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Scorecard Optimizer FAQs (Beta)

Gainsight NXT

 

Note - BETA Article

Please note that this is a BETA document. You might notice changes in the final version of this document.

If you have any feedback, feel free to share with us at docs@gainsight.com.

 

This article provides a list of Frequently Asked Questions (FAQs) about Scorecard Optimizer.

Scorecard Optimizer, powered by HorizonAI, assists you by offering optimal configuration recommendations to set up new scorecards, and improve the effectiveness of existing scorecards in your tenant orgs. Scorecard Optimizer uses historical renewal data (GS Opportunity) and customer data (Adoption Explorer, Surveys, and Cases) to provide recommendations that drive growth and customer engagement through early prediction of risks and opportunities.

Currently, Scorecard Optimizer is only available for Company Scorecards. Scorecard Optimizer support for Relationship Scorecards will be introduced in the future. However, the timeline is not yet confirmed.

Yes, you can run Scorecard Optimizer for both new and existing Scorecards in your tenant provided the org meets the data requirement. For more information on how to run the optimizer, refer to the Run Scorecard Optimizer article.

Scorecard Optimizer requires both renewal data and customer data to generate optimal configuration recommendations for your scorecard. For more information on the optimizer data requirements for tenants with and without Renewal Center, refer to the Prerequisites section of the Set up Scorecard Optimizer Data in Orgs With Renewal Center and Set up Scorecard Optimizer Data in Orgs Without Renewal Center articles, respectively.

The Scorecard Optimizer conducts specific checks at the scorecard level to determine if the provided data is sufficient to generate results. These data eligibility checks assess whether the model can successfully generate optimizer results or not.

  • Data Eligibility Check for New Scorecards:

  1. Identify company records that meet the scorecard criteria.
  2. Fetch opportunity data within the selected time frame from these records. Opportunities in the "Renewal" booking type and in "Closed" stages (both won and lost) are considered.
  3. Collect customer data from various categories—Adoption, Surveys, Cases, and Timeline—for the company records considered in step 2. The data from each category should cover a specific date range prior to the 'Renewal Closed Date' minus the 'Prediction Window'. 

A minimum of 30 eligible records is required for the model to generate results successfully.

  • Data Eligibility Check for Existing Scorecards:

  1. Identify company records that meet the scorecard criteria.
  2. Fetch opportunity data within the selected time frame from these records. Opportunities in the "Renewal" booking type and in "Closed" stages (both won and lost) are considered.
  3. Fetch scorecard history to identify eligible company records for the companies considered in step 2. The 'Overall Score' from the scorecard history for each company is considered from a specific date range prior to the 'Renewal Closed Date' minus the 'Prediction Window'. 

A minimum of 30 eligible records is required for the model to generate results successfully.

Currently, Scorecard Optimizer does not consider any exceptions in the Overall and/or Group scores as part of its analysis. Optimizer analyzes historical scorecard data, renewals data, and customer data to generate results.

Currently, Scorecard Optimizer only considers one Adoption Explorer project which has the highest number of records.

The Run Optimizer button is deactivated if the optimizer data is not completely configured. Check that all the below mentioned conditions are met.

  • GS Opportunity data is synced.
  • Opportunity Stage data is synced and mapped. Optimizer requires closed won and lost opportunities.
  • Booking Types for renewals are configured.
  • Company records are available in the opportunity filtered scorecard criteria.

Scorecard Optimizer is an admin only feature. CSM and other end users are not required to perform any configuration.

Scorecard Optimizer uses Time Frame to identify all renewal opportunities of companies that are using the Scorecard on which the optimizer is run. After identification, it studies the historical score of each renewal opportunity. The historical score is determined based on the Prediction Window.

For example, consider that the Time Frame selected is 18 months where the From Date is 1st May 2022 and the To Date is 31st October 2023, and the Prediction Window selected is three months. One of the renewal opportunities, identified by the optimizer during the selected Time Frame, renewed on 1 September 2023. For this opportunity, Prediction Window will study its health score as on 1 June 2023, which  is three months prior to 1 September 2023.

Gainsight’s default period  for Time Frame is set to 18 months of renewal data for both new and existing scorecards. The default period  for Prediction Window is 3 months.

After the completion of the optimization process, the optimizer results are available in the Recommendations step of the scorecard configuration wizard view. In the scorecard list view page, click View Results to navigate to the Recommendations step.

Scorecard Optimizer FAQs (Beta) 1.jpg

The following types of recommendations can be provided by Scorecard Optimizer, subject to certain conditions:

  • New measures are only recommended if customer data is available.
  • Revised measure weights are mostly recommended, subject to the new measure selection and high/moderate/non-predictive measure categorization.
  • Scheme range is recommended if there is only one scorecard in the tenant.

The following new measures are recommended by Scorecard Optimizer, subject to the customer data provided:

Category (Customer Data)

New Measure Name

Survey

NPS

Survey

CSAT Score

Survey

Survey Response Rate

Adoption

Daily Active Users

Adoption

Weekly Active Users

Adoption

Weekly Unique Users

Adoption

Breadth of Adoption

Cases

Support Tickets Volume

Cases

Priority Support Tickets Volume

Cases

Support Resolution Time

Cases

Priority Support Resolution Time

Engagement

Engagement

The time taken by the optimizer to generate results varies for new scorecards and existing scorecards in a tenant. You will receive an in-app notification and an email when the optimizer result is available.

Gainsight recommends running Scorecard Optimizer again if the last results are more than 2 months old.

Yes, the Download Results button is available in the Recommendations step of the scorecard configuration wizard view. The results are downloaded in a PDF file format.

Scorecard Optimizer FAQs (Beta) 2.jpg

Gainsight tags a scorecard as optimized when you proceed with the optimizer recommendations and publish the scorecard. Even if you customize the recommended optimizer configurations and publish the scorecard, it gets tagged as optimized. 

Note: It is advised to Skip recommendations if you do not wish to accept them to avoid getting the scorecard tagged as optimized.

Scorecard Optimizer does not track existing measures. It considers the data from your sources to define which measures show predictability and provide them as recommendations. If the measure already exists, it can be a subset of data or possibly have a different criteria definition recommended. Gainsight suggests checking the Measure Definition before adding the recommended measure to your scorecard.

Scorecard Optimizer aims to enhance the overall effectiveness of your scorecard through measure and weight recommendations. However, it's important to note that these recommendations can be subjective, depending on the type of data being processed. You can assess the optimizer's results, which include recommendations and prediction power, by modifying the configuration and then comparing the recommended results with the modified results. To view the modified results, you can use the Scorecard Prediction Preview feature before publishing the scorecard.

Note: Scorecard Optimizer is currently a Beta program. This program helps us to identify unique scenarios of unexpected results and gradually improve our model.