Resonate Append is an enrichment solution that drives deeper audience understanding, smarter targeting, more relevant personalization, and more predictive models. Fill the gaps in your first-party data with 15K+ attributes or custom commissioned attributes, delivering unmatched depth of recent consumer understanding for any industry.
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Data Append - Key Definitions File
Data Append - Use AI to Unlock Ideas with your Summary Report
Data Append - Output File
When Resonate completes your data append, the first file you'll receive is your Output File.
This file contains your customer records, now enriched with Resonate data appended to each matched ID. Only records that were successfully matched are returned, so every row you see represents a real, enriched customer.
You'll receive this file in one of two formats depending on what you selected: Standard or Pivoted. The one we’re looking at first is the Standard
In the Standard format, each row has four columns: your Customer ID, the match type — either Direct Match or Household — a comma-separated list of survey value keys where the value is true, and a fourth column for any redacted sensitive attributes.
In the Pivoted format, each attribute gets its own column, with a 1 for true, a 0 for false, and a null for any redacted sensitive attributes.
One important thing to note: these survey value keys are represented as numeric keys — not human-readable labels yet. For example, you might see "422753" in your file. To know what that means, you'll need the Key Definitions File — which we'll cover in a separate video.
When reading the predictions associated with a record, if the survey value key is attributed to that record, you can interpret that prediction to be true. If a survey value key is not associated with an ID, it should be considered false. There is one exception to the false prediction, if a Survey Value Key is listed within the Redacted Column this means there are sensitive data laws that impact this attribute and Resonate observes this ID within a state with these laws. In this case, we will withhold the prediction for that survey value key, and it is neither true nor false but instead Redacted. This ensures a layer of privacy protection for your use of the data.
Data Append - Key Definitions File
The Key Definitions File is your decoder.
When you open your Output File, you'll see the attributes represented by survey value keys which are numeric keys — like 422753 or 391131. Those numbers are efficient for file processing, but they don't mean much on their own. That's exactly what this file is for.
The Key Definitions File maps every numeric survey value key to its human-readable definition. So, 422753, for example, translates to "Age Group 18–24." 131941 becomes "Female." Each key has a clear, plain-language label, so your team knows exactly what attribute they're looking at.
Beyond the key-to-label mapping, this file also tells you:
- Every attribute and attribute value that was included in your append order
- Whether each attribute came from a single-select or multi-select question — which matters when you're doing percentage calculations or building segments
The definitions from this Key Definitions file should be joined together with the results of the Output File to enable you to represent the data with clear human-readable labels in your internal systems. Take the keys from Column 3 of your Standard output — or from the attribute columns of your Pivoted output — and match them to the "survey_value_key" column here to get the full attribute name.
Data Append - Summary Report
The final file you'll receive is the Summary Report — this one is all about verification.
Before your team dives into an analysis, you want to make sure your Output File and Key Definitions File are correctly aligned and that the data looks as expected. That's what the Summary Report is designed to help you do.
Here's what's inside: the report includes the full taxonomy of each attribute appended to your records, the survey value key associated with it, a distinct count of how many IDs have that attribute set to true, and the percentage of your total matched records that value represents.
Use those counts and percentages to QA your output. If your Output File shows 10,000 records with a particular age attribute, that should line up with what the Summary Report says. This helps ensure you’re joining the Output file and Key Definitions file correctly.
One thing to keep in mind: percentage values for mutually exclusive attributes may not sum to 100% in some cases. That's expected — it's because sensitive attributes are redacted for individuals in certain restricted states, so some records are excluded from those calculations. Additionally, some attributes may or may not be applicable to IDs that show a certain behavior. For instance, sometimes to have a preference for a certain type of product, a person has to also be predicted to have used that product within a certain time span.
Data Append - Use AI to Unlock Ideas with your Summary Report
Once you receive your Summary Report, you're holding a snapshot of who your customers really are — what they believe, how they shop, what media they consume, and so much more. Here's how to read it and put it to work.
The file has a few key columns to focus on. Column D is the survey question — for example, "How do you prefer to receive marketing messages?" Column E is the answer — "Email." Column G is the number of your customers who answered that way, and Column H is the percentage.
That percentage is your signal. When you see that 68% of your customers prefer to receive marketing via email, that tells you where to invest your channel strategy. When you see that 67% describe themselves as family-centric travelers, that's a creative brief. The more you scan these percentages, the more your customers start to reveal themselves — not as a list of IDs, but as real people with real preferences.
But here's where it gets really powerful: once you have this file, you can drop it directly into Claude or any AI assistant and let it generate ideas for you.
Here's a simple prompt to get started:
"Looking at this summary file, Column D is the survey question, Column E is the answer, Column G is the count, and Column H is the percentage. Based on these percentages, help me come up with ideas for how [your company name] can use this data — for lifecycle marketing, persona development, segmentation, email targeting, site personalization, or modeling."
In seconds, you'll get a tailored list of activation ideas built directly from your actual data — not generic assumptions.
Your Customer Success Manager can also walk you through the results and help you prioritize. Think of the Summary Report as your starting point — the data is there, and now it's just a matter of putting it to use.
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