Practise vocabulary for measuring search quality: click-through rate, MRR, NDCG, zero-results rate, and A/B testing for search.
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___ (Mean Reciprocal Rank) measures search quality by rewarding systems that rank the relevant result higher in the results list.
MRR = average of 1/rank for the first relevant result across all queries. If the relevant result is rank 1, score is 1.0; rank 2 is 0.5; rank 3 is 0.33. Higher MRR means users find relevant results closer to the top.
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NDCG (Normalised Discounted Cumulative Gain) is an offline search metric that accounts for both ___ of results and their position.
NDCG accounts for multiple relevance grades (highly relevant, relevant, not relevant) and position (results at rank 1 matter more). It normalises the DCG score by the ideal ranking, producing a value between 0 and 1.
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A high ___ rate in search analytics indicates users can't find what they're looking for — the index lacks relevant content or queries aren't matched.
Zero-results rate (or no-results rate) tracks queries that return no results. High rates indicate content gaps (the product doesn't exist) or matching failures (synonyms not configured, spelling variants not handled). It's a key diagnostic metric.
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CTR (Click-Through Rate) in search measures the percentage of ___ that result in a user clicking a search result.
Search CTR = clicks / impressions (queries). Low CTR on top results indicates poor relevance — the titles/snippets don't match user intent. Analysing CTR by query identifies which queries have poor result quality.
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___ analytics tracks what users type after an initial search, indicating their first query was unsuccessful.
Query refinement tracking identifies when users modify their initial query (adding terms, removing terms, rephrasing) — a signal that the initial results were unsatisfactory. High refinement rates for specific queries point to relevance problems.
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Review Comment: 'The search analytics for the /users endpoint show a low Mean Reciprocal Rank (MRR). This suggests that even when users find relevant results, they're not appearing high enough in the list to be immediately useful. Do we need to re-evaluate our indexing strategy here?'
MRR (Mean Reciprocal Rank) directly addresses the ranking of search results. It penalizes systems that rank highly relevant results lower in the list. The question highlights a potential issue with the indexing strategy and its impact on user experience – a common concern in search analytics.
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Slack Message: 'Hey team, we're seeing a high NDCG score for the 'product recommendations' search. It's great that users are finding what they want, but the lower-ranked results are getting a lot of clicks. Should we investigate whether our ranking algorithm is prioritizing diversity over precision?'
NDCG (Normalized Discounted Cumulative Gain) is a key metric that considers *both* the relevance of search results *and* their position within the list. A high score doesn't automatically mean perfect; it signals potential issues with prioritizing precision over diversity – which is what this Slack message raises.
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PR Description: 'Implemented a new query expansion feature for the search index. This should improve the CTR (Click-Through Rate) by providing users with more relevant results and reducing irrelevant clicks.'
CTR (Click-Through Rate) is the percentage of times a search result is clicked. Implementing query expansion – adding related terms to the initial search – *should* increase relevance and therefore the likelihood that users will click on a relevant result, leading to a higher CTR.
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Standup Update: 'I've been digging into the search analytics for the payment processing API. We're seeing a lot of users entering 'transaction status' into the query box, but they aren't finding the information they need. This suggests that our initial queries weren't matching what they were actually looking for.'
Tracking what users type after an initial unsuccessful search (often called 'after-the-fact' queries) provides critical insights. This data reveals that the index isn't matching user intent – likely due to variations in terminology or a lack of relevant content covering those specific query patterns.
'The session_after_query field indicates that after the initial search for 'product details', users subsequently searched for 'product specifications'. This suggests a potential need to broaden our index coverage to include more detailed product information.'
Analyzing the 'session_after_query' provides valuable context. It shows that users initially searched for a broad term ('product details') but then refined their query with more specific terms ('product specifications'). This highlights an opportunity to expand our index and provide richer content to meet evolving user needs.
What will I learn from the "Search Analytics Vocabulary" exercise?
Practise vocabulary for measuring search quality: click-through rate, MRR, NDCG, zero-results rate, and A/B testing for search.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to use with no account, sign-up, or paywall required.
How many questions are in this exercise?
This set contains 10 multiple-choice questions, each with a detailed explanation shown after you answer.
Do I need to create an account to track my progress?
No account is required. Your progress bar and score reset each time you reload the page, but you can retry the exercise as many times as you like.
Who is this Search Engineering Language exercise for?
This exercise is built for IT professionals and non-native English speakers who need to read, write, and discuss search engineering language topics confidently at work.
What happens if I answer a question incorrectly?
You will see the correct answer highlighted along with a detailed explanation of why it is correct -- so every wrong answer becomes a learning moment, not just a lost point.
Can I retry this exercise?
Yes -- click "Try again" on the results screen at any time to reset your score and go through all the questions again.
How long does this exercise take to complete?
Most learners finish all 10 questions in under 10 minutes, since each question is answered by clicking a single option.
Where can I find more Search Engineering Language exercises?
See the full Search Engineering Language exercises hub for more vocabulary drills on this topic.
Is this exercise mobile-friendly?
Yes -- the exercise works on any device with a modern browser, including phones and tablets, with no app download required.