Personalisation Starter Kit

What this covers

Personalisation isn't just email marketing. It's about adapting your product, messaging, and support to who someone is and what they're trying to do. This kit helps you identify where personalisation will have the biggest impact and how to implement it without overcomplicating things.

1. Strategy & Goals

What problem are you solving?

What's the business goal?

Where will you start?

How will you measure success?

2. Personalisation Use Cases

Pick 1-2 to start

Don't try to personalise everything. Focus on the moments where relevance matters most: typically onboarding, conversion points, or retention triggers.

In-Product Experience

Show different features, flows, or defaults based on user segment or behaviour

Your approach:

Onboarding Paths

Different setup flows for different customer types or goals

Your approach:

Email & Notifications

Send different messages based on what people have/haven't done

Your approach:

Help & Support

Surface relevant docs, FAQs, or support based on context

Your approach:

Pricing & Upsells

Show relevant plans or features based on usage patterns

Your approach:

Content & Recommendations

Suggest relevant resources, templates, or next steps

Your approach:

3. Customer Segments

Start simple: 2-3 segments maximum

You need to be able to clearly identify who's in each segment. Behaviour-based segments (what they do) work better than demographic ones (who they are).

Common Segment Examples to Consider

Segment Type	Who They Are	Why Personalise for Them
New Users	Signed up in last 7-30 days, low activity	Need onboarding, quick wins, education. High drop-off risk
Active Users	Regular activity (weekly+), using core features	Want efficiency, new features, advanced capabilities
Power Users	Daily use, 5+ features, high engagement	Need advanced tools, shortcuts, API access. Potential advocates
Dormant/At-Risk	Paid but inactive 14+ days, declining usage	Churn risk. Need re-engagement, value reminders, support
Trial Users	On free trial, not yet converted	Need to see value quickly, understand pricing, see ROI
Enterprise/Team Users	Multiple seats, admin permissions, higher spend	Need collaboration features, admin controls, priority support
Use-Case Based	e.g., Marketers vs Sales vs Support teams	Different goals, workflows, features matter to each
Freemium Users	On free plan, sustained usage	Potential to upgrade. Show paid feature value, not just lock them out

Your segments

Pick 2-3 that map to your business priorities. Define them clearly in the table below.

Segment Name	Definition / Criteria	What They Need	How You'll Personalise

4. Implementation Plan

Data & Tracking

What data do you need to segment users?

Where does this data live?

What events need tracking?

Tools & Systems

Email / Messaging Platform

In-Product Personalisation

Analytics / Segmentation

A/B Testing Tool

Timeline & Ownership

Launch Date

Owner

Review Date

5. Testing Approach

What's your first test?

Start small: one segment, one change, clear measurement

Success metric

Minimum sample size needed

Example Test Plan:

Control: All new users see generic welcome email. Treatment: Marketing managers see email focused on campaign features, sales managers see email focused on pipeline features. Measure: % who complete first action within 7 days.

6. Common Mistakes to Avoid

Personalising before you have enough data. Don't segment on less than 100 users per group
Making segments too narrow. You'll split your audience into unusably small groups
Personalising everything. Focus on high-impact moments, not every interaction
Forgetting the default experience. Always have a fallback for users who don't fit segments
Not testing. Validate that personalisation actually improves outcomes vs. generic
Over-personalising based on outdated data. Someone's behaviour from 6 months ago may not be relevant
Ignoring privacy. Be transparent about data use, give users control
Setting and forgetting. Personalisation needs ongoing tuning as behaviour changes

7. Ready to Launch?

Segments are clearly defined with specific, measurable criteria
Data needed to segment users is being tracked reliably
Personalisation logic is documented so others can understand it
A/B test or pilot is set up to validate approach
Fallback experience exists for users who don't match segments
Success metrics are defined and baseline is measured
Review date is set to assess results and iterate
Team knows how to update segments or logic as you learn