In our quest to create tailor-made solutions that become a perfect fit, "style counsel" and taking measurement meticulously are essential. That is why we start in each project with an in-depth needs and structure analysis of the data sources to be processed and the planned system for data production.

In close contact with our customers, we also develop evaluation benchmarks for an ongoing quality assurance. We understand the deduction of suitable reference figures and target specifications as a decisive building block for successful modeling.

Having many years of experience in media research, we take special care to ensure that external and internal structures of a data set are made as stable and consistent as possible during the modeling process.

• Data structure must be decomposed to a level of detail that allows to identify the anchor that ties one data set to the other.
• Framework and setting of reference quotas must happen in a way that helps to maintain relationships within the data. In terms of media research that means to understand specific structure of media products and their audiences. This is a prerequisite to design a proper modelling. If that is a given, it is secondary where the specific data is derived from (sourced from household or individual samples, survey, tracking or log file data etc.)
• Design of data processing can then follow this condition to optimize results within a multi-dimensional structure:

• • audience composition
  • usage patterns (such as devices, channels)
  • media properties

Facing the challenges of our clients’ projects, we are used to thinking along holistically from preparation of data input till the assessment of data output. That is why we consider right from the beginning about how the modeling work will be integrated into a regular data production.

What does a talented tailor do with a hole in his pants? You are right, he stuffs it instead of simply patching it with another piece of fabric. This is what we do by imputation:

In audience research we observe a tendency of datasets that remain incomplete from surveying. Caused by technical measurement of media consumption which cannot be deployed identically to all households. Or in case of lacking acceptance of certain questions, such as for net income or economic situation.

Dealing with that situation, we design algorithms and processes to impute the missing data. As an example, we enhance responses to query on income of a household for surveys of agma and Radiotest. Parameters are derived from the existing data in a bespoken model, and we inject those into the gaps. In contrast to the method of fusion, distribution of the results compared to the originally collected data might be different.

Our models are mainly designed to achieve a high plausibility of the imputed data. During the integration into the pipelines and the automated processing, it is important to care about measures for persistent quality assurance and validation.

Our clients usually see the predictable determination of reach and frequency in media as the major constituent of their work.

So, we do aim to exploit the measurement results on media consumption for the planning of advertising campaigns. The task is to develop prospective data models which can be applied on the probability of usage. We focus on validly determining the accurate number of consumers (net reach) and seek to keep the overlaps of various audiences reliable.

There are mostly two major use cases when forming the net reach by a data model:

• occasionally collected survey data

• continuously recorded measurement data

Survey: questionnaires on media consumption often contain specifically formulated questions on the last media contact with good recall and the frequency of usage. Assuming a sufficient sample size, we can employ segmentation as the method for calculating probabilities of usage and the net reach. This is what we have established as a proven model for some of agma’s projects. We aim to differentiate media users into segments by individual probabilities of usage from infrequent to regular use. This information can be derived from additionally surveyed variables, correlating with media usage.

In case of smaller samples, we rely on a lean procedure such as optimization of frequency cluster. For this method we derive the probability of usage from optimizing statistical distribution of target groups across the various clusters of frequency.

Continuous measurement: if this type of data is available, we apply discrete modelling methods, such as (negative) Binominal- or Poisson-models. Measurement data is considered as external reference in this case. By a multi-step approach, we determine the net-reach for various dimensions (e.g., devices, audiences) and several levels of hierarchy (e.g., channels, programmes, content, ad inventory). Key is that the modelling results must fit within the boundaries of the measurement results.

Those methods from our toolset can be applied to form data sets on respondent level (micro modelling) as well as for statistical projection of aggregated results across content and ads (macro modelling). For many media research projects, the combination of the two perspectives must be seen as the best way for net-reach model. This is how we adjust the net-reach at the respondent level to specifications derived from aggregations of results (Calibration).