Postulating consumers : how marketers conceptualise consumers in the era of big data analytics
Bibliographic record
Abstract
The proliferation of big data analytics in marketing appears to be having significant effects on the field, such as changing how marketers perceive their consumers and how they act on them. As I discuss in my research, marketers are not satisfied to work solely with approximate, imagined conceptualisations of consumers as a basis for advertisements and offers. Instead, they are looking for exact virtual data doubles of existing and potential consumers, which is something they hope to achieve through big data analytics. In my thesis, I explore the question of how and why marketers conceptualise consumers differently when using big data analytics compared with traditional market and consumer research methods. This is embedded in the theory of the co-production of knowledge and empirically relies on interviews with marketers and data analysts, case studies, and participant observations at industry conferences. In my research, I show to what extent the idea of the data double consumer conceptualisation is considered an ideal case for marketers, and that it is believed to be made possible through big data analytics, which is expected to create an exact knowledge about consumers. However, my findings show that in practice, big data analytics should be considered a sociotechnical assemblage that produces knowledge which contains inaccuracies, errors and uncertainties. Knowledge about consumers is not just discovered – neither through traditional market and consumer research methods nor through big data analytics. Instead, it is the outcome of a co-production that involves different steps, individuals, teams, normativities, and technologies. Hence, knowledge about consumers is never an exact representation of reality, irrespective of its methods of production. Consequently, consumer conceptualisations expected to be exact data doubles cannot be attained. Instead, postulations are established that are believed to be accurate, without having actual proof. Yet, my findings show that knowledge resulting from big data analytics has a higher credibility and epistemic authority amongst the participants, explaining the persistence of the data double consumer conceptualisation in digital marketing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.021 | 0.055 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".