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Act before the market
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.
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.)
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.
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.
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:
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.
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.
