McKinsey’s research on personalisation shows customers now expect brands to recognise them, and the companies that deliver grow faster. The good news: you no longer need a data-science team to do it.
- The headline numbers
- What customers actually mean by personalisation
- Why personalisation drives growth
- The personalisation ladder
- Personalising the sales conversation
- Templates you can adapt
- Where AI fits
- Guardrails that build trust
The headline numbers
In its November 2021 report, “The value of getting personalization right, or wrong, is multiplying,” McKinsey shared several findings that every business owner should know.
McKinsey reported that personalisation most often drives a 10 to 15 percent revenue lift, with company-specific results ranging from 5 to 25 percent depending on the sector and the ability to execute. Faster-growing companies drive about 40 percent more revenue from personalisation than their slower-growing counterparts.
What customers actually mean by personalisation
Personalisation is not just putting someone’s first name in an email. For a service business, it usually means:
- Remembering what the person asked about and referring back to it
- Recommending the next step that fits their situation, not a generic offer
- Reaching out at the right moment (after a milestone, before a renewal)
- Using the channel they prefer, whether that is email, WhatsApp or a call
Why personalisation drives growth
It is easy to see why the numbers look the way they do. Relevant messages get read. Recommendations that fit the situation get accepted. Customers who feel understood stay longer and refer others. Each effect is small on its own; together they compound across the whole customer journey.
The personalisation ladder
- Level 1, segment: group leads by need (for example “wants more leads” vs. “wants less admin”).
- Level 2, context: reference what they asked for, the page they came from or the tool result they got.
- Level 3, timing: trigger messages from behaviour, such as booking a call, missing a call or finishing onboarding.
- Level 4, one-to-one: every reply reads as if it was written for one person, at scale.
Personalising the sales conversation
The highest-leverage place to personalise is the first conversation after someone enquires. Ask one question about their goal, reflect their answer back in their own words, and recommend one specific next step. Then use the same words in your proposal. Customers buy faster when they hear their own problem described accurately.
Templates you can adapt
After a lead magnet: “Hi [Name], I saw your scorecard result came out lowest on [lever]. That’s the most common leak we see. Want me to send the two quickest fixes for it?”
After a missed call: “Hi [Name], sorry we missed each other. You mentioned [goal]. Would [time] or [time] work better?”
Before a renewal: “Hi [Name], it’s been 90 days since we started on [goal]. Here are the three wins so far and what I’d focus on next.”
Where AI fits
Doing all of this manually used to require a big team. AI agents can now read the context of each conversation, pull the right details from your CRM and write a relevant reply in seconds, while following your rules. That is how a small business can deliver the kind of personalisation McKinsey describes without hiring an army.
Our AI agents are built for exactly this: personal, fast and consistent.
Guardrails that build trust
- Only use data customers knowingly gave you
- Be clear when someone is talking to an AI assistant if they ask
- Give an easy way to reach a human
- Never invent details you don’t have
- Review conversations regularly and improve the rules
A worked example
Illustrative example, not a client case study. A marketing consultant runs a free scorecard on her website. Before, every lead received the same three follow-up emails. After applying the personalisation ladder, leads are grouped by their weakest area. Someone who scored lowest on follow-up receives a message about response time, a link to a relevant briefing and an offer to review their process. Someone who scored lowest on traffic receives content about ads and outreach instead.
On sales calls, she opens by repeating the prospect’s own scorecard answers and asks what changing that one number would mean for their business. Her proposals then quote the prospect’s exact words back to them. Nothing about this requires a big team. It requires a system that remembers context and uses it at the right moment.
The data you actually need
Most small businesses already hold enough data to personalise well: what the lead asked about, which page or ad they came from, any tool or quiz results, what they bought and when, and how they prefer to be contacted. The goal is not to collect more data but to use what customers already told you, consistently and respectfully.
Frequently asked questions
Is personalisation creepy?
It becomes creepy when you use information people did not knowingly share. Using what customers told you, to help them, feels like good service.
Where should I start?
Start with your follow-up after an enquiry. Segment by need and reference what the person asked about. That alone puts you ahead of most competitors.
Can AI personalise without sounding robotic?
Yes, when it is given clear rules, your tone of voice and real context. Review conversations regularly and refine the instructions.
Key takeaways
- Most customers expect personalisation and dislike its absence
- Typical revenue lift is 10-15% when done well
- Climb the ladder: segment, context, timing, one-to-one
- Use the data customers already gave you, with clear guardrails
Source: McKinsey & Company, “The value of getting personalization right, or wrong, is multiplying” (Nov 2021). This briefing summarises published third-party research for educational purposes. It is not original research by Aashish Maru. Statistics belong to their authors; our commentary is our own.