In the age of digitalisation, the volume of information accessible online – and its speed of availability – are steadily increasing. As a result, there exists rather an excessive supply of typically generalised, non-specific patchworks of information from a variety of inconsistent and difficult-to-link sources. Despite this overall surplus, the kind of information that is relevant to the making of strategic decisions vis-à-vis business intelligence, competitive intelligence and market volume is typically not available in an up-to-date form at the click of a button.

Leading data-driven enterprises, the so-called “GAFAs” (Google, Amazon, Facebook, Apple), have a huge head start in this sector. This affords them intrinsic competitive advantages in a number of areas, but particularly in relation to new technologies such as artificial intelligence (AI), cloud computing and predictive intelligence.

Against this backdrop, the field of market intelligence is concerned with business intelligence, competitive intelligence, customer intelligence and predictive intelligence. In short, market intelligence focuses on the acquisition of information and its analysis and processing for business-relevant strategic decisions regarding the market, competition and customers.

The data basis for market intelligence studies is comprised of three main pillars:

Figure 1: Key pillars of data for the market intelligence field

From a process perspective, the topic of market intelligence is usually dealt with as an initial step – e.g. as part of the development of an E-Commerce strategy. The reason for this is that a fact-based foundation is needed at the very start of the process in order to assess the existing market potential and identify the most attractive segments. Only where adequate potential exists can the development of a corresponding strategy be meaningfully pursued. However, market intelligence is also a topic of ongoing relevance. The option to call up real-time data offers a number of advantages; for example, competitor intelligence data (e.g. market shares) or campaign analyses for the marketing strategy can be evaluated using up-to-the-minute information. Accordingly, marketing intelligence is important not only for the preparation of strategic decisions, but also in regard to ongoing performance management.


Figure 2: Overview of the key advantages of a data-driven analysis 

The advantages of analyses based on crawling data are:

  •   Effective business planning through tailored choice of categories

Typically, both top-down and bottom-up business planning are based on general market studies that provide only very rough information on the product categories that are truly relevant. The infiltration of irrelevant market data gives rise to massive uncertainties, even during the planning stage. The advantage of an analysis based on crawling data is that the analysis is custom-tailored and that only relevant data is obtained, which means that uncertainties arising from irrelevant data are avoided.

  • A meaningful data basis for up-to-the-minute analysis at the level of individual products 

Crawling data is based on the daily tracking of transactions at an individual product level. This data is then aggregated at higher levels, e.g. at the level of product categories or brands. As such, it is possible both to conduct detailed evaluations and to obtain a comprehensive overview.


Based on our comprehensive, cross-sector project work and on the sources of data available for collection, we have identified the following areas of focus for market intelligence:

  • Market Intelligence Strategy: Creating market transparency and enabling data-driven decision-making in e-commerce with the help of a market intelligence strategy
  • Potential analysis (demand, product line, pricing): A fact-based data analysis (using real-time crawling data) of the channel-specific overall market potential (e.g. Amazon, eBay, etc.) in addition to the obtaining of competition data such as market shares, top seller products, price strategy, product line performance.
  • Customer Journey Analysis: The achievement of a broad-reaching understanding of relevant customer contact points (from the origin of the need to the after-sale) within the context of a customer journey, the standardisation of customer group types on the basis of specific characteristics, and individual user behaviour traits within the context of a persona analysis.
  • Online Competitive Analysis: The creation of a comparative scoring system for e-commerce maturity. Assessment of various relevant criteria (incl. digital visibility, online maturity, online marketing activities, etc.).
  • Artificial Intelligence (AI) / Machine Learning (ML): Artificial intelligence solutions are a combination of big data, artificial intelligence-based algorithms and the necessary high-performance hardware.

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Markus Fost, MBA, is an expert in e-commerce, online business models and digital transformation, with broad experience in the fields of strategy, organisation, corporate finance and operational restructuring.

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Markus Fost

Managing Partner
Markus Fost, MBA, is an expert in e-commerce, online business models and digital transformation, with broad experience in the fields of strategy, organisation, corporate finance and operational restructuring.

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