Scorecards help you quantify and easily track the health of your customers. Scorecards use advanced data analytics to combine objective measures such as product usage and customer survey scores, with subjective elements – such as executive relationships and reference-ability. You can then define your overall customer score as a weighted average of these data-driven or subjective measures. In February 2017 Gainsight released Scorecards 2.0. In Scorecards 1.0 (Account and Relationship Scorecards) you have to create Account and Relationship scorecards separately and manage them at different places in Gainsight. With Scorecards 2.0, you can manage Account/Relationship scorecards in one place and use new features such as Multiple Scorecards. All new customers are encouraged to use Scorecards 2.0, and Gainsight recommends customers on 1.0 to migrate to 2.0. 

The table below compares Scorecards 2.0 and 1.0: 

  Scorecards 2.0 Account Scorecards (1.0) Relationship Scorecards (1.0)
Created in  MDA  SFDC (not recommended)  MDA
Multiple Scorecards Yes No  No
Reporting  Scorecard Fact and history from postgres Usage Data and Scorecard Fact Scorecard Fact and Snapshot
Group Measures Yes  Yes Yes
Score Grouping Yes No  Yes
Score Override Yes  No Yes
Scorecard Mass-Edit Yes Yes Yes
Exceptions for Overrides Yes No Yes

In this article, you will learn about: 

  1. Scorecards 2.0 
  2. Scorecards 1.0
    1. Account Scorecards (1.0)
    2. Relationship Scorecards (1.0)
  3. Methods to Set Scores
  4. Suggested Reading

Scorecards 2.0 

Scorecards 2.0 provides a single place in Gainsight to configure and manage both Account and Relationship scorecards. You can access this feature at Administration > Scorecards 2.0. With Gainsight’s May 2017 release, you can also create multiple scorecards. Multiple scorecards enable you to have different scorecard configurations in Gainsight which can be applied to different Accounts/Relationships automatically based on criteria like customer stage or lifecycle, etc. If you use multiple scorecards, you will set one of them as the Default, so that any Accounts/Relationships that do not match your criteria will be assigned the Default scorecard configuration. You can only assign one scorecard to each Account/Relationship at a time. For more information, see Configure Scorecards 2.0

Account Scorecards (1.0)

Account Scorecards are used to track Customer metrics, Key Performance Indicators (KPIs), or other measures of Customer Health. Scorecards can be segmented by groups as well as optionally have weights applied to them to increase their representation. You can access and create Account Scorecards at Administration > Account Scorecards. For more information on Account Scorecards, see Configure Account Scorecards(1.0)

Relationship Scorecards (1.0)

Relationship Scorecards help you create scorecards for different relationship types. Similar to an account scorecard, a relationship scorecard is based on a weighted average of measures that define the relationship.  A measure refers to an area of customer health that you wish to track and monitor, such as usage, NPS score, adoption, support cases, and so on. In addition, you can assign different weights (values) to these measures. This helps you control how different measures impact the overall health score of a customer or relationship.

Note: You must enable Relationships and Redshift to see the tab under Administration > Health Scoring > Relationship Scorecards.

For more information on Relationship Scorecards, see Relationship Scorecards(1.0).

Methods to Set Scores

Gainsight offers four scoring methods to support a comprehensive scorecard that considers the full picture of customer health. Each method of scoring is suited to different types of data for different use cases. Following are the general ways of updating the scores in Gainsight: 

  • Manual Scoring
 (setting the scores manually from 360-view, Gainsight Dashboards, and Report Builder)
  • Automatically setting the scores using Rules Engine
  • Smart Scores
 (works only with Scorecards 1.0)
  • Predictive Scoring


For descriptions of the different scoring methods, see this blog post

Suggested Reading