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 tops ranking of best state for investors in new report

by the numbers

Texas ranks third on a new list of the best states for investors and startups.

Investment platform BrokerChooser weighed five factors to come up with its ranking:

  • 2024 Google search volume for terms related to investing
  • Number of investors
  • Number of businesses receiving investments in 2024
  • Total amount of capital invested in businesses in 2024
  • Percentage change in amount of investment from 2019 to 2024

Based on those figures, provided mostly by Crunchbase, Texas sits at No. 3 on the list, behind No. 1 California and No. 2 New York.

Especially noteworthy for Texas is its investment total for 2024: more than $164.5 billion. From 2019 to 2024, the state saw a 440 percent jump in business investments, according to BrokerChooser. The same percentages are 204 percent for California and 396 percent for New York.

“There is definitely development and diversification in the American investment landscape, with impressive growth in areas that used to fly under the radar,” says Adam Nasli, head analyst at BrokerChooser.

According to Crunchbase, funding for Texas startups is off to a strong start in 2025. In the first three months of this year, venture capital investors poured nearly $2.9 billion into Lone Star State companies, Crunchbase data shows. Crunchbase attributes that healthy dollar amount to “enthusiasm around cybersecurity, defense tech, robotics, and de-extincting mammoths.”

During the first quarter of this year, roughly two-thirds of VC funding in Texas went to just five companies, says Crunchbase. Those companies are Austin-based Apptronik, Austin-based Colossal Biosciences, Dallas-based Island, Austin-based NinjaOne, and Austin-based Saronic.

Autonomous truck company rolls out driverless Houston-Dallas route

up and running

Houston is helping drive the evolution of self-driving freight trucks.

In October, Aurora opened a more than 90,000-square-foot terminal at a Fallbrook Drive logistics hub in northwest Houston to support the launch of its first “lane” for driverless trucks—a Houston-to-Dallas route on the Interstate 45 corridor. Aurora opened its Dallas-area terminal in April and the company began regular driverless customer deliveries between the two Texas cities on April 27.

Close to half of all truck freight in Texas moves along I-45 between Houston and Dallas.

“Now, we are the first company to successfully and safely operate a commercial driverless trucking service on public roads. Riding in the back seat for our inaugural trip was an honor of a lifetime – the Aurora Driver performed perfectly and it’s a moment I’ll never forget,” Chris Urmson, CEO and co-founder of Pittsburgh-based Aurora, said in a news release.

Aurora produces software that controls autonomous vehicles and is known for its flagship product, the Aurora Driver. The software is installed in Volvo and Paccar trucks, the latter of which includes brands like Kenworth and Peterbilt.

Aurora previously hauled more than 75 loads per week under the supervision of vehicle operators from Houston to Dallas and Fort Worth to El Paso for customers in its pilot project, including FedEx, Uber Freight and Werner. To date, it has completed over 1,200 miles without a driver.

The company launched its new Houston to Dallas route with customers Uber Freight and Hirschbach Motor Lines, which ran supervised commercial pilots with Aurora.

“Transforming an old school industry like trucking is never easy, but we can’t ignore the safety and efficiency benefits this technology can deliver. Autonomous trucks aren’t just going to help grow our business – they’re also going to give our drivers better lives by handling the lengthier and less desirable routes,” Richard Stocking, CEO of Hirschbach Motor Lines, added in the statement.

The company plans to expand its service to El Paso and Phoenix by the end of 2025.

“These new, autonomous semis on the I-45 corridor will efficiently move products, create jobs, and help make our roadways safer,” Gov. Greg Abbott added in the release. “Texas offers businesses the freedom to succeed, and the Aurora Driver will further spur economic growth and job creation in Texas. Together through innovation, we will build a stronger, more prosperous Texas for generations.”

In July, Aurora said it raised $820 million in capital to fuel its growth—growth that’s being accompanied by scrutiny.

In light of recent controversies surrounding self-driving vehicles, the International Brotherhood of Teamsters, whose union members include over-the-road truckers, recently sent a letter to Lt. Gov. Dan Patrick calling for a ban on autonomous vehicles in Texas.

“The Teamsters believe that a human operator is needed in every vehicle—and that goes beyond partisan politics,” the letter states. “State legislators have a solemn duty in this matter to keep dangerous autonomous vehicles off our streets and keep Texans safe. Autonomous vehicles are not ready for prime time, and we urge you to act before someone in our community gets killed.”

Houston cell therapy company launches second-phase clinical trial

fighting cancer

A Houston cell therapy company has dosed its first patient in a Phase 2 clinical trial. March Biosciences is testing the efficacy of MB-105, a CD5-targeted CAR-T cell therapy for patients with relapsed or refractory CD5-positive T-cell lymphoma.

Last year, InnovationMap reported that March Biosciences had closed its series A with a $28.4 million raise. Now, the company, co-founded by Sarah Hein, Max Mamonkin and Malcolm Brenner, is ready to enroll a total of 46 patients in its study of people with difficult-to-treat cancer.

The trial will be conducted at cancer centers around the United States, but the first dose took place locally, at The University of Texas MD Anderson Cancer Center. Dr. Swaminathan P. Iyer, a professor in the department of lymphoma/myeloma at MD Anderson, is leading the trial.

“This represents a significant milestone in advancing MB-105 as a potential treatment option for patients with T-cell lymphoma who currently face extremely limited therapeutic choices,” Hein, who serves as CEO, says. “CAR-T therapies have revolutionized the treatment of B-cell lymphomas and leukemias but have not successfully addressed the rarer T-cell lymphomas and leukemias. We are optimistic that this larger trial will further validate MB-105's potential to address the critical unmet needs of these patients and look forward to reporting our first clinical readouts.”

The Phase 1 trial showed promise for MB-105 in terms of both safety and efficacy. That means that potentially concerning side effects, including neurological events and cytokine release above grade 3, were not observed. Those results were published last year, noting lasting remissions.

In January 2025, MB-105 won an orphan drug designation from the FDA. That results in seven years of market exclusivity if the drug is approved, as well as development incentives along the way.

The trial is enrolling its single-arm, two-stage study on ClinicalTrials.gov. For patients with stubborn blood cancers, the drug is providing new hope.

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