market companies rely heavily on individual knowledge and experience rather than formal data management systems. As the sales and marketing landscape has evolved to be more digital, so must business. Sales professionals can leave and a company must retain the knowledge of the buyers and potential buyers. CRM systems not only collect data, they also provide the history of customer relationships.

Businesses need to capture data at all the various touchpoints, including lead generation, prospect qualification, customer interactions, and order fulfillment. Regular analysis will help with accuracy. The key is to derive actionable insights from the data.

B2B data integration challenges

Integrating various data sources in B2B data analysis used to be much more difficult. With the advent of business intelligence software such as Tableau and Power BI, data analysis is much more accessible with a less significant investment. Businesses do need access to resources to effectively use the tools.

CRM and ERP systems store a wealth of data, including contact details, interactions, and purchase history. Marketing automation platforms capture additional information from website forms, social media, and email campaigns. Because of these multiple sources, connecting data points and cleansing the data is a necessary step in the process.

When analyzing B2B data for account based marketing (ABM) purposes, there are some unique considerations to keep in mind. Industries like healthcare and financial services, for instance, have specific regulations that dictate how a business can use customer data.

Leveraging B2B data analysis for growth

B2B data analysis is the foundation for any sales and marketing strategy. Collecting and using data from multiple sources allows revenue teams to uncover gaps, trends, and opportunities for continued growth.

Acknowledging what’s different about B2B data and tracking all of the customer journey touchpoints is important as a business identifies a target market, develops an ideal customer profile, and monitors their competitors. Insights from data also single out gaps in the sales pipeline, use predictive analytics for demand forecasting, and optimize pricing strategies.

This comprehensive approach gives B2B companies the tools they need to make informed decisions, accelerate their sales and marketing efforts, and achieve long-term growth in a competitive market.

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Libby Covington is a Partner with Craig Group, a technology-enabled sales and marketing advisory firm specializing in revenue growth for middle-market, private-equity-backed portfolio companies.

From Your Site Articles
Every situation is unique and deserves a one-of-the-kind data management plan, not a one-size-fits-all solution. Graphic by Miguel Tovar/University of Houston

Houston research: Why you need a data management plan

Houston voices

Why do you need a data management plan? It mitigates error, increases research integrity and allows your research to be replicated – despite the “replication crisis” that the research enterprise has been wrestling with for some time.

Error

There are many horror stories of researchers losing their data. You can just plain lose your laptop or an external hard drive. Sometimes they are confiscated if you are traveling to another country — and you may not get them back. Some errors are more nuanced. For instance, a COVID-19 repository of contact-traced individuals was missing 16,000 results because Excel can’t exceed 1 million lines per spreadsheet.

Do you think a hard drive is the best repository? Keep in mind that 20 percent of hard drives fail within the first four years. Some researchers merely email their data back and forth and feel like it is “secure” in their inbox.

The human and machine error margins are wide. Continually backing up your results, while good practice, can’t ensure that you won’t lose invaluable research material.

Repositories

According to Reid Boehm, Ph.D., Research Data Management Librarian at the University of Houston Libraries, your best bet is to utilize research data repositories. “The systems and the administrators are focused on file integrity and preservation actions to mitigate loss and they often employ specific metadata fields and documentation with the content,” Boehm says of the repositories. “They usually provide a digital object identifier or other unique ID for a persistent record and access point to these data. It’s just so much less time and worry.”

Integrity

Losing data or being hacked can challenge data integrity. Data breaches do not only compromise research integrity, they can also be extremely expensive! According to Security Intelligence, the global average cost of a data breach in a 2019 study was $3.92 million. That is a 1.5 percent increase from the previous year’s study.

Sample size — how large or small a study was — is another example of how data integrity can affect a study. Retraction Watch removes approximately 1,500 articles annually from prestigious journals for “sloppy science.” One of the main reasons the papers end up being retracted is that the sample size was too small to be a representative group.

Replication

Another metric for measuring data integrity is whether or not the experiment can be replicated. The ability to recreate an experiment is paramount to the scientific enterprise. In a Nature article entitled, 1,500 scientists lift the lid on reproducibility, “73 percent said that they think that at least half of the papers can be trusted, with physicists and chemists generally showing the most confidence.”

