Houston expert shares how to implement thought leadership into your B2B marketing

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What is thought leadership and how can it help you achieve your marketing goals? This Houston expert explains. Photo via Getty Images

Did you know that 52 percent of decision-makers and 54 percent of C-level executives spend an hour or more per week reading thought leadership content? This is according to a recent Edelman and LinkedIn survey on thought leadership.

I often counsel my clients about the role of thought leadership in B2B marketing. Thought leadership remains a strategic approach that can set a company apart, establish credibility and a strong brand voice and position it as a trusted expert in its industry. But what exactly is thought leadership, and how can it support a B2B marketing strategy?

Why a thought leadership strategy matters

Thought leadership marks a commitment to provide value through insights beyond mere selling. It involves producing content and ideas that address the company's target audience's most pressing challenges and questions. This content helps position the company as a service partner, go-to resource and industry advisor.

Builds credibility and trust: Trust remains vital in a B2B context where longer sales cycles and purchasing decisions undergo scrutiny. Thought leadership lets a company demonstrate its expertise, solution-based thinking and value meaningfully to decision-makers. According to industry data, an estimated 75 percent of decision-makers say an organization's thought leadership content is more trustworthy for assessing its capabilities and competencies than its marketing and product sheets.

Differentiates from competitors: By sharing insights, a company can differentiate itself in a crowded market. Thought leadership helps companies stand out by proving their deep understanding of the customer's challenges and needs and the solutions available for more efficient and cost-effective operations.

Enhances brand awareness: Regular publication of insightful content, whether through blogs, webinars or white papers, can increase brand visibility and keep the company top of mind for customers and potential customers.

Supports sales efforts: Well-crafted thought leadership content can powerfully warm up leads. It provides sales teams with material that resonates with prospective customers' pain points and aspirations. According to the Edelman report, nine in 10 decision-makers and C-suite executives said that they are moderately or very likely to be receptive to sales or marketing outreach from a company that consistently produces high-quality thought leadership.

How to implement a thought leadership strategy

Identify key insights and topics: Start by understanding the questions and challenges the target audience faces. Use this insight to create content that addresses these issues, offers solutions or provides novel perspectives. Include strong research and data, and offer case studies or practical steps. Depending on where the audience spends its time, consider publishing on LinkedIn, industry blogs, podcasts or webinars.

Remember that consistency is key: Thought leadership isn't a one-and-done approach. Build an ongoing and consistent content program. Keeping to a schedule helps maintain audience engagement and reinforces the organization's position as an industry leader.

Measure and adapt: Like any marketing strategy, measuring the effectiveness of your thought leadership efforts remains critical. Setting clear objectives provides the foundation for defining success and measuring outcomes effectively. Metrics could include media coverage, website traffic, social media engagement and business development leads. Additionally, sales impact can be measured by actions such as first-time discovery calls and sales-qualified leads.

Thought leadership proves an invaluable strategy for B2B marketing. It aims to assert the expertise of a company and build meaningful connections with its audience. A business can establish a strong, credible brand that attracts and retains customers by providing valuable insights and solving real-world customer challenges through high-quality content.

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Melanie Taplett provides communications and public relations services to the energy, manufacturing, technology, engineering and construction industries. Contact her at mtaplett@taplycom.com.

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How you can use your data to improve your marketing efforts. Photo via Getty Images

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

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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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Rice University MBA programs rank among top 5 in prestigious annual report

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Rice University’s Jones Graduate School of Business MBA programs have been ranked among the top five in the country again in The Princeton Review’s 2025 Best Business Schools rankings.

The university's MBA program in finance earned a No. 3 ranking, climbing up two spots from its 2024 ranking. Finance MBA programs at the University of Virginia's Darden Graduate School of Business and New York University's Leonard N. Stern School of Business were the only ones to outrank Rice, claiming No. 2 and No. 1 spots, respectively.

Rice's online MBA program was ranked No. 5, compared to No. 4 last year. Indiana University's Bloomington Kelley School of Business' online program claimed the top spot.

“These rankings reflect the commitment of our faculty and staff, the drive and talent of our students and the strong support of our alumni and partners,” Peter Rodriguez, dean of Rice Business, said in a news release. “They are exceptional honors but also reminders — not just of our top-tier programs and world-class faculty and students but of our broader impact on the future of business education.”

Rice also ranked at No. 6 for “greatest resources for minority students."

The Princeton Review’s 2025 business school rankings are based on data from surveys of administrators at 244 business schools as well as surveys of 22,800 students enrolled in the schools’ MBA programs during the previous three academic years.

"The schools that made our lists for 2025 share four characteristics that inform our criteria for designating them as 'best': excellent academics, robust experiential learning components, outstanding career services, and positive feedback about them from enrolled students we surveyed," Rob Franek, The Princeton Review's editor-in-chief, said in a press release. "No b-school is best overall or best for all students, but to all students considering earning an MBA, we highly recommend these b-schools and salute them for their impressive programs."

Rice's finance program has ranked in the top 10 for eight consecutive years, and its online MBA has ranked in the top five for four years.

Rice and the University of Houston also claimed top marks on the Princeton Review's entrepreneurship rankings. Rice ranks as No. 1 on the Top 50 Entrepreneurship: Grad list, and the University of Houston ranked No. 1 on Top 50 Entrepreneurship: Ugrad. Read more here.

