he Nordic eCommerce report dives into the eCommerce market in Sweden, Finland, Norway and Denmark. The report is free and available for download here.

What to expect inside?

Looking into data from 79.000 online retailers that sell physical goods we analysed what type of commerce platforms are popular, which payment providers are mostly used as well as delivery methods and product categories.

Interested in knowing more about our data, or are you looking to reach a specific type of webshops? Contact our sales here for a short intro.

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Previous E-commerce Reports

Baltic E-Commerce Market Intelligence Report (Published January 2024)
Nordic e-commerce Market Intelligence Report (Published October 2023)

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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?

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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.

Gathering quality webshop 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

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Deep dive into commerce platforms in European countries

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.

E-commerce platforms in Denmark

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.

E-Commerce Platforms in Estonia

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).

E-Commerce Platforms in Finland

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.  

E-Commerce Platforms in Latvia

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.

E-Commerce Platforms in Lithuania

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.  

E-Commerce Platforms in The Netherlands

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.

E-Commerce Platforms in Norway

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.

E-Commerce Platforms in Slovakia

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.

E-Commerce Platforms in Sweden

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).

E-Commerce Platforms in Switzerland

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.

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Better market intelligence

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.

The evolution of decision-making processes

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.

The Analytics Value Escalator

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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.  

The importance of quality data

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’s limitations in business decision-making

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.  

The future of decision-making with Prescriptive Analytics

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.

Conclusion

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.

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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.

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s we approach the year's final quarter, the stakes for last-mile delivery companies couldn't be higher. With the majority of revenue generated from B2C webshops, Black Friday, Cyber Monday, and the Christmas season represent crucial opportunities to maximise profits.  

However, preparation for these peak periods involves more than ramping up staff, fine-tuning routing, and increasing throughput.  

At Tembi, having helped over 40 last-mile providers across Europe, we understand that strategic planning on the commercial side can make or break your Q4 performance. To help you in the process we have collected a five of our key learnings on the topic.

1. Make Sure Your Bases Are Loaded

Instead of focusing solely on acquiring new clients, ensure you're optimally positioned with your existing ones. Monitoring your position in their checkout process can yield significant returns. Being positioned as the top delivery provider at the delivery checkout can dramatically increase the number of orders you receive, often doubling or even tripling them.  

From several of our Last-mile delivery clients, we have witnessed an average of 30%-50% increase in top-1 rankings working tactically with this. Typically, this amounts to a total increase of 20%- 33% in revenue from the existing client base!  

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2. Target the Right Clients, Not Just More Clients

Strategic client acquisition is essential. Focus on attracting webshops that boast a strong infrastructure, high order volumes, and the right geographical locations that align with your logistics.  

These targeted efforts can significantly enhance your profit margins and operational efficiency.

On the other hand, failing to identify the clients that are right for you means losing time and money on unsuccessful outreach, attending irrelevant meetings, and seeing your closing rate decline. And even worse, potentially attracting a non-profitable client for your business.  

Market research or a good market insight & sales intelligence tool will help ensuring you target the right clients. More is not always better.

3. Leverage Your Unique Advantages

Understand where you stand out compared to your competitors and highlight your unique selling points to differentiate yourself in a crowded market. Are your delivery times faster? Do you offer more sustainable options? Is your service reliability superior?

Tembi’s E-commerce Market Intelligence solution provides users with a comprehensive, data-driven market overview. This enables last-mile delivery companies to understand their performance and how they measure up against competitors. Our data not only visualises your strengths but also serves as credible evidence of your advantages.

Combining this data with comprehensive insights into each webshop in your market provides a significant advantage in sales meetings. You can tailor your pitch using up-to-date information, demonstrating how your solution will enhance the delivery experience for your customers' clients. This personalised approach showcases the specific benefits and improvements your service offers, making a compelling case for why your company is the best choice.

4. Plan and Work with Your Clients

Q4 is a vulnerable time for webshops, where faulty shipments and slow deliveries can be extremely costly. Success often stems from a partnership approach between webshops and last-mile providers.  

Engage deeply with your clients to ensure they see you as a trusted partner they can rely on during these critical periods.

In essence, this is where you want your sales and account management team to spend the majority of their time, which can be enabled by strong processes and the right tools/technologies to help your team be even more efficient.

5. Don’t wait - Start Today

Effective planning and execution require time, structured outreach, and meticulous account management. There is no easy way. The sooner you start, the better positioned you'll be to capitalise on the high season's opportunities. The time is now – not in October.

Get Ahead Of The Competition With Tembi

At Tembi, we bring years of experience in delivering market insights and partnership services that drive success.  

Our market intelligence solutions provide last-mile delivery companies with continuously updated data and insights into webshops, delivery provider rankings, export markets, technology usage, product categories, and much more - allowing companies to react swiftly to changes, maintain top rankings, and increase revenue from their existing client base.  

We tailor our supportive services to each client's needs, and we would love nothing more than to set up a free, non-committal session to discover how our e-commerce market intelligence solution could help your business achieve its revenue goals—both in Q4 and throughout the year.  

ith Tembi you don’t just get enriched B2B company data, we’ve actually visited every webshop on the market to ensure it is operating, analysed its products to understand what product category it belongs to, and connected traffic data from SimilarWeb to understand how its popularity has developed. 

A similar exercise would take 82 years for a person if s/he worked without a pause. And we do it bi-weekly.

Tembi for sales and marketing teams

At Tembi we are fascinated by the challenge of large-scale data gathering and analytics, and the more complicated, the more creative our product and data science team gets.Our Market Intelligence solution for companies targeting webshops - E-commerce Core – visits bi-weekly any active webshop in the European market capturing data on:

 • Technology platform (WooCommerce, Shopify, Magento etc.)
• Payment providers/systems (Klarna, Ayden, Stripe etc.)
• Product data (Products sold, number of products, product growth etc.)
• Company data (Ownership, address, warehouse(s), financial data etc.)
• Operating markets (languages, export markets etc.)

On top of this, we use proprietary AI-models to categoriSe each webshop into a product category using both image recognition and large language models (LLM) to ensure high quality data when you filter our database.

Granular filters to match your ICP

We’ve been there ourselves, looking for that last filter to get a precise search result –why we’ve added over 50 filter options to our product to ensure you can find exactly the webshop you’re looking for. Filter or cross-filter on product categories, growth stage, number of employees, website traffic, number of products, languages –and if you would lack a filter, our team is quick to add it (if we have the data of course).

Advanced analytics that generate insights

With deep data on each webshop, we can uncover insights by combining data in different ways. Our econometric and AI-models can today predict revenue estimations, company growth and for example technological investments – adding a deeper understanding of the maturity of a webshops operations. 

Combining these insights with webshops data further increases your possibility of narrowing your targeting, as well as better understanding your current clients, or where you’ve had success lately.

 With better data, we can get better insights that helps us reach our goals faster. If you’re interested in getting a demo or better understand how our clients use Tembi – don’t hesitate to book a call - or find more material about our E-commerce Core Solution here.

n today’s business world, being data-driven is no longer a question; it is a necessity. Organisations that don’t understand how to work with data and leverage it risk falling behind or even going out of business. However, merely being data-driven is not enough anymore. The rapid growth of access to artificial intelligence (AI) and lowered computing cost has amplified the significance of data, driving a shift towards predictive (and even prescriptive) intelligence to stay ahead of the competition.  

Transitioning from a data-driven to an AI-driven organisation presents immense opportunities, enabling companies to understand the competitive landscape better, and leverage both market predictions to gain an edge, as well as improving operations to lower operating expenses. This transition requires a fundamental change in how we operate and organise the company. Secondly, we need to decide where to start, and whether to build, or buy a solution.

Here we share five, simple, steps to ensure your organisations success in this transition.  

1. Management must clearly state that it is a goal

Achieving success with a transition is a strategic choice and an executional leadership challenge. It is crucial for management, whether top-level executives, business unit leaders, or team managers, to clearly communicate that the goal is to capitalise on the benefits of being data-, AI-, or analytics-driven, and where these benefits will have an impact, and why the transition is imperative for the organisation’s success. Leaders should:

• Clearly state the necessity of this transition for organisational success.
• Be transparent about the potential challenges of the transition.
• Accept that the transition might take longer time than anticipated, especially if immediate benefits are not apparent.
• Repeat the goal, ensure regular follow-ups on the agenda, at least monthly, preferably weekly, and support.

