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arcplan is the first software tool for business intelligence (BI) which received the renowned quality seal  HICHERT®IBCS by BARC and HICHERT+FAISST. The high degree of flexibility of the tool 'arcplan Enterprise' made it possible to fully meet all requirements regarding graphics, tables, structures and comments. Since then, arcplan has invested further development efforts in order to simplify the creation of "IBCS compliant" -reports for the user (report developer) and thus save a lot of time and resources in the course of report development.

arcplan 8.5 offers a portfolio of completed and 100% IBCS compliant graphics which can be integrated into the application by a few clicks. Of course, 'Quick Steps' also offers the full arcplan flexibility and can be modified, extended and tailored to the specific requirements.

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Overwhelming Offer in the Webshop

Two weeks ago, a frequently used online mail-order company, whose reminds of a river in South America, called my attention to a campaign by a friendly information email. Namely, three music CDs from a large selection were offered to me for 15€.

As in the past, I still enjoy buying music on physical sound carriers and decided to have a closer look at the offer. It turned out that approx. 9,000 CDs were offered on about 400 pages in the online shop. This shop provides the possibility to sort the offers by popularity or customer ratings. However, if I view the popularity in descending order, I find many titles which do not quite correspond to my age group. On the other hand, if I sort the offers by customer ratings, it turns out that the shop processes the ratings in an unweighted manner. That means that a CD with only one 5 star rating is listed above another CD with 4.9 stars over 1,000 ratings.

arcplan applications frequently offer the possibility for a user to rewrite by means of inputs into the application and/or the underlying database. This is particularly the case with forecasting applications, but in simplified form also with comment inputs.

R is the Open-Source All-rounder with a Difficult Learning Curve

Approximately three years ago, I switched from a commercial statistics solution (that was similar to SPSS) to R.  I can now say with conviction that I don't need another tool for advanced analytics. Especially in combination with IDE "R-Studio", the software has now reached a level of maturity that allows it to be used in big data science projects without any concerns.

There is, however, no need to delude oneself that one can install R easily and get started immediately. The learning curve is comparatively steep because there are multiple ways to do things due to the variety of packages, amongst other reasons.  Frequently, I was annoyed during my evaluation when I was suddenly tripped up by a trivial step and this meant I had to research how to solve the problem in R before continuing. Therefore, in this introduction (hopefully with many more parts to follow), I would like to present some tips and tricks that I would have appreciated knowing when I started.

Creating Campaign Intelligence in the Mobile App Channel

Mobile engagement is the next big topic in the BI environment - and rightfully so. Potential customer contacts at any location and in all kinds of contexts offer huge chances for relevant communication and a highly effective relationship management. Against this background, the ´Mobile App´ channel with all its possibilities should be integrated into the intelligent management of campaigns. In this context, existing mobile app engagement solutions offer (only) the basic requirements - and thus need professional integration into the existing CRM strategy and central campaign intelligence.

The customer lifetime value, which for a long time tended to be much more frequent in dissertations than in reality, is finding its way into practical application. The driving force is, in particular, the digital economy. The increasing distribution of the customer lifetime value opens up great opportunities for a targeted acquisition and management of customer relationships. However, it also exposes the fact that certain misunderstandings are widespread. For this blog, we have collected a few of them and contrasted them with the facts.

In addition to decision trees, logistic regression is the workhorse in the modelling in order to forecast the occurrence of an event. Fortunately, both methods are designed in a way that one can basically use any kind of predictor for the prediction, whether dichotomous categories, multi-level categories or continuous variables on interval scale level.  Especially the logistic regression, however, has no possibility to reasonably deal with missing values. In social science research or market research, one often makes do with limiting analyses to complete data sets.

A very frequent use case in reporting is the linking and displaying of information from either different tables or even different data sources. Frequently, a database view or even an intermediate step via a database layer, in which the data from different sources is merged, is required for this purpose. With the "Assign" function, arcplan offers a simple and quick possibility to link data in the report and in real time.

I have moved recently and was asked to paint the walls and ceilings of my old apartment by the former property management company. I went ahead right away and tried to get quotes from painting and decorating businesses online. There, I was asked immediately to provide the surface area to be painted in square-meters ... mmh, of course, I could have provided the floor area and the number of rooms straight away, and I had hoped that the businesses would use a simple projection for preparing the quotes. But to directly calculate the surface area to be painted appeared somewhat more complex to me than an off the cuff estimate.

In the direct marketing environment, success measurement of implemented campaigns continues to play only a minor role in many businesses. However, well performed campaign success measurements provide knowledge about the effectivity of the respective campaigns and facilitate informed decisions about their design and future optimization. It should be the objective to implement a standardized success measurement for direct marketing measures and to improve new campaigns with the generated knowledge and thus achieve a higher turnover and profit.

Use Cases

Categorical variables can be used as original texts in SPSS, which results in a substantial loss of performance in the case of large amounts of data, or as numerical codes with labels. The second way is not only drastically more performant but also the right way because although it makes the code in the SPSS syntax more difficult to read, it also makes it absolutely immune to changes in notation.

Starting Situation

Campaign Marketing Tools are often bridging between Marketing and Data management Teams, giving marketing ideas structure and shape within a form of coordinated and well measured marketing activities.

There is an obvious tendency on the international market to choose solutions that can be easily rolled out to all countries being part of a global strategy to simplify and unify technical infrastructure and processes.