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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.

Let us have a joint look into our toolbox in this blog entry. The topic provides material for more than one blog entry and we will get back to it time and again in this blog.

Background of the Problem

Many will remember the game show "Let´s make a Deal" from the 90ies where candidates had to choose one of three gates. Behind one gate, the prize was always hidden, and behind the other gates lurked blanks, i.e. the Zonk or, in the USA with presenter Monty Hall, goats. At the start, the candidate always chooses a gate behind which he believes the prize to be hidden. Then, the presenter can try to change the candidate´s mind to other gates by offering cash. He can also open gates in order to increase the excitement.

Starting Point

In order to be able to use an effective customer relationship management, it is of great importance to have robust knowledge about one's own customers and customer structures - especially in the area  of campaign management. Personal information about customers, events and changes in the customer situation and behaviour in a CRM system can be used as opportunity for communication. By means of this existing data, a personalized interaction with the customer, such as offers and services, becomes possible.

Many businesses are currently starting to make initial experiences with the use of big data methods. By means of statistical methods, these methods promise to generate additional knowledge about the customers from huge amounts of mostly unstructured data in very short time. Frequently, automatically generated data such as log files or clickstreams collected from the use of the website, a web shop or data from social networks, is used for this purpose. This knowledge can be of good use for CRM activities. In this context, the question of the significance of data quality in the use of big data methods arises. Because: The analysis and thus the generated knowledge can only be as good as the quality of the data used.  

I have been an intensive SPSS user since my time as psychology student. Accompanying me across all versions during this time were the simple brief commands in order to show me descriptive statistics. These short commands quickly become second nature and thus enable a fast data viewing.

Currently, my tool focus is on R. It is an excellent alternative but despite extensive experience with this open source tool, I still feel a lack of the usability of SPSS. I simply miss my short commands. However, it is relatively easy to add simple commands similar to SPSS as functions in R yourself.