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Act before the market
hen starting a webshop, you have two options: build a custom site from scratch or choose a ready-to-go commerce platform to manage inventory and sell products or services online. Given that webshops have existed since the early days of the internet, there are now numerous providers catering to both entrepreneurs and established businesses.
A variety of commerce platforms power European webshops, from large international providers like Shopify and WooCommerce to smaller local specialists such as Dandomain in Denmark and Voog in Estonia. Larger platforms often offer the benefits of scale, while local providers might offer specialized solutions and compliance with regional regulations that enhance scalability.
Choosing the right platform is not just important for those building webshops, but also for the ecosystem surrounding commerce platforms. Not all plug-ins and solutions are compatible with every framework, and understanding a platform’s market penetration can be a strong indicator of its success and investment in that region.
In this article, we take a deep dive into the most widely used commerce platforms across 10 European markets, examining which solutions are the most popular. It’s likely no surprise that Shopify and WordPress’s open-source WooCommerce plugin dominate, but who are the other key players?
Looking at Switzerland, The Netherlands, Slovakia, Denmark, Finland, Sweden, Norway, Lithuania, Latvia and Estonia we’ve identified a total of 242.061 active webshops. With over 100.479 webshops, or 32%, Shopify is trailing behind WooCommerce with 9%. Looking at these 10 markets, WooCommerce is today the preferred e-commerce platform with around 129.480 webshops.
The fact that we only identified 6.682 custom-built webshops (2,1% of the dataset), shows just how powerful commerce platforms are today, where both large and small webshops can benefit from the platform's investments in technology and solutions that make it easy, and possible, to operate and grow a business online.
Before diving into the specifics of each market’s platform penetration, let’s quickly explain how we gather and maintain the quality of this data.
Monitoring hundreds of thousands of webshops on an ongoing basis demands a robust validation process to maintain high-quality data. At Tembi, we automatically filter out inactive webshops, businesses in bankruptcy, and webshops not registered as official companies, and we can only to this by actually visiting the webshops and analyze their operations continuously. We’re not B2B lead list generation company per se, but our data is used by many companies to improve sales and help identify business opportunities.
Once the validation process is complete, and we’ve analyized the webshops products, our system categorizes each webshop into a product category and enriches the data with for example website traffic data and company data.
If you're interested in learning more about how our technology works, be sure to check out our article: Insights from every Webshop on the Market
Having looked how the distribution looks over 10 European countries, let’s examine which E-Commerce platforms are popular in each country and see what insights we can uncover into regional preferences and market trends.
In Denmark, we can find a total of 32.720 webshops. We have identified that 13.567 webshops are built using WooCommerce, and 11.703 are built with Shopify. Just as it also shows in the picture of the ten European markets, WooCommerce and Shopify power the majority of the webshops. The remaining 24% (7.450 webshops) utilize various other providers. With 2.164 webshops, Dandomain stands as the third most used platform in Denmark, likely due to its local roots and strong integration with popular hosting services in the country.
Estonia has a total of 8.568 webshops, with WooCommerce as the clear market leader. WooCommerce is used by 5.846 webshops, representing 68% of all Estonian market. In second place, like in most markets, Shopify follows, but with only 9% of the market, totaling 739 webshops. WooCommerce’s strong presence in Estonia gives it the highest market share in the group of the analysed countries. In third place we find the local e-commerce platform, Estonian Voog, powering 570 webshops. Voog offers native language support and is perfect for small to medium-sized companies, which could also explain why WooCommerce owns such a big portion of the market.
The remaining 23% of E-Commerces, without the ones using WooCommerce and Shopify, are built using various other providers (1.983 webshops).
Finland has a total of 15.092 webshops, with WooCommerce and Shopify being the market leaders. 6.953 webshops in Finland use WooCommerce (45% of the Finnish market), while Shopify is used by 4.014 webshops, accounting for a 26% market share.
The remaining 28% (4,125 webshops) utilize various other providers. Notably, 644 webshops (5% of the market) are custom-built, highlighting a segment of businesses opting for fully tailored E-Commerce solution. With a strong tech and design culture, Finnish businesses likely leverage local expertise to create bespoke solutions cater directly to their target market. MyCashFlow, a Finnish E-Commerce Platform, is the third most used one in the country, accounting with 1.327 webshops, a 9% of the total.
