Relationship Intelligence Data in Data Studio

Prerequisites

To use this feature, a user must have access to the Data Studio and have access to Propensity to Purchase and Relationship Scoring as part of their Terminus subscription.

If you have any questions regarding your current subscription plan, please reach out to your Customer Success Manager.

Feature Overview

Relationship Scoring gives true insight into the quality of relationships and level of engagement within your target accounts, so you can activate sales and trigger high-touch marketing campaigns.

Relationship data also forms the basis of sales and customer success coaching and pipeline forecasting, indicating how well your reps are doing at building 1-to-1 relationships within accounts, and whether those relationships are ramping up or fading away.

Feature Details

Within the Terminus Data Studio, Relationship Data will be surfaced as columns in the Data Studio and Opportunity Insights reports, and within the Terminus Global Filters. This means that this data can be used to:

• Generate customized reporting views in Data Studio
• Create prioritized lists of accounts for sales and customers success teams
• Orchestrate targeted advertising campaigns and personalized website experiences

Configuring Relationship Data in Data Studio

In order to access Relationship data in Data Studio, your Onboarding Specialist or Customer Success Manager will first provision an Email Experiences instance for you. For more details on how to grant access to your email data, please reference these articles:

• O365 Relationships Enablement
• GSuite Relationships Enablement

Data Studio & Opportunity Insights Columns

In the Data Studio data table, there will be four selectable columns related to Relationship Data:

These columns include:

• Relationship Score: An AI-driven grade (from “Weak” to “Strong”) that analyzes the all-time email and calendar patterns of your employees across all accounts to help marketers prioritize accounts based on previous brand recognition. This score measures the volume of your interactions within an entire account, and will slowly decay over time (as activity declines).

• Propensity to Purchase: An AI-driven score (from “0” to “100”) that analyzes the recent email and calendar patterns of your employees across all accounts to help marketers trigger timely, personalized campaigns. This score is more reflective of the frequency and recency of your interactions. The scoring grouping for this metric include:
• 0
• 1-10
• 11-20
• 21-30
• 31-40
• 41-50
• 51-60
• 61-70
• 71-80
• 81-90
• 91-100
• First Email: The date of the first recorded email interaction (tracked by Email Experiences) with a contact at the account.
• Last Email: The date of the most recent recorded email interaction(tracked by Email Experiences) with a contact at the account.

These columns are also available in the Opportunity Insights report.

Columns can be sorted by ascending or descending values, and exported via .CSV from the data table.

Global Filters

Within the “Terminus” drop-down in Global Filters, users will be able to filter by Propensity to Purchase and Relationship Score.

Use Cases for Relationship Data

Use Case #1: How do I scale the delivery of the right content at the right time?

• Launch display advertising in lockstep with your email signature and website personalization campaigns to deliver a tailored content experience to prospects and customers.
• Target dynamic audiences based on relationship and 1st party intent data (re: Propensity to Purchase), in addition to Terminus firmographic, behavioral, and 3rd party intent data.

Use Case #2: Which accounts are engaging on our website, but have weak relationships?

• Build a target account list within Data Studio by applying engagement spike and/or intent filter with a low Relationship Score value (e.g. “Below Average”).
• Collaborate with Sales to prioritize these accounts, and track if they become an engaged account and/or result in new pipeline created on your ABM Scorecard.

Use Case #3: How likely is my pipeline to close?

• Use relationship and marketing analytics to identify forecasted pipeline that need additional activation within Opportunity Insights
• Collaborate with Sales by creating multi-channeled campaigns for the identified accounts to provide marketing coverage throughout the sales process

Use Case #4: Are my ABM programs creating new relationships?

• Build a target account list within Data Studio by applying a low Relationship Score value (e.g. “None”, “Below Average”).
• Execute personalized campaigns against the list, and measure the growth of their Propensity to Purchase score and Relationship Score values in Data Studio.
• Track if they become an engaged account and/or result in new pipeline created on the ABM Scorecard.