Digital media offers oceans of 'real data' but cannot of itself identify the human meanings. Semiotics enables us to identify the structures which define meaning but is entirely qualitative. We combined both to create 21st-century Qual & Quant.
This research seeks to create and validate a new approach for brands to develop a fusion based approach aiming at more meaningful and differentiated digital communication strategies. This methodology focuses on enabling marketers to contextualize the online data and derive digital campaigns for brands that have consumers at their heart.
This research seeks to create and validate a new approach for brands to develop a fusion based approach aiming at more meaningful and differentiated digital communication strategies. This methodology focuses on enabling marketers to contextualize the online data and derive digital campaigns for brands that have consumers at their heart.
This paper is about the fusion of quantitative with qualitative research. It showcases how quantitative segmentation (and the resultant prime target market identification) and qualitative prime target market motivators can be undertaken in a single source study. The fusion of Stage 1 Quantitative Segmentation with Stage 2 Qualitative Motivators enables costs to be reduced by approximately 25% compared to traditional two staged research. In essence, the result is quantitative segmentation, qualitative motivator, single source data and qualitative insight with quantitative numeracy.
Innovation of qualitative/quantitative fusion, evidenced through a case study. Envisage a segmentation study that also identifies psychological motivators: deep qual insight with quant numeracy. But wait, there's more, costs down 25% and time down 50%!
Guest speaker: Jonathan Mall, CEO & Founder of Neuro Flash.
Data edge summary from Peter Nash.
This paper reports on AGBs introduction of a new single source type survey into the New Zealand and Australian markets. Combining the industry accepted currencies of TV viewing and print readership the New Zealand model adds a product consumption diary to allow marketers to analyse and reach buyer-graphic targets. The product, Panorama, reopens the fusion debate. Even the worlds most successful single source surveys have failed to stand the test of time and it is inevitable that fusion must be at least an evolutionary step in the direction of the Holy Grail.
The main objectives of this paper are to demonstrate the relevance and value of data fusion, as a technique, in the Media research area, by reference to two case studies; and to discuss the variety of opinions and difficulties attaching to the assessment of the 'quality' of data fusion results. Subsequent to this introduction, we first briefly recapitulate the decisions to be taken in relation to any specific fusion. We then review, rather more fully, the criteria which are or which should be (in our opinion) employed, post hoc, to test data fusion output. An account of our case studies follows, with emphasis on the particular. Problems they presented and the solutions adopted. We conclude this introduction with a brief word on terminology. We shall use the term fusion' to imply the merging of data from two initially discrete files, whether or not the transfer of data is in both directions, A > B and B > A, or only one- way, whilst recognizing that others may prefer to label only the former 'fusion' and to term the latter, unidirectional transfer 'ascription' or 'injection'. We shall denote any variable common to both files as X and recognize three sub-sets of common variables: critical Variables, on which the values of a data-donor and a data recipient must match exactly, if information is to be transferred between them; matching variables, which are common and employed in the pairing of donors and recipients, but nonuritical; and (rarely) control variables, which are common but held out from the matching process, for testing purposes. We shall employ Y to denote any variable initially unique either to the donor file or to the receiver file. Yd will stand for any donor variable; Yf for a donor variable, after fusion and then present, therefore, in the receiver file; and Yr any variable unique to a receiver file. For both X's and Y's, subscripts will only be added when it is necessary to distinguish between two or more variables in any group.