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.

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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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Houston neighbor named richest small town in Texas for 2025

Ranking It

Affluent Houston neighbor Bellaire is cashing in as the richest small town in Texas for 2025, according to new study from GoBankingRates.

The report, "The Richest Small Town in Every State," used data from the U.S. Census Bureau's American Community Survey to determine the 50 richest small towns in America based on their median household income.

Of course, Houstonians realize that describing Bellaire as a "small town" is a bit of misnomer. Located less than 10 miles from downtown and fully surrounded by the City of Houston, Bellaire is a wealthy enclave that boasts a population of just over 17,000 residents. These affluent citizens earn a median $236,311 in income every year, which GoBankingRates says is the 11th highest household median income out of all 50 cities included in the report.

The average home in this city is worth over $1.12 million, but Bellaire's lavish residential reputation often attracts properties with multimillion-dollar price tags.

Bellaire also earned a shining 81 livability score for its top quality schools, health and safety, commute times, and more. The livability index, provided by Toronto, Canada-based data analytics and real estate platform AreaVibes, said Bellaire has "an abundance of exceptional local amenities."

"Among these are conveniently located grocery stores, charming coffee shops, diverse dining options and plenty of spacious parks," AreaVibes said. "These local amenities contribute significantly to its overall appeal, ensuring that [residents'] daily needs are met and offering ample opportunities for leisure and recreation."

Earlier in 2025, GoBankingRates ranked Bellaire as the No. 23 wealthiest suburb in America, and it's no stranger to being named on similar lists comparing the richest American cities.

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This article originally appeared on CultureMap.com.

How a Houston startup is taking on corrosion, a costly climate threat

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Corrosion is not something most people think about, but for Houston's industrial backbone pipelines, refineries, chemical plants, and water infrastructure, it is a silent and costly threat. Replacing damaged steel and overusing chemicals adds hundreds of millions of tons of carbon emissions every year. Despite the scale of the problem, corrosion detection has barely changed in decades.

In a recent episode of the Energy Tech Startups Podcast, Anwar Sadek, founder and CEO of Corrolytics, explained why the traditional approach is not working and how his team is delivering real-time visibility into one of the most overlooked challenges in the energy transition.

From Lab Insight to Industrial Breakthrough

Anwar began as a researcher studying how metals degrade and how microbes accelerate corrosion. He quickly noticed a major gap. Companies could detect the presence of microorganisms, but they could not tell whether those microbes were actually causing corrosion or how quickly the damage was happening. Most tests required shipping samples to a lab and waiting months for results, long after conditions inside the asset had changed.

That gap inspired Corrolytics' breakthrough. The company developed a portable, real-time electrochemical test that measures microbial corrosion activity directly from fluid samples. No invasive probes. No complex lab work. Just the immediate data operators can act on.

“It is like switching from film to digital photography,” Anwar says. “What used to take months now takes a couple of hours.”

Why Corrosion Matters in Houston's Energy Transition

Houston's energy transition is a blend of innovation and practicality. While the world builds new low-carbon systems, the region still depends on existing industrial infrastructure. Keeping those assets safe, efficient, and emission-conscious is essential.

This is where Corrolytics fits in. Every leak prevented, every pipeline protected, and every unnecessary gallon of biocide avoided reduces emissions and improves operational safety. The company is already seeing interest across oil and gas, petrochemicals, water and wastewater treatment, HVAC, industrial cooling, and biofuels. If fluids move through metal, microbial corrosion can occur, and Corrolytics can detect it.

Because microbes evolve quickly, slow testing methods simply cannot keep up. “By the time a company gets lab results, the environment has changed completely,” Anwar explains. “You cannot manage what you cannot measure.”

A Scientist Steps Into the CEO Role

Anwar did not plan to become a CEO. But through the National Science Foundation's ICorps program, he interviewed more than 300 industry stakeholders. Over 95 percent cited microbial corrosion as a major issue with no effective tool to address it. That validation pushed him to transform his research into a product.

Since then, Corrolytics has moved from prototype to real-world pilots in Brazil and Houston, with early partners already using the technology and some preparing to invest. Along the way, Anwar learned to lead teams, speak the language of industry, and guide the company through challenges. “When things go wrong, and they do, it is the CEO's job to steady the team,” he says.

Why Houston

Relocating to Houston accelerated everything. Customers, partners, advisors, and manufacturing talent are all here. For industrial and energy tech startups, Houston offers an ecosystem built for scale.