2. Organise the transition

Clarifying responsibility is essential as well as identifying the right person to lead the operational work of the transition. Allocate funding centrally rather than locally to prevent initiatives from being perceived as competing with short-term operational needs. By centralizing funding and clarifying responsibility, organisations can ensure that the transition to an AI-driven approach is viewed as a strategic investment rather than an operational cost.

3. Disseminate the solution broadly

It is unfortunate when initiatives become confined to a single department or individual. The benefits of an AI-driven approach are significant and extend across the entire organisation. Therefore, it is crucial to integrate solutions into as many teams as possible where there is a business case. Engaging more teams in the adoption phase offers several benefits:

• Shared costs across more teams.
• A unified language and collaborative efforts towards success.
• Accelerated transition and higher combined ROI.

Avoid placing the burden on a single individual. Employ the innovative power of the entire organisation to achieve greater success.

4. Embed new solutions in daily routines

For new solutions and strategies to work, they must be integrated into daily operations. Overcoming existing habits and ways of working requires repetition until the new practices become habits. Incorporate the use of data and analytics tools into the organisational rhythm, such as in weekly meetings or daily stand-ups. Measure the impact of these new practices and share the progress with the entire organisation. Highlight how the transition is improving efficiency compared to previous methods.  

5. Embrace an Adaptive Mentality

Fostering an adaptive mindset is crucial for the transition to an AI-driven organisation. This mindset should infiltrate the company culture, regardless of role. Here are three tips for building a stronger adaptive mindset:

• Identify and support superusers who can inspire and motivate others.
• Hire individuals with an innovative mindset, both leaders and employees.
• Promote a supportive culture through promotions, celebrating successes, and sharing positive results.

It might sound simple, but actively working on lifting and promoting the right people is very often overlooked. Make sure it is part of the leaderships action plan so this practice doesn’t fall between two chairs, or is forgotten within a couple of quarters.

Conclusion

Building a data and AI-driven organisation is essential for maintaining competitiveness in today’s business environment. Transitioning from being merely data-driven to embracing AI and predictive intelligence offers significant advantages, including a better understanding of the competitive landscape, leveraging market predictions, and improving operational efficiencies.

To ensure success in this transition, organisations should follow five key steps. First, management must clearly articulate that becoming an AI-driven organisation is a strategic goal. This involves transparent communication about the importance and challenges of the transition, along with regular follow-ups and continuous leadership support.

Second, organising the transition is crucial. This includes clarifying responsibilities and centralizing funding to ensure that AI initiatives are viewed as strategic investments rather than operational costs.

Third, disseminating the solution broadly across the organisation is vital. Integrating AI solutions into multiple teams enhances collaboration, shares costs, and accelerates the transition, leading to a higher overall ROI.

Fourth, embedding new solutions into daily routines ensures that these practices become ingrained in the organisation’s operations. Regular use and measurement of the impact help highlight the efficiency improvements over previous methods.

Finally, fostering an adaptive mentality is essential. This involves supporting superusers, hiring individuals with an innovative mindset, and promoting a culture that celebrates successes. An adaptive mentality ensures the organisation remains agile and responsive to new opportunities.

By following these steps, organisations can effectively leverage data and AI, achieving sustained success in an increasingly AI-driven world.

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iscover data and insight around webshops in Sweden, Denmark, Finland & Norway. This report is free and available on LinkedIn for download.

What to expect inside?

We've visited and analysed over 70.000 active webshops in Sweden, Denmark, Finland & Norway. Orginsed around three topics you'll find:

➜ Insights around distribution of product categories
➜ Data on delivery prices and delivery methods
➜ Discover which technology platforms power the webshops
And much more ⏩

Go to our linkedIn page and view, or download your copy.

Previous E-commerce Reports

Baltic E-Commerce Market Intelligence Report (Published January 2024)
Nordic e-commerce Market Intelligence Report (Published October 2023)

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hen you're pitching as a real estate agent, knowing the details well shows your professionalism and sets you up for success. In commercial real estate, understanding the area around your property can really make a difference. It's not just about showing the space; it's about explaining its future potential and what's happening in the market around it.

While Tembi’s Market Intelligence Platform for Real Estate helps agents find tenants by predicting company relocations, this data is also very useful in pitches to Real Estate Owners.

Here are a few ideas and ways to improve your pitch using data from Tembi’s platform, moving it from a generic presentation to an insightful conversation around the commercial property and its area.

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Present deep area insights

Know the types of buildings, available spaces, and current tenants. This lets you understand the area's composition and the types and sizes of businesses likely to move in. Analysing moving patterns helps you see where companies are coming from, how often relocations occur, and how long companies stay.  

Predicting Company movements

The commercial real estate market constantly changes, with companies of all sizes reassessing their location needs. Pointing out these potential moves can strengthen your pitch significantly. Clearly explain which businesses might move to the area and why with the help of Tembi’s Moving Probability Score. This shows you understand market trends and aligns with what your clients need.

Space requirements: Tailoring your approach

Knowing how much space a prospective tenant might need is crucial. This isn’t just about numbers; it’s about tailoring your offerings to the market’s needs. Discussing square meters in the context of tenant demands positions you as a knowledgeable partner, not just a facilitator. For example, consider whether it makes more sense to split a 400 square meter office into two or three units based on moving patterns and the area’s composition.

Catering to the right tenants

The types of companies moving into an area can greatly influence a property’s value. Whether it’s tech startups or law firms, understanding this dynamic can transform your pitch. Match the property’s features with the expectations and culture of incoming companies.

Data-driven discussions

Back up your claims with data, market analysis, or company data. Having this data at your fingertips boosts your credibility. It shows you’re informed, and your insights are based on reliable sources. By using targeted, data-backed information, you reduce time spent on generic preparations. This lets you create a more impactful presentation that addresses specific client concerns and market opportunities.

Lifting the conversation

Move from generic pitches to discussions that resonate more deeply. Talk about the property and its area not just as they are, but as they could be. This encourages deeper engagement from potential clients who see you understand their long-term success.

Knowing how the area around “your potential” property is developing gives you a strategic edge. It allows you to anticipate changes and position your property as a smart choice in an evolving landscape.

In commercial real estate, winning a pitch often comes down to how well you understand and convey a property’s location potential. By focusing on the broader context, you turn a simple sales pitch into a compelling vision of the future, clearly showing why the smart choice is to act now, with you.

Interested in knowing more about Tembi’s Real Estate solution, don’t hesitate to book a meeting with Dennis.

Read more about our Platform.

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n the ever-evolving landscape of e-commerce, the race to secure customers and meet their delivery expectations has never been more intense. Last-mile delivery providers are constantly seeking new e-commerce clients, but what if I told you that there's a critical factor many overlook? It's not just about acquiring new clients; it's about optimising your position in their checkout process. Here's why:

1. The Power of Checkout Positioning

At Tembi, we understand the significance of where a delivery provider stands in the checkout process. Did you know that up to 60% of final package orders go to the top-ranking delivery provider? This means that by being ranked number 1 instead of 2, 3, or 4 at your clients, you could double or even triple the number of orders from a client.

2. Cost and Ranking

Interestingly, but not surprisingly, our data shows that the top-ranking delivery provider is the cheapest option in up to 80% of cases.  

End-user delivery fees depend on the independent deal between last-mile providers and webshops. Other than lowering the delivery price paid by the e-commerce company, there are several ways to affect this.

Progressive discounts based on order volume, collaborative logistic offerings, reliance on service, and related solutions are all options that can help e-commerce companies offer your last-mile delivery service as the cheaper option for the end user.  

3. Beyond Cost: Delivery Time, Sustainability, and Collaboration

However, it's about more than just being the cheapest option. Factors such as delivery time, delivery method, sustainability options, and collaboration with the delivery company also play significant roles. One or more of these are always present when the cheapest option differs from the top-ranked delivery option.  

Consumers are increasingly conscious of environmental impact and delivery speed, making these factors crucial in their decision-making process. Therefore, they are also weighed in terms of the webshop owner's priorities and systems.

4. Reacting to Changes

Losing your top ranking can be disastrous, but it's often discovered too late. Sometimes, you only realise this at the end of a quarter when financial results reflect the drop in orders. It's crucial to act swiftly on changes at your clients.  

The Tembi Market Intelligence Solution for e-commerce gives you updated insights into your market's webshops, including delivery providers, checkout rankings, export markets, technology use, product categories, and much more.

This not only enables you to identify new ideal client profiles but also to quickly react to changes in your existing clients – like when you lose or win a top 1 position.

