Conference Agenda

Overview and details of the sessions of this conference. Please select a date or room to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Session Overview
C 2: Business Analytics with Social Media I
Thursday, 19/Mar/2015:
10:45 - 11:45

Session Chair: Axel Maireder, GfK
Location: Room 149
Fachhochschule Köln/ Cologne University of Applied Sciences
Claudiusstr. 1, 50678 Cologne


Combining Survey and Social Media Monitoring in an Airline Customer Satisfaction Study

Charlotte Nau, Melanie Arens

TNS Infratest, Germany

Relevance & Research Question: Traditional customer satisfaction research with its heavy emphasis on surveys has come under pressure. There is a strong demand for surveys to become shorter, be conducted less frequently and at lower costs. At the same time, an increasing number of people share their experiences with products and services via social media, making the internet a continuously growing source of customer feedback. In this project, we investigate how to combine customer satisfaction surveys with social media monitoring for greater insight and efficiency.

Methods & Data: We conducted a two-component study focussing on a large European airline. The first component was a survey among 2,000 recent customers of that airline. It included measures of overall satisfaction and loyalty, detailed evaluations of different service aspects, and brief ratings of the airline’s major competitors. The second component was a social media monitoring where we collected online conversations about the airline over a three-month period, leading to around 130,000 relevant documents. A sample of 4,500 documents was coded manually.

Results: There was a strong relationship between how often a competitor airline was mentioned on social media and its attractiveness ratings in the survey, indicating that social media can serve as an early warning system for customer defection. Also, we found the volume of social buzz around a service topic (e.g. check-in) to not be predictive of its impact on customer satisfaction, suggesting that survey research remains necessary to estimate the urgency of issues. Qualitatively, the social data were able to explain why service aspects were rated positively or negatively.

Added Value: The combination of survey and social media data led to insights which were more comprehensive and detailed than what both of the methods would have revealed alone. While surveys are able to provide a strategic overview of a company’s customer relationships, social media can provide qualitative explanations for the ratings. On the other hand, social media data – which pour in unsystematically and in large quantities – are given context and structure by the customer survey, hence making them manageable. Finally, social data can replace some survey components altogether, allowing for shorter and less expensive interviews.
Nau-Combining Survey and Social Media Monitoring in an Airline Customer Satisfaction Study-137.pdf

Social ratings as the new currency of marketeers? – A comparison of influences from Likes and test seals on product ratings

Christian Bosau1, Liesa Henriette Pöppel1, Ulrich Rieger2

1Rheinische Fachhochschule Köln (RFH), Germany; 2MW Research GmbH, Germany

Relevance/Research-Question: In the so-called web 2.0 customers became more active and social ratings, e.g. Facebook-Likes, got steadily more important. Due to social psychological theories like normative and informational conformity (Deutsch & Gerard, 1955) it could be assumed that social ratings might influence customers attitudes (Bak & Keßler, 2012), maybe even more than official test seals, like “Stiftung Warentest”. However, the almost uncritically claim that good social ratings are always positive creating favorable outcomes for any marketeer has seldomly been tested yet. The interaction effect with test seals has not been analyzed at all.

Methods/Data: Therefore, this experimental 3x2x4-study compared the influence of the number of Likes (little, medium, high) and the test seal “Stiftung Warentest” (positive, negative) on product ratings (measured by quality rating, purchasing intention & customer recommendation) using four different products (2x shopping goods: TV, Laptop; 2x convenience goods: water, chocolate; repeated measurement). Potentially influencing variables (existing Facebook-Account, actual purchasing purpose, sociodemographics) were also measured. The products presented realistically in a web-based offer were rated by a representative sample (N=1170) drawn via a panel-database.

Results: As expected, the 2-way-ANOVAs showed a significant positive main effect for the quality seal for all 4 products. A clear positive main effect of Likes could not be found. Instead, the influence of Likes differed depending on a combination of the rated product, the actual purchasing purpose, the rating of the test seal and an existing facebook account; e.g. for TVs and Laptops with a negative test rating more Likes had indeed a more negative effect, this effect was even stronger for people who had an actual purchasing purpose and a facebook account. Thus, Likes had – other than expected – a complete reverse effect. For products with a positive test rating more Likes had almost none effect at all.

Added Value: The study tested the believe that is often taken for granted that a high number of Likes has always positive effects and showed, that this believe has to be discussed more critically. By combining several different products in one study it also showed that dependence on the product category.
Bosau-Social ratings as the new currency of marketeers – A comparison of influences-158.pdf

Brand Engagement Topologies on Instagram

Simeona Petkova

Amsterdam University of Applied Science, Netherlands, The

Brand engagement on social media has been mainly studied through its communicative potential and difference from offline marketing strategies (Jensen and Jepsen, 2006), through content analysis of nongovernmental organisations’ Facebook profiles (Walters et al. 2009) and Facebook messages per specific industry (Kwok and Yu, 2012). The most comprehensive typology on brand engagement on Facebook pages across industries have been provided by Coursaris et al. (2013) that also appeals for the creation of more engagement typologies on other social media platforms. This research takes upon this appeal and aims to provide a typology of brand engagement on Instagram by asking which are the most prominent engagement types detected through the profile tabs and the branded hashtags of the top three brands on Instagram.

We have coded the emerging types of brand engagement through the content and the interactions on the three most prominent brands profile tabs (Nike, Starbucks and Adidas Originals) and the images generated by Instagram users and tagged with the corresponded branded hashtags (#nike, #starbucks, #adidasoriginals). Our dataset consisted of 91 500 images (captured 27 October - 7 November from the branded walls and the API) from which we took 1% sample and registered the images’ content, caption, hashtags, comments, and the number of likes. We used a descriptive registration, complemented with open and thematic coding.

Preliminary results showed that engagement types vary per branded profile and branded hashtag posts. While the most occurring typologies on the branded profiles are campaigns (49%), promotion (19%) awareness (9%), the user engagement per branded hashtag is dominated by products and the ways they are used (respectively 35% and 27% of the provisional sample). Starbucks profile, for example, focuses on building community, running various themed campaigns, giving appreciation of its customers while the posts generated by Instagram users for the #starbucks are related to the products (featuring the products, mentioning the flavours).

Our findings signify that marketeers need to have a deeper understanding of the way the users of a specific platform engage with their branded content that goes beyond the registration of likes and comments.
Petkova-Brand Engagement Topologies on Instagram-191.pdf

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