Understanding InsideView Match Score Thresholds

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A match score is a number from 1 to100 that indicates the likelihood that a match obtained by InsideView is accurate. Match scores provide insight into the quality of a matched record.

The match score is also by used InsideView Data Integrity, InsideView Enrich, InsideView Refresh, and InsideView Diagnose products to return a quality matched record based on your input values.

Note: Match scores do not exactly correspond with raw probability; that is, a match score of 60 does not mean that a match is 60% likely to be accurate. In fact, a score of 60 corresponds with accuracy rates of about 90%.

How are Match Scores calculated?

Match scores are composed of additive and subtractive components. These additives (adding to the score) components affect the match score thresholds based on:

  • The number of data fields provided as input by you—lesser the data fields lesser the match score is because the accuracy of data depends on the number of fields for which InsideView has the information that can be matched.
  • The number of potential matches in the pipeline—higher the number of potential matches in the pipeline lower the match score is because if the number of alternatives is many, then matching and choosing from multiple alternatives is a difficult choice.

Any company with a lot of potential matches in the database would be subtracted from the score proportionately to the number of companies that could potentially dilute the match results.

Why you should use Match Thresholds?

When configuring InsideView Enrich, account administrators may set a match score threshold—i.e. a minimum match score below which enriched leads will not be sent into the marketing automation system. The appropriate match threshold for your use case will depend on your business requirements.

Because match scores are correlated to levels of data accuracy, the choice of where to set the match score threshold is, in essence, a question of how many false positives can be tolerated in the lead supply.

Generally speaking, a lower threshold will conduce to a greater number of leads with a higher proportion of false positives, whereas a higher threshold will yield better accuracy but at a lower volume.

How to Set Match Score Thresholds

InsideView Data Integrity, InsideView Enrich, and InsideView Refresh provide an easy mechanism for system administrators to set match score thresholds, which gives you control over the quality of data you receive from InsideView for updating and enriching your records. We use these thresholds to assign matches into these categories:

Category

Description

Acceptable/Good 

InsideView data records that match your input data based on an accepted match score threshold defined by you.

Rejected 

InsideView data records that are not suitable matches for your input data because of low match scores.

Uncertain 

InsideView records that have a “probable” match score with multiple potential matches for your input data. You may choose to review these manually.

 

Best Practices for Setting Match Score Thresholds

When setting match score thresholds, it’s important to note that precision and recall work in an inverse relationship. If you are focused on precision, fewer records will be returned, meaning recall will be reduced. If you’re focused on a high match rate (high recall), precision is likely to be sacrificed. 

Here are guidelines to help you decide what fits your need:

  • Very high precision with decent recall: Set the confidence score to 0.7
  • High precision with high recall: Set the confidence score to 0.5
  • Decent precision with very high recall: Set Confidence score to 0.3

Match Score  Benchmarks

This table shows the overall match score (aka confidence score) for various data matching results, based on a sample size of 100,000 records:

 Match Score Benchmark Data

Match Score 

True Positive (TP)

True Negative (TN)

False Positive (FP)

False Negative (FN)

FP_FN

Precision

Recall

F1 Score

0.3

62182

13273

15909

2727

5909

74.03%

87.80%

80.33%

0.5

62000

18454

10727

3455

5364

79.39%

87.55%

83.27%

0.7

54000

28091

1091

14818

2000

94.59%

76.25%

84.43%

0.9

14455

28726

455

56273

91

96.36%

20.41%

33.69%

 

Use Case Example

An InsideView customer from the tech industry wanted to fine-tune InsideView Enrich with a match score, which would enable them to have the right balance of accuracy versus enrichment.

InsideView’s approach was to provide this customer with three options. The following table illustrates these options, showing levels of accuracy, false positives, and enrichment for match score thresholds of 70, 60, and 50 respectively:

Accuracy versus Enrichment Balance Match Score Threshold Percentage of Actual Match Accuracy Percentage of False Positives
High Accuracy & Low Enrichment 70 94% 5%
Medium Accuracy & Medium Enrichment 60 90% 10%
Low Accuracy & High Enrichment 50 85% 15%

In the case of the technology company, our customer chose the middle option, since it was the most accurate option that would enable them to meet their lead volume requirements. Your needs may differ, and we can help you decide the threshold that is best for you.

The InsideView Professional Services team can work with you to select options based on your requirements. Please send an email to our Data Services team at services@insideview.com.

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