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.

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 startup's revolutionary automotive recycling tech to begin commercial operations

houston innovators podcast episode 267

Vibhu Sharma observed a huge sustainability problem within the automotive industry, and he was tired of no one doing anything about it.

"Globally, humans dispose 1 billion tires every year," Sharma says on the Houston Innovators Podcast. "It's a massive environmental and public health problem because these tires can take hundreds of years to break down, and what they start doing is leaking chemicals into the soil."

Today, 98 percent of all tires end up in landfills, Sharma says, and this waste contributes to a multitude of problems — from mosquito and pest infestation to chemical leaks and fire hazards. That's why he founded InnoVent Renewables, a Houston-based company that uses its proprietary continuous pyrolysis technology to convert waste tires into valuable fuels, steel, and chemicals.

While the process of pyrolysis — decomposing materials using high heat — isn't new, InnoVent's process has a potential to be uniquely impactful. As Sharma explains on the show, he's targeting areas with an existing supply of waste tires. The company's first plant — located in Monterrey, Mexico — is expected to go online early in the new year, an impressive accomplishment considering Sharma started his company just over a year ago and bootstrapped the business with only a friends and family round of funding.

"It's about 16 months or so from start to commercial operations, which is phenomenal when you consider what it takes to build and operate a chemical or petrochemical facility," Sharma says.

Currently, with the facility close to operations, Sharma is looking to secure customers for the plant's products — which includes diesel, steel, and carbon black — and he doesn't have to look too far out of the automotive industry for his potential customer base. Additionally, the plant should be net zero by day one, since Sharma says he will be using the output to fuel operations.

While the first facility is in Mexico, Sharma says they are already looking at potential secondary locations with Texas at the top of his list. Houston, where Sharma has worked for 26 years, has been a strategic headquarters for InnoVent.

"When it came to doing the research and development, we were able to work with experts in the Houston and Texas areas to test out our idea and validate it," Sharma says. "One thing that gets under appreciated about Houston is how well it's connected to the rest of the world. There are so many direct connections between Houston and Latin America, as well as Europe, Middle East, and Asia."

"I also find that the Houston ecosystem is very supportive of new companies and helping them grow," he adds.

Houston expert on what AI is changing in the workplace — and why employers need to recognize the 'human edge'

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When OpenAI's GPT-4 made headlines by passing the bar exam and scoring in the top 10 percent on medical licensing tests, I noticed something fascinating: everyone focused on AI replacing professionals, but they missed the deeper story. AI isn't just disrupting work – it's exposing fundamental flaws in how we've built our entire workplace ecosystem. It's holding up a mirror to our organizations, revealing just how far we've strayed from what makes us uniquely human.

The World Economic Forum tells us 44 percent of workers' skills will need updating by 2027, but that statistic only scratches the surface. In my conversations with business leaders, I'm watching a transformation unfold in real-time. Take the accounting industry, where I've observed forward-thinking firms like Deloitte and PwC turning their accountants into strategic business advisors while other firms continue training junior staff for tasks that AI will soon handle. This isn't just a skills mismatch – it's a fundamental misunderstanding of human potential.

The challenge runs deeper than individual industries. McKinsey predicts 30 percent of hours worked globally could be automated by 2030, but I believe they're missing a crucial point. We've spent decades designing jobs around industrial-era ideals of efficiency and standardization – the very qualities that make them perfect targets for AI automation. In our obsession with measuring, standardizing, and streamlining everything, we've created workplaces that treat humans like machines rather than the complex, creative beings we are.

What's emerging is a striking paradox: as work becomes more automated, our workplace cultures are growing more disconnected. Microsoft researchers identified a "collaboration deficit" in remote work environments, with 56 percent of employees reporting a decline in workplace friendships. This cultural shift is occurring precisely when we need human connection most. During the Great Resignation of 2021, 47 million Americans quit their jobs, they weren't leaving because of salary considerations or technological inadequacies. The most common reasons cited were lack of human connection, purpose, and authentic leadership.

