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  • Cross-selling and up-selling in digital commerce.

    Advanced up-selling and cross-selling strategies are essential for businesses intent on establishing, maintaining or expanding their presence in the modern digital market. Inherent to any successful strategy is the use of predictive analytics methods to ascertain, through an assessment of individual user behaviour, a set of optimal marketing decisions. Learn why approaches that go beyond simple product recommendations lead to greater success.

  • Fraud detection with data analytics.

    The use of data-driven fraud detection is increasing rapidly across diverse fields, due to the increased availability of data and to methodological innovation. An example is the use of analytics-driven fraud detection as part of an annual financial audit. Find out more in our article in the German auditing trade journal "Die Wirtschaftsprüfung", entitled "Zur Beurteilung des Fraud-Risikos im Rahmen der Abschlussprüfung2 ("Assessing Fraud Risk for the Purposes of Annual Audits" by tax consultant Stephan Knabe, Dr. Sebastian Mika (idalab), Prof. Dr. Klaus-Robert Müller (Technische Universität Berlin), Dr. Gunnar Rätsch and auditor Prof. Dr. Wienand Schruff (KPMG).

  • Understanding and exploiting the Digital Customer Journey .

    The hot topic in digital retail is the concept of the Customer Journey. New tracking technologies enable the compilation of digital contact point data, recording a snapshot of a customer's journey to purchase. The key to its usefulness as a sales-boosting tool lies in its integrated analysis of multiple touch points and in the pointers it provides as to the relative impact of different channels. Existing media/marketing strategies can be re-evaluated and future budget planning made more efficient. Find out more: The challenges of Customer Journey attribution.

  • Big Data is not a strategy.

    In essence, Big Data refers to two important developments. Firstly, the success of data-analytics-driven business approaches in an increasing number of economic sectors, and the rise of business models incorporating data sets as productive capital. Secondly, the phenomenon of a dramatically expanding mass of data and the corresponding innovation in methods of capitalising on it. A key factor in determining the success and profitability of any Big Data project is the correct assessment of both its potential and its possible pitfalls. Find out more: What is Big Data? | What is Predictive Analytics? | Big Data: achieving success from the outset.

  • Intelligent algorithms instead of parallelism.

    Data have their full impact in many aplpications only if analytics run in real-time with low latency. This is particularly interesting if it enables the direct control of processes. Examples include monitoring applications, real-time bidding or fraud detection. However, if the rate of data is high classical big data technologies like big data do not scale well - or only at prohibitive initial investments. But modern algorithms allow for a drastic redcution of computational efforts. Find out more: Intelligente Echtzeitanalyse

idalab - The Analytics Consultants

idalab supports its clients in all aspects of data analytics. Using innovative mathematical, statistical and algorithmic methods, we derive value from client's data. From the precise formulation of your objectives, to the creation of tailor-made solutions, through to implementation in software systems, we deal with the entire analytics value chain.

Our principle areas of expertise are customer relationship managementrisk analysis and fraud detectiondigital marketing and data strategy. Clients from the fields of digital commerce, insurance and banking, and digital advertising form the core of our business.

With 10 years of industry experience, covering some 100 successful collaborations, idalab has the expertise to help you achieve your objectives. Talk to us.

Our Services

Management Consulting

We help decision-makers evaluate the potential, pitfalls, and capabilities of data analysis in the context of their business.

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Data Analytics Solutions

Working together with our clients, we develop solutions to meet their objectives using advanced data analytics methods.

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Software, Systems and Processes

We develop software to implement data analytics solutions, integrating them into our customers' existing IT landscape.

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Our Clients

idalab's clients are from nearly all business areas, whether on- or offline. We support small and medium enterprises as well as multinational cooperations. A short list of typical clients can be found here.

 

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