LS ELECTRIC — entity recognition without commercial coverage

• Regional LS ELECTRIC site: 66 AI citations on the target topic.
• 64 of 66 citations landed on the homepage; 2 on the support section.
• Zero citations on product, distributor, comparison, or catalogue pages.
• Global LS ELECTRIC site held 170 citations on the same topic.
• Fix targets commercial-intent depth, not brand awareness.

Entity recognition is not commercial visibility. AI systems clearly knew LS ELECTRIC existed — the homepage was being cited 64 times for relevant queries. But anyone asking "where can I buy [LS product] in [region]" or "[LS product] vs [competitor]" was getting answers from someone else, because the site had no content built to answer those questions.

The 64-versus-170 gap between regional and global sites is the cleanest manufacturing example of the same pattern: same brand, same authority signals, deeper commercial content wins.

We ran a forecast audit on the regional LS ELECTRIC site against the commercial-intent query map for the target product lines. The shape of the citation distribution told the story:

• 66 citations total on the target topic.
• 64 of 66 landed on the homepage.
• 2 of 66 landed on the support section.
• 0 on product pages, distributor pages, comparison pages, or catalogue pages.
• 170 citations on the global LS ELECTRIC site for the same topic.

This is a textbook "the entity is known, the commercial layer is empty" pattern. AI systems use the homepage as the fallback when they cannot find a more specific page to cite.

The forecast and plan target commercial-intent depth, not brand awareness:

1. Built a commercial-intent query map. "Where to buy [product line] in [region]", "[LS product] vs [competitor]", "distributor [city / region]", "product specification [model number]", "alternatives to [product line]".
2. Designed chunk-ready pages per cluster. Product pages, distributor pages, comparison pages, and catalogue answer pages — each built for retrieval extraction with self-contained answer paragraphs.
3. Rolled out Schema.org markup. Product, Distributor, Organisation, and FAQ structured data on the new commercial-intent pages.
4. Set measurement targets against both sites. The regional site is the active target; the global site is the benchmark — when regional commercial citations cross 50, the gap is closing.

Three lessons that rewrite how a multinational industrial brand should treat regional sites:

1. Homepage-only citation is a red flag, not a win. It signals the entity is known but the commercial-intent pages do not exist for AI to cite.
2. Schema is the technical floor; commercial content is the editorial floor. Both have to be in place before answer-layer presence catches up to brand presence.
3. Use the global site as the benchmark. The same brand with deeper commercial content already earns 2.6× the citations — the playbook is a known proof, not an experiment.

This is published as a forecast / baseline case, not a finished growth outcome. The 66-versus-170 split is the headline insight: same brand, different content depth, very different AI answer share. The same diagnostic pattern repeats for any manufacturer with strong global entity recognition and weak regional commercial pages.

Full case study: https://www.linkedin.com/pulse/ls-electric-ai-search-visibility-forecast-gregory-shevchenko-o9ulf