There are 4.903 webshops in Latvia. Of this number, 1.841 webshops are built with WooCommerce (37% of Latvian webshops) and 1.201 webshops are built with Shopify (24%). The other 1.861 webshops (38%) use different providers.
Lithuania has a total of 12.077 webshops, with WooCommerce as the most popular platform, powering 6.568 stores, or 55% of the market. Shopify is the second most used (2.198 webshops) making up 18% of Lithuanian online stores. The remaining 26% (3.311 webshops) use various other providers, with PrestaShop ranking third, supporting 1.506 webshops and capturing 12% of the market. As we can see, PrestaShop ranks very closely to Shopify. We see how two Lithuanian E-Commerce platforms, such as Shopiteka and Verskis, are too the most used ones.
The Netherlands have a highly developed E-Commerce market with 81.224 webshops. WooCommerce has by far most clients, powering 38,316 stores, or 46% of all online shops. Shopify follows with 21,534 webshops, accounting for 26% of the market. The remaining 27%, or 21.374 stores, are distributed across various other providers.
Norway has an E-Commerce market with 13.469 webshops. WooCommerce leads the way, powering 5.346 webshops, or 39% of the market. Shopify is a close second, used by 4.931 webshops, making up 36% of the market. The remaining 24%, or 3.192 webshops, utilize various other providers. The competition between Shopify and WooCommerce is tight in Norway, with only 415 webshops more (a 3%) built with the latter. The third one is MyStore, an E-Commerce provider created in Norway.
There are 15.429 webshops in Slovakia. WooCommerce leads the market, powering 6.399 of these webshops, accounting for 41%. Shoptet follows with 3.502 webshops, making up 22% of the market. The remaining 36%, or 5.528 webshops, are built using a variety of other providers. Slovakia’s case is specially interesting, as Shopify is not the second choice. In its place we find Shoptet, a Czech platform that offers marketplace integrations to the Central European market. This can be relevant for companies looking to increase visibility and brand recognition in the region.
Sweden's E-Commerce landscape is strong, with a total of 31.588 webshops. WooCommerce has a solid position on the market, powering 13.293 of these stores, or 39%, showcasing its popularity among Swedish businesses. Shopify isn’t far behind, with 11.354 webshops, making up 34% of the market. The other 6.941 webshops, representing 26%, use a variety of different providers. We find similar data in Norway, the competition between WooCommerce and Shopify is close, with only a 4% market share of difference (roughly 2.000 webshops).
Switzerland is home to 26.991 webshops, with WooCommerce and Shopify leading the market. 12.168 of these webshops are built with WooCommerce (45% market share), making it the most popular E-Commerce platform in the country. Shopify follows closely, with 9.841 webshops, representing 36% of the market. The remaining 19% (4.739 webshops) are built using different providers. Of the most used platforms in Switzerland, only PepperShop is Swiss company.
The data from analyzing 242.061 webshops confirms that WooCommerce and Shopify hold a dominant position, commanding 73% of the market share. Breaking this dominance is no easy task, as it would not only require mass migration but also new solutions that offer greater value than the globally leading commerce platforms.
However, despite the dominance of these major providers, there are still over 80.000 webshops using other frameworks. For instance, with over 15,000 webshops on PrestaShop and more than 13,000 using Magento, there remains a significant opportunity to develop plug-ins and solutions for these platforms.
Whether you're developing plug-ins or building software reliant on specific frameworks, understanding your total addressable market (TAM) is a key indicator of potential and helps determine if an investment is worthwhile. Additionally, knowing how different markets are penetrated provides insights into where to focus future sales and marketing efforts. The more data you have, the better informed your decisions will be.
If you’re interested in more data around the webshops, don’t hesitate to contact us on hello@tembi.io. We are adding more countries continuously so sign up for our newsletter to get the latest updates.
he amounts of available data is growing in an overwhelming speed, on one hand presenting an increased difficulty to collect and access the data, on the other hand an increased opportunity to better understand markets and competitors.
With continuously increased computing power and a steadily growing democratisation of access to advanced analytics, the way we approach decision-making is evolving. What has been historically a process of intuition and experience is now increasingly guided by data-driven insights. This transformation is enabling companies to not only understand past and present trends but also to predict and shape future outcomes.
Let’s dive into how data and analytics are reshaping business decision-making, from traditional methods to the advanced analytics techniques of the future.