5. Strategic Monitoring and Reaction

Many of our clients establish a business case for using our market intelligence solution for e-commerce based on new client acquisition alone. However, the value of working strategically and tactically to monitor and react to changes in checkout positions at existing clients often significantly exceeds the value of client acquisitions alone.

From several of our Last-mile delivery clients, we have witnessed an average of 30% - 50% increase in top-1 rankings working tactically with this. Typically, this amounts to a total increase of 20%- 33% in revenue from the existing client base!

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6. Take Action

Curious to learn more? Eager to get started?  

Contact Tembi for a commitment-free discussion about our solutions and services to help you optimise your revenue from your current and future e-commerce clients. With Tembi's market intelligence, you can stay ahead of the competition and secure your position at the top of the checkout page.

In the fast-paced world of e-commerce, every advantage counts. By focusing on client acquisition and optimising your checkout positioning, you can ensure your last-mile delivery business thrives in today's competitive market.

Click here to schedule a call today.  

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ccess website traffic data inside Tembi.

We are happy to share that we’ve entered into a data partnership with Similarweb where we display monthly website data traffic inside our Market IntelligencePlatform.

Using Tembi, you can now use the data from Similarweb to filter your search based on traffic volumes, see historical data on selected webshops and be able find the growing webshops within different product categories.

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For access or inquires, please contact our e-commerce responsible Peter.

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ead list generators is a red ocean. Thousands of companies offer multiple ways to filter company or technology data to narrow down a segment of potential clients.

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The challenge:  Precision is quite poor as data is not contextualised.

Customer problem: Lead list is very inaccurate, leading to many disqualified leads and lost of time. (And irritation.)

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How we’ve created a world-class categorisation system using analytics

Our machines have been learned and optimised over the course of years to automatically categorise webshops based on product category. We started with 16 categories two years ago, and today we have 38 product categories (i.e. Shoes, Fashion, Beauty, Car parts, Beverages, etc.).

This is how we do it, and this is why our clients see a reduction of disqualified leads with over 80% after switching to Tembi. (If time is money, then an 80% reduction is quite a lot of money.)

Web Scraping
‍We’ve built custom web scraping tools that automate the collection of data from each webshop. We run hundreds of different scrapers to ensure we can gather the right data.

Data Cleaning
‍We process the scraped data to remove irrelevant information, correct errors, and prepare it for analysis.

Natural Language Processing (NLP)
‍We use NLP techniques to analyse the text data from webshops and product descriptions and names. Which helps us understand the context and categorising products based on their characteristics.

Machine Learning (ML) Classification
‍We've trained a machine learning model on a dataset where the product types are already known. This model is used to classify the products in webshops into predefined categories.

Manual Review and Feedback Loop
‍We constantly review the process to ensure accuracy and continually improve the ML model through a feedback loop.

And this is why when you look for a website that sells clothes on Tembi, you will only find webshops that sell clothes.

To date we have validated over 1 million webshops around Europe, and update our data several times per month to ensure a very high quality that help sales and marketing teams find the webshops that match their ICP.

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Interested in knowing more about our e-commerce solution and how we categorise webshops? Download the product presentation or book a meeting for a demo.

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ind the webshops you’ve always been looking for - Small or large, local or international, on the rise or in need of a strategic nudge.

At Tembi we look beyond general company and webshop data, and visit each webshop to better understand which type products are being sold, the technological platforms in use, and for example, what software's are powering the webshops communications, performance and operations.  

Our machines do the equivalent job of thousands and thousands of people daily researching the web, collecting information, gathering data and putting it together in one place. Webshop after webshop.

“Securing the right targets at the right moment isn’t just about closing deals—it’s about gaining a strategic advantage. With Tembi’s latest market intelligence innovation for Software Providers and Digital Agencies, whether your focus is business development, sales, or marketing, you’ll gain an excellent perspective on market dynamics.”
‍Michael Bugaj, CMO at Tembi

Access exclusive data with precision

We're not just about scratching the surface. Our goal is to deeply understand each webshop, connecting them to their parent companies to unveil their financials, interconnected webshops, and operational domains.

By integrating information from multiple places into a singular, comprehensive view, we enable sales and business development teams to better segment the market, and see where new opportunities are developing, and link that to individual companies, refining lead list and decreasing disqualified leads with up to 80%.

Identify relevant webshops to your business

When we initially ask potential clients how many webshops they believe there is in their market the typical answer is around 1/3 of the market, meaning most of the market goes unnoticed.

Who are the emerging stars? The niche players about to grow? With Tembi, these questions find their answers. Our platform allows you to input your ideal customer profile, creating alerts for when potential leads meet your criteria.

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Benefits of using our Tembi’s e-commerce solution

Besides significantly simplifying the process of gathering data, here are five immediate benefits:

1. Extensive filtering options to match your ICP

Filter on location, products, markets, financials, ownership, and +50 other possibilities to find exactly the cohort that matches your ICP and/or campaign.

2. Rapidly grow your pipeline

With your ICP in place, see immediately the whole lead list on your market, and set up notification for automated new matches.

3. More information on your ideal targets

Understand you targets business and its situation better. Get clarity around their product offering and development and how you can help.

4. Spend your time right

Up to 80% less disqualified leads frees more time to be able to focus on closing the deals.

5. Understand your market

We monitor the market weekly and visualise trends and movements so that you can better understand how the market develops and identify new opportunities.

Interested in knowing more about our e-commerce solution? Download the product presentation or book a meeting for a demo.

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About Tembi

Tembi is a data and analytics company, providing a software solution designed to deliver market information and insights that give our customers a better understanding of their market and how it develops. Our Market Intelligence Platform gathers and connects data from multiple industries to give a more precise and correct overview the market and its companies.

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ast month we published our commercial relocation predictions for 2024 in Denmark, and today, we're publishing our Market Intelligence report for Swedish Commercial Real Estate.

Our data science and econometrics team looked through Swedish data from the last six months together with our predictions models to see which companies in Sweden scored a high moving probability. Looking at companies with minimum five employees, we predict that 4698 companies and production units will change address next year.

Inside the report you'll find, besides relocation predictions, relocation data from the last six months, which industries saw the most movement as well as moving velocity - how many companies have relocated in the three biggest areas in Sweden during the last three years.

Access the full report here.

After reading the report, If you're interested in getting a sample of which companies will relocate next year in Sweden, book a demo, and we will prepare data for you.

Book a demo

s the Swedish real estate market continues to evolve, new trends are emerging, particularly within the commercial sector. Our latest Market Intelligence Report offers an in-depth look into these shifts, providing deep insights and predictions for 2024that could help the industry better understand the market. And where to look for opportunities.

The past six months we have seen a significant activity within the Swedish commercial real estate scene. Over 3,005 companies, each with more than five employees, have relocated, showcasing a trend that follows previous years patterns. Notably, businesses ranging from 5 to 9 employees formed the majority of these moves, highlighting a high activity within this segment.

‍The Predictive pulse of 2024

Looking ahead, our analytical models have identified a high relocation indicator for 4,698 companies having more than five employees. This indication, drawn from AI models and extensive market data, suggests an active year with a lot of potential. Interestingly, the bulk of this movement is expected from companies with 10 to 49 employees, pointing to a important reshuffling in the commercial real estate space. 

Regional Revelations

The report sheds light on the geographical nuances of these relocations. While the Stockholm region has traditionally been a hub of activity, the forthcoming year places a spotlight on Gothenburg, anticipating a higher volume of larger entities on the move. This regional redistribution of commercial real estate activity underscores the diverse opportunities unfolding across Sweden.

In light of that, we also see that companies and production units with more than 20employees in Stockholm tend to move more often than in other parts of the country. 33% of companies in Stockholm changed address during the last three years, while the same number in Gothenburg is 27%.

The Industries on the Move 

Diving deeper, certain industries emerge as more mobile than others, including sectors like insurance, information services, and staffing solutions.

Why This Matters

For businesses and investors, understanding these patterns is crucial. The shifting sands of the commercial real estate market offer both challenges and opportunities. For investors, it's a chance to anticipate demand in growing areas and sectors. For businesses, the insights provide a roadmap for strategic decisions about where to plant their flags in a competitive landscape. 

Access the full report by clicking on the picture below

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he e-commerce sector in the Baltic region has seen consistent growth throughout the last ten years, opening up a number of opportunities for investment. Estonia and Latvia, in particular, stand out as some of the most rapidly expanding online retail markets within the Central and Eastern Europe (CEE) area.

