A Novel Method to Investigate the Effect of Social Network “Hook” Images on Purchasing Prospects in E-Commerce
Bibliographic record
Abstract
Background. Social network visual shopping trends are growing e-commerce at unprecedented levels. Images are used as product marketing material; however, image posts are triggering very low consumer behavior and low sales conversion.Objective. To explore how online stores can increase the purchasing prospects of their products using images on social networks.Methods. We introduce a theoretical probabilistic model to estimate consumer behavioral intention and purchasing prospect on social networks, outline parameters that can be exploited to increase click-rate and conversion, and motivate a new strategy to market products online. The model explores increasing online stores’ sales conversion by utilizing a product collection landing page that is marketed to consumers through a single “Hook” image. To implement the model, we developed a novel technological method that enabled online stores to post different “Hook” images on social networks and hyperlink them to the product collection landing pages they created.Results. Stores and marketers developed four types of “Hook” images: themed-collaged product images, single product images, lifestyle images, and model images. Themed-collaged product images accounted for 60% of consumer traffic from social network sites. Moreover, consumer purchasing click rate increased at least twofold (4.94%) with the use of product collection landing pages.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".