Bibliographic record
Abstract
Over the past three to four decades, explosive growth in data collection, storage, and processing has changed marketing practice substantially. For example, in business-to-business industries, advanced marketing analytics has supported salespeople’s decision-making by providing predictions based on new types of information. Furthermore, companies can reach consumers and ask their opinions more easily. The first essay of my dissertation quantifies the impact of the availability of such information on the marketing-sales interface. While marketing analytics intended to aid salesforce decision-making has developed rapidly, there is little empirical understanding of how the adoption of such marketing analytics may affect sales performance. Using data from a global business-to-business information technology company, we provide empirical evidence that the adoption of a new marketing analytics tool improved salespeople’s performance. By further exploring the outcomes by salespeople- and account-specific characteristics, we find that marketing analytics enabled high-performing salespeople to achieve greater sales from customers without recent transactions. In contrast, for low-performing salespeople, marketing analytics led them to winning more sales opportunities from accounts with recent transactions. Overall, marketing analytics empowers the high performers to reach a more balanced customer account portfolio and supports the low performers to seize the opportunities that might have been missed. Digitization has not only provided more information on customers but enhanced the means of communications between companies and their customers. The extant literature has explored positive behaviors that can occur after completing a survey, sometimes called the mere-measurement effect, but has neglected how the effect may depend on the valence of previous experience. In the second essay, we examine the impact of product performance on the mere-measurement effect using a unique natural experiment dataset from a securities brokerage company. Our results confirm mere-measurement effects in transaction intensity, transaction volume, and sales of other products. More importantly, the effects are greater for customers with negative pre-survey performance. The finding suggests that participation in satisfaction surveys leads to asymmetric positive effect. These results can help companies assess the return of resources invested in conducting surveys beyond the value of information and more accurately understand the relation between consumer behavior and marketing interventions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".