A Benefits Model for Search – Part 3

The Cost of “No-Hit” Searches

In previous blogs (Part 1 and Part 2) , I outlined a benefits model for search based on the position of the most appropriate content, as returned on a SERP (Search Engine Result Page).

The model in parts 1 and 2 is based on the “popular searches” and does not take account of searches that user attempted that resulted in “no hits”. In some cases these would have been attempted before arriving at a popular search term. In other cases, a no hit result may lead to the user abandoning the site and / or using another channel to find the information required. (The cost of abandonment and repeat searches will be covered in later post).

The “no-hit” results page could be caused by one of a number of problems:

  • a spelling mistake in the search term
  • using the incorrect search syntax e.g. use of AND when it is not supported
  • using incorrect punctuation (e.g. spaces, hyphens, full stops) – for instance “i Pod” rather than “iPod”
  • selecting a combination of words for the search query that is too narrow and leads to zero results (a problem if the search defaults to AND
  • there may genuinely be no information for the search provided
  • poor operation of a search engine.

If we look at the total number of “no-hit” searches, which is easily done using search logs, then it is possible to estimate the cost to a business or organisation of these.

Assuming:
t = time for a user to construct and run a search in seconds

n0 = number of no hit searches (for a search term)
c = equipment cost of running a search (A widely-reported article in 2009 estimated that each search on Google produced 7grams of CO2.)

Time cost of a search = t  x   n0 x salary costs

Resources cost of search = c  x  n0

There are a variety of techniques that can be adopted to reduce the number of “no-hits” results. For instance:

  • implementing a synonym capability (or “did you mean?” suggestion) for common misspellings – some search engines can be set up to do this automatically, given a  list of synomyns
  • add synonyms for common misspellings as content (in metadata)
  • providing auto filled drop-down boxes that suggest correct spellings for popular searches
  • faceted search to allow the user to narrow down a search – if the list of facets includes the number of results for each facet, then the user will know which criteria will produce a number of results
  • configure the search engine to deliver both “all words” and “any words”, with priority given to the “all words” results.

Using the above model, it is possible to measure the number of “no-hits” before and after changes and estimate the benefits.

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One Response to A Benefits Model for Search – Part 3

  1. Pingback: A Benefits Model for Search – Part 4 | Ted Carroll's Corporate Search Blog

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