Purpose: Share a non-comprehensive set of formula examples to cover the majority of customer use cases

0. Data to power formulas

Here's a list of data you can use in Pylon to power formulas

• Issue data

• Meetings (via Google Cal Integration or Meeting Recorder Integration)

• Custom Activities

• Custom Account fields

  • Manually created Pylon Account fields

  • CRM synced Account fields

  • Product usage data, or any other data synced in from a data warehouse

1. Standard formulas

These are standard formulas for what you see in Excel or Google Sheets. Type directly into the formula editor and you will see the list of formulas and options. Some common ones we use:

• Logic: If, And, Or, Not

• Math: Sum, Average, Max

• Date: Today, Day, Month, Year

2. Query formulas

These look like simple formulas, but are actually querying your customer data. The one below will return the number of issues that each account submitted, that is tied to a Linear ticket, in the last 90 days

Type in any query formula via the editor prompts and you will see the options here

3. AI formulas

AI formulas require the following sequence to setup:

• Create a custom Account notebook with the relevant data you'd like to reference

• Create an AI formula field that uses the Account notebook as its context. In the below explain, Mentioned competitor is an Account notebook AI block. To create such formula, create a Text, Select, Multi-Select, or True/False Account and turn on the Enrich with AI option

Common formula patterns

A. Signal, explanation, next step

Set up an Account notebook specific for a given signal (upsell or risk), and create a series of formulas to extract the relevant insights. In this below case, we created a notebook for identifying customers that are interested in Account Intelligence product

In the below example, Indicated Interest and Next steps are two different AI fields within our custom Account notebook

Overall signal that aggregates multiple signals

The signal is an AI indicator of a possible upsell or risk

The explanation is an AI explanation of why the signal was flagged

Next step is an AI recommended of how to address the signal

B. Health scores

Aggregate health score weighted across multiple sub-scores

Sub-health score based on your org's definitions