Traditionally, business decisions were often made based on intuition, experience, and a limited set of data. Executives relied heavily on their gut feelings or the historical knowledge of their industry. While this approach worked in the past, it more than often led to suboptimal outcomes due to the lack of comprehensive information and understanding of the market.
The emergence of data-driven decision-making marked a significant shift in this process. Businesses began to collect and analyse large internal and external datasets, to inform their strategies and tactics. A development that has been rapidly accelerated by the introduction of BI software. Decisions were no longer solely based on instinct but were supported by quantitative evidence.
As technology advanced, so did the decision-making process. We have now entered an era of analytics-driven decisions, where businesses use sophisticated analytical tools to forecast future trends (predictive analytics) and even prescribe specific actions to achieve desired outcomes (prescriptive analytics). For instance, Amazon uses predictive analytics to manage inventory, ensuring that products are in stock when customers want them while minimising storage costs. Our company, Tembi, has developed a beta product that uses prescriptive analytics to recommend development and construction companies what to build in certain locations to reach full capacity. And this is the only beginning of how data and analytics will assist us in making better decisions.
To understand the full impact of analytics on decision-making, it’s essential to explore the concept of the Analytics Value Escalator developed by Gartner. This model describes the progression of analytical methods, each offering increasing value and complexity.
1. Descriptive Analytics
Descriptive analytics answers the question, “What happened?” It involves summarising historical data to understand past performance. For example, sales reports, web analytics, and financial statements fall into this category. While descriptive analytics provides valuable insights, it is often limited to hindsight and does not explain the reasons behind the data.
2. Diagnostic Analytics
Diagnostic analytics delves deeper, addressing the question, “Why did it happen?” By identifying correlations and patterns within the data, businesses can uncover the root causes of specific outcomes. This method is more powerful than descriptive analytics but still focuses on past events.
3. Predictive Analytics
Moving up the escalator, predictive analytics answers the question, “What is likely to happen?” It uses historical data, machine learning algorithms, and statistical models to forecast future trends and behaviors. For example, retailers might use predictive analytics to anticipate customer demand or optimise inventory levels.
4. Prescriptive Analytics
At the top of the escalator is prescriptive analytics, which addresses the question, “What should we do?” This advanced method not only predicts future outcomes but also recommends specific actions to achieve the best possible results. For instance, a logistics company might use prescriptive analytics to determine the most efficient delivery routes, considering variables like traffic, weather, and fuel costs.
No matter how advanced the analytics methods are, their effectiveness is fundamentally dependent on the quality of the data they analyse. Poor quality data or analytics conducted on incomplete data-sets can lead to misleading conclusions and can hence create unreliable insights.
Common data issues include data silos, where information is trapped in isolated systems; inconsistent data formats; and incomplete or outdated data.
To ensure data quality, businesses must adopt best practices such as regular data cleaning, integration across departments, and robust data governance policies.
For instance, Procter & Gamble invested in a comprehensive data governance framework to ensure consistency and accuracy across its global operations, which has been crucial in maintaining the integrity of their analytics initiatives.
“We’re also now able to take our data analytics and AI to the next level because we have a solid, reliable base of product data that can be matched with external consumer data. That possibility gets our business leaders really excited!”
Laura Becker, President of Global Business Services at Procter & Gamble
Generative AI, a cutting-edge technology that enables machines to create new and original content, has revolutionised various industries by producing text, images, music, and even complex data patterns. Its ability to generate content that mimics human creativity has opened up exciting possibilities in fields like marketing, design, entertainment, and more. However, despite its remarkable capabilities, generative AI faces notable limitations, particularly in the context of business decision-making.
In business environments, decision-making often requires a deep understanding of nuanced contexts, the ability to interpret complex and sometimes ambiguous data, and the capacity to foresee the broader implications of certain choices. While generative AI can assist by providing insights, generating scenarios, or offering creative solutions, it lacks the human intuition and judgment needed to fully comprehend the strategic, ethical, and long-term consequences of business decisions.
Another significant limitation is the lack of transparency in how generative AI models arrive at their outputs. These models often function as "black boxes," where the decision-making processes are not easily interpretable or understandable, even to those with technical expertise. This opacity can be problematic in business settings, where leaders need to understand the rationale behind decisions and recommendations. Without transparency, it becomes challenging to trust and validate the AI's outputs, increasing the risk of relying on potentially flawed or biased information. For example, in finance, where decisions can have significant consequences, the lack of transparency in generative AI’s recommendations might lead to regulatory concerns.
