Each month, Restaurantology inserts thousands of new RestaurantologyLogs, which capture firmographic, technographic, territory, and trend data for each Concept and Company we detect and monitor.

These Logs function as a monthly snapshot of the restaurant landscape, allowing you to track things like unit count changes or the first appearance of a new tech provider across a multi-unit brand.

Why it matters

RestaurantologyLogs aren’t just informational. They’re designed to drive automation and improve CRM data hygiene. When matched 1:1 with Salesforce Accounts, Logs can power:

• Automated field updates via triggers or flows
• Enrichment of sales territories and rep assignments
• Better downstream reporting and insights

The goal: ensure that every RestaurantologyLog is matched to a single Salesforce Account whenever possible.

Matching logic

Before each Log is inserted, Restaurantology attempts to auto-match it to an Account by checking whether the Restaurantology Internal ID already exists on an Account record.

There are two possible outcomes:

1. Match found: The ID is found, and the RestaurantologyLog is inserted with an associated Account ID.
2. Match not found: The ID is not found, and the RestaurantologyLog is inserted without an Account match.

In cases where no match occurs, Admins are expected to resolve these unmatched Logs by either:

• Linking the Log to an existing Account using one of the available methods, or
• Creating a new Account and linking the corresponding Logs

Two mapping methodologies

There are multiple ways to link RestaurantologyLogs to existing Salesforce Accounts, but not all methods scale equally. It’s important to understand your options and choose the right one based on your dataset and CRM maturity.

Matching methods fall into two categories:

Mass mapping

• Data Matching Helper
• Scan and Match Data

Manual mapping

• Updating Account > Restaurantology Internal ID by hand
• Using a one-time import to populate IDs in bulk

In the next few articles, we’ll walk through each option and explain when—and why—to prioritize at-scale solutions over manual work.