AI Personalises Your Product
Personalisation is the single most effective driver of product engagement, retention, and expansion revenue. AI makes deep personalisation achievable without a data science team — delivering the right experience to the right user at the right moment.
From Basic to Advanced
| Personalisation Level | Description | Technology Required | Impact |
|---|---|---|---|
| Name personalisation | Hello [First Name] | CRM field merge | Minimal — table stakes |
| Segment-based content | Different copy for different industries/roles | CRM segmentation + content variants | Low–Medium |
| Behaviour-based recommendations | Show content based on what user has done in product | Event tracking + rules engine | Medium–High |
| AI-generated personalised insights | Insights generated specifically from this user's data | Event tracking + LLM API | High |
| Predictive next best action | AI recommends what user should do next based on similar user patterns | ML model + event data | Very High |
| Fully adaptive experience | Every element of the product adapts to the individual user's patterns | Deep ML + significant data volume | Very High — requires scale |
Practical Implementation
Personalised dashboard and home screen
Instead of showing every user the same dashboard, AI generates a personalised home screen based on their role, usage patterns, and current goals. A sales manager sees pipeline metrics and team performance; an individual contributor sees their own tasks and upcoming activities. Implementation in Bubble: user role and usage data stored in the database, a dynamic dashboard page that queries and renders based on role and recent activity, AI-generated summary section that synthesises the user's key metrics into a plain-language status update.
Personalised onboarding paths
Instead of every new user following the same onboarding sequence, AI generates a personalised path based on intake information: their role, their primary use case, their technical sophistication, and their stated goal. A technical user skips the basics; a non-technical user gets additional scaffolding. Implementation: intake form on signup, AI-generated personalised onboarding plan stored in the user record, dynamic checklist that surfaces the relevant steps for this user's specific situation.
AI-generated proactive alerts and insights
The product proactively surfaces insights the user did not know to ask for: your email open rates dropped 15 percent this week compared to your last 4 weeks — your subject lines may be losing relevance or your list may need cleaning. This proactive insight is generated by AI analysing the user's specific data against their own historical patterns, not generic benchmarks. Implementation: daily Bubble scheduled workflow analyses user data, passes to Claude for insight generation, surfaces significant insights in the product UI and via email.
Technical Architecture
Instrument every meaningful user action
Personalisation requires data. Log every user action in a Bubble database: feature used, page visited, action taken, setting changed, content viewed. Each event record: user ID, event type, event properties (what specifically was done), timestamp. This event stream is the foundation — without it, personalisation is guesswork.
Build user preference and behaviour profiles
A daily Bubble workflow aggregates each user's recent event stream into a behaviour profile: most used features, least used features, typical active hours, actions taken in the most recent session, onboarding completion percentage, and any explicit preferences set. Store this profile in the user record. This profile is what AI uses to personalise every interaction.
Generate personalised in-app content with Claude
When a user opens their dashboard, a Bubble server-side workflow calls Claude: Given this user's profile and recent activity, generate a personalised dashboard greeting that: (1) references something specific they did recently, (2) highlights a metric that matters to their role, (3) suggests one action that would improve a metric they have been tracking. User profile: [profile]. Keep the greeting to 2 to 3 sentences, conversational tone. The greeting is different for every user, every day, without any manual curation.
Test personalisation impact on retention
Compare 90-day retention rates for users who received personalised experiences versus those who did not (A/B test the personalised dashboard). Track NPS scores for each group. Measure feature adoption breadth — personalised feature recommendations should drive adoption of features users had not discovered independently. Use this data to justify expanding the personalisation system and to optimise the AI prompts that generate the personalised content.
How much user data is needed before personalisation is effective?
Meaningful personalisation requires a minimum of 5 to 10 meaningful user events (actions taken in the product, not just page views). Role-based personalisation can be activated from the moment of signup if the user indicates their role. Behaviour-based personalisation improves progressively — after 7 days of usage, personalisation is noticeably better than day 1; after 30 days, it is significantly better. Design for progressive personalisation improvement rather than waiting for perfect data.
Does personalisation create privacy concerns?
Yes — users should understand that their usage data is being used to personalise their experience. Disclose this in your privacy policy and, for products in GDPR jurisdictions, in your consent framework. In practice, most users respond positively to personalisation that is clearly improving their experience — the negative reaction comes when personalisation feels intrusive or when data usage is unexpected. Transparent, product-improving personalisation rarely generates privacy pushback.
Want AI Personalisation Built Into Your Bubble.io Product?
SA Solutions builds personalisation engines for Bubble.io applications — from user profiling and event tracking through AI-generated personalised content and retention-driving adaptive experiences.