However, according to Kelsey Piper at Vox, “an attempt to replicate studies from top journals Nature and Science found that 13 of the 21 results looked at could be reproduced.”

That's so meta

The archivist Jason Scott said, “Metadata is a love note to the future.” Learning how to keep data about data is a critical part of reproducing an experiment.

“While this will be always be determined by a combination of project specifics and disciplinary considerations, descriptive metadata should include as much information about the process as possible,” said Boehm. Details of workflows, any standard operating procedures and parameters of measurement, clear definitions of variables, code and software specifications and versions, and many other signifiers ensure the data will be of use to colleagues in the future.

In other words, making data accessible, useable and reproducible is of the utmost importance. You make reproducing experiments that much easier if you are doing a good job of capturing metadata in a consistent way.

The Big Idea

A data management plan includes storage, curation, archiving and dissemination of research data. Your university’s digital librarian is an invaluable resource. They can answer other tricky questions as well: such as, who does data belong to? And, when a post-doctoral student in your lab leaves the institution, can s/he take their data with them? Every situation is unique and deserves a one-of-the-kind data management plan, not a one-size-fits-all solution.

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This article originally appeared on the University of Houston's The Big Idea. Sarah Hill, the author of this piece, is the communications manager for the UH Division of Research.

Here's your university research data management checklist. Graphic by Miguel Tovar/University of Houston

Tips for optimizing data management in research, from a UH expert

Houston voices

A data management plan is invaluable to researchers and to their universities. "You should plan at the outset for managing output long-term," said Reid Boehm, research data management librarian at University of Houston Libraries.

At the University of Houston, research data generated while individuals are pursuing research studies as faculty, staff or students of the University of Houston are to be retained by the institution for a period of three years after submission of the final report. That means there is a lot of data to be managed. But researchers are in luck – there are many resources to help navigate these issues.

Take inventory

Is your data…

  • Active (constantly changing) or Inactive (static)
  • Open (public) or Proprietary (for monetary gain)
  • Non-identifiable (no human subjects) or Sensitive (containing personal information)
  • Preservable (to save long term) or To discard in 3 years (not for keeping)
  • Shareable (ready for reuse) or Private (not able to be shared)

The more you understand the kind of data you are generating the easier this step, and the next steps, will be.

Check first

When you are ready to write your plan, the first thing to determine is if your funders or the university have data management plan policy and guidelines. For instance, University of Houston does.

It is also important to distinguish between types of planning documents. For example:

A Data Management Plan (DMP) is a comprehensive, formal document that describes how you will handle your data during the course of your research and at the conclusion of your study or project.

While in some instances, funders or institutions may require a more targeted plan such as a Data Sharing Plan (DSP) that describes how you plan to disseminate your data at the conclusion of a research project.

Consistent questions that DMPs ask include:

  • What is generated?
  • How is it securely handled? and
  • How is it maintained and accessed long-term?

However it's worded, data is critical to every scientific study.

Pre-proposal

Pre-proposal planning resources and support at UH Libraries include a consultation with Boehm. "Each situation is unique and in my role I function as an advocate for researchers to talk through the contextual details, in connection with funder and institutional requirements," stated Boehm. "There are a lot of aspects of data management and dissemination that can be made less complex and more functional long term with a bit of focused planning at the beginning."

When you get started writing, visit the Data Management Plan Tool. This platform helps by providing agency-specific templates and guidance, working with your institutional login and allowing you to submit plans for feedback.

Post-project

Post-project resources and support involve the archiving, curation and the sharing of information. The UH Data Repository archives, preserves and helps to disseminate your data. The repository, the data portion of the institutional repository Cougar ROAR, is open access, free to all UH researchers, provides data sets with a digital object identifier and allows up to 10 GB per project. Most most Federal funding agencies already require this type of documentation (NSF, NASA, USGS and EPA. The NIH will require DMPs by 2023.

