Relevance & Research question: Sustainability is the topic of the day. But how can customers who tend towards an ecological lifestyle be identified? Customer segmentation is a well-known method to create more efficient marketing and sales strategies. One of the core problems is the intelligent linking of very specific customer data with suitable general market data and ultimately the precise local determination of potential. This paper shows an innovative way to classify potential buyers at address level.

Methods & Data: First, a survey is conducted with about 10,000 respondents. Questions on general needs, actions and attitudes are used to calculate a sustainability score. By geocoding the respondents’ addresses, the results can be enriched with more than 700 microgeographic data, including information on sociodemographics, building type, living environment, energy consumption or rent. A cluster analysis identifies five sustainability types, two of which tend towards sustainability. By means of a discriminant analysis, the generated segments are transferred to all 22 million addresses and 41 million households in Germany.

Results: As a result, households prone to a sustainable lifestyle can be easily identified. Type 1 of these sustainable households are mostly married couples with children, on average 51 years old, living in single-family houses with solar panel in medium-sized towns and rural communities. They have a monthly net income of €2,500 to 3,500 and sustainability, innovation, family and pragmatism are important to them. Type 2 representatives live in big cities in apartment buildings, are between 18 and 49 years old, often single and have a monthly income between €1.500 and 2500. To facilitate the application of the typology in marketing and sales practice, the typical representatives of both cluster types were developed and described as personas.

Added value: The resulting information can be combined with existing customer data and thus used to identify corresponding sustainability attitudes within one's own customer portfolio. For the specific acquisition of new customers, the address-specific knowledge can be aggregated to any higher (micro-) geographical level and then used in sales and marketing strategies. Advertising activities can then be precisely targeted to the right type of potential buyers.

Relevance & Research Question:

In recent years there has been a steady increase of interest in sensor- and app-based data collection which can provide new insights into human behavior. However, the quality of such data lacks research focus and still needs further exploration. The aim of this paper was to compare various methods of using GPS paradata in CAPI surveys for monitoring interviewers and assess GPS data quality differences among different CAPI interviewers and survey regions.

Methods & Data:

We compared geofencing (distance between locations at the beginning of an interview and at the end), curbstoning (tests if there are too dense groups of interview locations within some area), and interwave geofencing methods (distance between location of interviews with same respondent between panel waves) to check whether they identify interviews that have lower data quality in terms of completion times, criterion validity, and test-retest reliability based on CAPI data of 26th and 27th waves of Russia Longitudinal Monitoring Survey with 491 and 631 respondents, respectively. In case of GPS data quality, we compared missing data rate and geolocation measures’ accuracy by interviewers and regions.

Results:

We found that the geofencing method was quite efficient in flagging “suspicious” interviews that have lower data quality. Curbstoning method can be quite useful, however, the problem concerns the selection of the thresholds of area and density of interviews within this area. In addition, using accuracy-based measures as GPS measurement error instead of selecting a threshold was found to be more efficient. In terms of data quality, the region of an interview proved to be the main factor associated with lower data quality in terms of missing data and measurement error compared to others with remote regions providing GPS data of higher quality.

Added Value:

The comparison of various methods of interviewers’ monitoring shows ways of how GPS-paradata can be used and which of the approaches allow to detect interviewers with lower data quality. GPS data quality assessment is useful in terms of future geolocation data employment within social science research as it shows the possible sources of measurement and item nonresponse errors.

Relevance & Research Question: In times of big data, we have increasing numbers of easily accessible data which can be used to describe human behavior and organization's activities on large sample sizes without the well-known biases of survey data. Yet, big data is not particular good in measuring attitudes and opinions, as well as information that has not been made public. Thus, the question rises how survey and big data can be combined to enable a more complete picture of reality.

Methods & Data: As a case study, we analyse the facebook communication of political parties in Germany. We seek to describe and explain facebook activities. While the descriptive part of our study can be investigated on basis of data that was gathered via the official web interface of facebook, the “why” is examined via an online survey among the district associations of the most important political parties in Germany (n= 2,370), which began on 2 May 2017 and ended on 16 June 2017.

Results: By combining big data and survey data, we are able to describe the facebook usage of the district associations over a time period of eight years, as well as identify several key factors explaining the very different facebook activities of political parties in Germany, like the number of members and certain expectations of the chairman regarding the merits of social media for political communication activities. Furthermore, we can show that almost half of our respondents perceive its local party chapter as a very active one, while API data indicates that they are, if any, moderate social media communicators.

Added Value: It was only possible through the combination of survey data and big data, to draw a rich picture of the political usage of facebook on the local level in Germany. Our findings also indicate that ”objective” big data and an individual’s perception regarding the same issue, might differ substantially. Thus, we recommend analyst to - whenever possible - combine big and survey data and be aware of the limitations.