Yet instead of heeding this wake-up call, the rise of AI is pushing us further apart. A decade ago, the concept of "workplace family" was commonplace – now it's often dismissed as manipulative corporate rhetoric. This shift reveals a troubling blindspot in our thinking about work. Consider this: we spend more than 90,000 hours at work over our lifetime – more time than we spend with our own families – yet we're increasingly treating these relationships as purely transactional. In our rush to establish boundaries and protect ourselves from corporate exploitation, we've overcorrected, creating sterile workplaces stripped of human connection.

This timing couldn't be worse. As someone who studies the intersection of technology and workplace culture, I've observed a clear pattern: the more we automate routine tasks, the more our success depends on distinctly human qualities like trust, emotional sensitivity, and the ability to navigate complex interpersonal dynamics. Yet we're systematically dismantling the very cultural foundations that enable these qualities to flourish. It's as if we're entering a boxing match by tying one hand behind our back – at precisely the moment we need every advantage we can get.

The real crisis isn't that AI might replace jobs – it's that we're creating workplace environments that suppress the very qualities that make us irreplaceable. When we treat our colleagues as mere interfaces rather than complex human beings, we don't just damage relationships – we damage our capacity for innovation, creativity, and the kind of deep collaboration that complex problem-solving requires.

Some companies are starting to get it right. When I look at examples like IKEA, who chose to retrain their call center workers as interior design advisors rather than simply replacing them with chatbots, I see a glimpse of what's possible. They recognized something profound: you can't automate the human ability to understand what a frustrated customer really needs, or the intuition to read between the lines of what they're saying.

This is what I call the "human edge" – and it's far more nuanced than most leadership teams realize. It's the marketing manager who can sense team tension during a video call and address it before it derails a project. It's the sales representative who builds such strong relationships that clients stay loyal through market upheavals. It's the team leader who knows exactly when to push for more and when to show compassion. These aren't just nice-to-have soft skills – they're becoming our most valuable business assets.

But here's the challenge: we're still trying to measure workplace success like it's 1990. We track productivity metrics, sales numbers, and project timelines, but how do we quantify someone's ability to defuse a tense client situation? How do we measure the value of a team leader who creates an environment where people feel safe to innovate? These human capabilities – empathy, emotional intelligence, relationship building, creative problem-solving – are increasingly what separate successful companies from failing ones, yet they're nearly impossible to capture in a performance review.

When I talk to business leaders, I tell them bluntly: if a job can be reduced to a process, AI will eventually do it better. Our value lies in all the messy, human things that happen between the bullet points of a job description. Instead of asking "How many tasks did you complete?" we should be asking "How did you help your team navigate that difficult change?" Instead of training people to follow processes, we should be developing their ability to build relationships and navigate complexity.

It's time we started treating these human capabilities not as soft skills, but as core business competencies. The question isn't whether AI will change work – it's whether we'll use this moment to finally build workplaces that enhance rather than diminish our humanity.

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Nada Ahmed is the founding partner at Houston-based Energy Tech Nexus and author of Amazon Bestseller “Determined to Lead- The Disruptive Woman's Guide to Stop Playing Small and Transform your Career through Agile Leadership.”

Houston robotics co. closes series B after year of growth

money moves

Houston- and Boston-based Square Robot Inc. closed a series B round of funding last month.

The advanced submersible robotics company raised $13 million, according to Tracxn.com, and says it will put the funds toward international expansion.

"This Series B round, our largest to date, enables us to accelerate our growth plans and meet the surging global demand for our services,” David Lamont, CEO, said in a statement.

The company aims to establish a permanent presence in Europe and the Middle East and grow its delivery services to reach four more countries and one new continent in Q1 2025.

Additionally, Square Robot plans to release a new robot early next year. The robot is expected to be able to operate in extreme temperatures up to 60 C. The company will also introduce its first AI-enabled tools to improve data collection.

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.

The company was one of the first group of finalists for the Houston Innovation Awards' Scaleup of the Year, which honors a Bayou City company that's seen impressive growth in 2024. Click here to read more about the company's growth.

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This article originally ran on EnergyCapital.