Start out strong

Remember, although documentation is due at the beginning of a project/grant proposal, sustained adherence to the plan and related policies is a necessity. We may be distanced socially, but our need to come together around research integrity remains constant. Starting early, getting connected to resources, and sharing as you can through avenues like the data repository are ways to strengthen ourselves and our work.

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This article originally appeared on the University of Houston's The Big Idea. Sarah Hill, the author of this piece, is the communications manager for the UH Division of Research.

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Intuitive Machines lands $148M as part of NASA Moon Base funding

to the moon

Houston-based Intuitive Machines has been awarded $148.3 million to deliver its Nova-C lander to the moon by 2028. The funding is part of $600 million that NASA recently awarded to three companies as part of the agency’s Moon Base Program.

The contracts aim to support sustained human presence and commercial operations on the Moon. Austin-based Firefly Aerospace was awarded $144.2 million by NASA for one mission and Pittsburgh-based Astrobotic netted $297.9 million for two lunar landings. Intuitive Machine's award is the company's sixth task order under NASA's Commercial Lunar Payload Services (CLPS) program.

“We’re building a proving ground for Moon Base operations,” Ryan Stephan, NASA’s Moon Base acting director of cargo landers, said in a news release. “Accelerating our Moon mission ordering cadence and launch opportunities enable us to move quickly to learn, iterate, and improve.”

Under the latest task order, Intuitie Machines will deliver three scientific and operational payloads to the moon, which include a:

  • Linear Energy Transfer Spectrometer (LETS) radiation monitor to gather critical environmental safety data
  • Advanced stereo cameras to analyze surface-plume interactions (SCALPSS)
  • Laser retroreflector array (LRA) for precise cislunar positioning

The funding breakdown includes a $68.6 million base contract and a $79.7 million performance incentive for Intuitive Machines.

The company says the funding will allow it to create a standardized and repeatable "lunar utility pipeline" for delivering cargo to the moon.

"We are shifting the paradigm from custom aerospace engineering to commercial mass production of lunar infrastructure," Steve Altemus, CEO of Intuitive Machines, said in a separate news release. "Our flight-proven Nova-C platform allows us to build, test, and deploy multiple landers in parallel using Industry 4.0-powered manufacturing. This contract directly advances our core mission to provide persistent, reliable, and commercial baseline of transport, connectivity, and operations that allows our customers to stay longer and achieve more on the Moon."

NASA also shared that it is exploring plans to send PROMISE, a rover based on the Mars Perseverance and Curiosity rovers, to the moon and it plans to seek proposals for additional lunar lander missions, technology demonstrations, a communications and navigation satellite network, and new science payloads to support its lunar outpost. NASA is developing its Moon Base near the lunar South Pole. The agency expects it to come to fruition sometime after 2032.

Intuitive Machines had received its last CLPS award for $180.4 million in March 2026. It will be the first mission to utilize the company's larger cargo lunar lander, Nova-D. The company was also recently awarded a $1 million grant from Maryland Gov. Wes Moore to expand its robotics operations in the state.

UT team develops wearable technology for atmospheric water harvesting

In The Air

Engineers at the University of Texas at Austin have developed a prototype jacket that harvests clean drinking water directly from the atmosphere, and it works even in the driest desert conditions.

The research, published in Science Advances, marks the latest milestone in nearly a decade of work by materials scientist and chair professor Guihua Yu and his team at the Cockrell School of Engineering's Walker Department of Mechanical Engineering and Texas Materials Institute. The wearable technology marks a significant leap: instead of a bulky, stationary machine, this jacket does the work.

Photo courtesy of UT Austin

"We have been working on atmospheric water harvesting technology for a number of years," Yu says. "This current version is even more wearable. We're transitioning from conventional, more stationary water harvesting to something truly portable and personal."

Yu's lab first published work on hydrogel-based water harvesting around 2019, and the jacket is the latest evolution of that platform, now called AirGel. Last year, the broader AirGel invention won the top prize in the graduate category of the National Collegiate Inventors Competition.

The jacket is woven with specially engineered hydrogel fibers; ultra-porous materials that attract and absorb moisture from the surrounding air much like a household desiccant. Unlike a desiccant, the material doesn't require intense heat to release that water. The hydrogel is thermally responsive, meaning a modest rise in temperature — even from mild solar heating — is enough to release the water it has captured.

