Other best practices

Finally, we want to provide you with a list of some additional and potentially useful principles regarding the setup of an on-site search experience.

Index pages, not products

Each document we put in Elasticsearch corresponds to an URL.

We do this because we think that different page types—for example, brand pages, category pages, or CMS pages—can be relevant for the same search. There is no reason why somebody interested in shipping prices should not be able to find corresponding information using the search bar of a website (unfortunately, this is rarely the case), so we put it in the same index as products, using the same document structure:

{
  "type": "cms-page",
  "search_result_data": {
    "id": "7",
    "title": "Versandkosten | contorion.de",
    "name": "Versandinformationen",
    "url": "/versandkosten"
  },
  "search_data": {
    "full_text_boosted": [
      "Versandinformationen"
    ],
    "full_text": [
      "<p>Die Versandkosten innerhalb Deutschlands betragen 5,95€ pro Bestellung. Ab einem Warenwert von %freeShippingPrice% liefert Contorion versandkostenfrei.</p><p>Contorion.de liefert im Moment nur nach Deutschland.</p> <p>Die Versandkosten innerhalb Deutschlands betragen 5,95€ pro Bestellung. Ab einem Warenwert von %freeShippingPrice% liefert Contorion versandkostenfrei.</p><p>Contorion.de liefert im Moment nur nach Deutschland.</p>"
    ]
  }
}

Furthermore, our generic page-based schema allows for other search operations such as rendering a staple page (an overview of different variants of a product; often with their own facet navigation such as the aforementioned Senkkopf-Holzbauschraube):

The query looks very similar to a normal faceted navigation, except that it searches only within a specific staple.

Explicit attribute management

A usage-driven search schema and document structure puts more burden on importers because document attributes must be duplicated in multiple fields with varying formats. To handle this additional complexity and keep the import code maintainable, we recommend to explicitly decide which attributes to put in which field; ideally as data—for example, in a database table and ideally as part of an already existing attribute management system:

Bonus points for providing a user interface for this purpose. It allows category or product managers to make fine-grained conscious decisions on how to use certain attributes in search. For example, a numeric attribute hammer_weight can be used as a string facet and completion term, whereas another numeric attribute hammer_handle_length can only be used as a number facet.

Product management for search

Everybody has an opinion on how search should work, and being a product owner or product manager for search is definitely not the easiest task.

What is good product management for search? Certainly, it is not of technical nature (the suggestion like “please use technique X” is usually suck).

Rather helpful are concrete examples of expected and actual behavior from a user perspective (“If I search for a hammer, I want to find a hammer”).

This is an excerpt of the actual input we got from various stakeholders at Contorion:

A final remark: Search problems are very often data quality problems. Search cannot fix issues of missing attributes, bad product descriptions, or wrong categorizations. In general: the better the underlying document material, the better the search experience.