Availability: Enterprise*
*This feature is available as a part of the paid PandaDoc CPQ add-on. If this is something you're interested in, reach out to your Customer Success Manager or sign up for a demo.
Lookup tables in PandaDoc enable quick referencing of frequently used data in rules, such as zip code lookups, unit conversions, price lists, and more. This feature is particularly useful for automating and streamlining complex workflows by dynamically updating quotes or adding products based on pre-defined data.
What are lookup tables?
Lookup tables are structured data tables with rows and columns. They allow users to match and retrieve specific data by referencing one or more columns. Examples of use cases include:
Pricing by region and segment: Setting up regional price lists for various customer segments.
Customer multipliers: Applying specific multipliers based on customer type, renewal terms, etc.
Rate tables: Managing multiple pricing periods or tables with thousands of SKUs.
Currency exchange rates: Updating daily FX rates.
Tax and fee management: Configuring taxes or fees at the country or vendor level.
Language variations: Localizing product catalog information, including names, descriptions, and prices.
Cross-sell/Up-sell options: Defining product combinations for cross-selling or up-selling opportunities.
Setting up a lookup table
Follow these steps to create and upload a lookup table in PandaDoc:
Open your configuration table or create a new one using Google Sheets or Microsoft Excel.
Use the first row to specify column names.
Fill in the data for each column.
Export the document as a .csv file.
Go to Workflows > Lookup tables.
Select +Lookup table.
Specify a unique table name and add a description if needed.
Upload the .csv file and select Create table.
Your lookup table is now ready to use in the rules.
Using lookup tables in rules
Lookup tables integrate seamlessly with the quote step in PandaDoc Workflows, allowing you to dynamically update your quotes or automate the additions of new products. Here’s how you can use them:
1. Add products dynamically
You can create rules that pull data from lookup tables to automatically add products from the catalog based on defined conditions. This allows you to streamline upselling or cross-selling processes by adding relevant products to the quote dynamically.
Suppose your lookup table contains data about different bicycle models, including SKU, Category, and associated features like Pedals, Lights, and more. You want to automate the addition of recommended accessories (e.g., lights or pedals) to quotes based on the selected product model.
Example rule setup:
Condition: If your Lookup table contains elements with SKU column that matches the selected product's SKU [data field.item].
Action: Add the recommended accessories from the catalog to the Add-ons section
2. Customize prices for different regions and segments of customers
You can create rules that dynamically adjust prices in quotes based on region-specific criteria using lookup tables. This setup ensures pricing is tailored to customer needs without manual intervention.
Watch a video overview:
Another example setup for regional pricing:
Pre-condition: Your lookup table includes columns for:
Base Price
CA Multiplier (or other region-specific multipliers)
Additional product details as needed.
Condition setup:
Line items in the quote must contain elements where:
The Description includes the text CA (indicating the Canadian region or a relevant line item).
The lookup table must contain rows where:
The CA Multiplier column is not empty.
Action setup: Update the price of the relevant line item using the formula:
Price = [Base Price] * [CA Multiplier].
How it works:
When the rule is triggered, the workflow identifies all line items with descriptions containing CA.
It then references the lookup table to find the corresponding Base Price and CA Multiplier.
The price for each qualifying line item is recalculated by multiplying the base price by the regional multiplier (e.g., 1.24 for Canada).
This setup ensures that prices in the quote adjust automatically based on the customer’s region.
Managing lookup tables
Currently, you can only delete the lookup tables you've added. To remove unwanted tables from the list, click three vertical ellipses on the far right of the table and select Delete from the dropdown menu.
Limitations
While Lookup tables are a powerful feature, there are some limitations to keep in mind:
Maximum table size is 3 MB.
All data in the lookup table are treated as strings, even numeric values like prices.
Tables cannot be edited directly in PandaDoc; updates must be made via a new .csv upload.
Tables are flat, and no joins are possible.
Table names must be unique within the Workspace.
The table columns must be unique within the table.
Best practices
Column naming: Use clear and consistent names for columns to avoid confusion.
Data validation: Double-check your data before exporting to ensure accuracy.
Backup: Keep a backup of your original spreadsheet for easy updates or corrections.
Optimize file size: Ensure your table is under the 3 MB limit by removing unnecessary data or splitting large datasets.