Behavioral Economics-Informed
Contrastive Learning
for Sales Coaching

• Deep read: CL4SRec (Xie et al., 2021) — annotate contrastive pair construction methodology 3h
• Deep read: Contrastive RL as Goal-Conditioned RL (Eysenbach et al., NeurIPS 2022) 2h
• Create annotated bibliography doc — tag each paper by relevance layer (core / adjacent / peripheral) 1h

• Read: Kahneman & Tversky Prospect Theory (1979) — extract formulas for loss aversion weighting function λ 2h
• Read: Thaler's Mental Accounting (1985) — identify observable CRM signals for segregation / integration of outcomes 2h
• Draft 1-page "BE Constructs → Observable Signals" mapping table (loss aversion, present bias, anchoring, sunk cost) 2h

• Read: Behavioral Data Representation Learning survey (OpenReview 2026) — focus on contrastive learning section 3h
• Review: Fine-tuning LLMs in behavioral psychology for health coaching (npj 2025) — extract transfer methodology 2h

• Search Semantic Scholar + arXiv: "contrastive learning sales" "behavioral economics AI coaching" — log all results 2h
• Confirm novelty: verify no existing paper combines BE-informed contrastive pairs with goal-conditioned sales modeling 2h
• Write 500-word "gap statement" — the precise unoccupied research space this paper fills 2h

• Draft research proposal skeleton (Title, Abstract, Problem, Novelty, Method preview, Expected contributions) 3h
• Weekly review: what's unclear, what needs deeper reading, blockers for Week 2 1h

• Define the 4 core BE constructs to operationalize: (1) Loss aversion, (2) Present bias, (3) Anchoring effect, (4) Sunk cost fallacy 2h
• For each construct: write formal definition, cite canonical source, draft measurable proxy in CRM event logs 3h

• Construct the "Contrastive Pair Definition" framework — for each BE construct, define: anchor rep behavior (negative), goal-aligned behavior (positive), and labeling heuristic 4h
• Example: loss aversion → rep who avoids re-engaging lost deals (negative) vs. rep who systematically follows up (positive) 1h

• Read Locke & Latham Goal Setting Theory (2002) — map 5 principles (clarity, challenge, commitment, feedback, complexity) to contrastive RL reward structure 2h
• Draft "Goal State Space" definition — how quota milestones translate to goal vectors in embedding space 2h

• Write Section 2 (Related Work) first draft — 3 paragraphs: CL methods, BE in tech, goal-conditioned RL 3h
• Identify 2 academic advisors / collaborators with relevant expertise (ML + behavioral science); draft outreach email 1h

• Produce v1 theoretical diagram: how BE constructs → contrastive pairs → embedding space → goal-conditioned nudges 3h
• Internal presentation: share framework with team, collect feedback on operational feasibility 1h

• Research publication venues: KDD, NeurIPS (ML + social impact), ICIS (IS + decision science), WSDM — review submission guidelines & deadlines 2h
• Review 3 accepted papers from target venue to understand expected rigor, contribution framing, and experiment design 3h

• Write 3 formal hypotheses: H1 (BE pairs outperform random pairs), H2 (model outperforms non-personalized nudges), H3 (goal-conditioned RL learns faster with BE-informed pairs) 3h
• For each hypothesis: define null hypothesis, success metric, statistical test to use, minimum detectable effect 2h

• Write formal problem statement section — define: input space X (rep activity sequences), output space Y (goal attainment probability), contrastive objective function 4h

• IRB/ethics review: assess data privacy requirements for using real rep behavioral data (GDPR, internal consent policies) 2h
• Draft data use agreement template for customer data used in research 2h

• Finalize 4-page research proposal — submit internally to advisory board / co-founders for alignment sign-off 4h

• Set up research code repository (GitHub) with modular structure: /data, /models, /experiments, /evals, /paper 2h
• Select baseline models to compare against: (1) random pair contrastive, (2) SimCLR on activity sequences, (3) standard LSTM next-activity prediction, (4) rule-based nudge system 2h

