Personalization you can measure | Nautilus Tech Labs

Customers expect it. In McKinsey's 2021 research, 71 percent of consumers said they expect companies to deliver personalized interactions, and 76 percent said they get frustrated when it does not happen.

So many companies buy a personalization platform, switch on recommendations, and report that engagement went up. Often nobody can say whether it went up because of the personalization or because of the season, a price change or a marketing campaign that ran the same month.

Personalization is only worth what you can prove it changed.

Start with rules

The first useful personalization is usually simple and based on things you already know for certain about a customer:

what they bought or use today,
where they are in their relationship with you: new, renewing, or at risk of leaving,
what they told you they want, through settings or preferences,
and what they did recently, such as an unfinished order or an open support case.

A handful of clear rules built on those facts can do a lot. A new customer sees onboarding help instead of upgrade offers. A customer with an open support case does not get a sales email that week. A customer with one product sees the add-on that most people like them buy next.

Rules have a big advantage early on. Anyone can read them, explain them and switch them off.

Keep a control group

This is the part that makes personalization measurable. For every rule or model, keep a small group of comparable customers who see the standard version instead. Then compare the two groups on the number you care about: conversion, renewals, repeat contacts or revenue per customer.

If the personalized group does better, keep the rule. If it does not, remove it, even if it seemed like a good idea. Over time you build a set of rules that each have a measured result behind them.

Add models where rules run out

Once the rules are working and measured, machine learning can help with the decisions that are too many or too fine for rules: which of two hundred products to show, which article to suggest, when to send a reminder. The same discipline applies. Every model runs against a control group, and it stays only if it beats the rules it replaces.

Respect the data you use

Personalization runs on customer data, so trust matters as much as accuracy. Use data customers have agreed to share. Be able to explain why someone saw what they saw. Avoid anything that feels like surveillance, such as referring to things the customer never told you directly. And make sure your legal team has signed off on what data is used and how.

Where the platform fits

A headless setup, where your website, apps and email all draw from one customer profile and one set of rules, makes personalization much easier to keep consistent. We wrote about when headless architecture pays off separately.

A first step

Write down three rules you could run next month using data you already have, and decide the control group for each. That is enough to learn what works for your customers. Our customer experience team can help design and measure them.