Frequently Asked Questions
Find answers to most common questions or get in touch with us
Who are LINKS Analytics?
LINKS Analytics is a Netherlands-based firm that has provided global supply chain intelligence solutions since 2009. The firm has developed a proprietary supply chain data-driven decision-making solution that covers over 40 largest countries and 70 industries. The firm’s clients include the largest financial institutions in Europe, with combined assets under management of over €550 billion.
What do LINKS Analytics propose to industrial and transport companies?
LINKS Analytics’ ClearD3 platform helps companies improve gross margins and revenues by anticipating changes in market environment and managing pricing and capacity accordingly. By using thousands of pre-populated external data items, the platform monitors the development of events and data several steps along the supply chain (e.g. clients of clients, suppliers of suppliers) and translates those to expected changes in volumes and pricing for the company. A typical company will experience a revenue improvement of 2-6% without new investment.
What does LINKS offer to financial institutions?
The history of LINKS begins with serving the financial community – pension funds, insurance companies and asset managers. Mira ABM – the Main Investment Risk Application is design to carry out asset allocation, scenario analysis and stress testing in a fast-changing environment, when historical statistical data and analysis become less helpful. For more on LINKS Mira ABM please refer to the product page.
Where does the ClearD3 data come from?
Data in itself is not everything, as the major contribution of the ClearD3 platform is making sense of available data. However, LINKS does integrate, source and organize a very large dataset, so customers do not need to worry about sourcing data.
Many data items in ClearD3 are sourced from reputable publicly available sources, such as national statistical offices (e.g. US BEA, Eurostat), central banks (e.g. FRED, ECB), US and European government agencies (e.g. US BLS), international organizations (e.g. OECD, the World Bank, WTO, IMF). These data series provide a general background information, but they are not sufficient for a reliable and specific industry-level intelligence.
LINKS also generates its own data series in each industry by applying machine learning methods on road and marine traffic data, satellite imaging, weather and other sources. Please reach out to us for examples of data relevant for your industry.
What is your solution about?
ClearD3 leverages thousands of external data series. Normally, this would be an impossible undertaking given the amount of data, but ClearD3 uses supply chain relationships to determine the relevance of different data items for each company. For example, a food processing company would naturally see agricultural commodity prices as important, but also weather, energy prices, bulk chemical prices, exchange rates of major agricultural product exporting countries. Machine learning algorithms sift through the data and assess the expected impact on volumes and prices of a company’s products three to six months forward.
What are the challenges of your customers?
Competitors and clients consolidate, so many local companies now compete against larger regional or global players with greater access to information about global pricing environment. Knowing well “your own corner of the world” is not sufficient any more.
Even larger companies struggle with increased pace of change and volatility; fixed list prices end up either being too low or too high given the environment. Put it simply, the has either too low-capacity utilization or too low margins compared to better informed competitors.
In order to react quicker and in a more informed way, the company has to incorporate external data/intelligence in decision making. But what data and how? In order to even begin to answer this question, the company would need to invest in a data team and/or hire external consultants. It is hard for the company to judge what the actual return on investment for such an investment can be.
What are the constraints of your customers?
Internal data team: organizing data-driven decision process requires an internal team of data scientists, data vendor experts and business analysts who can correctly formulate business problems to data scientists. Building such capacity is a major undertaking requiring multi-year investments.
Staff outside the “data bubble”: companies are still staffed with hundreds and thousands of people who make decisions on the daily basis. It’s not enough to have a data-driven decision making aid. Such a solution must be fully integrated with the existing processes and people often need to adjust their way of working to benefit from data solutions. This places tremendous pressure on an organization to fundamentally restructure the way it does business and possibly replace staff.
Lack of objective evaluation process: without a structured way to assess the added value of data-driven solutions it is impossible for the management to make the change. Often, there are some expected ROI calculations attached to data projects, but how realistic are they? In such circumstances the management often has to make a leap of faith.
What are available choices to address the challenges?
The status quo of doing nothing: the competing companies, particularly more data-driven ones, will achieve better margins than their direct competition with products that may not be even superior, which over time will translate into larger and larger financial gap.
Review the entire pricing process: the manual reviews are one-off expensive exercises that only temporarily fix the problem. The current volatile environment may require major changes three, four or five times a year, which cannot be done with manual (external) review.
Internal effort to become data-driven: internal effort would require a multi-year data project with uncertain ROI’s. This would entail developing in-house competence of data vendor selection, data analysis and machine learning to collect and digest external data.
How can we be sure it works? What is ClearD3 Test Lab?
Since the goal of data-driven pricing is to improve the financial performance, ClearD3 implementation begins with creating a “Test Lab” – a double-blind randomized trial environment (also known as A/B Testing), where only a small proportion of the company’s business is priced using ClearD3 in random. This means that companies are able to run very small-scale tests of different pricing approaches and get hard statistical evidence from the ground that the decision-making process adds contribution margins or revenues (or both).
The company may also use ClearD3 Test Lab platform to test the ROI/effectiveness of third-party or own data-driven decision systems.
What is the required investment to implement ClearD3?
There is no up-front investment or commitment from companies. Following an introductory discussion and assessment of potential ROI’s to be achieved, LINKS maps the company’s business in ClearD3 and activates the use of the system. The company pays flat monthly fees and the use can be cancelled every month.
How does LINKS compare with alternatives?
Machine learning modules of generic CRM providers: these tools usually leave it to the user to select and add relevant external data, build and validate models. This still requires deep data source and machine learning expertise.
Industry-specific specialized service providers: if a company is in a very specific, usually B2C industry, there are specialized solutions. However, there are no such solutions for more opaque B2B markets, and in those fields where there are those solutions, they still do not consider the impact of broader global environment on the industry, so they are by default limited.
ClearD3 is able to reliably assess and forecast B2B pricing environment even when the market is opaque and no data are available, by monitoring the industries further along the supply chain and making inferences about the business environment.
What is your track record?
ClearD3 has been validated by major financial institutions (professional teams with Assets Under Management over €100 billion) using the system based on rigorous and objective requirements:
– Beating human analysts
– Beating best available statistical methods
– Generating actual superior performance on the basis of actual forecasts
These results and references of existing clients are available for companies interested in learning more about ClearD3.
What are the benefits of your approach?
Test-prove-expand approach: ClearD3 Test Lab enables reliable testing and validation of not only ClearD3 pricing module, but also any third-party data solutions.
“Batteries included”, non-invasive approach: all relevant external data of thousands of macroeconomic and industry-specific series are included by default.
No pressure on human capital: no need to learn new systems, toolkits, data; sales, finance, pricing and C-suite continue to work with their existing systems, with reporting availabled in BI tools, such as Power BI or Tableau
Easy, non-invasive: works with the existing informational environment of the company (CPQ/CRM/ERP)
No disruption: no lengthy project setup, new IT infrastructure requirements
ROI focus: no up-front investments and lengthy implementations, no long-term commitment. ClearD3 establishes cash generation targets and monitors achievement, making reliable ROI calculation a cornerstone of the system.
How do you implement?
Introduction: at this stage you can have access to some of our unique data relating to your end markets
Assessment: we can estimate the potential gross margin and revenue gains within an hour
Roll-out of ClearD3 Test Lab: testing phase of one to three months the system is monitored in a A/B testing environment and the ClearD3 Pricing impact is objectively assessed.
ClearD3 Pricing Live: at this stage you may decide to go live for any fraction of your business volume and monitor the performance
Number of countries
Revenues improved (%)