• Implement SimCLR baseline on toy sequential data (can use public CRM-adjacent dataset like ATIS or synthetic sales logs) 4h

• Implement InfoNCE loss from scratch — verify gradients, test on small synthetic dataset with known structure 3h
• Prototype BE pair labeling function for "loss aversion" using synthetic data: flag reps who drop deals after first rejection 2h

• Review JaxGCRL codebase (ICLR 2025) — assess integration feasibility for goal-conditioned RL component 3h
• Select evaluation metrics: NDCG@K for nudge ranking, goal attainment Δ%, behavioral shift rate, A/B test framework 1h

• Phase 1 retrospective: complete all deliverables checklist, identify gaps, finalize Phase 2 data plan 2h

• Export and audit all available CRM event types: calls, emails, demos, stage transitions, deal values, time gaps, outcomes 1 day
• Build feature inventory: for each event type document (a) volume, (b) completeness %, (c) temporal resolution, (d) rep-level vs. deal-level granularity 1 day

• Profile data quality: missing data rates, duplicate events, timestamp inconsistencies, rep coverage (% of reps with ≥6 months history) 1 day
• Run preliminary EDA: distribution of deal outcomes by rep, activity frequency histograms, seasonality analysis 1 day

• Write "Data Feasibility Report" — assess whether real data supports all 4 BE construct proxies; identify fallback to synthetic data if needed 4h

• Define the sequence representation schema: {rep_id, timestamp, event_type, deal_stage, deal_value_bucket, outcome_flag, days_to_close} 1 day
• Decide sequence windowing strategy: rolling 30-day windows vs. deal-lifecycle windows vs. quota-period windows half day
• Implement sequence tokenizer: map event types to integer tokens with positional encoding; test on 100 sample sequences 1.5 days

• Test 3 augmentation strategies (from CL4SRec): cropping, masking, reordering — implement all 3 and document which best preserves BE-relevant signal 2 days

• Loss Aversion labeler: detect reps who show >40% drop in outreach velocity after a deal loss vs. control window — implement + unit test 4h

• Present Bias labeler: detect systematic preference for short-cycle deals (<30 days) over high-value long-cycle deals, controlling for territory — implement + unit test 4h

• Anchoring labeler: detect reps whose follow-up timing is anchored to first prospect response rather than optimal cadence — implement + unit test 4h

• Sunk Cost labeler: detect reps who continue investing time in stalled deals (>60 days no response) rather than reassigning capacity — implement + unit test 4h

• Run all 4 labelers on full dataset — compute label prevalence rates and inter-construct correlation matrix; flag unexpected correlations 4h

• Implement pair construction algorithm: for each BE construct, sample matched positive-negative pairs controlling for rep tenure, industry, quota size 2 days

• Human validation of 100 pairs per construct with behavioral economist collaborator — compute inter-rater reliability (Cohen's κ) 4h
• Threshold: accept labeling function if κ > 0.7; revise and re-test if below 2h

• Build final dataset: train/val/test split (70/15/15), stratified by construct type and rep performance tier 1 day
• Document dataset statistics: N pairs per construct, sequence length distributions, goal attainment rates in positive vs. negative groups 1 day

• Build end-to-end reproducible data pipeline (Prefect or simple Make) from raw CRM export → tokenized sequences → labeled pairs → DataLoader 3 days
• Write data section of paper (Section 3): dataset description, labeling methodology, statistics, ethical considerations 2 days

• Contingency: if real data volume is insufficient (< 500 labeled pairs per construct), implement synthetic data generation using LLM + BE scenario templates 3 days
• Phase 2 review: confirm all data components ready before modeling begins; hold gate-review with team 1 day

• Implement Transformer sequence encoder for sales event sequences — positional encoding, multi-head attention, feed-forward blocks 3 days
• Verify encoder on reconstruction task: can it learn to predict masked events? Confirm learning signal before adding contrastive objective 2 days

• Implement BE-weighted InfoNCE loss: weight positive/negative pair distances by BE construct severity score (e.g., higher λ for stronger loss aversion signal) 3 days
• Add goal-conditioned head: given current rep state embedding, output probability distribution over next-best-actions toward quota milestone 2 days

Do BE-informed pairs produce better representations than randomly constructed or augmentation-based pairs?

