Salesforce account matching - we have a challenge where he have hotel properties that sit within hotel groups. we are trying to check if new leads already exist in SF but are struggling to identify unique fields to do this - websites and linkedin could be the group and also the property. has anyone got some clever ideas about how we could run a matching process? (can we do an ai lookup against multiple data points?)
For matching hotel properties within groups in Salesforce, you can use Clay's enrichment capabilities to improve your process: 1. Use the "Lookup Records" action to check if companies already exist in your Salesforce database before creating new records. 2. Implement Clay's enrichment actions like "Find Companies" to gather additional unique identifiers beyond websites and LinkedIn profiles. 3. Create a custom matching logic using Clay's "Formula Tool" that considers multiple data points simultaneously (location, size, industry specifics) to distinguish between properties and parent groups. 4. After enriching your data, use the "write to table" feature to organize your results before pushing to Salesforce. This approach allows you to build a more robust matching system that can differentiate between hotel properties even when they share group-level websites or social profiles.
challenge is the company names may not match because they are in different formats. websites can also be the same for multiple hotels within the same brand.
can the group have location city = property city too?
For matching hotel properties in Salesforce, you can implement a multi-layered approach: 1. Use Clay's "Normalize company names" cleaning function to standardize inconsistent company names by removing legal suffixes and unnecessary prefixes, making your hotel property names more uniform. 2. Leverage the "Lookup Record" action with Salesforce to check if companies already exist in your database, but set the "Exact Match?" parameter to false to perform a contains search rather than requiring exact matches. 3. Enrich your data with additional unique identifiers beyond websites using Clay's "Find Companies" action to gather more matching data points. 4. Consider using the company lookalike functionality to identify relationships between properties within the same hotel group, which can help establish connections between related entities. After processing, use the "write to table" feature to organize your results before pushing to Salesforce.
yes but it is nested in a adress field in SF rather than a standalone data point.
For matching hotel properties with nested location data in Salesforce, you can: 1. Use Clay's "Lookup Record with Salesforce" action with "Exact Match?" set to false to perform a contains search rather than requiring exact matches. 2. Extract and normalize the state information from the nested address field using Clay's "Normalize Location with Mapbox" action, which can parse address components into separate fields including state. 3. Create a multi-criteria matching approach that combines partial company name matching with location data to distinguish between properties in the same group but different states. 4. Leverage Clay's location-based data points like "Company Location - State" to enrich your records with standardized location information that can be used for matching. After processing, use the write to table feature to organize your results before pushing to Salesforce.
so if we can extract the city from the nested field we can rely upon it to decide if it is a lead or not? if yes then extraction is possible
lookup
formula to check presence
if presence is not found then it is a new lead?
Yes, you can extract the city from a nested address field in Salesforce and use it as part of your matching criteria, but relying solely on checking for "SF" may not be completely reliable for determining if a lead is new. Instead, I recommend using Clay's "Lookup Record with Salesforce" action with "Exact Match?" set to false to perform a contains search that can check address components. You can then use Clay's location-based data points to extract and standardize the city information. For more reliable matching: 1. Extract city/state from the nested address field 2. Combine this with other identifiers (company name, website) 3. Use a formula to create a multi-criteria matching approach This will give you more accurate results than relying on a single data point like "SF" in the address.
i can extract city from SF but it's still not matching - am I right in thinking by default it's a 'fuzzy' match?
When using Clay's "Lookup Record with Salesforce" action, the default is a "contains" search, not an exact match. This means it performs a partial or "fuzzy" match by default. You can see this in the action settings where "Exact Match?" is a toggle option that's off by default. When this option is disabled, Clay performs a contains search across all your specified search fields. For extracting city information specifically, you can use the "Normalize Location with Mapbox" action after extracting the address data. This will parse the address components and provide standardized location fields including city. This combination of contains search and location normalization gives you flexibility when matching records with inconsistent formatting.
yeah it is a regex match, alternative is to feed the address into an AI column and letting it decide if it is the same city or not and output a checkbox field
Yes, you can use an AI formula column in Clay to analyze address fields and determine if two records are in the same city, outputting a checkbox result. Here's how to set it up: 1. Add a new "formula" column to your table 2. Use the AI to generate code that compares city information from your address fields 3. The formula can extract city names from nested address fields and normalize them for comparison 4. Configure the output as a boolean (true/false) which will display as a checkbox This approach is more credit-efficient than using the AI integration directly, as AI formulas are credit-free ways to process your data. For better city extraction and matching, you can leverage Clay's location-based data points like "Company Location - State" or "Contact Location - City" to standardize location information before comparison.
i've extracted the data but both the account name and city are nested in SF and clay can't match them.
When dealing with nested fields in Salesforce, you can use a multi-step approach in Clay: 1. Use the "Lookup Record" action with "Exact Match?" toggled off to perform a contains search rather than requiring exact matches. 2. Extract the nested data (account name and city) from Salesforce into separate columns in your Clay table using the Salesforce integration. 3. Create a formula column to normalize or standardize the extracted data for better matching. 4. Use the "Lookup Multiple Rows" integration to scan for matching values across your data, which allows you to identify if leads already exist based on multiple criteria. 5. After processing, use the write to table feature to organize your results before pushing to Salesforce. This approach gives you more flexibility when working with nested fields that can't be directly matched.
are you using the formula column to do the matching? if that isn't working. try an AI column with a prompt.
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