Frequently Asked Questions

ClearD3™ is sister operation of LINKS Analytics B.V., 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. Our clients include the largest financial institutions in Europe, with combined assets under management of over €550 billion.

The 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, from clients of clients and suppliers of suppliers. It then translates related event chains into expected changes in volumes and pricing for the company. A typical company will experience a revenue improvement of 2-6% without new investment.

What’s needed is to make sense of available data. Data items in ClearD3™ are sourced from reputable publicly available sources, such as national statistical offices (US BEA, Eurostat), central banks (FRED, ECB), US and European government agencies (US BLS), international organizations ( OECD, the World Bank, WTO, IMF).
But these data series only provide a general background information. By themselves, they are not sufficient for a reliable and specific industry-level intelligence. ClearD3™ 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.

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.

Many local companies find themself competing against larger regional or global players with greater access to information about global pricing environment. Knowing “your own corner of the world” is not sufficient anymore.
Companies, small and large, all struggle with the increased pace of change and volatility. Fixed list prices often end up either being too low or too high and do not accurately respond to new demand in a changed environment. Put simply, the result is 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, your company has to incorporate external data/intelligence in decision making. But what data and how? In order to even begin to answer this question, you would need to invest in a data team or hire external consultants. Yet it is hard to judge what the actual return on investment for such an investment can be.

Internal data team: organizing a 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 staffed with many people making many decisions on a 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.

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.

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.

Test-prove-expand approach: ClearD3™ Test Lab enables reliable testing and validation. Not only of the ClearD3™ pricing module, but also of 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 available 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.