Loading…
Topic
7 posts about Personalization. Every summary links to the original.
See the idea
An agentic ad stack wires governed customer signals to campaign execution through a shared tool interface and server-side guardrails. The diagram shows each layer, the direction of a tool call, and where control stays human.
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Newest first
Differentiating user onboarding is essential because a one-size-fits-all approach fails to address varying learning styles and paces. Drawing from educational principles, the post advocates scaffolding through self-serve resources like documentation, videos, and blog posts to support different learning preferences. Live conversations via a support tool enable real-time clarification and uncover unanticipated questions, while personal demos and webinars provide one-on-one or group tutoring for deeper engagement. Data-driven messaging schedules, triggered by user events and attributes, ensure customers receive pertinent guidance just in time without being overwhelmed. This strategic variety helps users extract value efficiently, reducing churn and support load, and ultimately converting them into satisfied, paying customers.
Customer service is the support provided to customers throughout their journey, aiming to resolve issues and help them get the most from a product or service. The post cites that 93% of customers are likely to make repeat purchases after excellent service, while 80% stopped doing business due to poor experience. Key traits of good service include personalization, fast response times under 10 minutes, and problem resolution. Support teams providing personalized service are twice as likely to report higher customer satisfaction and retention. Different types of customer service include live chat, phone, email, and self-service, each with varying efficiency and personalization.
Customers increasingly expect personalized marketing, with 66% reporting companies do not understand them despite data showing 80% are more likely to purchase from personalized experiences. However, 85% of marketers believe personalization should be a higher priority. To address this, the post outlines four methods: using first-party data collected directly from customers to enrich messaging; integrating the tech stack for cohesive journeys; reaching people through the right channel at the right moment based on behavior; and building targeted campaigns across multiple channels using a visual campaign builder. Such approaches allow marketers to trigger relevant messages based on custom attributes, actions, and segments without requiring code. The result is more engaging, timely, and measurable marketing that improves customer relationships and business outcomes.
Existing systems estimate user price sensitivity from spending behavior or demographic proxies, missing residential property values and creating one-dimensional profiles. This invention proposes a four-step method that combines public property data with LLM-assisted data structuring, building-level price estimation, hierarchical clustering, and user mapping. The method collects public property records, uses LLMs to standardize attributes such as postcode, transaction price, and property size, then aggregates transactions within buildings to calculate weighted average sale prices. A hierarchical clustering model groups buildings into affluence tiers based on estimated resale values, accounting for regional market differences, and users are mapped to these tiers via aggregated geographic signals. The approach remains unimplemented and untested in live markets, with potential applications in personalization, advertising, and operational planning subject to future technical validation and privacy approvals.
Designed artifacts are ontological, shaping what becomes possible, yet systems enabling user authorship are typically evaluated on usability rather than ontological boundary negotiation. Two open-ended probes using a Wizard of Oz technique were created to let participants experience training a personalized machine learning system on phenomena they defined themselves. In a week-long exploratory study, participants used one of two probes during their everyday lives, allowing observation of boundary negotiations. Four sites of ontological boundary negotiation emerged: the boundaries of a phenomenon, the subject as part of relations, distinguishing signal from noise, and the objectivity of data. The paper offers starting points for supporting boundary negotiation through design and proposes open-ended probes as a method for ontological design.
A Twilio survey of 660 business leaders and consumers reveals a gap: 98% of organizations train AI to act more human, yet customers rank issue resolution, speed, accuracy, and availability above personality. Only 54% of consumers want human-like AI, versus 73% of brands. Behaviors customers call empathetic—acknowledging the specific situation, offering multiple solutions, using conversational language, offering compensation—depend on personalization and context, not tone. 51% of consumers find AI robotic, and 39% of businesses cite weak emotional understanding as a top dissatisfaction driver. The report recommends prioritizing context and personalization over personality, using tools like Twilio Conversation Memory and Conversation Orchestrator to make agents useful before charming.
The post examines the typical lifecycle of customer data in companies, revealing that most data is captured but never effectively used. A survey of 660 business leaders found that 48% struggle to connect customer data across channels, making it the top obstacle over budget or headcount. Data is often captured in volume but siloed across systems, duplicated into separate records for the same customer, and reliant on stale overnight batch updates. This leads to AI agents working with outdated profiles, causing 71% of consumers to abandon conversations when the AI fails to recognize them. The post proposes shortening the path between capture and use through real-time data synchronization, exemplified by Twilio’s Conversations and Conversation Memory products.
The best engineering reads, once a week.
