How to Build an Effective Data Strategy for Your Business

From 2021 to 2022, online traffic increased worldwide by 25%. This growth reflects both a significant expansion in the number of internet users, as well as an increase in the number of companies offering content, products, and services online. 

With more and more business happening online, business leaders are recognizing the power and importance of data in developing better digital business strategies. 

A 2022 Data and AI Leadership Executive Survey showed that 91.7% of companies are increasing investments in data and AI, and 92% are already achieving returns on their investments. This trend is reflected in search volumes for the keyword “data”. Comparing 2022 to 2018, global monthly search volumes have increased by 350K. 

Of the companies surveyed, however, only 26% reported they’ve achieved the goal of becoming data-driven. The remaining companies are still working to make data a fundamental part of their culture. This gap reflects the challenges businesses face in implementing data. 

In this article, we’ll explore the topic of data strategies from several angles. And to provide the best possible insights, we’ll refer to the expert advice provided by industry experts in the recent Semrush webinar: “How to Implement Data to Strengthen Your Marketing Strategy.” 

Why Is a Data Analytics Strategy So Important for Businesses? 

Running a business requires constant adaptation and improvement. Businesses need to track and analyze a variety of important metrics to adjust to changing market conditions and gain a competitive advantage. Some of those metrics include: 

This list is by no means inclusive, but each of these examples require data to measure. As webinar guest, Natalie Luneva, a SaaS Entrepreneur and Growth Advisor, says, “What gets measured gets improved.” This idea gets to the fundamental importance of working with data in the business context. Data is key to understanding, and understanding allows for improvement.

How to Overcome Data’s Biggest Challenges 

While data can provide a big competitive advantage, building a data strategy framework isn’t easy. This is the reason why only 26% of the surveyed companies said they’ve successfully become data-driven. The rest are still overcoming challenges. Let’s take a look at a few struggles businesses face with regard to data and listen to the experts to figure out how to overcome them. 

A changing data landscape 

In the last decade, businesses have benefited tremendously from targeted marketing made possible by third-party data providers. But with Google and other big data providers phasing out third-party cookies, many businesses are wondering what the future of data will look like. 

This change also speaks to a larger issue. New technologies, consumer sentiment, and changing regulations will impact the kinds of data that are available and how you can use it. For companies, this means keeping a close watch on the ever changing data landscape. 

What’s the solution? 

In a world of heightened data privacy, new data sources and new data management strategies are becoming crucial. As one solution, Expert Marketer, Martin Henning, suggests, “the earlier companies begin to switch to a first-party data strategy the better.” 

First-party data is collected by companies directly from their customers. In order for a first-party strategy to work, quality content that attracts visitors will become increasingly important. “If the content is that good,” Hennig continues. “I’m confident that customers will find their way to us.” 

Additionally, in this new era of restricted data access, segmentation, channel awareness, and a focus on broader trends will become increasingly important for long term success. 

Aligning teams 

Companies often struggle to figure out how to help teams collaborate and use data to track and achieve common goals. Different business units are often interested in different metrics. In isolation, there’s no problem with the sales team focusing on sales metrics and the marketing teams focusing on marketing metrics. But what happens when these units need to work together? Confusion around the most important metrics is a common challenge. 

A possible solution: 

When collaborating, Luneva suggests, “Bringing different types of data into one common denominator.” Across teams, she says, “We’re often using different languages. But once we’re able to bring all of those data points and metrics together into a single common denominator, everything becomes easier.” 

Along similar lines, Hubspot’s SEO Team Lead, Jennifer Lapp, spoke to her success collaborating across teams. “We created shared goals,” she says. “And the alignment in the efforts between those teams has helped us understand what we should be measuring.” 

Before diving into work across units, take time to discuss goals, define key metrics, and think about what Key Performance Indicators (KPIs) best reveal success. 

Translating data for stakeholders 

Just as different business units are interested in different kinds of data, stakeholders often aren’t interested in fine details of the data. They’re most interested in the impact on the bottom line. As Lapp says, “The struggle is finding ways to deliver data so it generates buy-in and support from leadership.” Unfortunately, unclear communication or a focus on the wrong details can leave stakeholders confused, or worse, resistant to further support for a given project. 

A possible Solution: 

The skill of translation data for individuals outside of your team is key. It takes not only practice, but time to translate data in an effective way. Likewise, as with any kind of presentation, it’s crucial you understand your audience. “You need to tie the data to business metrics they care about,” Marcus Tober, Head of Enterprise Solutions at Semrush, suggests, “Translate it into a language stakeholders understand.”

How do you turn data into data-driven strategies? 

So far, we’ve disused the importance of data and some of the challenges. Now, let’s take a look at how to actually work with some data to generate strategy-strengthening insights. 

For these examples, we’ll pretend we work for a small bicycle manufacturer who wants to expand into the broader U.S. bicycle market. We’ll use Semrush Traffic & Market Toolkit to dig up some data to explore how the insights might inform our strategy.

Exploring market trends

The first thing we might do when trying to bring our small bicycle manufacturing company to a broader national audience is take a look at the market as a whole. We could start broadly by looking at some industry reports and reading up about top players in the industry. 

