This document discusses analyzing customer usage data trends using Gainsight. It provides an overview of the types of analyses that can be done with Gainsight data, including cause and effect, correlation, and metrics analyses. Examples of questions that can be answered include determining which features successful customers adopt and how implementation methods impact usage. The document reviews steps for building an effective analysis, including starting with an actionable question, determining required data, and choosing a reporting format. Report Builder and dashboards are demonstrated as tools for visualizing analyses in Gainsight.

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Identifying Trends in Usage Data
Andrew Serpa, Senior Business Operations Associate

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Agenda
• Building an effective analysis
• Analyses available using Gainsight data
• Report Builder overview
• GS team analysis

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Agenda
• Building an effective analysis
• Analyses available using Gainsight data
• Report Builder overview
• GS team analysis

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“What features do my most successful customers tend to
adopt?”
“How long does it take for my customers to get their first
DAU? Their second?”
“How long does it take for us to resolve Risks with our
customers? If we decrease this, will it improve our customer
retention?”
Gainsight enables you to access a wealth of data on your
customers

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Steps to creating high yield report
• Begin with a question you want to solve with data
• Make sure that the question is actionable, i.e., once you have the data
you can do something about it
• Determine what data you’ll need to solve the question
• Based on data needed for analysis, determine the best format (i.e., GS
Report Builder vs. Excel Export)
• (Optional) Plan which set of analyses will collectively tell your story in a
Dashboard format

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• Determining the
relationship between
multiple data sets
• Often involves
segmenting groups based
on shared attributes
(Promoter score,
implementation method,
etc.)
• Examples:
• “What features do super
users tend to adopt?”
• “How do our implementation
tracks impact usage?”
Types of data relationships observed
Cause and effect Correlation Metrics
• Compare the effect of an
independent variable on a
dependent variable
• Use to measure effect of
actions we control on
customer outcomes
• Examples:
• “How have our recent
outreaches impacted NPS?”
• Performance based
tracking
• Examples:
• How has our average
customer NPS changed
over time?
• What is our ratio of
CSM:ARR?

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Examples of data analysis types
Cause and effect Correlation Metrics
• Consider testing the
relationships between the
following data in GS:
• Independent variables:
• Hours of CSM
involvement
• EBR frequency
• CTAs closed per week
• Dependent variables:
• Health score
• Daily Active Users
• NPS
• Consider grouping
Customers along the
following characteristics to
determine patterns:
• Segmentation:
• Promoters vs. Detractors
• Size of Customer
(licenses)
• Implementation method
• Qualities:
• Use of platform features
• Time to adoption
• Support Activity
• Customer snapshot:
• Habits Scores
• NPS
• Habits improvements
• Team metrics:
• CSM:ARR
• Open Risks by CSM

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Agenda
• Building an effective analysis
• Analyses available using Gainsight data
• Report Builder overview
• GS team analysis

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• Copilot Analytics (unique open-rate, open-count, click-count,
bounce-rate, cut across specific outreaches)
• Operational reporting (Cockpit)
• LRM (premium), Transactions tab
• Surveys tools
• Filter by fields
• Time periods
• One-off reports (Report builder)
• GS dashboards
There are several reporting features in GS that you can
use to analyze customer behavior
Focus of today’s discussion

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Agenda
• Building an effective analysis
• Analyses available using Gainsight data
• Report Builder overview
• GS team analysis

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• [Screenshot of Report builder with relevant features highlighted]
Report Builder – Setting up a report
Select the
Object that
contains the
data of interest
2
Access Report
Builder from the
top ribbon
1
The “Show me” field
represents the actual data
that is being pulled, often
quantitative in nature (e.g.,
Pageviews, NPS, DAU,
etc.)
3
The “By” field
represents the
segmentation of the data
that is being pulled,
typically categorical or
descriptive in nature
(e.g., Page, Customer,
other custom buckets,
etc.)
4
Tip: When producing
charts, you will need to
specify how quantitative
“Show me” fields are
displayed (i.e., Sum,
Average, etc.)
Use the Filter
functionality to
exclude
unwanted data
(i.e., limit data to
a specific date
range, etc.)
5
Choose the desired
presentation of Report
output (Table, Bar Chart,
Line Graph, etc.)
6
Save Reports to the
Repository for later
access and to use in
Dashboards
7
Export underlying data
to Excel or CSV format
8

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• Filter for Aggregation level in Usage data; specify whether you are after Account
level data or User level data, otherwise you’ll get both
• Beware the difference between “Date” and “Created Date” for records
• Ex: Milestones have a “Created Date” which represents when you made the record and
“Date” which represents the actual date of action for the Milestone
• For CTAs:
• “CSM” is always the CSM on the account
• “Assignee” is the owner of the CTA
• If you find yourself frequently needing to segment data, build the segmentation into
GS using Rules Engine
• Ex: Team Manager
• Add fields to Customer Object (i.e., segmentations and tags) to make quick
analyses easier!
Report builder tips

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Creating Dashboards from Report Builder

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• Combine similar Reports from Repository in Dashboards to
provide more complete picture for analyses
• Dashboard filters: Fast and efficient way to pivot data quickly
• Note: To enable a Dashboard level filter, the filter must be present on
all reports on the Dashboard. Use “dummy” filters on original reports to
enable Dashboard level filters
Dashboard tips

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• Multi-Object data analysis
• Custom segmenting
• Large tabular record keeping
Exporting data from GS vs. Report Builder / Dashboards
Report Builder / Dashboard Export data to Excel or CSV
• Single-Object data analysis
• Customer
• Usage
• Calls to Action
• Milestones
• …
• Recurring report creation
• Data visualization

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Customer data analytics examples

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Analysis Example 1: Determining which features are
correlated with Green habit users
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Averageweeklypageviews
Weeks since OCD
Average weekly use of features - Green habit score
Cockpit Copilot Gainsight Home Customers
Survey Account Widget Opportunity Widget Case Widget
Customer Success 360 Administration - UI Settings Rules Manager Administration - Customers WIP
Reporting 2.0 Service Cloud Adoption - Usage WIP Integrations
SAMPLE DATA FOR ILLUSTRATION ONLY

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Analysis Example 2: Gauging the effectiveness of 3
different onboarding methods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Average%DAU
Weeks since OCD
% DAU vs. weeks since OCD
Implementation 1 Implementation 2 Implementation 3
SAMPLE DATA FOR ILLUSTRATION ONLY

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0 50 100 150 200 250 300 350 400
DaystoreachGreensinceOCD
Original OCD date (indexed to 0)
Analysis Example 3: Determining whether an
onboarding initiative is working
SAMPLE DATA FOR ILLUSTRATION ONLY

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Questions?

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Backup

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Steps to creating high yield report (Backup)
Step Example
1
Begin with a question you want to solve with
data
Which features do my most successful customers
tend to adopt?
2
Make sure that the question is actionable,
i.e., once you have the data you can do
something about it
Once we know which features are highly
correlated with successful customers, we can:
• Survey successful customers on use of these
features
• Tailor our onboarding programs and feature
positioning
3
Determine what data you’ll need to solve the
question
Data required:
• Page visits by customer (Usage data)
• Customers segmented by habit score
(Customer data)
4
Based on data needed for analysis,
determine the best format (i.e., GonG Report
Builder vs. Excel Export)
Because we’re taking data from two different
objects, this analysis will be easiest in an Excel
export
5
(Optional) Set up a Dashboard to pair similar
Reports