• Train 4 model variants: (a) BE-pairs, (b) random pairs, (c) CL4SRec augmentation, (d) supervised baseline — identical encoder for fair comparison 4 days
• Visualize embeddings with t-SNE/UMAP: do BE-pair models cluster reps by behavioral pattern more cleanly? 1 day

• Evaluate representations on downstream probe task: linear probe predicting quota attainment from frozen embeddings 2 days
• Statistical analysis: compute effect sizes, run permutation tests, report 95% CIs for all metrics 2 days
• Write Experiment 1 results section — include all tables and figures in paper draft 1 day

Do nudges generated from BE-contrastive representations lead to better goal attainment than generic ML nudges or human-authored nudges?

• Implement nudge generation pipeline: given rep embedding + goal vector, decode to ranked action recommendations using goal-conditioned head 3 days
• Prepare offline evaluation: use held-out data where ground truth optimal actions are known from high-performer trajectories 2 days

• Evaluate nudge quality: NDCG@5, precision@3, and behavioral alignment score (% of recommended actions consistent with high-performer sequences) 2 days
• Expert panel evaluation: have 3 sales managers rate 50 nudge samples (blinded) for usefulness and behavioral soundness on 5-point scale 2 days
• Write Experiment 2 results section 1 day

Which BE constructs contribute most? Can the model identify which specific bias a rep exhibits and personalize accordingly?

• Ablation study: train 4 models each with one BE construct removed (leave-one-out) — measure performance degradation per construct 3 days
• Single-construct models: train 4 models each with only one BE construct — measure individual contribution ceiling 2 days

• Interpretability analysis: use attention weights to identify which sequence events most activate each BE construct dimension 2 days
• Case studies: write 3 illustrative rep examples showing how the model identifies bias and generates corrective nudge — qualitative analysis for paper 2 days
• Compile all experiments into complete Results section draft 1 day

• Recruit 30–50 sales reps across 3 performance tiers (low/mid/high quota attainment) for 2-week A/B pilot 3 days
• Deploy treatment (BE-contrastive nudges) vs. control (current product or no nudge) — randomize at rep level, not deal level 2 days

• Monitor pilot: track nudge acceptance rate, behavioral change metrics, rep-reported usefulness (daily 30-second survey), deal progression 3 days
• Conduct 10 structured interviews with pilot reps: what nudges felt right, what felt off, which BE construct was most recognized 2 days

• Assemble full paper from existing section drafts — unify voice, notation, figure style; target 9–10 pages (NeurIPS/KDD format) 3 days
• Write Abstract (150 words): problem, approach, key results — use exact metric numbers half day
• Write Introduction (1.5 pages): motivate with sales industry data, state 3 contributions clearly as bullet points 1.5 days

• Internal review round 1: circulate to co-founders + academic collaborator — collect structured feedback on claims, experiments, limitations 2 days
• Revise based on feedback — strengthen limitations section, add reproducibility statement, confirm all code will be open-sourced 2 days
• Write Discussion: what does this mean for BE theory? For AI coaching? What are the open questions? 1 day

• Final paper polish: proofread, format to venue style guide, generate camera-ready figures at 300dpi 2 days
• Post pre-print to arXiv immediately upon submission — establishes timestamp for IP priority half day
• Consult IP attorney: file provisional patent on BE-informed contrastive pair construction methodology + goal-conditioned nudge system 2 days

• Submit paper to target venue 1h
• Write product integration spec: how does the research model become the engine for the startup's coaching product? 2 days
• Prepare 10-minute research talk for investor/partner demos: what we built, what it means, where it goes next 2 days
• 24-week retrospective: lessons learned, roadmap to Phase 2 research (multi-modal data, cross-industry generalization) half day