Houston named ‘star’ metro for artificial intelligence in new report

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A new report declares Houston one of the country’s 28 “star” hubs for artificial intelligence.

The Houston metro area appears at No. 16 in the Brookings Institution’s ranking of metros that are AI “stars.” The metro areas earned star status based on data from three AI buckets: talent, innovation and adoption. Only two places, the San Francisco Bay Area and Silicon Valley, made Brookings’ “superstar” list.

According to Brookings, the Houston area had 11,369 job postings in 2024 that sought candidates with AI skills, 210 AI startups (based on Crunchbase data from 2014 to 2024), and 113 venture capital deals for AI startups (based on PitchBook data from 2023 to 2024).

A number of developments are boosting Houston’s AI profile, such as:

Brookings also named Texas’s three other major metros as AI stars:

  • No. 11 Austin
  • No. 13 Dallas-Fort Worth
  • No. 40 San Antonio

Brookings said star metros like Houston “are bridging the gap” between the two superstar regions and the rest of the country. In 2025, the 28 star metros made up 46 percent of the country’s metro-area employment but 54 percent of AI job postings. Across the 28 metros, the number of AI job postings soared 139 percent between 2018 and 2025, according to Brookings.

Around the country, dozens of metros fell into three other categories on Brookings’ AI list: “emerging centers” (14 metros), “focused movers” (29 metros) and “nascent adopters” (79 metros).

Forecasting retail sales just got smarter, thanks to Google searches

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Imagine being able to predict the future of retail sales — not with months-old economic reports, but with real-time digital breadcrumbs left behind by consumers. Thanks to new research from K. Ramesh (Rice Business) and Gary Lind (University of Pittsburgh), that future is already here.

Key findings

  • Search trends predict future retail revenue — up to three quarters ahead.
  • Markets don’t fully price in search-based insights, especially before earnings announcements.
  • Trading strategies using search data outperform traditional asset-pricing models by 2–3%.
  • Analysts often underuse this data, resulting in predictable forecast errors.
  • At the macro level, search trends closely track — and often lead — official retail sales and GDP.

Their study shows that Google search trends are powerful predictors of consumer demand, capable of forecasting U.S. retail sales up to three quarters in advance. The implications span industries and audiences — from Wall Street traders to Main Street retailers, and from data-driven CFOs to curious business students.

A smarter, faster crystal ball for retail demand

Retail sales drive nearly 70% of U.S. gross domestic product (GDP), making them one of the most important indicators of economic health. Yet traditional forecasting relies heavily on backward-looking data and broad economic indicators. Ramesh and Lind’s work offers a leap forward: search data from Google Trends — publicly available and easy to use — can offer a faster, more precise signal of where sales are headed.

“If you want to understand consumer demand,” says Ramesh, the Herbert S. Autrey Professor of Accounting at Rice Business, “look at what people are searching for — not just what companies report after the fact. These digital footprints can tell us where revenue is going, often before the market catches up.”

This low-cost approach stands in stark contrast to expensive alternatives, such as satellite images of parking lots, in-store foot traffic from mobile data, or retail scanner feeds — tools often reserved for hedge funds and large institutional investors. Google Trends offers a scalable, accessible option for anyone seeking insight into consumer behavior.

Why this matters for investors and analysts

The researchers analyzed search patterns for nearly 200 publicly traded U.S. retail companies between 2004 and 2019. They found that firms with rising search volumes tend to report higher revenues — often well before analysts or investors react.

In fact, investment strategies built around these trends earned 2–3% higher returns than traditional models would predict. And most of these gains were concentrated around earnings announcements — a sign that markets were slow to recognize what search data had already revealed.

The advantage is even more striking for companies that fly under Wall Street’s radar. For firms with limited analyst coverage, the predictive power of search data on stock performance was more than 80% stronger.

A new tool for analysts — if they’ll use it

Despite the clear signal in the data, the study finds that analysts routinely fail to incorporate search-based insights into their forecasts. That gap translates into missed revenue predictions and market inefficiencies — which sophisticated investors can exploit.

“The search data are out there,” says Lind, a Rice Business Ph.D. ’19 graduate now on faculty at Pitt Business. “They’re public, powerful, and free. Analysts who ignore them are missing an opportunity.”

The impact of this research extends well beyond equity markets. By aggregating Google search data across the retail sector, the authors found strong alignment with U.S. Census retail sales figures and GDP trends. In other words, these digital signals reflect not just firm-level interest, but macroeconomic momentum.

A data wake-up call

This makes search trends a potentially valuable early-warning system for policymakers, central banks, and anyone responsible for interpreting the economic landscape. During volatile or uncertain periods, such tools could offer precious foresight into shifting consumer behavior.

In a digital era where consumers research before they shop — whether online or in-store — it’s no surprise that their Google queries leave valuable clues. What’s surprising is how often businesses, investors, and even economists still overlook them, Ramesh says. “Search data is a goldmine. And it's just sitting there, waiting to be used.”

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This article originally appeared on Rice Business Wisdom.

Lind and Ramesh, “Using Internet Search Data to Predict Aggregate Retail Sales and Enhance Firm-Level Revenue Expectations,” Contemporary Accounting Research (April 2025): 1-32. https://doi.org/10.1111/1911-3846.13043.

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