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Act before the market
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.
This is our starting point, where we use historical data to understand past performance.
Here, we dig deeper, using the data to uncover the root causes of past events.
Leveraging statistical models and machine learning, we forecast future trends and behaviors.
This is the pinnacle of analytics maturity, where we not only predict the future but also provide actionable recommendations to shape desired outcomes.
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.
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.
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.
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.”
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.
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.
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.
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.
“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.”
“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.”
“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.”
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%.
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.
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.
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.
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.
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.
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.
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.
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.”
Christian Mejlvang, head of product at Tembi
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.
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.
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.
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.
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 step-by-step decision-making process includes most commonly these seven parts:
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.
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.
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.
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.
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.
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:
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.
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 (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.
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 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 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.
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.
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 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.
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.
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.
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.
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.
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he Nordic eCommerce report dives into the eCommerce market in Sweden, Finland, Norway and Denmark. The report is free and available for download here.
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.
Baltic E-Commerce Market Intelligence Report (Published January 2024)
Nordic e-commerce Market Intelligence Report (Published October 2023)
