AI Personalises Your Product
Every user of your product is different — different goals, different skill levels, different usage patterns. A product that treats them all the same serves none of them optimally. AI enables genuine personalisation at scale: every user getting the experience that fits them.
What Can Be Personalised in a SaaS Product
| Dimension | Generic Experience | Personalised Experience | Business Impact |
|---|---|---|---|
| Onboarding path | Same 5 steps for everyone | Steps relevant to user’s stated role and goal | Higher activation rate |
| Dashboard default view | Same widgets for everyone | Widgets most relevant to this user’s usage patterns | Higher daily engagement |
| Feature discovery | All features shown equally | Features introduced when user behaviour suggests readiness | Higher feature adoption |
| Help and guidance | Generic help articles | Context-sensitive help for the specific action user is attempting | Fewer support tickets |
| Email communications | Same segment-wide email | Triggered by individual usage patterns and milestones | Higher open and click rates |
| Pricing and upgrade prompts | Usage limit reached = upgrade prompt | Usage pattern analysis = right offer at right moment | Higher conversion to paid |
The Technical Architecture
Instrument your application for personalisation signals
Personalisation requires signals: what has this user done, what have they not done, what do they seem to be trying to accomplish? Every meaningful user action must be tracked: features used (which ones, how frequently, in what sequence), goals set or stated (if your onboarding captures the user’s primary goal), content consumed (which help articles, which tutorial videos), team size and role (for B2B products — different roles need different experiences), and plan type (free users need different nudges than paid users). Store all events in the user record or a separate events table in Bubble.
Build the user segment classification
A weekly Bubble workflow classifies each user into a personalisation segment based on their behaviour: Power User (logs in daily, uses 5+ features, has completed all onboarding steps), Engaged User (logs in 3+ times per week, uses 2-3 core features), Casual User (logs in weekly, uses 1 core feature repeatedly), At-Risk User (login frequency declining, using fewer features than 30 days ago), and Dormant User (no login in 14+ days). Each segment receives a different in-product and email experience — personalisation that scales because it is segment-level rather than individually generated for every user.
Build the AI recommendation engine
For individual-level personalisation within segments: when a user opens the dashboard, a Bubble workflow retrieves their recent activity and passes to Claude: This user has been using . Their recent activity: [activity summary]. Their stated goal: [goal]. Recommend the single most valuable next action for this user to take in the product today — the action most likely to help them achieve their goal based on their current usage pattern. Return: the recommended action, the reason it is the best next step for this user, and the specific UI location where they can take it. Display as a personalised daily prompt in the dashboard.
Build personalised email triggers
Rather than segment-wide email blasts, build behavioural triggers: user has not used Feature X in 14 days despite having used it previously (re-engagement nudge with a specific tip), user has completed 4 of 5 onboarding steps but not the final one (targeted completion encouragement), user’s usage has grown 50% this month (expansion conversation trigger — they may be approaching plan limits or ready for the next tier), user achieved a significant milestone (celebration email that reinforces product value and encourages sharing). Each trigger a Make.com scenario generating a Claude-written personalised email.
Does in-product personalisation require a large user base to be effective?
Segment-level personalisation (creating 4 to 6 user experience tracks) works from day one — even with 50 users, you can identify meaningful differences in usage patterns and tailor the experience accordingly. Individual-level AI personalisation (generating specific recommendations for each user) adds more value as the user base grows and usage patterns become more diverse and data-rich. Start with segment-level personalisation immediately; add individual AI recommendations when you have 200+ active users with sufficient usage history to generate meaningful patterns.
How do I personalise without making users feel surveilled?
The key distinction is personalisation that helps vs personalisation that reveals uncomfortable surveillance. Helping a user discover a feature relevant to what they are trying to do feels helpful; showing them that you know they visited the pricing page 3 times last week feels intrusive. Personalisation in the product should be framed as assistance (based on how you use , you might find [feature] useful) rather than surveillance (we noticed you…). The same data, used thoughtfully, produces very different user reactions.
Want AI Personalisation Built Into Your Bubble.io Application?
SA Solutions builds Bubble.io personalisation systems — user behaviour tracking, segment classification, AI recommendation engines, and personalised email trigger workflows.
