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

guest column

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.

------

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.

------

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.

------

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.

Ad Placement 300x100
Ad Placement 300x600

CultureMap Emails are Awesome

Property Showcase

Houston robotics co. unveils new robot that can handle extreme temperatures

Hot New Robot

Houston- and Boston-based Square Robot Inc.'s newest tank inspection robot is commercially available and certified to operate at extreme temperatures.

The new robot, known as the SR-3HT, can operate from 14°F to 131°F, representing a broader temperature range than previous models in the company's portfolio. According to the company, its previous temperature range reached 32°F to 104°F.

The new robot has received the NEC/CEC Class I Division 2 (C1D2) certification from FM Approvals, allowing it to operate safely in hazardous locations and to perform on-stream inspections of aboveground storage tanks containing products stored at elevated temperatures.

“Our engineering team developed the SR-3HT in response to significant client demand in both the U.S. and international markets. We frequently encounter higher temperatures due to both elevated process temperatures and high ambient temperatures, especially in the hotter regions of the world, such as the Middle East," David Lamont, CEO of Square Robot, said in a news release. "The SR-3HT employs both active and passive cooling technology, greatly expanding our operating envelope. A great job done (again) by our engineers delivering world-leading technology in record time.”

The company's SR-3 submersible robot and Side Launcher received certifications earlier this year. They became commercially available in 2023, after completing initial milestone testing in partnership with ExxonMobil, according to Square Robot.

The company closed a $13 million series B round in December, which it said it would put toward international expansion in Europe and the Middle East.

Square Robot launched its Houston office in 2019. Its autonomous, submersible robots are used for storage tank inspections and eliminate the need for humans to enter dangerous and toxic environments.

---

This article originally appeared on EnergyCapitalHTX.com.

Houston's Ion District to expand with new research and tech space, The Arc

coming soon

Houston's Ion District is set to expand with the addition of a nearly 200,000-square-foot research and technology facility, The Arc at the Ion District.

Rice Real Estate Company and Lincoln Property Company are expected to break ground on the state-of-the-art facility in Q2 2026 with a completion target set for Q1 2028, according to a news release.

Rice University, the new facility's lead tenant, will occupy almost 30,000 square feet of office and lab space in The Arc, which will share a plaza with the Ion and is intended to "extend the district’s success as a hub for innovative ideas and collaboration." Rice research at The Arc will focus on energy, artificial intelligence, data science, robotics and computational engineering, according to the release.

“The Arc will offer Rice the opportunity to deepen its commitment to fostering world-changing innovation by bringing our leading minds and breakthrough discoveries into direct engagement with Houston’s thriving entrepreneurial ecosystem,” Rice President Reginald DesRoches said in the release. “Working side by side with industry experts and actual end users at the Ion District uniquely positions our faculty and students to form partnerships and collaborations that might not be possible elsewhere.”

Developers of the project are targeting LEED Gold certification by incorporating smart building automation and energy-saving features into The Arc's design. Tenants will have the opportunity to lease flexible floor plans ranging from 28,000 to 31,000 square feet with 15-foot-high ceilings. The property will also feature a gym, an amenity lounge, conference and meeting spaces, outdoor plazas, underground parking and on-site retail and dining.

Preleasing has begun for organizations interested in joining Rice in the building.

“The Arc at the Ion District will be more than a building—it will be a catalyst for the partnerships, innovations and discoveries that will define Houston’s future in science and technology,” Ken Jett, president of Rice Real Estate Company, added in the release. “By expanding our urban innovation ecosystem, The Arc will attract leading organizations and talent to Houston, further strengthening our city’s position as a hub for scientific and entrepreneurial progress.”

Intel Corp. and Rice University sign research access agreement

innovation access

Rice University’s Office of Technology Transfer has signed a subscription agreement with California-based Intel Corp., giving the global company access to Rice’s research portfolio and the opportunity to license select patented innovations.

“By partnering with Intel, we are creating opportunities for our research to make a tangible impact in the technology sector,” Patricia Stepp, assistant vice president for technology transfer, said in a news release.

Intel will pay Rice an annual subscription fee to secure the option to evaluate specified Rice-patented technologies, according to the agreement. If Intel chooses to exercise its option rights, it can obtain a license for each selected technology at a fee.

Rice has been a hub for innovation and technology with initiatives like the Rice Biotech Launch Pad, an accelerator focused on expediting the translation of the university’s health and medical technology; RBL LLC, a biotech venture studio in the Texas Medical Center’s Helix Park dedicated to commercializing lifesaving medical technologies from the Launch Pad; and Rice Nexus, an AI-focused "innovation factory" at the Ion.

The university has also inked partnerships with other tech giants in recent months. Rice's OpenStax, a provider of affordable instructional technologies and one of the world’s largest publishers of open educational resources, partnered with Microsoft this summer. Google Public Sector has also teamed up with Rice to launch the Rice AI Venture Accelerator, or RAVA.

“This agreement exemplifies Rice University’s dedication to fostering innovation and accelerating the commercialization of groundbreaking research,” Stepp added in the news release.

View All Listings