The engines are capable. Most implementations underdeliver because nobody assembled what they need to run on.
Personalisation is one of the easiest things in marketing to buy and one of the hardest to make work. The software is genuinely capable. The reason so many implementations produce a banner with a first name in it is that the data and content required to drive anything more sophisticated were never assembled.
Evaluating these platforms on features therefore tells you very little. The more useful comparison is what each approach demands as an input.
Rules-based personalisation
The simplest approach. A marketer defines conditions and the corresponding experience: if the visitor is from this industry, show this case study.
What it needs is attributes you actually hold at the moment of the visit, and enough content variants to fill the rules. It fails when a team writes fifty rules and produces content for twelve of them, or when the attribute driving the rule is only available for a fraction of visitors.
It is underrated. For many B2B sites, a small number of well-chosen rules delivers most of the achievable value, and it is auditable, which matters when someone asks why a particular visitor saw a particular thing.
Behavioural and model-driven personalisation
The system learns from behaviour rather than following stated rules, selecting content based on patterns across previous visitors.
What it needs is traffic volume. These approaches learn from observed outcomes, and low-traffic sites do not generate enough of them for the learning to converge. B2B sites with modest visitor numbers frequently find the model never gets past exploration, and the experience is effectively random with extra latency. It also needs a content library large enough to select from - a model choosing between three assets is not doing much selecting.
Real-time orchestration across channels
The most ambitious version. A unified profile updates continuously and drives coordinated experiences across web, email, product and sales touchpoints.
What it needs is identity resolution that works, event data flowing in near real time from every relevant system, and a content operation capable of supplying variants at the rate the orchestration consumes them. Each of those is a substantial programme in its own right.
This is where the largest gap between purchase and outcome tends to appear, because the platform can be live in weeks while the prerequisites take quarters.
The B2B complication
Most personalisation technology was designed around consumer patterns: one person, one identity, a short consideration period, an individual purchase decision.
B2B breaks each of those. The buying decision involves a committee whose members visit separately, often anonymously, over months. Personalising to an individual visitor may be personalising to one member of a group whose collective view is what matters. Account-level personalisation is a better fit conceptually, and it depends on resolving a visitor to an organisation reliably, which is exactly where the coverage question in your data becomes decisive.
How to decide
Work backwards from what you have. Establish your actual traffic volume, your realistic content production capacity, and how much of your visitor base you can identify at a useful level. Then choose the most sophisticated approach those three can support, and no more.
A well-executed rules-based implementation beats an underfed model every time, and it costs a fraction as much to run.
Before the demo, answer three questions: how many visitors do we get, how many content variants can we realistically produce and maintain, and what proportion of visitors can we identify? Those answers narrow the shortlist more than any feature grid.
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