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In our series titled "The next generation of CRM – May the MarTech be with you", we have already explained the marketing technology trend's actual sustainability, the dos and don'ts to be observed during introduction, and the means of identifying one's own MarTech maturity level.

In this article, we will next take a look at the MarTech architecture – our map on the mission through the MarTech universe. Because precisely this aspect is becoming increasingly technical, complex and confusing. In our digital world, customers have numerous options at their disposal – and their demands in terms of customer experience are rising. But digitization is also bringing changes for marketers. Though the traces which customers leave behind along digital contact channels such as an online shop, app or website allow very versatile use – they also increase the complexity of marketing.

The use of marketing clouds is growing rapidly – driven by changing customer needs and the desire for more effective and more efficient data-based dialog with customers. At the same time, marketing clouds are becoming increasingly complex. Consequently, many of those responsible for selecting and implementing a marketing cloud platform lack the experience to do so. This can cause a number of problems and ultimately lead to the failure of the project.

Digital transformationin the Interet of Thing (IoT) pose a major challeng to many companies. The IoT apdoption framework supports transformation through a structured and technologicaly independent approach.

MarTech - what is it really? And how does it differ from CRM, KMS & Co.? We have already answered this in Part 1 of our series titled "The next generation of CRM – may the MarTech be with you". In this article we ask the question: Which setup do I need to actually make my MarTech stack operational?

As a consultancy, we are asked this very often. Admittedly, selecting marketing technologies can make marketers quickly feel like a kid in a confectionery store: You'd love to have a little bit of everything! However, both situations are also similar in terms of outcome: An excessive appetite usually ends in a stomach ache. At the end of the day, the objective shouldn't be to obtain as many features as possible within the allocated budget, or obtain the most recent, "cool" tool, but to achieve as much added value as possible - isn't that so? But how do I "assemble" the MarTech stack which is optimal for me?

Microsoft Power BI is one of the most popular BI tools on the market, and offers numerous ways to process and visualize data. For even greater flexibility, you can also extend standard functions extensively by using Python and R scripts. This article describes how to integrate Python scripts, what they enable, and what to keep in mind.

In business intelligence, "quality" is always a key topic but dealt with in a variety of ways. This is due to a lack of a consistent understanding in this area, and consequent absence of a standardized approach to BI quality.

That this is becoming increasingly problematic is also signified by renaming of the current Gartner Quadrant. Data quality "tools" are now called data quality "solutions". The reason: Whereas many manufacturers now proclaim to be bearers of "quality", the common denominator for this is usually infinitesimally small: To find, understand and solve problems. To their credit, this is still the source of all technical progress according to the philosopher Karl Popper. However, it does not yet necessarily have anything to do with data quality per se. In this article, I will therefore sort out and structure the topic of BI quality somewhat, and accordingly call the whole thing "BI quality management". This leaves plenty of scope for "total BI quality management", as applied and taught in the engineering disciplines for some time now.

When preparing reports, we repeatedly face the challenge of processing large quantities or complex information in a way which is clear and comprehensible for the report's consumers. Visualizations in the form of diagrams and charts can serve as an effective means here.

In this article, I would like to point out some important aspects which need to be taken into consideration when creating visualizations in reports, so as to achieve meaningful communication.

The other day, I played "Guess Who?", the classic game for children from about 6 to 9 years, with my six-year-old son. While we were playing, we both tried to work out the best way to win the game. This article series is the result of our search for an effective game plan. Part 1 is aimed at the whole family. OK - let's find out how to win!

Having tested Exasol's "lift and shift" migration approach, b.telligent consultant Simon explains step by step how to migrate to the cloud with confidence

Algorithms for Personalized Recommendations

Users do not always leave behind enough personalized information along their customer journey. For instance, new customers can be acquired or existing customers might browse an e-commerce website without being logged in. Non-personalized recommendation systems, such as those based on proposals for products frequently purchased together, still offer recommendation opportunities for companies in this case. However, the more individually these are tailored to the customer, the better.

Anyone who evaluates data using well-known visual analytics tools such as Tableau or Qlik Sense wants to create, quickly reach the limits of applications — at the latest as soon as he works with unstructured or incomplete data sets. To avoid frustration, we tested both tools for their data preparation options and created a short list of best practices.

How much does it cost me to use the tool? Who should work with it: marketeers or IT professionals? And can I also manage my data in the cloud? Many factors play a role in choosing the right tool for visual analytics. b.telligent consultant Daniel Erlhöfer explains the technical side of Tableau and Qlik Sense in this blog post: What are the differences in the data preparation of both tools?

Starting Situation

One may expect widespread approval when describing the objective of modern campaign management today as follows: The focus of marketing must be the individualized and consistent interaction with the customer via his/her preferred channels - self-evidently containing relevant messages at the respective best time. While this statement is not very surprising nowadays, it still shows that behind each of these adjectives, there is a potential source of errors for a customer-oriented campaign strategy.