# Customer Householding ## Overview In order to analyze customer behavior across channels, Daasity uses **householding** logic to link customer records. If the same person exists in multiple systems (with the same email or other identifiers), Daasity can assign them a common Household ID or unified customer ID. *This allows you to see metrics like “unique customers” without double-counting the same person across Shopify and Amazon, for instance.* ## What is householding? Householding is the concept of combining multiple individual customer accounts to create a single "householded" account. Customers may use different information depending on where they purchase. For example, to order online a customer will provide a physical address and an email address; to purchase in a store a customer might use an email address or a phone number to get a receipt but will likely not provide an address. Customer information can also vary over time or even across platforms when we capture the same information: * A new or slightly differing address (Ave. Vs Avenue) * A slightly different first and/or last name (Mike vs Michael or married vs maiden name) * Different email addresses (work email vs home email) All of these can lead to multiple customer records created for the same individual. Similarly, merchants may have different information in their communication platforms: our email platform will have email and our SMS platform a phone number. ## Why is householding important? Applying a householding process to your data allows you to better understand what is truly happening at a unique customer level. A lack of householding can have a significant impact on analysis if not properly taken into account: * A single customer's LTV curve will be split into multiple customers, making them both incorrect * Retention data and repurchase rates will look incorrect as only one of the records for a customer might be credited with a repurchase * Customer segmentation will be incorrect and could even lead to an individual receiving multiple emails from a single email campaign Daasity's transformation code uses some common fields other than just email to map duplicated customer records together into a single record. ## Householding logic 1. If an email address matches with another, those records will be mapped together regardless of whether the names or addresses are different. 2. If the email addresses differ, the first and last name, city and zip code will be analyzed, giving each customer a “score” depending on how many fields are present (so a customer with first, last and zip code will have a higher “score” than someone with only a first and last name available but no zip code or city information). Records with the same number of fields available will use the most recent order as the tie-breaker. 3. Each customer without an email address will then have their record updated based upon the score determined by the second step, resulting in a mapping table with a simple mapping of customer ids. 4. The mapping table created is used in our Orders data to tie customers together, but is not currently used in other elements of our data. ## Amazon and Customer Identification Amazon's Orders API does not include buyer email, buyer name, or shipping name in its order data. These fields are used by Daasity to generate the customer ID and power householding logic, so their absence has historically limited how accurately Amazon customers can be identified and matched across orders. **For Amazon-fulfilled (AFN) orders**, Daasity now sources buyer email, buyer name, and shipping name from the FBA Amazon Fulfilled Shipments Report when those fields are not available in the Orders API data. This improves customer ID generation and householding match rates for AFN orders. **For merchant-fulfilled (MFN) orders**, data sourcing is unchanged. **What this means for your data:** You may see minor changes in Amazon customer counts or customer profiles as previously unmatched records are now correctly attributed. No action is required. If you have questions about specific customer records, contact or your merchant success manager. ## FAQ ### Does Daasity household all customer records? Daasity runs the householding transformation code against every customer record we ingest from all data sources. However, this does not mean that we successfully combine every customer record that should be combined. Some data sources do not expose all customer fields through their API, which can limit how accurately records can be matched. {% hint style="warning" %} **Amazon note:** Amazon does not provide buyer email, buyer name, or shipping name through its Orders API for Amazon-fulfilled (AFN) orders. For AFN orders, Daasity now supplements these fields using data from the FBA Amazon Fulfilled Shipments Report, which improves customer identification and householding accuracy for this channel. Merchant-fulfilled (MFN) orders are unaffected. If you notice shifts in your Amazon customer counts or customer profiles, this is expected — previously unmatched records are now being correctly attributed. {% endhint %} ### Can I see the non-householded customer records in my data? Daasity does not currently expose the non-householded customers in any explore. However if there are specific records that you would like to see, contact or your merchant success manager. ### Where do I see the householded customer records in my data? Customer data can be seen in several explores including [Order & Order Line Revenue Explore](https://help.daasity.com/core-concepts/data-models/data-explores/digital-analytics-unified/ecommerce-orders-and-revenue-by-order-date-explore), [The Transactional Sales Report Explore](https://help.daasity.com/core-concepts/data-models/data-explores/digital-analytics-unified/transactional-sales-report-explore), [Lifetime Value Explores](https://help.daasity.com/core-concepts/data-models/data-explores/digital-analytics-unified/lifetime-value-explore), and [Customer Details Explore](https://help.daasity.com/core-concepts/data-models/data-explores/digital-analytics-unified/customer-details-explore) \\ Customer data typically comes from either Shopify or Amazon. We ingest this customer data into a single table and then use our householding transformation to match up customers from different stores or platforms based on several variables.

How Customer Data Flows in Daasity