Our first Market Intelligence report for the e-commerce Market in the Baltics looks into the foundation of the Industry and its suppliers

Find data & insights around:

🛍️ Number of Webshops in Estonia, Latvia and Lithuania.

📊 Webshop product category distribution per country

⚖️ Market specialisation

📦 Delivery providers, prices and methods

🖥️ Technology platforms that power the webshops

...and much more.

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Access the full report for free by clicking on the image below!

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f you're a marketer or sales representative in any B2B market, you know the challenges of identifying your Ideal Customer Profile (ICP) among your total market. It's not always a straightforward process and getting it wrong can be costly.

Failing to identify the ICP means wasting time and money on unsuccessful outreach, attending irrelevant meetings, and seeing your closing rate decline.  

On the other hand, getting it right can lead to successful outreach, higher client relevancy, an increased win rate, and acquiring more clients with fewer resources. There is a lot to gain.

At Tembi, we've spent countless hours helping our clients identify and target their ICP. To help you start your journey, we've created a 5-step guide on how to get your ICP work right.

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Step 1: Analyse Your Current Customers

One of the interesting aspects of identifying your Ideal Customer Profile (ICP) is that you can often find the answer right in front of you - among your existing clients. Try to identify patterns among your most successful and satisfied customers. Look for similarities in industry, company size, location, and purchasing behaviour.

Finding common characteristics among your top customers will help you gain insights into the type of companies that are best suited to your solutions.

Step 2: Develop Detailed Buyer Personas

Create detailed buyer personas that represent your ideal customers within different segments of your target market. Consider job roles, responsibilities, goals, challenges, and decision-making criteria.  

Use both quantitative data and qualitative insights to flesh out these personas. Give each persona a name and backstory to make them relatable and memorable for your sales and marketing teams.

Step 3: Consider the factors that are impacting your industry.

To gain a better understanding of the market in which your B2B solutions operate, it's important to dive into market insights. By staying up-to-date with emerging trends, challenges, and opportunities within your target industries, you can effectively identify common pain points or needs that your solutions can address.  

To achieve this, intelligence solutions like Tembi can be helpful. By using the right insight tool, you can identify patterns across your market and discover what is currently driving change for your potential clients.

Step 4: Identify and Target Your ICP

Identifying and targeting your Ideal Customer Profile (ICP) can be a challenging task, even with your ICP and buying persona in hand. There are several useful tools available that can help you identify industry players, but when it comes to narrowing down your segments, you only have three options:

1. Devote lots of manpower and resources from your team to conduct research in your segment.

OR

2. Seek paid consultancy to support the research needed.

Or  

3. Use a market intelligence solution that delivers the necessary insights into your market

At Tembi, we specialise in the latter option. We provide deep insights into entire markets, with the ability to cross-filter and monitor your potential and existing client base.

Step 5: Iterate and Refine Your ICP

Building an ideal customer profile is an iterative process that requires ongoing refinement.  

• Continuously gather feedback from your sales and marketing teams as they engage with prospects and customers.  
• Monitor the effectiveness of your messaging and tactics, and be open to adjusting your ICP based on new insights or changes in the market environment.  
• Regularly revisit and update your buyer personas to ensure they remain accurate and aligned with your target audience.

Finally, make sure you have the intelligence solution that allows you to identify new targets quickly when tweaking your ICP.

Looking to start or improve your market targeting process?

Tembi is here to help. Our team of experts has spent thousands of hours supporting clients from various industries, providing deep market insight solutions that are tailored to their specific needs.

Our Tembi Market Intelligence solution can help you quickly identify and target new clients, giving you an edge in winning their hearts.  

Whether you're looking to learn more about Ideal Customer Profiles (ICPs) or want some qualified input into your targeting process, our team of targeting specialists is ready to assist you.

You can book a free and non-committal assessment with one of our specialists today by clicking the link below.  

Book a free consultancy

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About the Author

Thomas, our CCO, has over 15 years of experience working with client targeting and acquisition across various industries. Trust us to help you take your business to the next level!

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ogether with Andre Veskimeister and Parcel Locker Central we've published the Tembi Delivery Index - a monthly index that tracks the average price that e-commerce businesses charge private customers for delivery.

The index is a useful tool for logistics companies, e-commerce retailers, and consumers to understand the delivery landscape's pricing dynamics over the three most common delivery methods: Home delivery, Parcel locker and PUDO (Pick Up Drop Off).

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The Tembi Delivery Index covers today Denmark, Norway, Sweden, Finland, Estonia, Latvia, and Lithuania. More markets will added during 2024.

All the data is drawn from Tembi’s Market Intelligence Platform from a sample of over 100.000 webshops by picking a random product outside the free delivery range and analysing the average delivery prices & delivery methods.

urious about the Danish Real Estate market and how it moved? Our new Market Intelligence Report provides a comprehensive analysis of the Danish commercial real estate market. Key highlights include:

1. Extensive Relocation Data (2023): Insights into the significant movement within the Danish corporate landscape, focusing on company relocations.
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2. Predictions for 2024: Using AI analytics, the report offers predictions on market movements and company relocations for the upcoming year.
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3. Detailed Market Insights:  Valuable insights on relocation patterns, the type of premises companies are moving into (leasing vs. owning), and how often companies relocate based on their size.
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4. Regional Analysis:  Breakdown of relocation trends across different regions and municipalities in Denmark.

This report is a valuable resource for anyone interested in the dynamics of the Danish commercial real estate market, providing data-driven insights and predictions to inform strategic decisions.

Access a free version of the report here.

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ata analytics has become the cornerstone of strategic business decision-making. But what is the difference between diagnostic and predictive analytics? This visual and simple guide represents the evolutionary journey of analytics, from basic understanding to advanced prediction and optimisation.

🔍 Descriptive Analytics: "What happened?"

This is our starting point, where we use historical data to understand past performance.

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🔎 Diagnostic Analytics: "Why did it happen?"

Here, we dig deeper, using the data to uncover the root causes of past events.

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📈 Predictive Analytics: "What will happen?"

Leveraging statistical models and machine learning, we forecast future trends and behaviors.

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🎯 Prescriptive Analytics: "How can we make it happen?"

This is the pinnacle of analytics maturity, where we not only predict the future but also provide actionable recommendations to shape desired outcomes.

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As we move up the ladder from hindsight through insight and towards foresight, the difficulty increases, as well as the data requirement - but it significantly amplifies the value and optimisation capacity of our decision-making processes.

s a real estate professional, you know that timing and information are everything. Identifying which businesses are planning to relocate mean the difference between closing a deal and missing out.  

But what if you could predict these moves before they happen?  

At Tembi we have developed a solution that gives real estate professional market foresight, and a real competitive edge establishing early client relationships. Our advanced artificial intelligence platform provides you with the ability to anticipate company relocations, transforming the way you secure leads and grow your business.

The challenge of predicting moves

Traditionally, figuring out which companies are planning to move offices has been a matter of luck or extensive networking and marketing campaigns based on limited data. By the time, a company is ready to look for a new location, or inverse a property hits the market, it is already a race against dozens of other real estate professionals who are also in the know.

Tembi's predictive edge

At Tembi, we have leveraged artificial intelligence to change the game. Our Real Estate Market Intelligence solution is not just another database – it is a predictive tool that can forecast whether a company will move in the next 6 to 12 months, often before the company itself has identified the need to relocate.

How it works

Our proprietary machine learning models analyse vast amounts of data points, from building data and economic trends to company growth patterns, to provide a prediction score on companies likely to move. This insight gives you a significant head start to prepare a proposal, reach out, build a relationship, and maybe even secure a deal before others even know there is an opportunity.

And if you own properties, our Moving Prediction Score is a great tool to health check your current tenants and where they “stand.”

Precision score

Over time, our machine learning models have become very precise. When we estimate that a company will grow, we are right nine out of ten times , giving it a 90% precision rate. And most companies that will move, we capture.

Access comprehensive data

But we do not just stop at predictions. Tembi provides you with access to comprehensive company data, including size, financial health, and industry segmentation. This information allows you to tailor your approach to each potential client's unique needs and preferences.  

Gain a competitive advantage

With Tembi’s solution, you are not just getting leads; you are getting a consultant's perspective. Understanding the dynamics of the real estate market is crucial, and we give you the knowledge and insights to navigate it effectively. This means you can position yourself strategically in the market and close deals faster, giving you that competitive edge.

