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Anyone who knows my former blogs knows that I am a big fan of both R and Python in daily work.

As powerful as R is in terms of functionalities for data analysis and modeling, as quickly is the motivation subdued in case of "number crunching" when RAM runs at maximum.

In this context, a nice server installation with a lot of metal (e.g. 96Gig-RAM) works wonders.

As this option is not always available, I have made a virtue of necessity and turned towards the more performant alternative, namely the Python based R alternatives, especially since I have been using Python for ETLs and data preparation for a long time.

Starting Situation

The controlling division of a telecommunications business is to be supported regarding the forecasting of the monthly development of gross adds figures. "Gross adds" is the key figure which reports the gross new customer growth within a defined period, where the number of lost customers is not taken into account. The key figure "gross adds" is primarily used in the telecommunications industry and reflects the number of newly concluded contracts (postpaid and prepaid).

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arcplan facilitates the creation of standardized reports, which support (and thus make more efficient) the daily work of the employees of a business. In particular, this is the case if reports present the included data in a meaningful, concise and user-friendly manner. Large volumes of information are thus often structured and presented in the form of tables. In order to illustrate relations and/or hierarchies of data within the table and avoid redundancies, it is required to choose columns and line headings which, in addition, are supposed to be placed expediently.

This article deals with the following three questions on a high level and very briefly:

• What does a data-driven person think when he hears contentions?
• Which tool is more practical for data analyses: R, Python, Java, MATLAB?
• Can sporting disciplines be the next application area for data analyses and machine learning

Ready-Made Data Sets Which Explode the Limits

I am frequently confronted with raw data that is provided to me for analysis and which, when uncompressed, can easily encompass files of half a gigabyte or more. Starting from one gigabyte and over, the desktop-supported statistics tools slowly become strained. There are, of course, tool options for only selecting part of the columns, or only loading the first 10,000 lines, etc.

But what should you do when you only want to take a random sample from the data provided? You should never rely on the file being randomly sorted. It may already have gained systematic sequence effects due to processes in the database export. It also may be the case, that you only want to analyse a tenth of a grouping, such as the purchases made by every tenth customer. To this end, the complete file has to be read as otherwise it is impossible to ensure that all of the purchases of the filtered customers are taken into account.

Uplift modelling can support campaign managers in managing and planning campaigns as it supplements the classic response model of campaign scoring.

Uplift modelling is based on the principal idea that campaign responders are grouped in two categories: those who would have reacted even without the campaign and those who would not have responded without the campaign. Unlike classic scoring, which equally aims at both groups, uplift scoring tries to exclusively isolate the second group and, wherever possible, ignore the first. For this purpose, the response information from the control group is used, which remains unused in classic campaign scoring

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.