How to Build an Effective Data Strategy for Your Business

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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. 

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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. 

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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.” 

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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. 

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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 .Trends to dig up some data to explore how the insights might inform our strategy. 

The first thing we might do when thinking about 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 broad by looking at some industry reports and reading up about top players in the industry. Then, we could use the Market Explorer Overview Report 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. 

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Looking at the left side of the screen, we discover the market has a moderately low-level of consolidation, with Trek as the market leader with 47% of the market share. Specialized and Giant are next in line with 25% and 11% 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 the market traffic this month is up by 15%, and the market size is large with some room to grow. These are all good signs for our company. 

But, as any business owner or marketer knows, it’s important to look at data across time. What happens when we look at the market over the last year? 

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Looking to the right side of the screen, we discover some less encouraging signs. Though the month of October looks strong, over the past year market traffic has declined nearly 24%. Likewise, we can see some growth and decline in the market size. This is likely due to seasonality, as the peak in market size is centered around the spring and summer months. 

Moving from data to strategy: 

  • Trek has consolidated almost half the market—we might look for segments of the market where Trek’s hold isn’t so strong. This way we can gain a foothold as we expand. 
  • Market Traffic has declined over the past year—we might focus on getting our bikes into brick and mortar locations first. 
  • There’s a large peak in market expansion in June, which could indicate strong seasonality in our market—we might launch our website in the spring and work on driving traffic during the seasonal spike in early summer.

Analyzing the market audience

Now, let’s turn our attention to the market audience. The Audience report in Market Explorer can provide some data regarding demographic, socioeconomic, and employment statuses. Here’s a look at the Demographics.

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For the bicycle market, nearly 80% of the audience are men and most are millennials (between ages 25-44). Interestingly, looking at the right hand side of the chart, it appears the percentage of women in the market are higher in the 18-24 category and the 65+ category. 

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Looking into the Socioeconomics of our market, we discover some additional insights. Our audience is composed mostly of people who work full time (52%), who have a college degree (48%), have a low or medium income (49% low and 30% medium), and live in a home with 3-4 people (38%). 

Moving from data to strategy: 

  • Our market is mostly men in their 30’s, though younger and older women are more likely to exist in the market—considering the trend around younger and older women, we shouldn’t ignore bikes designed for women in our manufacturing and marketing strategy. 
  • Our audience works-full time, but they don’t earn massive incomes—We need to build bikes that are affordable for people. We shouldn’t sacrifice quality, but we need to factor in price.
  • Most of our customers likely have kids—We should create bikes for every member of the family and be sure to include family focused images and language in our marketing.

Dissecting competitor strategies 

Knowing how competitors market to their audiences provides insights into what works and illuminates opportunities. Using the Market Explorer Benchmarking Report, let’s compare strategies among major players in the bicycle market.

Here’s a look at the Traffic Generation Strategy and Social Media Distribution Strategy graphs. 

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Examining outliers can often lead to interesting insights. For example, on the right, we discover that Giant Bicycles has the largest traffic percentage coming from Search. And on the right, we find GT Bicycles gets nearly 80% of their social media traffic from Youtube. Likewise, only Specialized and Giant seem to be using instagram. 

Moving from data to strategy: 

  • Giant Bicycles receives 63% of their traffic from Search Engines—Something about Giant’s SEO strategy is working. We might analyze their content and figure out what they’re doing. Then, we might try to capitalize on some keywords they rank for. 
  • GT Bicycles receives nearly 80% of their traffic from Youtube—We might explore GT’s youtube channel. When it comes to bicycles, high-quality and high-action video content may be appealing to customers, so we might invest in video content.
  • Only Specialized and Giant use Instagram, and their traffic from the platform is under 3%—Instagram seems like a missed opportunity. We could probably beat Specialized and Giant’s strategy on Instagram and capture some additional traffic. 

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? 
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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. 

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