How many unique contacts do you market to?
Who are they? Is this person you’re sending your mailer gonna get it 2, 3, and 4 times because your data reflects that many duplicate versions of the customer? Data isn’t the real world, but we want it to represent the real world, so if we use it at face value there are real costly consequences.
The duplication of customer records is a natural outcome when you source data from multiple channels and application systems. These channels of data are just so variant and in high volume that identifying unique customers that touch those channels is a very expensive endeavor. This is where the Identity Resolution industry comes into the picture. Over the past decade, this industry has enabled organizations to tag a unique ID to the customers that touch the organization's channels. But getting that information requires a complex back and forth with third-party Identity Resolution companies and is ridiculously expensive to implement. What if you could resolve duplicate customer identities right within the data landscape you use every day? Truelty gives you exactly that.
Take the Code
to the Data,
NOT the Data
to the Code
You’ve already done the work getting the data into Snowflake, why not use the power of Snowflake to compute customer uniqueness where the data lives? Truelty brings the Identity Resolution code to your Snowflake instance, allowing you full control over the security of your data.
Additionally, this means that the unique customer clusters can live right where your data already is. No querying an outside service. Additionally, the Truelty GUI has no access to your private data elements, rather it just controls the Truelty Identity Resolution application which pushes down the necessary code in your Snowflake instance to generate the unique customer clusters within your data. So you get all the benefits of unique record cluster IDs without ever risking the loss of data custody.
Truelty produces a column of Cluster IDs which attach to each of the channels of data you’re piping in. When the IDs overlap you know you have a duplicate record across channels. The beauty of this approach is that you have an immediate way of leveraging unique records in your existing reports, analytics, data science, and operational elements queryable within your existing Snowflake instance.