#personalization

Deliver personalized demos that convert. Pre-demo research (30 mins): 1. Company website, recent news, LinkedIn. 2. Prospect's role, responsibilities, pain points from discovery. 3. Current tools they use (from conversation or SimilarWeb). Demo customization: 1. Use prospect's company name in demo environment. 2. Import sample data relevant to their industry. 3. Show workflow that mirrors their process. 4. Address specific pain points discovered. 5. Skip features they don't care about. Opening: 'Based on our conversation, I've customized this to show...' Throughout: ask confirming questions ('Is this how you currently do it?'). End: clear CTA and next steps. Follow-up: personalized recap email with screenshots.

Build recommendation systems using collaborative filtering, content-based filtering, and hybrid approaches for personalization. Collaborative filtering approaches: 1. User-based CF: find similar users, recommend items liked by similar users, cosine similarity calculation. 2. Item-based CF: find similar items, recommend similar items to liked items, Pearson correlation. 3. Matrix factorization: SVD, NMF for dimensionality reduction, latent factor modeling. Content-based filtering: 1. Feature extraction: item attributes, TF-IDF for text features, categorical encoding. 2. Profile building: user preference vectors, weighted feature importance, learning user tastes. 3. Similarity computation: cosine similarity, Jaccard similarity, recommendation scoring. Deep learning approaches: 1. Neural Collaborative Filtering: user/item embeddings, deep neural networks, non-linear interactions. 2. Deep autoencoders: collaborative denoising, missing rating prediction, feature learning. 3. Recurrent neural networks: sequential recommendations, session-based filtering, temporal dynamics. Hybrid systems: 1. Weighted combination: linear combination of different approaches, weight optimization. 2. Mixed systems: present recommendations from different algorithms, user choice. 3. Cascade systems: hierarchical filtering, primary and secondary recommendation stages. Evaluation metrics: 1. Precision@K: relevant items in top-K recommendations, practical relevance measure. 2. Recall@K: coverage of relevant items, completeness assessment. 3. NDCG (Normalized Discounted Cumulative Gain): ranking quality, position-aware evaluation. Cold start problem: new user recommendations, new item recommendations, demographic-based initialization, content-based bootstrap, popularity-based fallback strategies.

Use personalized video to stand out. When to use video: 1. High-value accounts (worth the time). 2. Multiple failed touch attempts. 3. Complex value prop (better shown than written). 4. Re-engagement of cold leads. Video structure (60-90 seconds): 1. Personalized intro (10s): 'Hi [Name], I'm [You]. I was looking at [Company]...' 2. Relevant observation (20s): '...and noticed you recently [trigger event]. Congrats!' 3. Value prop (20s): 'We help companies like yours [achieve outcome].' 4. Specific example (15s): 'For example, [Customer] saw [specific result].' 5. Call to action (5s): 'Would 15 minutes next week work to explore if this fits?' Production: use Loom or Vidyard, share screen showing their website/LinkedIn, show your face (builds trust), smile and be enthusiastic. Send via: email (embed thumbnail), LinkedIn message. Track: video view rate (60%+), watch time (>75%). Reply rate: typically 3-5x higher than text email.

Handle customization requests. Response: 1. Confirm customization options available. 2. Explain customization process. 3. Provide pricing for custom work. 4. Show examples of previous customizations. 5. Outline additional lead time. 6. Clarify return policy for custom items. 7. Request detailed specifications. 8. Assign point of contact for project. Turn inquiry into personalized sale.

Personalize outbound emails efficiently. Research (2-3 mins per prospect): 1. Recent LinkedIn post or company news. 2. Mutual connections. 3. Technology they use (BuiltWith, SimilarWeb). 4. Recent job postings (indicates growth/pain). Personalization tiers: High-value accounts (custom per person): '[Name], saw your post about hiring 3 SDRs. Are you also scaling your sales tech stack?' Medium-value (templated with custom first line): 'Noticed [company] is using [tool]. How is that working for [specific pain point]?' Low-value (segment-based): '[Industry] companies typically face [challenge]. Curious if you're experiencing this?' Tools: Phantombuster for data enrichment, ChatGPT for variation generation, Instantly/Lemlist for sending. Batching: research 50 prospects, write custom lines, plug into sequence. Track: personalized emails get 3-5x reply rate vs generic. Test different personalization levels to find ROI sweet spot.