Currently, Tembi's Real Estate Market Intelligence is available to real estate professionals operating in Denmark and Sweden, with plans to expand to other markets soon.

Are you interested in getting more information. Please fill out the form below and we will get back to you as soon as possible.

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f you knew which company would move within the next six, or twelve months, what would you do with that prediction?

During the last month I have talked with many Real Estate professionals within different sectors in the real estate industry, asking them the question how an understanding of companies moving intentions would change their work and approach. As expected, they all had different answers and saw different possibilities.  

Below I have paraphrased three answers that stood out during my conversations.

The Real Estate Agent

“As a Real Estate Agent, I would analyze our commercial rental pipeline to identify nearby businesses in the right segment with a high probability of moving. Then reach out to them. But I would also use that knowledge when I try to close potential new clients.”

The Real Estate Asset Manager

“Check our tenant status and see if anyone is about to move. The dialogue with our tenants is the most important thing, and this insight will allow me to reach out to them proactively, talk about their journey, and understand if their needs will change soon. So, we can make sure that they will be relocated and stay as tenants with us.”

The Real Estate Developer

“Of course, twelve months is a short time frame for us. However, previously, 12 months is far too short for us, but we are experiencing more frequently than before that large companies are not as inclined to commit to leases 24-36 months in advance. In addition to being very interested in understanding how an area is developing and where there will be a need for future offices, we would also use that knowledge to ensure we find tenants for our upcoming projects.”

Tembi’s Moving Prediction Score

Predicting if a company will move or not is not science fiction anymore. By continuously collecting and gathering millions of data points, we have at Tembi developed a Moving Prediction Score (MPS) that can predict with over 90% precision if a company will move within the next twelve months. That is about 20 times as good as a random guess.

Using artificial intelligence (AI) and gathering unique data, we can predict how a company will grow, how the number of employees will change and when they would need to move to new offices, as well as understand where they might want to move. Calculating the MPS, we do not only look at historical data, we combine different machine learning models and use this across industries and geographies. Our models are trained on 80 % of the company locations, and we test the models the remaining 20 % to see if we are right or wrong. And that is how we can reach a validated precision of 90%.

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If you are interested in hearing more about how our Moving Prediction Score works, or how it can be applied to your business, do not hesitate to reach out to us.

ith our E-commerceMarket Intelligence Report, we have taken a deep dive into the e-commerce industry in Sweden, Finland, Denmark, and Norway to better understand delivery price differences, who are the dominant delivery providers and i.e. which technology providers power all the webshops.

The report is packed with data & insights to give the reader a better understanding of the market as well as the competitive landscape.

Nordic E-commerce market

The nordic e-commerce is growing fast, with a market size of over €38 billion distributed over 76.000 webshops. Out of the regions 27 million people, more than 19.5million are online shoppers. It's also an exciting place for startups — over the past three months, 4,848 new webshops have opened up online. 

All data in this report comes from Tembi’s E-commerce Intelligence Platform (EIP).

We don’t talk about consumer data in this report. We only focus on the businesses in thee-commerce industry, such as webshops, delivery providers, and technology providers.

Type in your details below, and get a version asap in your inbox.

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n the fast-paced last-mile delivery sector, market intelligence is essential for success. By understanding your customers, competitors, and market trends, you can make informed decisions that lead to growth and profitability.

Market intelligence can help you identify new market opportunities, improve operational efficiency, and develop new products and services. It can also help you stay ahead of the competition and differentiate yourself from the crowd.

In this blog post, we have outlined a few specific examples of how last-mile delivery companies are using market intelligence to grow their businesses.

Staying ahead of the competition
‍Market intelligence can help last-mile delivery companies understand the competitive landscape and identify new ways to differentiate themselves. For example, a company might use market intelligence to identify new technologies that can help them improve their delivery services, or to develop new pricing strategies that are more competitive.

Identifying new market opportunities
‍By tracking market trends and customer behaviour, last-mile delivery companies can identify new markets to expand into or how green delivery is developing. For example, a company might identify a growing demand for same-day delivery in a particular city or region, or an understanding of the competitor's solution and market penetration of different delivery solutions.  

Understanding website traffic patterns and consumer purchase behaviour
‍Last-mile delivery companies can today track which product categories are growing and which webshop’s are growing in popularity, as well as which international sites are exporting to one’s country. By doing so, last-mile delivery companies can establish early partnerships abroad and better equip themselves for future demands and growth.

Developing new products and services
‍Market intelligence can help last-mile delivery companies understand the needs of their customers and develop new products and services that meet those needs. For example, a company might develop a new service that delivers packages to customers' workplaces, or a possibility to get delivery at very specific times in the evening.

Improving operational efficiency
‍Market intelligence can help last-mile delivery companies optimise their delivery routes, reduce costs, and improve delivery times. For example, a company might use market intelligence to identify the best locations for new warehouses, or to develop more efficient delivery schedules.

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Getting good data for Market Intelligence is not easy, as it requires a lot of time, and quite often a big investment in data infrastructure and a plan to keep high quality and ensure data is actualized. Hence, many decisions are taken without bringing external factors into the mix or using poor data as a ground.

Different Market Intelligence platforms collect different types of data and can help companies better understand the market dynamics. Here are a few tips and suppliers for getting started with market intelligence.

Getting started with Market Intelligence

As with any strategic decision, starting the process, you need to define your goals. Market intelligence is not an answer, it is a tool. Are you looking for growth within a particular type of webshops, or price development of different delivery methods? Or a more complex question around identify new market opportunities. Once you know your goals, you can start to identify the data and insights you need.

Collect data
‍There are many different sources of market intelligence data, including customer surveys, industry reports, and government statistics. You can also collect data from your own internal systems, such as sales data and customer feedback.

Analyse the data
‍Once you have collected data, you need to analyse it to identify trends and insights. You can use a variety of tools and techniques to analyze data, such as data analytics software or more advanced methods using machine learning.

Share the insights
‍Once you have gained insights from your market intelligence data, you should to share them with your team to gather input, feedback, and get new ideas so you can keep iterating your work. You can either do a presentation or set up a dashboard that monitors the data and actualises your insights.

Tembi and Market Intelligence for Last-Mile Delivery

Our E-commerce Intelligence Platform – EIP – monitors every webshop on the market, and provides data around providers, prices, and delivery methods. This data can be filtered from a webshop category perspective or for example revenue, providing a comprehensive overview and intelligence of the market and competitors. Hence, EIP both collects and analyses the data, and provides (shares) the insights in simple overview. In other words, decision-ready intelligence.

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ith the E-commerce Intelligence Platform (EIP), we have set out on one of our most ambitious data and analytics ventures yet: to authenticate and catalog every webshop globally, defining product categories, individual products, and the delivery infrastructure. Our aim is to build the most expansive and current e-commerce database, one that can proactively empower webshops, carriers & delivery providers, and suppliers to navigate through the dynamic, ever-expanding market.

EIP was first introduced in Denmark in 2021 and has since extended its reach to Sweden, Norway, Finland, the Netherlands, Latvia, Lithuania, and Estonia. To date, we systematically and repeatedly index, validate and analyze over 200,000 webshops, classifying them into different product categories.

So, why embark on this colossal task?

The objective behind EIP is to provide the industry with unparalleled Market Intelligence. To achieve this, it was imperative for us to go beyond the surface-level offerings and gain a deep understanding of the last-mile delivery mechanics, the various providers involved, and the pricing structures.

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Benefits of EIP

All webshops in one place
‍EIP offers a comprehensive market overview, identifying and validating every operational webshop, while discarding inactive ones. We have established a direct link between each webshop and its owner, detailing ownership, headquarters, and financial figures. By evaluating the webshops' offerings and categorizing their products, we understand the technological platforms utilized and the delivery services provided, including pricing and export capabilities.

Our "Market Scrape" equips users with a detailed snapshot of all webshops in a specific market. For deeper insights, particularly into the largest, custom-built webshops, our "Custom Scrape" service offers an in-depth analysis.

Checkout monitoring
‍Understanding the last-mile market, we monitor each delivery checkout on all webshops, gathering information about providers and their position on the list of delivery options, delivery methods and prices, free-delivery threshold, and green delivery options – giving us comprehensive view of the shipping market and how it evolves from a public perspective.

We keep a pulse on the last-mile delivery market by continuously monitoring checkout processes across webshops. This monitoring captures data on delivery providers, their ranking in delivery options, pricing strategies, thresholds for free delivery, and eco-friendly shipping options, thereby offering an overview of the evolving shipping landscape.

