Checking Our Blind Spots: The Most Common Mistakes Made by Social Marketers
Bibliographic record
Abstract
Background: The work of social marketers and the environment in which they work is complex, which inevitably results in mistakes being made and sometimes, the failure of a social marketing program. Unfortunately, social marketers do not often report their own mistakes. Even when failures or mistakes are reported, it is usually for the purpose of one study, as opposed to a wider understanding of mistakes made by social marketers in the field. This is a significant gap in the development of social marketing practice since understanding the nature of the most common mistakes made by social marketers could assist them in assessing their own shortcomings and potentially lead to more effective programs. Focus: This article is related to research and evaluation of the social marketing field. Research Question: What are the perceptions of social marketing experts regarding the most common mistakes made by social marketers? Importance to the Field: A greater understanding of the common mistakes made by social marketers will allow practitioners to assess their own shortcomings, improve program outcomes, and raise the status of the social marketing field. Methods: This research is qualitative and exploratory, with a constructivist, grounded theory methodology. In-depth interviews with 17 social marketing experts were conducted. Experts were purposefully chosen based on a set of criteria including the number of years of experience they had in the field. Results: The interviews revealed nine mistake categories: inadequate research, poor strategy development, ad hoc approaches to programs, mismanagement of stakeholders, poorly designed program objectives, weak evaluation and monitoring, poor execution of pilots, inadequate segmentation and targeting, and poor documentation. Additionally, the interviews revealed two other emergent, crosscutting themes that affect the mistakes being made: external influences that the social marketer may not have direct control over and the social marketer’s own preconceptions that they bring to the program. Recommendations for Research or Practice: Future research may explore (1) the extent to which external influences lead to social marketing program success or failure, particularly in comparison to mistakes made by social marketers and (2) perspectives from the social marketing community as to the most common mistakes made by social marketers. Social marketers may consider being more reflexive in their work, including reporting their own mistakes and failed programs, as well as challenging the biases they may bring to the work that they do. Limitations: The sample size is small and therefore not generalizable to all social marketing experts or the social marketing community. Also, there are many parts of the world in which social marketers practice, but which are not represented by the social marketing experts. Additionally, the “mistakes” listed are based on opinion as opposed to direct observation, which may make them more susceptible to bias.
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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 | 0.055 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".