In sales and marketing, knowing your leads and their behaviors is critical. PQL scoring is a method that helps businesses identify leads who have shown buying intent by engaging with your product.
But the intricate process of data analysis and the task of attributing a Product Qualified Lead (PQL) score can be intimidating because of the following:
- Scattered data across multiple systems, making it difficult to gain a comprehensive view of leads.
- Time-consuming processes that delay insights and slow down decision-making.
- Lack of product engagement activity used in traditional lead-scoring methods, leading to missed opportunities and a seemingly heavy lift required to shift to PQL scoring.
Whether your company is solely focused on product-led or attempting to experiment with a product-led motion, youâre in luck. PQL scoring can actually be fairly simple and implemented today.
This blog post will guide you on how you can utilize Coefficient and AI to compute a PQL score from your database and directly load it into your CRM, so your team can focus efforts on the highest potential leads.
How-to-Guide: Streamlining Product Qualified Lead Scoring
Coefficient is a free Google Sheets add-on that allows you to connect your business systems to Google Sheets and sync real-time data into your spreadsheet.
Before you start cleaning your data, install Coefficient.
Open Google Sheets. Click Extensions in the top ribbon and select âAdd-onsâ -> âGet add-ons.â
Input âCoefficientâ in the Google Workspace Marketplace search menu and select the Coefficient app.
Click âAllowâ to grant Coefficient access to your Google account.
Return to your spreadsheet menu and start Coefficient by clicking Extensions -> Coefficient -> Launch.
Now you can start pulling data from your database into Google Sheets. In this example, weâll pull Snowflake data into Google Sheets. Click âImport fromâŚâ on the Coefficient sidebar to do this.
Select Snowflake as your data source.
Provide the required credentials to set up the Google Sheets Snowflake connection.
Coefficient lets you connect Snowflake to Google Sheets through two main methods: Tables and columns or through custom SQL queries.
Note: In this example, weâve already imported our data. Check out this blog for a full tutorial on how to export your Snowflake Data into Google Sheets.
Once the data is imported, weâll create a formula to calculate the PQL score based on the lead’s activities using GPT Copilotâs Formula Builder.
Select âGPT Copillotâ from the Coefficient menu.
Click âFormula Builder.â
Next, weâll describe the formula we want the Formula Builder to create for us, using three variables: Last_Login_Data, Imports_Created, and Shared_Reports.
In our example, weâve assigned a scoring system:
- If a user in the activity database has logged in, created an import, or shared a report, their PQL Score will be 5.
- If theyâve only logged in and created an import without sharing a report, their PQL score will be 3.
- If theyâve only logged in without any activity, their PQL score will be 1.
- If they havenât done anything, their score will be 0.
Describe the criteria to the Formula Builder using the text box: âcalculate a PQL score that looks to see whether column C3, D3, and E3 are empty. If all three columns are empty, then PQL score is 0. If two of the three columns is blank, then the PQL score is 1. If one of the three columns are blank, then PQL score is 3. Otherwise the PQL score is 5. Source dataset is in âProduct Activity by Customerâ!A3:E3.â
Click âBuild.â
Copy and paste the formula into the empty cell below âPQL Score.â
Click the checkmark in the pop-up to apply the formula to the entire column automatically.
Now that youâve calculated the PQL score, youâll want to export the data to Salesforce so your Sales Team knows which leads are the most promising and should be prioritized.
In this example, our Salesforce contacts are in the second tab of our sheet alongside a blank column without any PQL Scores, indicating we need to update this information in Salesforce.
Now, weâll use the Formula Builder to create a formula that will connect our data sets by Contact ID: âLook up a Snowflake Userâs Salesforce Contact ID by using their Snowflake User ID to return the Salesforce Contact ID. The Snowflake User ID is on tab Product Activity by Customer in cell A3. The Salesforce Contact ID is on tab â Salesforce Contactsâ in column B.â
Click âBuild.â
Copy the formula and paste it into the empty cell below âSalesforce ID.â
Stop exporting data manually. Sync data from your business systems into Google Sheets or Excel with Coefficient and set it on a refresh schedule.
Accept the suggestion to autofill the entire column once more.
Now letâs export this data to Salesforce to update the PQL score.
Return to the Coefficient menu and click âExport toâŚâ
Click âSalesforceâ as the source.
Select âRow 2â in the Header row dropdown.
Select âContactâ as your object and âUpdateâ as your action and click âNext.â
Scroll down to the Salesforce ID in the Field Mappings menu.
Map the Salesforce ID to âContact ID.â
Click âNext.â
Confirm your information is entered correctly, and click âExportâŚâ
Choose to update âAll rows on sheetâ and click âNextâŚâ
Wait for your data to update.
Click âDoneâ when the update is complete.
Return to the Salesforce Contacts tab to verify the update.
Click âRefreshâ at the top of the screen.
And with just a few clicks and the Coefficient Formula Builder, youâve analyzed Snowflake product activity to assign a PQL score and upload it to Salesforce.
https://www.youtube.com/watch?v=8RaB0jPjiwU
Effortlessly Prioritize Leads Using Coefficient and ChatGPT to Calculate a PQL Score
With the combined power of Coefficient and GPT, analyzing data to assign leads a PQL score has never been easier. Plus, you can seamlessly push these updates to Salesforce in just a few clicks without leaving your spreadsheet.
Ready to get started? Install Coefficient for free today and see firsthand how it can streamline your marketing and sales Operations.