Charting value creation strategies B2B salespeople use throughout the sales process: learning from social media influencers
Bibliographic record
Abstract
Purpose This paper aims to explore how business-to-business (B2B) salespeople use social media and emulate value creation strategies used by social media influencers. Design/methodology/approach Using 28 interviews with salespeople, this paper develops six propositions and a conceptual framework that outlines when and how B2B salespeople use social media in value-creating sales. Findings This study’s findings provide a critical analysis of when social media are most effective and beneficial in supporting salespeople’s value-creating sales in various stages in the sales process (e.g. prospecting, opening relationships, qualifying prospects and serving accounts) and when they are less effective (e.g. presenting sales messages and closing sales). Research limitations/implications This research yields a substantive understanding of the evolving role that social media play in B2B sales by examining B2B salespeople’s value creation strategies through the lens of social media influencers’ practice and outlines ideas for future research on B2B salespeople’s social media strategies. Practical implications The findings of this research can be used by B2B organizations to structure the training of B2B salespeople to use social media to the fullest extent by aligning specific strategies with different parts of the sales process. Originality/value This paper contributes by summarizing the B2B sales literature on social media and integrating recent insights from the social media influencer literature; empirically identifying how B2B salespeople use social media to create value, thus validating previous findings and extending understanding by offering a set of six theoretical propositions; and delineating B2B salespeople’s social media practice into 11 value creation strategies that are critically explored for their place in the sales process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".