What's Next

Corrolytics is preparing for broader pilots, commercial partnerships, and team growth as it continues its fundraising efforts. For anyone focused on asset integrity, emissions reduction, or industrial innovation, this is a company to watch.

Listen to the full conversation with Anwar Sadek on the Energy Tech Startups Podcast to learn more:

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Energy Tech Startups Podcast is hosted by Jason Ethier and Nada Ahmed. It delves into Houston's pivotal role in the energy transition, spotlighting entrepreneurs and industry leaders shaping a low-carbon future.

This article originally appeared on our sister site, EnergyCapitalHTX.com.

These 50+ Houston scientists rank among world’s most cited

science stars

Fifty-one scientists and professors from Houston-area universities and institutions were named among the most cited in the world for their research in medicine, materials sciences and an array of other fields.

The Clarivate Highly Cited Researchers considers researchers who have authored multiple "Highly Cited Papers" that rank in the top 1percent by citations for their fields in the Web of Science Core Collection. The final list is then determined by other quantitative and qualitative measures by Clarivate's judges to recognize "researchers whose exceptional and community-wide contributions shape the future of science, technology and academia globally."

This year, 6,868 individual researchers from 60 different countries were named to the list. About 38 percent of the researchers are based in the U.S., with China following in second place at about 20 percent.

However, the Chinese Academy of Sciences brought in the most entries, with 258 researchers recognized. Harvard University with 170 researchers and Stanford University with 141 rounded out the top 3.

Looking more locally, the University of Texas at Austin landed among the top 50 institutions for the first time this year, tying for 46th place with the Mayo Clinic and University of Minnesota Twin Cities, each with 27 researchers recognized.

Houston once again had a strong showing on the list, with MD Anderson leading the pack. Below is a list of the Houston-area highly cited researchers and their fields.

UT MD Anderson Cancer Center

  • Ajani Jaffer (Cross-Field)
  • James P. Allison (Cross-Field)
  • Maria E. Cabanillas (Cross-Field)
  • Boyi Gan (Molecular Biology and Genetics)
  • Maura L. Gillison (Cross-Field)
  • David Hong (Cross-Field)
  • Scott E. Kopetz (Clinical Medicine)
  • Pranavi Koppula (Cross-Field)
  • Guang Lei (Cross-Field)
  • Sattva S. Neelapu (Cross-Field)
  • Padmanee Sharma (Molecular Biology and Genetics)
  • Vivek Subbiah (Clinical Medicine)
  • Jennifer A. Wargo (Molecular Biology and Genetics)
  • William G. Wierda (Clinical Medicine)
  • Ignacio I. Wistuba (Clinical Medicine)
  • Yilei Zhang (Cross-Field)
  • Li Zhuang (Cross-Field)

Rice University

  • Pulickel M. Ajayan (Materials Science)
  • Pedro J. J. Alvarez (Environment and Ecology)
  • Neva C. Durand (Cross-Field)
  • Menachem Elimelech (Chemistry and Environment and Ecology)
  • Zhiwei Fang (Cross-Field)
  • Naomi J. Halas (Cross-Field)
  • Jun Lou (Materials Science)
  • Aditya D. Mohite (Cross-Field)
  • Peter Nordlander (Cross-Field)
  • Andreas S. Tolias (Cross-Field)
  • James M. Tour (Cross-Field)
  • Robert Vajtai (Cross-Field)
  • Haotian Wang (Chemistry and Materials Science)
  • Zhen-Yu Wu (Cross-Field)

Baylor College of Medicine

  • Nadim J. Ajami (Cross-Field)
  • Biykem Bozkurt (Clinical Medicine)
  • Hashem B. El-Serag (Clinical Medicine)
  • Matthew J. Ellis (Cross-Field)
  • Richard A. Gibbs (Cross-Field)
  • Peter H. Jones (Pharmacology and Toxicology)
  • Sanjay J. Mathew (Cross-Field)
  • Joseph F. Petrosino (Cross-Field)
  • Fritz J. Sedlazeck (Biology and Biochemistry)
  • James Versalovic (Cross-Field)

University of Houston

  • Zhifeng Ren (Cross-Field)
  • Yan Yao (Cross-Field)
  • Yufeng Zhao (Cross-Field)
  • UT Health Science Center Houston
  • Hongfang Liu (Cross-Field)
  • Louise D. McCullough (Cross-Field)
  • Claudio Soto (Cross-Field)

UTMB Galveston

  • Erez Lieberman Aiden (Cross-Field)
  • Pei-Yong Shi (Cross-Field)

Houston Methodist

  • Eamonn M. M. Quigley (Cross-Field)
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