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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Texas solar power poised to surpass coal for the first time in 2026

Powering Texas

Solar power promises to shine even brighter in Texas this year.

A new forecast from the U.S. Energy Information Administration (EIA) indicates that for the first time, annual power generation from utility-scale solar will surpass annual power generation from coal across the territory covered by the Electric Reliability Council of Texas (ERCOT).

Solar generation is expected to reach 78 billion kilowatt-hours in 2026 in the ERCOT grid, compared with 60 billion kilowatt-hours for coal, the EIA forecast says. The ERCOT grid supplies power to about 90 percent of Texas, including the Houston area.

“Utility-scale solar generation has been increasing steadily in ERCOT as solar capacity additions help meet rapid electricity demand growth,” the forecast says.

Although natural gas remains the dominant source of electricity generation in ERCOT, accounting for an average 44 percent of electricity generation from 2021 to 2025, solar’s share of the generation mix rose from four percent to 12 percent. During the same period, coal’s share dropped from 19 percent to 13 percent.

EIA predicts about 40 percent of U.S. solar capacity, or 14 billion kilowatt-hours, added in 2026 will come from Texas.

Although EIA expects annual solar generation to exceed annual coal generation in 2026, solar surpassed coal in ERCOT on a monthly basis for the first time in March 2025, when solar generation totaled 4.33 billion kilowatt-hours and coal’s totaled 4.16 billion kilowatt-hours. Solar generation continued to exceed that of coal until August of that year.

“In 2026, we estimate that solar exceeded coal for the first time in March, and we forecast generation from solar installations in ERCOT will continue to exceed that from coal until December, when coal generation exceeds solar,” says EIA. “We expect solar generation to exceed that of coal for every month in 2027 except January and December.”

For 2027, EIA forecasts annual solar generation of 99 billion kilowatt-hours in the ERCOT grid, compared with 66 billion kilowatt-hours of annual coal generation.

In April, ERCOT projected almost 368 billion kilowatt-hours of demand in ERCOT’s territory by 2032. ERCOT’s all-time peak demand hit 85.5 billion kilowatt-hours in August 2023.

“Texas is experiencing exceptional growth and development, which is reshaping how large load demand is identified, verified, and incorporated into long-term planning,” ERCOT President and CEO Pablo Vegas said. “As a result of a changing landscape, we believe this forecast to be higher than expected … load growth.”

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This article first appeared on EnergyCapitalHTX.com.

Intuitive Machines strikes $49.3M deal to expand lunar communications network

space deal

Houston-based Intuitive Machines is bulking up its space-to-ground data network with the acquisition of United Kingdom-based Goonhilly Earth Station and its U.S. arm, COMSAT.

The $49.3 million cash-and-stock deal would add 44 antennas to Intuitive Machines’ network. The acquisition is expected to close in the third quarter.

Intuitive Machines, a space infrastructure and services company, designs, builds, and operates spacecraft and data networks for lunar and deep-space missions. Goonhilly operates a satellite Earth station in Cornwall, England.

Intuitive Machines says Goonhilly’s and COMSAT’s civil, commercial, and government customers will complement its current customer base and broaden its reach into related sectors.

“Customers have been clear that they want a single, integrated, and resilient solution for their communications and [position, navigation, and timing] needs as they accelerate missions at an unprecedented pace,” Steve Altemus, co‑founder and CEO of Intuitive Machines, said in a news release.

Kenn Herskind, executive chairman of Goonhilly, says the acquisition “will allow us to scale that capability globally and directly support the next era of lunar exploration. Together, we will be creating a commercial lunar communications network that is interoperable, resilient, and ready to support Artemis and international missions.”

Modular nuclear reactor co. NuScale Power moves into Houston market

New to Hou

The nuclear energy renaissance continues in Texas with an announcement by NuScale Power. The Oregon-based provider of proprietary and innovative advanced small modular reactor (SMR) nuclear technology announced in April it would be opening office space in Houston’s CityCentre.

“Opening this space in Houston underscores our commitment to meeting rising energy demand with safe, scalable nuclear technology,” John Hopkins, NuScale president and CEO, said in a news release. “This move expands our presence in a key market for partners, prospective customers, and stakeholders in addition to positioning us for the future as we focus on the near-term deployment of our industry-leading technology. Texas is leading the way in embracing advanced nuclear for grid resilience and industrial decarbonization, and we’re proud to expand our footprint and capabilities in this important region.”

Interest in nuclear power has been growing in recent years thanks to tensions with oil-rich nations, concerns about man-made climate change from fossil fuels, and the rapidly increasing power needs of data centers. Both Dow and Texas A&M University have announced expanded nuclear power projects in the last year, with an eye of changing the face of Texas’s energy industry through smaller, safer fission reactors.

Enter NuScale, founded in 2007 from technology developed at the University of Oregon. Their modular SMR technology generates 77 megawatts and is one of the only small modular reactors (SMR) to receive design approval from the U.S. Nuclear Regulatory Commission (NRC). These advances have led to runaway success for NuScale, whose stock has risen by more than 1,670 percent since the start of 2024.

The new operations campus in CityCentre is expected to facilitate the movement, installation and coordination of NuScale technology into the various energy systems. Typically, SMRs are used for off-grid installations, desalination operations, mining facilities and similar areas that lack infrastructure. However, the modularity means that they can be easily deployed to a variety of areas.

It comes none too soon. ERCOT projects that Texas data centers alone will require 77,965 megawatts by 2030.

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This article first appeared on EnergyCapitalHTX.com.

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