Then, we could use the Market Overview dashboard to do some research on the digital landscape.

Here’s a look at the Market Summary for six of the top domains in our bicycle market in the month of October, 2022.

Looking at the left side of the screen, we discover the market has an average level of consolidation, with Trek as the market leader with 45% of the market share. 

Specialized and Giant are next in line with 38% and 17% of the market share. For a new entrant, this market may be a little challenging to enter, but by no means impossible. 

We can also see that the TAM far exceeds the SAM, meaning the market has room to grow. 

Moving from data to strategy: 

• Differentiate to Break Into a Consolidated Market
  With Trek holding 45% of the market and the next two players controlling most of the rest, new entrants must identify underserved segments or unmet needs. Focus on product differentiation, unique branding, or specialized features to avoid head-on competition with dominant players.
• Prioritize High-Potential Growth Areas
  The significant gap between TAM and SAM signals untapped market potential. Target strategies toward converting more of the TAM into SAM—such as expanding product lines, reaching new customer segments, or optimizing distribution in underpenetrated regions.
• Position for Market Share Expansion Over Time
  Although initial entry may be difficult, long-term gains are possible by gradually capturing niche audiences and scaling. Use competitor benchmarking to identify weaknesses in existing offerings and build a strategy that chips away at the incumbents’ share over time.

Analyzing the market audience

The Demographics dashboard offers a clear view of who’s engaging with top bicycle brands like Trek, Specialized, and Giant. 

The market is overwhelmingly male-dominated, with over 70% of the audience identifying as male across all three brands. 

In terms of age, the audience skews younger to middle-aged, with the highest concentration between 25–54 years old. Trek and Giant, in particular, show strong engagement from users aged 45–54, while Specialized stands out slightly among the 25–34 demographic.

Moving from Data to Strategy

1. Target the Core Buyer—but Don’t Ignore the Edges
   With men aged 25–54 making up the bulk of the market, initial campaigns and product lines should cater directly to this group. However, the data reveals that certain brands perform better with younger and older audiences. These underserved segments present opportunities for differentiation—through bikes designed with female riders in mind and marketing that reflects their presence.
2. Develop Age-Tailored Messaging and Models
   The diversity in age distribution—from younger millennials to boomers—calls for product segmentation. Feature performance and innovation for the 25–44 bracket, while emphasizing comfort, lifestyle, or family utility for the older segments

Dissecting competitor strategies 

Knowing how competitors market to their audiences provides insights into what works and illuminates opportunities. Using the Traffic Overview dashboard from Traffic & Market Toolkit, let’s compare strategies among major players in the bicycle market.

Trek leads the bicycle market in both traffic and engagement, with 5.3M monthly visits, the highest purchase conversion rate (0.65%), and the lowest bounce rate (43.1%). Giant, while smaller in scale, shows strong engagement depth—boasting the highest visit duration (8:34) and tying Trek in pages per visit (5.3), signaling high content interest. Specialized sits in the middle with solid traffic but lower conversion (0.16%) and a higher bounce rate.

When it comes to traffic sources, Direct and Organic Search dominate. Trek leads in Organic Search traffic, while Giant performs surprisingly well in SEO given its size. 

Referral, Paid Search, and Social channels are nearly absent across all players—highlighting missed opportunities. Competitors appear to be underinvesting in Email, Display Ads, and Paid Social, leaving space for a challenger to stand out with smart targeting and retargeting strategies.

Moving from data to strategy: 

• Double down on SEO by analyzing what’s driving Trek and Giant’s organic performance. Identify high-ranking keywords and content formats that resonate with their audiences—and build out targeted pages to compete in those spaces.
• Boost engagement through smart content by taking a cue from Giant’s high visit duration and pages per session. Develop content that keeps users exploring—like detailed product guides, comparison tools, or interactive features.
• Capitalize on underused channels like Email, Display, and Paid Social. With competitors barely investing here, there’s a clear opportunity to stand out with retargeting ads, lifecycle email campaigns, and social media promotions that drive both traffic and conversions.

The Big Picture: Does Your Business Need a Data Governance Strategy?

Now that you know why data is important, what some of the challenges are, and how to derive insights from data, you may be tempted to jump right in! First, we want to discuss Data Governance, which is an idea that will help you develop some guidelines for working with data inside your organization. 

Data governance is a big topic that can get quite complicated pretty quickly. For the sake of this article, we don’t need to get too deep into the weeds. To put it simply, data governance is the overall practice of gathering, organizing, and managing data across a business. The strategy component of data governance relates to those specific processes, procedures, and guidelines businesses put in place around data within the organization. 

Here are a few examples of issues that might be addressed in a data governance strategy: 

• How do we gather data? 
• What kinds of data do we gather and what data do we avoid? 
• How do we access and share data? 
• How do we ensure data quality and data security? 
• How do we store, organize, document, and discard data? 
• Who is in charge of what data and what is the process for sharing it? 

As a set of guidelines, a data governance program ensures that data is accessible, safe, and of high quality. As your business begins to grow, you can revisit your data governance practices and make adjustments as needed. Ultimately, the most important thing for any business is to avoid a haphazard approach to your data. Clarity, when it comes to working with data, is key.