Condenser test in AustinSo, somebody would be wearing the jacket, or perhaps carrying this gel-like textile as a blanket, as it passively absorbs moisture from the air. Then they would detach the textile panels and place them into a small, portable collector unit; essentially a compact heater. The water evaporates out of the textile, condenses inside the collector, and drips out as clean, drinkable water.

"It immediately becomes drinkable because it already goes through the distillation process," Yu explains.

In trials, the jacket produced between 400 and 900 milliliters of water per day depending on humidity, or roughly 14-30 ounces, nearly a quart, depending on the air's humidity. With one kilogram of the textile, the researchers found they could generate approximately 3.7-4 liters of water in arid conditions, and potentially double that in humid ones. So far, the team has tried the jacket out in very dry, semi-dry, and humid areas, and the jacket was able to pull water from each climate.

Lead researcher Chuxin Lei, a postdoctoral researcher on Yu's team and co-author on the paper, says the goal was to rethink who this technology could serve.

Portable bag contents

"Many current [atmospheric water harvesting] systems are still built as rigid or stationary platforms, making them less suitable for people who are moving, working outdoors, or operating in some remote environment. This lead us to ask whether we could build a water harvesting system that could become more like clothing — light, wearable, flexible, and naturally suited for personal use," Lei says.

The potential applications are wide-ranging. Yu's team has previously worked with the Department of Defense on water solutions for soldiers, where water logistics can be dangerous and costly. The technology could also serve hikers, emergency responders, disaster relief workers, and agricultural and field workers. Anyone who needs clean water on the go and far from infrastructure.

The team also sees a potential future where the technology complements large-scale centralized water systems rather than replacing them.

"Our solution cannot be a universal solution for all," Yu acknowledges. "But I think it's an extremely important alternative."

For now, the jacket is still a laboratory prototype, but Yu and Lei are optimistic. With the right industry partnerships, they say, the technology could realistically reach commercial scale within three to five years.

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This article originally appeared on CultureMap.com, written by Natalie Grigson.

Houston ranks among world’s top 30 emerging startup ecosystems

Startup Status

Long known as the Energy Capital of the World, Houston also ranks among the world’s top 30 emerging startup ecosystems, according to a new report.

The report from Startup Genome, a research and advisory organization, doesn’t assign a specific numeric ranking to Houston’s startup ecosystem. Rather, it puts Houston in the ranking range of 21 to 30 for emerging ecosystems. Startup Genome weighed factors such as early-stage funding, performance and talent to identify the top emerging ecosystems.

Houston also gained notice for being one of the world’s 20 emerging ecosystems with at least four unicorn startups in the past 10 years. Houston and nine other ecosystems each had four unicorns.

According to StartupBlink, a startup research platform, Houston’s startup ecosystem grew 24 percent in 2025, with over 1,300 startups and total startup funding exceeding $808 million. StartupBlink places Houston at No. 46 among the world’s top 100 startup ecosystems.

In a recent post on LinkedIn, David Horsup, executive in residence at the Rice Alliance Clean Energy Accelerator, wrote that Houston “has all the ingredients to be wildly successful if it stays true to its differentiated pillars that drive the economy — energy, medical, and aerospace.”

Mumbai topped Startup Genome’s list of emerging ecosystems, followed by Istanbul, Madrid, Salt Lake City-Provo and Barcelona. After Salt Lake City-Provo, the top U.S. ecosystems were Phoenix, Detroit, Minneapolis and Las Vegas.

Silicon Valley led Startup Genome’s ranking of the world’s top established ecosystems, followed by New York City, London, Tel Aviv and Boston. Austin landed at No. 18 in this category and Dallas at No. 27.

“For much of the past decade, this report has chronicled the welcome dispersion of opportunity beyond the traditional hubs,” Startup Genome writes. “That trend has not died — but it has been complicated. Capital and scale are consolidating once more, particularly in the United States, and the gap between leading and emerging ecosystems is widening.”

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