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-area lab grows with focus on mobile diagnostics and predictive medicine

mobile medicine

When it comes to healthcare, access can be a matter of life and death. And for patients in skilled nursing facilities, assisted living or even their own homes, the ability to get timely diagnostic testing is not just a convenience, it’s a necessity.

That’s the problem Principle Health Systems (PHS) set out to solve.

Founded in 2016 in Clear Lake, Texas, PHS began as a conventional laboratory but quickly pivoted to mobile diagnostics, offering everything from core blood work and genetic testing to advanced imaging like ultrasounds, echocardiograms, and X-rays.

“We were approached by a group in a local skilled nursing facility to provide services, and we determined pretty quickly there was a massive need in this area,” says James Dieter, founder, chairman and CEO of PHS. “Turnaround time is imperative. These facilities have an incredibly sick population, and of course, they lack mobility to get the care that they need.”

What makes PHS unique is not only what they do, but where they do it. While they operate one of the largest labs serving skilled nursing facilities in the state, their mobile teams go wherever patients are, whether that’s a nursing home, a private residence or even a correctional facility.

Diagnostics, Dieter says, are at the heart of medical decision-making.

“Seventy to 80 percent of all medical decisions are made from diagnostic results in lab and imaging,” he says. “The diagnostic drives the doctor’s or the provider’s next move. When we recognized a massive slowdown in lab results, we had to innovate to do it faster.”

Innovation at PHS isn’t just about speed; it’s about accessibility and precision.

Chris Light, COO, explains: “For stat testing, we use bedside point-of-care instruments. Our phlebotomists take those into the facilities, test at the bedside, and get results within minutes, rather than waiting days for results to come back from a core lab.”

Scaling a mobile operation across multiple states isn’t simple, but PHS has expanded into nine states, including Texas, Oklahoma, Kansas, Missouri and Arizona. Their model relies on licensed mobile phlebotomists, X-ray technologists and sonographers, all trained to provide high-level care outside traditional hospital settings.

The financial impact for patients is significant. Instead of ambulance rides and ER visits costing thousands, PHS services often cost just a fraction, sometimes only tens or hundreds of dollars.

“Traditionally, without mobile diagnostics, the patient would be loaded into a transportation vehicle, typically an ambulance, and taken to a hospital,” Dieter says. “Our approach is a fraction of the cost but brings care directly to the patients.”

The company has also embraced predictive and personalized medicine, offering genetic tests that guide medication decisions and laboratory tests that predict cognitive decline from conditions like Alzheimer's and Parkinson’s.

“We actively look for complementary services to improve patient outcomes,” Dieter says. “Precision medicine and predictive testing have been a great value-add for our providers.”

Looking to the future, PHS sees mobile healthcare as part of a larger trend toward home-based care.

“There’s an aging population that still lives at home with caretakers,” Dieter explains. “We go into the home every day, whether it’s an apartment, a standalone home, or assisted living. The goal is to meet patients where they are and reduce the need for hospitalization.”

Light highlighted another layer of innovation: predictive guidance.

“We host a lot of data, and labs and imaging drive most treatment decisions,” Light says. “We’re exploring how to deploy diagnostics immediately based on results, eliminating hours of delay and keeping patients healthier longer.”

Ultimately, innovation at PHS isn’t just about technology; it’s about equity.

“There’s an 11-year life expectancy gap between major metro areas and rural Texas,” Dieter says. “Our innovation has been leveling the field, so everyone has access to high-quality diagnostics and care, regardless of where they live.”

Aegis Aerospace appoints Houston space leader as new president

moving up

Houston-based Aegis Aerospace's current chief strategy officer, Matt Ondler, will take on the additional role of president on Jan. 1. Ondler will succeed Bill Hollister, who is retiring.

“Matt's vision, experience, and understanding of our evolving markets position us to build on our foundation and pursue new frontiers,” Stephanie Murphy, CEO of Aegis Aerospace, said in a news release.

Hollister guided Aegis Aerospace through expansion and innovation in his three years as president, and will continue to serve in the role of chief technology officer (CTO) for six months and focus on the company's technical and intellectual property frameworks.

"Bill has played an instrumental role in shaping the success and growth of our company, and his contributions leave an indelible mark on both our culture and our achievements," Murphy said in a news release.

Ondler has a background in space hardware development and strategic leadership in government and commercial sectors. Ondler founded subsea robots and software company Houston Mechatronics, Inc., now known as Nauticus Robotics, and also served as president, CTO and CSO during a five-year tenure at Axiom Space. He held various roles in his 25 years at NASA and was also named to the Texas Aerospace Research and Space Economy Consortium Executive Committee last year.

"I am confident that with Matt at the helm as president and Bill supporting us as CTO, we will continue to build on our strong foundation and further elevate our impact in the space industry," Murphy said in a news release. "Matt's vision, experience, and understanding of our evolving markets position us to build on our foundation and pursue new frontiers."

Rice University launches new center to study roots of Alzheimer’s and Parkinson’s

neuro research

Rice University launched its new Amyloid Mechanism and Disease Center last month, which aims to uncover the molecular origins of Alzheimer’s, Parkinson’s and other amyloid-related diseases.

The center will bring together Rice faculty in chemistry, biophysics, cell biology and biochemistry to study how protein aggregates called amyloids form, spread and harm brain cells. It will serve as the neuroscience branch of the Rice Brain Institute, which was also recently established.

The team will work to ultimately increase its understanding of amyloid processes and will collaborate with the Texas Medical Center to turn lab discoveries into real progress for patients. It will hold its launch event on Jan. 21, 2026, and hopes to eventually be a launchpad for future external research funding.

The new hub will be led by Pernilla Wittung-Stafshed, a Rice biophysicist and the Charles W. Duncan Jr.-Welch Chair in Chemistry.

“To make a real difference, we have to go all the way and find a cure,” Wittung-Stafshede said in a news release. “At Rice, with the Amyloid Mechanism and Disease Center as a catalyst, we have the people and ideas to open new doors toward solutions.”

Wittung-Stafshede, who was recruited to Rice through a Cancer Prevention and Research Institute of Texas grant this summer, has led pioneering work on how metal-binding proteins impact neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases. Her most recent study, published in Advanced Science, suggests a new way of understanding how amyloids may harm cells and consume the brain’s energy molecule, ATP.

According to Alzheimer’s Disease International, neurodegenerative disease cases could reach around 78 million by 2030 and 139 million by 2050. Wittung-Stafshede’s father died of dementia several years ago.

“This is close to my heart,” Wittung-Stafshede added in the news release. “Neurodegenerative diseases such as dementia, Alzheimer’s and Parkinson’s are on the rise as people live longer, and age is the largest risk factor. It affects everyone.”
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