Moreover, generative AI relies heavily on the quality and scope of the data it has been trained on. If the training data is biased, incomplete, or not representative of the current environment, the AI’s output may be flawed or misleading. This can be particularly problematic in business, where decisions based on inaccurate or biased data can lead to significant financial losses, reputational damage, or other unintended negative outcomes.
Looking ahead, prescriptive analytics is set to further transform how businesses make decisions, enabling them to be more proactive and confident in their choices. By processing large amounts of data—both historical and real-time—using advanced algorithms, prescriptive analytics not only analyses past events and predicts future trends but also recommends the best actions to take. This empowers everyone in an organisation, from managers to frontline employees, to make quicker and more informed decisions.
For example, industries like healthcare, finance, and supply chain management are already beginning to harness the power of prescriptive analytics. In healthcare, it can optimize treatment plans for patients by analyzing a wide range of factors, from medical history to genetic data. The Mayo Clinic is one institution exploring how prescriptive analytics can personalise treatments that hopefully can lead to better patient outcomes and reduced costs. By using simulations, companies can test different strategies in a virtual environment before implementing them, ensuring that decisions are more likely to lead to successful outcomes.
A key advantage of prescriptive analytics is its ability to combine internal data with external market intelligence. By integrating data from sources like customer feedback, industry trends, and competitive analysis, businesses can gain a more comprehensive view of the environment in which they operate. This broader perspective allows companies to better understand market dynamics, customer needs, and emerging opportunities. When internal data is enriched with external insights, businesses can make more informed decisions about where to allocate resources, how to optimise operations, and where to focus strategic efforts. This combination of internal and external data enhances the ability to deploy resources effectively, ensuring that efforts are aligned with both internal capabilities and market demands.
However, not every company will immediately or fully adopt prescriptive analytics. The extent to which businesses can leverage this technology depends on the quality of their data, the sophistication of their existing analytical capabilities, and their willingness to embrace advanced analytics. Companies with strong internal data and analytical resources will be the first to take full advantage of prescriptive analytics. In contrast, smaller businesses or those with less advanced data strategies may begin with specific applications and gradually expand its use. Alternatively, they can utilise Intelligence-as-a-Service providers such as Tembi to gain access to market data, analytics, and actionable insights, allowing them to benefit from advanced analytics without the need for extensive in-house capabilities.
The success of prescriptive analytics also hinges on the quality of internal data and the company’s analytical skills. To implement it effectively, businesses need to ensure their data is accurate, comprehensive, and up-to-date, requiring investment in data management and infrastructure. Skilled data scientists and analysts are essential for developing and maintaining the models that drive prescriptive analytics. Moreover, fostering a data-driven culture within the organisation is crucial, so that decision-makers understand and trust the recommendations provided by these tools.
As prescriptive analytics becomes more widespread, companies must also consider the ethical implications of relying on these advanced technologies. The potential for algorithmic bias, the need for transparency in decision-making processes, and concerns around data privacy and security are all critical issues, especially in industries handling sensitive information. Businesses will need to strike a balance between leveraging the capabilities of prescriptive analytics and maintaining human oversight to ensure responsible and effective decision-making.
The journey from traditional decision-making to an analytics-driven approach represents an important evolution in the business world. As data and analytics continue to advance, businesses are better equipped than ever to make informed, strategic decisions. However, the effectiveness of these decisions depends on the quality of the data, the appropriate use of analytical methods, and a clear understanding of the limitations of emerging technologies like generative AI.
To navigate this new landscape, businesses should consider the following steps:
Audit your data quality: Ensure that your data is clean, integrated, and well-governed.
Invest in analytics training: Equip your team with the skills needed to leverage advanced analytics tools.
Balance AI with human judgment: Use AI tools like generative AI and prescriptive analytics wisely, keeping human oversight in place.
As we look to the future, prescriptive analytics offers a promising glimpse into how businesses can navigate an increasingly complex world with confidence and foresight. By embracing these tools and strategies, companies can stay ahead of the curve and achieve sustained success in a data-driven world.
For further reading, consider exploring the ethical challenges of AI in business or case studies on successful data-driven decision-making in various industries.
Invitation for Discussion: How are you incorporating analytics into your decision-making process? What challenges or successes have you experienced? Share your thoughts with us at mbu@tembi.io.
