Strategy · 24 pages

The answer engine playbook for B2B

A working framework for getting a B2B brand retrieved, quoted, and recommended by AI assistants, from query mapping to measurement.

Key findings

01
Map buyer questions by intent before touching content. Category, comparison, and problem queries each need a different page type.
02
Lead every priority page with a direct, self-contained answer that can be quoted without surrounding context.
03
Treat entity consistency as infrastructure: one name, one description, one set of facts across your site, schema, and profiles.
04
Measure citation share per query set, not rankings alone. The gap between the two is where the work is.

Preview

Answer engines do three things: retrieve candidate sources, extract passages, and synthesize a response. Each step has its own failure mode, and most B2B sites fail at extraction, not retrieval.

1. Map the questions buyers actually ask

Start from intent, not keywords. Group questions into category ("best tools for X"), comparison ("X vs. Y"), and problem ("how do I fix Z") sets. Each set maps to a page type and a success metric.

2. Make every answer extractable

Open with a one- to two-sentence answer that stands on its own.
Use descriptive headings phrased the way the question is asked.
Prefer lists and tables for anything comparative or sequential.

3. Remove ambiguity about who you are

Models reconcile what your site says with what everyone else says. Consistent Organization and Product schema, matching profiles, and a clear About page reduce the chance of being misdescribed or skipped.

4. Measure citation share

Run a fixed prompt set against the major assistants on a schedule and record which sources are named. Track it next to rankings so you can see which pages rank but never get quoted.