Search Quality - A Business-Friendly Perspective

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Have you ever been asked to fix a bad search experience? Have you ever been asked to predict the outcome of a change to your search algorithm? Fixing search problems can feel like a game of whack-a-mole -- fixing one set of queries breaks others. It's a frustrating game of guesswork and trade-offs. But you're responsible for the overall performance of a search system, and the powers that be want assurances. Wouldn't it be great if you could report, with confidence, the overall quality of search? Search quality metrics such as MAP, MRR, and nDCG exist to help the search engineer, but mapping them to real-world business goals is challenging. And pointing to obscure metrics in the face of contrary perceptions is unsatisfying to everyone. Can we do better? In this talk, we'll look at some of these evaluation metrics and how to use them. Then, we'll explore the gap between these metrics and the business perspective on search quality. Finally, we'll explore tactics you can use to tell an effective story about search quality that non-experts can understand.

Technology

Search Quality
A Business-Friendly Perspective

1. DO YOUR HOMEWORK
2. GATHER PRELIMINARIES
3. INTERVIEWS
4. ANALYSIS
5. ROADMAPPING
6. PRIORITIZE & PLAN
7. EXECUTE ITERATIVELY
a. IRON TRIANGLE
b. FEEDBACK LOOPS
A METHOD TO THE MADNESS

Search Quality Anti-Patterns
● Search Quality is Bug Squashing
● Search Quality is Too Hard For You
● Search Quality is Off Limits
● Search Quality is a Feeling
● Search Quality is Relevance
● Search Quality is Mysterious

● Big Picture
○ Use Cases, Domain, IR Vertical
● Classes of Queries
○ Navigational, Informational, Transactional, Research
○ Length, Category, Intent
● Distribution of Queries
○ Head/Tail Analysis
○ Over Tail, Over Time
○ Relative Performance, Outliers
○ Quantify Scale/Impact
● Connect to Revenue
● Points of Interest
Analysis

Searching for People
Motivation: Many customers are
searching for movies by actor name.
~10% of random sample includes an
actor name in the query. These
queries have poor performance
relative to their peers as measured
by click-through.
We believe that recognizing named
entities (people names) will bring
performance in-line with peer
group.
IMPACT: HIGH
RISK: LOW
LOE: MEDIUM
SCOPE: Named Entities queries
SCALE: ~10%
MEASURE: Relative CTR
VALUE: $XX.XX

Iron Triangle of Search Quality
MEASUREMENT
OPTIMIZATIONDATA

Search Evaluation Metrics
● MRR
● MAP
● Precision @K
● NDCG
● LGTM

RELEVANCE JUDGMENTS
● relevance is approximately query similarity
● relevance is binary
● judgment lists agree with users
● judgment lists are complete and consistent

What are we talking about?
1. Does it deliver value?
2. At what cost?

Who are we talking about?
1. Marketing
a. define offerings
b. attract and retain customers
2. Management
a. set goals
b. plan and allocate resources

HIERARCHY OF BUSINESS OBJECTIVES
REVENUE
ENGAGEMENT
CONVERSIONSRETENTION
MARKET
SHARE
CUSTOMER
SATISFACTION
CTR ABANDONMENTACQUISITION

Search Behaviors
● behaviors make sense
● behaviors are measurable
○ even without relevance data
● behaviors tell a story
● you can map behaviors to user tasks

Development Framework for Search
LABOPERATIONS
INTEGRATION
CLICKSTREAM
A/B TESTING
EVALUATION
OFFLINE
RELEVANCE
ONLINE
AUTOMATED
TESTING
IRON TRIANGLE

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Search Quality - A Business-Friendly Perspective

1. Search Quality A Business-Friendly Perspective

2. Peter Fries Search Consultant

3. Search Quality

4. Successful Delivery of Search Projects

5. Successful Delivery of Search Projects

6. Search Project Quality

7. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

8. WHAT MADNESS?

9. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

10. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

13. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

15. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

17. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

19. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

21. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious

22. Search Quality Anti-Patterns ● Search Quality is Bug Squashing ● Search Quality is Too Hard For You ● Search Quality is Off Limits ● Search Quality is a Feeling ● Search Quality is Relevance ● Search Quality is Mysterious!

23. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

25. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

26. DO YOUR HOMEWORK

27. GATHER PRELIMINARIES

28. TALK TO THE PEOPLE

29. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

30. ● Big Picture ○ Use Cases, Domain, IR Vertical ● Classes of Queries ○ Navigational, Informational, Transactional, Research ○ Length, Category, Intent ● Distribution of Queries ○ Head/Tail Analysis ○ Over Tail, Over Time ○ Relative Performance, Outliers ○ Quantify Scale/Impact ● Connect to Revenue ● Points of Interest Analysis

33. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

34. ROADMAPPING

35. Searching for People Motivation: Many customers are searching for movies by actor name. ~10% of random sample includes an actor name in the query. These queries have poor performance relative to their peers as measured by click-through. We believe that recognizing named entities (people names) will bring performance in-line with peer group. IMPACT: HIGH RISK: LOW LOE: MEDIUM SCOPE: Named Entities queries SCALE: ~10% MEASURE: Relative CTR VALUE: $XX.XX

36. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

37. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

38. Iron Triangle of Search Quality MEASUREMENT OPTIMIZATIONDATA

39. Iron Triangle of Search Quality MEASUREMENT OPTIMIZATIONDATA

40. Iron Triangle of Search Quality MEASUREMENT OPTIMIZATIONDATA

41. Iron Triangle of Search Quality MEASUREMENT OPTIMIZATIONDATA

42. Search Evaluation Metrics ● MRR ● MAP ● Precision @K ● NDCG ● LGTM

43. RELEVANCE JUDGMENTS ● relevance is approximately query similarity ● relevance is binary ● judgment lists agree with users ● judgment lists are complete and consistent

45. Search Quality A Business-Friendly Perspective

46. What are we talking about? 1. Does it deliver value? 2. At what cost?

47. Who are we talking about? 1. Marketing a. define offerings b. attract and retain customers 2. Management a. set goals b. plan and allocate resources

48. HIERARCHY OF BUSINESS OBJECTIVES REVENUE ENGAGEMENT CONVERSIONSRETENTION MARKET SHARE CUSTOMER SATISFACTION CTR ABANDONMENTACQUISITION

49. Search Behaviors ● behaviors make sense ● behaviors are measurable ○ even without relevance data ● behaviors tell a story ● you can map behaviors to user tasks

50. HIERARCHY OF BUSINESS OBJECTIVES REVENUE ENGAGEMENT CONVERSIONSRETENTION MARKET SHARE CUSTOMER SATISFACTION CTR ABANDONMENTACQUISITION SEARCH-EXITS SERP-TO-CART THRASHING POGO-STICKING PAGINATION SEARCHES PER-SESSION REVENUE PER-SEARCH ZERO-CLICKS DWELL-TIME SEARCH-RATE

51. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

52. Development Framework for Search LABOPERATIONS INTEGRATION CLICKSTREAM A/B TESTING EVALUATION OFFLINE RELEVANCE ONLINE AUTOMATED TESTING IRON TRIANGLE

53. 1. DO YOUR HOMEWORK 2. GATHER PRELIMINARIES 3. INTERVIEWS 4. ANALYSIS 5. ROADMAPPING 6. PRIORITIZE & PLAN 7. EXECUTE ITERATIVELY a. IRON TRIANGLE b. FEEDBACK LOOPS A METHOD TO THE MADNESS

54. THANK YOU!

Search Quality - A Business-Friendly Perspective

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Search Quality - A Business-Friendly Perspective