Decision-ready Market Intelligence
‍Merging our data with metrics like order volumes allows last-mile delivery providers to proactively respond to changes in their checkout positioning, preventing potential revenue drops.

“Prior to Tembi, identifying a lost position at a store’s checkout could take up to six weeks, during which we would lose about 64% of order volumes. With EIP's immediate updates, we can swiftly address the issue, preventing significant revenue losses”

‍Webshop integration manager

Let us say you charge €3,0 per delivered package and expect 100 packages per day (on average). The daily revenue is €300. Losing 64% of the volume equals to a loss of €192 per day. During six weeks that loss amounts to €8.064.  

With EIP, as soon as a positioned is lost, you are notified, and can talk to the store, and manage your delivery operations immediately.  

From a strategic perspective, both as a webshop owner, as well as delivery provider, you can track which delivery methods are popular, what are the market prices, and where is the market developing, both on your own market, but also abroad.  

Automated lead generation
‍Understanding the supplier network of providers for webshops within different fields - delivery, payments, and technology - opens an overview of who works with whom. Giving providers competitive intelligence and a perfect data set for lead generation and prospecting.  

As a delivery provider, being able to see all your clients in one simple overview with metadata, you will equally see where you are not present. By understanding previous relationships and solutions used, you can improve your sales pitch and competitive edge.

EIP use-cases

There are multiple ways to use EIP and the data. Here are a couple of examples.

EIP for Account Managers
‍See what technologies your clients are using, and which providers they work with. If you work with last-mile delivery, you can see your position in each check-out and follow your client's business and get the latest data before your check-up.

EIP for prospecting
‍Whether you work with professional services for webshops or selling software, you can find each webshop on your market and find precisely the type of webshop you are looking for with our filters.

EIP for Business Development
‍See and follow market trends, track your competitors and always be up to date.  

EIP for Customer Success
‍From the moment you have a new client, follow the implementation and results. Track critical changes and get access to detailed customer business information.

EIP for Analysts and Business Intelligence
‍Via our API you can extract all our data to your own system and combine external data with your internal data to track correlations, get a full competitor, and market overview.

“A dynamic market requires ongoing data collection.”
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‍Christian Mejlvang, head of product at Tembi

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The technology behind EIP

Our data foundation is robust, encompassing over five billion data points, which include both real-time and historical data collected from 2021. We augment this repository daily with over one million data points to guarantee not only the high quality of our data but its relevance as well.

Utilizing diverse machine learning techniques such as AI (Artificial Intelligence), NLP (Natural Language Processing), LLM, and image recognition, we convert raw data into actionable intelligence, aligning with our commitment to transforming data into insight. This data undergoes a process of enrichment, contextualization, and multi-level automated verification to ensure its integrity. We categorize our data into three tiers of quality—Bronze, Silver, and Gold—and it is only the Gold-standard data that is displayed on the EIP platform, reflecting our dedication to the highest standards of excellence.

Our data acquisition strategy is multifaceted: 1) sourcing open data, 2) procuring datasets from various providers, 3) deploying our proprietary scrapers to gather exclusive data, and 4) generating novel data through analytical methods applied to the data we have. This fourth approach underpins our Predictive Market Intelligence service.

We employ a combination of econometric and predictive machine learning models to create proprietary datasets. These are instrumental in our analysis of market trends and trajectories, providing an innovative perspective on market dynamics.

Interested in knowing more about EIP? Contact us.

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here are many “intelligences” in the world of business. Besides the cognitive ability of a business’s staff, it refers to the information that has been gathered, analysed, and presented in a way that is useful for decision-making. It is not just raw data; intelligence is actionable information that provides insight into a particular subject, such as a competitor’s activities or internal business capabilities. "Intelligence" is a multifaceted term that usually denotes a high level of understanding, awareness, or information processing, whether by humans, collectives (like organizations), or technology.

What type of intelligence is needed often depends on what strategic decision you are looking to make, what type of resources you have, and the amount of data. Here are the ten most common ones:

Business Intelligence is a technology-driven process for analysing data, presenting actionable information to help executives, managers, and other corporate end users make informed business decisions. BI encompasses a variety of tools, applications, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards, and data visualizations. This process offers comprehensive business metrics, often in real-time, to support better decision-making. With BI, businesses can focus on data-driven strategies to address weaknesses and capitalize on strengths.

Market Intelligence is the gathering of relevant data about the entirety of a company's market space. It covers broad spectrums such as understanding industry trends, identifying market opportunities, and detailed insights into competitors and customers. This intelligence is crucial for forming market entry strategies, pricing models, business development and sales & marketing initiatives. It aids businesses in anticipating market shifts and consumer needs, enabling proactive rather than reactive strategies. The insight gained from market intelligence informs various strategic decisions, such as market opportunity assessment, market penetration strategy, and market development.

Marketing Intelligence is the practice of collecting data from a variety of sources about the market environment a business operates in. It includes the analysis of consumer behaviour patterns, campaign outreach, and purchase triggers. The focus is to understand the success of marketing efforts and to gauge the sentiment and preferences of current and potential customers. It influences tactical marketing decisions and helps businesses adapt their strategies to better meet consumer expectations, enhance brand loyalty, and optimize return on marketing investment.

Competitive Intelligence refers to the systematic collection and analysis of information about competitors and the competitive environment. CI aims to provide a complete picture of the marketplace and the forces at work within it, encompassing aspects such as competitors' strategies, market developments, new entrants, and technological advancements. Effective CI provides a legal and ethical means to anticipate competitive moves and stay ahead of industry trends, supporting strategic planning and risk management.

Customer Intelligence (CI) is a sophisticated analysis of customer data designed to create comprehensive portraits of ideal customers to better understand and predict their behaviour. It is an advanced step beyond basic customer service, seeking not just to address customer needs but to anticipate them. CI combines demographic and psychographic data with transactional and behavioural insights to paint a detailed picture of current and potential customers. This intelligence helps in personalizing marketing strategies, enhancing customer experiences, and boosting customer loyalty. In the age of big data, companies leverage machine learning and AI (Artificial Intelligence) algorithms to process vast amounts of information, providing a deep dive into customer preferences, pain points, and potential opportunities for cross-selling and up-selling.  

Financial Intelligence combines understanding a company's financial health with the savvy to use this data in making robust decisions. It involves the analysis of financial data like cash flow statements, balance sheets, and income statements to grasp a company's financial condition and forecast its future performance. It is not just about number crunching; it also includes reading between the lines of financial statements to identify the underlying performance factors, assessing the company's fiscal policies, and ensuring regulatory compliance. Financial Intelligence helps in capital budgeting, financial planning, and aligning financial goals with corporate strategy.

Operational Intelligence (OI) is the real-time dynamic, business analytics that delivers visibility and insight into data, streaming events, and business operations. OI solutions run query analysis on live feeds and event data to deliver real-time operational insights. It involves understanding and optimizing labour productivity, machinery performance, and other operational sectors. By integrating and analysing data from various operations, businesses can quickly identify and address inefficiencies, ensuring the smooth functioning of processes and supporting continuous improvement.

Sales Intelligence refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of information to help salespeople keep up to date with clients, prospect data, and drive business. It includes a range of activities, such as tracking customer data and interactions, social media monitoring, and sales forecasts. With accurate and insightful sales intelligence, sales teams can enhance their productivity, improve lead generation and conversion rates, and drive increased sales and profitability.

Product Intelligence involves collecting and analysing data concerning one's products and those of competitors. It is pivotal in understanding how a product performs across its lifecycle, which features resonate with customers, and what improvements should be prioritized. This intelligence is crucial for product development, management, and innovation, informing companies about user feedback, product usage patterns, and market demands. By leveraging product intelligence, businesses can tailor their product offerings to better meet customer needs and stay competitive in the market.

Technological Intelligence is the systematic gathering and analysis of information about the technological environment of a business to aid decision-making. It includes tracking trends in technology advancements, research and development within the industry, patent filings, and regulatory changes. With a solid technological intelligence strategy, a company can foresee technological disruptions, identify new business opportunities, innovate, and maintain a competitive edge. This intelligence is vital for strategic planning, particularly in industries where technology evolves rapidly and is a key differentiator.

Many types of intelligences are not exhaustive and often overlap. Businesses typically leverage a combination of these intelligence types to inform various functional and strategic areas within their organizations.  

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aking a decision is easy but knowing how to make the right decision at the moment of choice, now that is tricky. As the outcomes and consequences are only known after the decision has been made, we try hard to mitigate the risk of making a wrong one.  

Like a game of probability, we weigh different information and data, and play out the possible outcomes against each other to narrow down our choices, and, well, make a bet. Given the vast amount of information and data available, gathering the needed and relevant information can be a challenge. For the human mind it is impossible to grasp all inputs and data at once. And it is practically impossible. Additionally, as we learn new information, we may create new connections and gain new insights that open new possibilities. Which often leads to the question, "What if...?"

Lastly, before executing the decision, we weigh our options and evidence, and filter it through the personal and/or corporate value filter. By repeating this process, and adding a decision-review step, we learn how to make better decisions. The more we know, the more experience we have, the better our chances of making the best possible choice. And that is how it has been for the last ten of thousands of years.  

While we have evolved our ability to gather and access information with software, and made the analytical part simpler and more accessible, machine-assisted decision making and execution is about to change the decision-making process.

AI and decision making

The human brain can process 11 million bits of information per second, but our conscious minds can handle only 40 to 50 bits per second. And while we do not always forget, retrieving the right information at the right time is not straightforward.

Our ability to gather and analyse data is limited by our knowledge, time, and “computational power.” However, if we know what information we need, there are now thousands of tools that can help us gather the data and connect it with other data sources to uncover new insights and patterns.

Predicting the future based on historical patterns is not a complicated science, but rarely a trustworthy one. Machine learning algorithms have increased the accuracy and given us a better foresight of how decisions and events might unfold, making it possible to simulate different scenarios and study decision consequences without having to execute a decision. The possibility of setting up “What-if” scenarios and playing them against each other, pushes us closer of being able to make the right, rational decision.  

Building on the previous point about the importance of good data, let us talk about the challenge of data diversity. Machine learning models are only as good as the data they are trained on. If you train a model on a narrow dataset, it will only be able to make predictions that are relevant to that dataset. For example, an automated script writer that is only trained on movies and books written by Quentin Tarantino will always produce scripts that are similar to Tarantino's work. The same thing happens if you run your analytics only based on your company's internal data without considering external data such as market and competitor data.  

Powerful and accurate models combine data from a variety of sources to reduce bias, improve generalisation, and identify new patterns and insights. For example, a company that is developing a model to predict customer churn could combine data from its internal CRM system with data from external sources such as social media and customer reviews. This would help the company to identify patterns and insights that it would not be able to see by looking at its internal data alone.

Prescriptive analytics  

The one type of analytics that will profoundly change our decision-making process, and profoundly change how we work, is prescriptive analytics.

Prescriptive analytics is (currently) the final stage in the analytics spectrum, which includes descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics answers the question "What happened?", diagnostic analytics explain “Why it happened!”, predictive analytics addresses "What might happen?", and prescriptive analytics tackles "What should we do about it?", including all former analytics in its process.  

When we make decisions, all these analyses happen naturally in our brain and are part of our decision process. The extent of how much we analyse depends on the time we have, the number of people involved, and the consequences of the decision. If we have little time or the stakes are low, we may make a quick decision with minimal analysis. However, if we have more time or the stakes are high, we will spend more time trying to analyse the situation and considering our (imagined) options.

If we turn to machine-assisted decision making powered by prescriptive analytics many of parts of decision process become automated. Using machine learning, algorithms, and computational modelling, prescriptive analytics provide insights, simulates different scenarios, and suggest actionable steps in response to a predicted outcome or scenario.

For example, in supply chain management, prescriptive analytics might suggest optimal routes for delivery based on predicted weather conditions, anticipated traffic patterns, and historical accident data. Or, in finance, it could recommend investment strategies based on a forecasted economic downturn.

A New Paradigm of Decision-Making with Prescriptive Intelligence

A step-by-step decision-making process includes most commonly these seven parts:

1. Identify the decision
2. Gather information
3. Identify alternatives
4. Weigh the evidence
5. Choose among the alternatives
6. Take action
7. Review the decision

Imagine that you have a data foundation that gathers all your data in one place, both external open data (market, competitors etc.) and internal. You have billions of rows of present and historical data, cleaned, enriched, and contextualised. You are a Business Development Manager at a Last-Mile delivery company, and you are tasked with expanding sales to a new area. Where do you start?

1. Identify the decision

In which geographical area can we increase our revenue the most?  

2. Gather information

Where are our competitors present?  
What are our competitors' prices?  
Where are our terminals?  
How much are we today delivering in each area?
What delivery options are the most popular in which area?  
What investment will be needed for each area?  
Etc.  

3. Identify alternatives

All areas and options are listed. Business cases are presented.

4. Weigh the evidence

Alternatives are weighed against each other. Pros and cons are discussed.

5. Choose among the alternatives

Once you have weighed all the evidence, you are ready to select the alternative that seems best for the company. You may even choose a combination of alternatives.

6. Take action

You implement the chosen alternative. It is time for execution.

7. Review the decision

You review the results of the decision and see how your expansion plan is working out and iterate.  

With prescriptive intelligence in place, the machine assisted decision-making process is similar, but at the same quite different as the effort lies in the beginning, and not the collection of information. We assume here you have access to a tool that combines market data with internal data.

1. Identify the decision

In which geographical area can we increase our revenue the most?  

2. Goal formulation (prompting)

What are the results that you are looking to achieve and through what means. List interesting areas for exploration and factors you think are relevant.

3. Scenario evaluation

Alternatives and scenarios are simulated and presented by the AI describing the steps needed to reach formulated goal. Costs and risks are listed based on data that is available. You have the possibility to deep dive into areas to expand your analysis or follow the recommended path.

4. Weigh the scenarios

Recommendation is weighed against the other scenarios.

5. Scenario implementation

You implement the chosen scenario and measure against milestones and goals set by the AI.

6. Review the chosen scenario

The decision and chosen scenario are evaluated in real time with the AI to ensure ongoing learning and optimisation.

If we look past the fact that much of the decision-making process is automated, we move from hypothetical discussions around outcomes and consequences to an evaluation of the proposed steps to reach the decision and set goal. The proposed scenario is not unbiased and unemotional, it is guiding force explaining how to reach that goal with what is available.  

Science fiction?  

Prescriptive intelligence is not something we imagine anymore, it is being worked on today, and there are already solutions in the market for specific use cases. Our decision-making process will not only be faster (timewise), but we will also be able to be much more accurate in understanding outcomes and the decisions in between we need to make to reach a certain goal.

Finding the competitive edge

If everyone can afford the same tools and have access to the same data, isn't there a risk that we will all pull towards the same goals in our respective fields? Isn’t it all about increasing profit through expansion or decreasing costs?

The chances of that scenario are limited.

Not one company has the same data as another one. We can acquire datasets, predictions, but in the end how we operate, they people we employ, the decision we made, and our assets and business models are not the same. Each company has its own strategy, so even if we all access the same market intelligence, the outcome will be different. But just as generative AI has shown with ChatGPT and Midjourney, the playfield has become much more even.  

Market analysis and expensive data is becoming less expensive and available to a larger extent of companies, and not only the big ones.

Market intelligence

A general prescriptive analytics platform is still a couple of years in the future. At Tembi, we have built the data foundation for it, and are constantly working on adding new machine learning based prediction and econometric models to create better insights and foresights for our clients based on open data.

While companies have their internal data, we provide extensive access to open data, and ready-to-go-analytics – or market intelligence – that provide actionable insights to the decision-making process. Many of our clients use our API to connect their data with our data to examine and understand (i.e.) volume fluctuations (revenue drivers) with external events, and hence be able to understand how external factors impact their business, mitigate risk, or uncover new business possibilities.

The more we connect the world's Information the better we will understand the future, and the more impact our decisions will have. And that is why we work here at Tembi. Until we provide a general prescriptive intelligence platform for executing successful business decisions, we focus on providing market intelligence that is beyond what can be seen by a person online. We combine data from multiple industries and build market predictions models based on changes across different industries.  

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n today's data-driven world, the abundance of information and the advancement of analytical tools have sparked a competitive quest for insights. As data becomes more affordable and accessible, the ability to use this data effectively becomes a decisive factor in staying ahead. But having data is one thing; making sense of it to predict the future is quite another. It is a complex task that goes beyond just crunching numbers—it is about weaving together diverse parts of information, both old and new, to form a clear picture of what lies ahead.

This article aims to untangle the concept of Predictive Market Intelligence, demonstrating how it operates and its value in a business context. We will look at how this approach to data can lead to smarter decisions and how it is shaping the way companies move forward.  

What is Predictive Market Intelligence?

Predictive Market Intelligence (PMI) stands at the confluence where big data analytics, artificial intelligence, and advanced market research meet. It is the art and science of collecting vast amounts of open data - from (i.e.) market trends, company behaviour, to global economic indicators - and analysing them to forecast future market conditions. The aim of PMI is not only to investigate information based on past market performance – historical data – but to forecast the evolution of markets, specific industries, or companies, by employing diverse analytical methods and algorithms.

Unlike traditional market research, Predictive Market Intelligence is dynamic, constantly refining its insights with a steady stream of real-time data. This process enables businesses to not just interpret the present but also to anticipate and prepare for future market developments, gaining foresight and deepening their understanding of potential future scenarios.

Applications of Predictive Market Intelligence

If companies can use Predictive Market Intelligence to gain foresight, can PMI be applied everywhere, or are there particular interesting applications of this approach to market analysis and strategy? Here are a couple of examples:

Enhanced Forecasting Abilities

• Anticipating Market Trends: Predictive Market Intelligence allows companies to not just understand current market dynamics but to anticipate future trends. By analysing patterns in data, businesses can foresee changes in consumer preferences, economic shifts, or industry disruptions. This foresight enables them to adapt their strategies proactively rather than reactively, staying ahead of the curve.

• Identifying Emerging Opportunities: With Predictive Market Intelligence, companies can spot emerging opportunities in their industry. This could include untapped market segments, new product possibilities, or innovative service offerings that have not yet been fully realised by competitors.

Data-Driven Decision Making

• Reducing Uncertainty: In business, uncertainty can be costly. Predictive Market Intelligence significantly reduces this uncertainty by providing data-backed insights. When decisions are based on solid data, the risks associated with them are significantly lowered.

• Strategic Alignment: Predictive Market Intelligence aligns various aspects of a business - from marketing and sales to product development and supply chain management - with the overall market dynamics. This alignment ensures that every part of the business is working towards a common, data-informed goal.

Improved Customer Understanding

• Tailored Customer Experiences: By understanding customer behaviour and preferences through Predictive Market Intelligence, companies can tailor their products, services, and marketing efforts to meet the specific needs and desires of their target audience.

• Building Customer Loyalty: Businesses that consistently meet or exceed customer expectations foster stronger customer loyalty. Predictive Market Intelligence plays a crucial role in enabling businesses to understand and predict what their customers want, often before the customers themselves know.

Operational Efficiency

• Streamlining Operations: Predictive Market Intelligence can identify inefficiencies in operations, supply chains, and production processes. By addressing these inefficiencies, companies can reduce costs and improve their overall operational effectiveness.

• Resource Optimisation: With Predictive Market Intelligence, businesses can allocate their resources more effectively, whether it's human resources, capital investment, or marketing spend, ensuring that every dollar spent is optimised for maximum return.

Competitive Analysis

• Benchmarking Against Competitors: Predictive Market Intelligence tools can analyze competitors' performance, strategies, and market position. This insight allows companies to benchmark their performance and strategise accordingly to gain a competitive advantage.

• Adaptive Strategies: In fast-paced industries, what works today might not work tomorrow. Predictive Market Intelligence empowers companies to quickly adapt their strategies in response to competitive moves or market shifts.

Technology Behind Predictive Market Intelligence

Retrieving Market Intelligence is a question of gathering data from various sources, organising the gathered data, and applying different technologies to validate, enrich and put the data into context. The last step is to apply different analytical models depending what outcome one is looking for. So, where the first step is about gathering (open) data, the second analytical step is the creation of synthetic data (programmatically generated data).  

Each step of the process, from open data to intelligence, uses different technologies. Each plays a unique role and function, but applied together, collectively, these technologies can create incredibly precise projections. Let us dive into a couple of them.

Data Mining and Aggregation

Central to Predictive Market Intelligence is the process of data mining and aggregation. This involves the meticulous gathering of vast volumes of data from a multitude of sources like public information, financial reports, and for example websites. The objective is to amass a comprehensive dataset that encapsulates the diverse aspects of the market and company behaviors. This rich tapestry of data forms the foundation upon which further analysis is built.

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) stand at the core of Predictive Market Intelligence, processing and interpreting the extensive data collected. AI algorithms are adept at discerning complex patterns and relationships within the data, which are often imperceptible to the human eye. Simultaneously, ML models, with their ability to learn and improve from the data, continuously refine their insights, ensuring they remain relevant and accurate in a rapidly changing market.

Natural Language Processing

A key component in understanding context is Natural Language Processing (NLP). NLP technologies delve into text-based data, analysing news articles, pdfs, and websites. They are particularly effective in understanding the context of the written text, and being able to synthesis substantial amounts of data and help verify what the data is

Predictive Analytics

Predictive analytics brings a forward-looking perspective to Predictive Market Intelligence. By employing statistical and econometric models as well as forecasting algorithms, it anticipates future market behaviors, trends, and company needs. This facet of Predictive Market Intelligence is instrumental in risk assessment and scenario planning, allowing businesses to prepare for various future market scenarios.

Big Data Analytics and Cloud Computing

Big Data Analytics provides the muscle to process and analyze the immense datasets characteristic of Predictive Market Intelligence. It offers real-time analysis and sophisticated data visualization tools, making complex data understandable and actionable. Complementing this is cloud computing, which offers the necessary infrastructure for data storage and analysis. Its scalability ensures that businesses can adapt to varying data demands, while also offering cost-effective solutions compared to traditional in-house data centers.

For Experts and Beginners Alike

Predictive Market Intelligence is not only for experts. With platforms such as Tembi, PMI is today accessible for everyone, regardless of analytical skill set. While there are use-cases that require tailormade algorithms, predictions such as company growth, market trends and econometric forecasts are already available. And with decision-ready market insights, companies can quickly adapt to a data-driven decision process without heavy investments.

Predictive Market Intelligence for Experts

For the expert, Predictive Market Intelligence serves as an advanced tool that complements and elevates their analytical skills. PMI can be used to validate hypotheses, refine models, and conduct in-depth analyses that underpin robust, strategic decisions.

The technology used in Predictive Market Intelligence lets experts quickly sort through and understand huge amounts of data. This means they can get a clear picture of how markets are changing, what competitors are doing, and how companies are behaving. With this kind of intelligence, experienced professionals can make accurate predictions and find new business opportunities before anyone else does.

For less savvy analytical minds

For those new to Predictive Market Intelligence, it can seem both exciting and a bit overwhelming at first. But this technology simplifies the process of understanding the market by turning complicated ideas into clear insights. It provides easy-to-use tools and clear visuals that help make sense of complex data.

With Predictive Market Intelligence, even those just starting out can get a complete view of the market. They'll learn to spot the important signs that show changes in what consumers want or in the economy. This technology is like having a guide and a coach in one, helping new users think strategically and make decisions based on data.

A Convergence of Knowledge

Predictive Market Intelligence acts as a bridge between theory and practice, enabling a fluid exchange of knowledge across all levels of expertise. It is a field that values the knowledge of the expert and nurtures the growth of the newcomer. By fostering an environment where learning is continuous and insights are accessible, Predictive Market Intelligence ensures that all users, regardless of their level of expertise, can contribute to and benefit from the intelligence it provides.

What is next for Predictive Market Intelligence

The future of Predictive Market Intelligence looks particularly promising as cloud computing costs, which have been a significant factor in the past, are expected to continue their trend towards more economical and efficient services. As the price-performance ratio of technologies like GPUs improves, companies can leverage more powerful analytical capabilities at a lower cost. This could further democratize PMI, allowing smaller businesses to engage with what was only accessible to larger corporations. The integration of emerging technologies such as distributed cloud and advanced AI (Artificial Intelligence) algorithms will further enhance PMI's accuracy and speed, offering businesses of all sizes the predictive insights needed to stay ahead in an increasingly data-centric world.

What will be key, as always with the development of analytics and AI, is the quality and the amount of data. With a democratization of technology, the winners will be the ones that invest in good data gathering processes – both internal and external open data – and have solid data partnerships in place.

One thing is sure, we have only touched the very beginning of this approach. But already today, it is evident that companies that utilize external data in their decision process, have far better chances of making better decisions. Giving them a better competitive edge.

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